From 60ffe00be8ac1d77c60603146802af45296519a6 Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Thu, 2 Mar 2023 11:16:54 -0500
Subject: [PATCH 01/29] added torchvision

---
 integrations/torchvision/README.md            |  202 +
 ...rchvision-python-transfer-imagenette.ipynb | 8286 +++++++++++++++++
 .../tutorials/sparse-transfer-learning.md     |  168 +
 3 files changed, 8656 insertions(+)
 create mode 100644 integrations/torchvision/README.md
 create mode 100644 integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
 create mode 100644 integrations/torchvision/tutorials/sparse-transfer-learning.md

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
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+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML Torchvision Integration
+
+By integrating with Torchvision, SparseML enables you to train inference-optimized sparse versions of popular image classification models like ResNet-50 on your dataset.
+
+There are two pathways:
+- **Sparse Transfer Learning**: fine-tune a pre-sparsified checkpoint on your own dataset **[RECOMMENDED]**
+- **Sparsification from Scratch**: Apply state-of-the-art training-aware pruning and quantization algorithms to arbitrary Torchvision models.
+
+Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse for GPU-class performance on the CPU.
+
+## Installation
+
+Install with `pip`:
+
+```bash
+pip install sparseml[torchvision]
+```
+
+## Tutorials
+
+- [Sparse Transfer Learning with the CLI](tutorials/sparse-transfer-learning.md)
+- [Sparse Transfer Learning with the Python API](tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
+- Sparsification from Scratch with the CLI (coming soon!)
+- Sparsification from Scratch with the Python API (coming soon!)
+
+## Quick Tour
+
+### SparseZoo
+
+Neural Magic has pre-sparsified versions of common Torchvision models such as ResNet-50. These models can be deployed directly or can be fine-tuned onto custom dataset via sparse transfer learning. This makes it easy to create a sparse image classification model trained on your dataset.
+
+Check out the model cards in the [SparseZoo](https://sparsezoo.neuralmagic.com/?repo=ultralytics&page=1).
+
+### Recipes
+
+SparseML Recipes are YAML files that encode the instructions for sparsifying a model or sparse transfer learning. The SparseML image classification training script accepts the recipes as inputs, parses the instructions, and applies the specified algorithms and hyperparameters during the training process.
+
+### SparseML CLI
+
+SparseML's CLI enables you to kick-off sparse training workflows with various utilities like creating training pipelines, dataset loading, checkpoint saving, metric reporting, and logging handled for you.
+
+To get started, we just need to a couple key arguments:
+```bash
+sparseml.image_classification.train \
+    --checkpoint-path [CHECKPOINT-PATH] \
+    --recipe [RECIPE-PATH] \
+    --dataset-path [DATASET-PATH]
+```
+
+- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub (which SparseML uses to download a PyTorch checkpoint).
+
+- `--dataset-path` specifies the dataset used for training. It must be a local path to a dataset in the ImageNet format (see CLI tutorials for more details).
+
+- `--recipe` specifies the sparsity related parameters of the training process. It can either be a local path to a YAML recipe file or a SparseZoo stub (which SparseML uses to download a YAML recipe file). The `recipe` is the key to enabling the sparsity-related algorithms implemented by SparseML (see the CLI tutorials for more details on recipes).
+
+For full usage, run:
+```bash
+sparseml.image_classification --help
+```
+
+### SparseML Python API
+
+For additional flexibility, SparseML has a Python API that also enables you to add sparsification to a native PyTorch training loop.
+
+Just like the CLI, the Python API uses YAML-based recipes to encode the parameters of the sparsification process, allowing youto add SparseML with just a few lines of code.
+
+The `ScheduleModifierManager` class is responsible for parsing the YAML recipes and overriding the standard PyTorch model and optimizer objects, 
+encoding the logic of the sparsity algorithms from the recipe. Once you call `manager.modify`, you can then use the model and optimizer as usual, as SparseML abstracts away the complexity of the sparsification algorithms.
+
+The workflow looks like this:
+
+```python
+# typical model, optimizer, dataset definition
+model = Model()
+optimizer = Optimizer()
+train_data = TrainData()
+
+# parse recipe + edit model/optimizer with sparsity-logic
+from sparseml.pytorch.optim import ScheduledModifierManager
+manager = ScheduledModifierManager.from_yaml(PATH_TO_RECIPE)
+optimizer = manager.modify(model, optimizer, len(train_data))
+
+# PyTorch training loop, using the model/optimizer as usual
+
+# clean-up
+manager.finalize(model)
+```
+
+See the tutorials for full working examples of the Python API.
+
+## Quick Start: Sparse Transfer Learning
+
+### Overview
+
+Sparse Transfer is quite similiar to the typical transfer learing process used to train NLP models, where we fine-tune a pretrained checkpoint onto a smaller downstream dataset. With Sparse Transfer Learning, we simply start the fine-tuning process from a pre-sparsified checkpoint and maintain sparsity while the training process occurs.
+
+In this example, we will fine-tune a 95% pruned version of ResNet-50 ([available in SparseZoo](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none)) onto ImageNette.
+
+### Kick off Training
+
+We can start Sparse Transfer Learning by passing a starting checkpoint and recipe to the training script. For Sparse Transfer, we will use a recipe that instructs SparseML to maintain sparsity during training and to quantize the model. The starting checkpoitn and transfer recipe are specified by the following SparseZoo stub:
+```
+zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification
+```
+
+<details>
+   <summary>Click to see the recipe</summary>
+
+SparseML parses the `Modifers` in the recipe and updates the training loop with logic encoded therein.
+   
+The key `Modifiers` for sparse transfer learning are the following:
+- `ConstantPruningModifier` instructs SparseML to maintain the sparsity structure of the network during the fine-tuning process
+- `QuantizationModifier` instructs SparseML to apply quantization aware training to quantize the weights over the final epochs
+   
+```yaml
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.0005
+
+# quantization variables
+quantization_epochs: 6.0
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+# Phase 2 Apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(num_epochs - quantization_epochs)
+```
+
+</details>
+
+First, download and unzip the ImageNette dataset:
+
+```bash
+wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
+tar -xvf imagenette2-320.tgz
+```
+
+Run the following to transfer learn from the 95% pruned-quantized ResNet-50
+onto ImageNette:
+```bash
+sparseml.image_classification.train \
+    --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --arch-key resnet50 \
+    --dataset-path ./imagenette2-320 \
+    --batch-size 32
+```
+
+The script uses the SparseZoo stubs to identify and download the starting checkpoint and YAML-based recipe file from the SparseZoo. SparseML parses the transfer learning recipe and adjusts the trainign process to maintain sparsity during the fine-tuning process.
+
+The resulting model is 95% pruned and quantized and is trained on ImageNette!
+
+To transfer learn this sparsified model to other datasets you may have to adjust certain hyperparameters in this recipe and/or training script such as the optimizer type, the number of epochs, and the learning rates.
+
+### Export to ONNX
+
+The SparseML installation provides a `sparseml.image_classification.export_onnx` command that you can use to export the model to ONNX. Be sure the `--checkpoint_path` argument points to your trained model:
+
+```bash
+sparseml.image_classification.export_onnx \
+  --arch_key resnet50 \
+  --checkpoint_path ./checkpoint.pth \
+  --dataset-path ./imagenette2-320
+```
+
+### Deploy with DeepSparse
+
+Once exported to ONNX, you can deploy your models with DeepSparse. Checkout the DeepSparse repo for examples.
diff --git a/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb b/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
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index 00000000000..4ebb09561c3
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k39CzQqsOeQE"
+   },
+   "source": [
+    "# **Image Classification: Sparse Transfer Learning with the Python API**\n",
+    "\n",
+    "In this example, you will fine-tune a 95% pruned-quantized ResNet-50 model onto the Imagenette dataset using SparseML's Torchvision Integration.\n",
+    "\n",
+    "### **Sparse Transfer Learning Overview**\n",
+    "\n",
+    "Sparse Transfer Learning is very similiar to typical fine-tuning you are used to when training models. However, with Sparse Transfer Learning, we start the training process from a pre-sparsified checkpoint and maintain the sparsity structure while the fine tuning occurs. \n",
+    "\n",
+    "At the end, you will have a sparse model trained on your dataset, ready to be deployed with DeepSparse for GPU-class performance on CPUs!\n",
+    "\n",
+    "### **Pre-Sparsified Image Classification Model**\n",
+    "SparseZoo, Neural Magic's open source repository of pre-sparsified models, contains several families of image classification models including ResNet, MobileNet, and EfficientNet.\n",
+    "\n",
+    "In this case, we start the training process from a 95% pruned-quantized [ResNet-50 model](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none), which is identified by the following SparseZoo stub:\n",
+    "\n",
+    "```bash\n",
+    "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none\n",
+    "```\n",
+    "\n",
+    "\n",
+    "**Let's dive in!**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y0WybTbssU0g"
+   },
+   "source": [
+    "## **Installation**\n",
+    "\n",
+    "Install SparseML via `pip`.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OCGA94en19CF"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install sparseml[torch,torchvision]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OWXoz-YnPR8Z"
+   },
+   "source": [
+    "If you are running on Google Colab, restart the runtime after this step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MVf4UkqT9xXn"
+   },
+   "source": [
+    "## **Code Level APIs**\n",
+    "\n",
+    "If you need to integrate further into a system, you can use the code API directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "H5DZiJrH3DiO"
+   },
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision\n",
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "th1dNZiY-U56"
+   },
+   "source": [
+    "## **Step 1: Download the Sparse Checkpoint from the SparseZoo**\n",
+    "\n",
+    "We can use a SparseZoo stub to identify the model we want to use. In this case, we will use the 95% pruned and quantized version, which retains 99% of the baseline accuracy of the dense model. \n",
+    "\n",
+    "[Checkout the model card on SparseZoo](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Wk_EC5ar3F0L"
+   },
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "\n",
+    "ZOO_STUB = \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification\"\n",
+    "\n",
+    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"resnet50\",\n",
+    "    pretrained_path=ZOO_STUB,\n",
+    "    pretrained_dataset=\"imagenette\",\n",
+    "    num_classes=10,\n",
+    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
+    ")\n",
+    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
+    "input_size = input_shape[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oMIVY3hYAgI8"
+   },
+   "source": [
+    "## **Step 2: Setup The Dataset**\n",
+    "\n",
+    "Download the datasets. We will use Imagenette dataset for this example. Checkout the [PyTorch documentation](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) on Datasets and Dataloaders for more derails on how Datasets work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KZBaOzAe-fQj"
+   },
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "\n",
+    "# Define your train and validation datasets below, here we will use Imagenette\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AA-J71G1_c_H"
+   },
+   "source": [
+    "## **Step 3: Setup Training Loop and Objects**\n",
+    "\n",
+    "This training loop is all native PyTorch, there is nothing SparseML related."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "QX9y0eBe4Bu4"
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    # loop through batches\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # compute loss, run backpropogation\n",
+    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        # run evaluation\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    # return loss and evaluation metric\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GQL_dmO7QHQO"
+   },
+   "source": [
+    "Setup the dataloaders, loss, and optimizer. These are all native PyTorch, there is nothing SparseML related."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "In57pouf4P6r"
+   },
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "# hyperparameters\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "# setup data loaders\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dUx2vnrrAwdX"
+   },
+   "source": [
+    "## **Step 5: Add Sparse Transfer Learning Logic with SparseML**\n",
+    "\n",
+    "First, we will download the use the SparseZoo `Model` class to download the transfer learning recipe to the local directory. You can also download directly from the SparseZoo website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "PT_04J827o_a"
+   },
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model\n",
+    "\n",
+    "download_directory = \"./model_download_directory\"\n",
+    "zoo_model = Model(ZOO_STUB, download_path = download_directory)\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(recipe_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KCMRFLFN8024"
+   },
+   "source": [
+    "#### Inspecting the Recipe\n",
+    "\n",
+    "Here is the transfer learning recipe:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 10.0\n",
+    "init_lr: 0.0005\n",
+    "\n",
+    "# quantization variables\n",
+    "quantization_epochs: 6.0\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "# Phase 1 Sparse Transfer Learning / Recovery\n",
+    "sparse_transfer_learning_modifiers:\n",
+    "  - !ConstantPruningModifier\n",
+    "    start_epoch: 0.0\n",
+    "    params: __ALL_PRUNABLE__\n",
+    "\n",
+    "# Phase 2 Apply quantization\n",
+    "sparse_quantized_transfer_learning_modifiers:\n",
+    "  - !QuantizationModifier\n",
+    "    start_epoch: eval(num_epochs - quantization_epochs)\n",
+    "```\n",
+    "\n",
+    "\n",
+    "The `Modifiers` in the transfer learning recipe are the important items that encode how SparseML should modify the training process for Sparse Transfer Learning:\n",
+    "- `ConstantPruningModifier` tells SparseML to pin weights at 0 over all epochs, maintaining the sparsity structure of the network\n",
+    "- `QuantizationModifier` tells SparseML to quanitze the weights with quantization aware training over the last 5 epochs\n",
+    "\n",
+    "\n",
+    "To transfer learn this sparsified model to other datasets you may have to adjust certain hyperparameters in this recipe and/or training script. Some considerations:\n",
+    "\n",
+    "- For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly\n",
+    "- Adding more learning rate step milestones can lead to more jumps in accuracy\n",
+    "- Increase the learning rate when increasing batch size\n",
+    "- Increase the number of epochs if using SGD instead of the Adam optimizer\n",
+    "- Update the base learning rate based on the number of steps needed to train your dataset "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l5XhMbqb_7FM"
+   },
+   "source": [
+    "Next, we will use SparseML's `ScheduledModifierManager` to parse the instructions from the recipes and update the `optimizer` and `model` with the instructions encoded therein."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "xvbZ-avB4xdc"
+   },
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nrgF_ur_B66y"
+   },
+   "source": [
+    "## **Step 6: Run Training**\n",
+    "\n",
+    "We pass the modified `model` and `optimizer` objects to the training loop we build above. Since the `recipe` was applied, there is "
+   ]
+  },
+  {
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+   "execution_count": null,
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+    "executionInfo": {
+     "elapsed": 1988739,
+     "status": "ok",
+     "timestamp": 1674842564679,
+     "user": {
+      "displayName": "Robert Shaw",
+      "userId": "07936720368498181527"
+     },
+     "user_tz": 300
+    },
+    "id": "xy7JLoTM6RC_",
+    "outputId": "7f956859-081b-49fd-9723-e93a55e74c72"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "365d8cd0a55741b395ee0ba07f8404d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/10\n",
+      "Training Loss: 0.20641206579269014\n",
+      "Top 1 Acc: 0.9458662944004964\n",
+      "\n",
+      "Running Validation Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5719a6f214c946fc82b46274ee89ba63",
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/10\n",
+      "Val Loss: 0.07535588860628195\n",
+      "Top 1 Acc: 0.978\n",
+      "\n",
+      "Running Training Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b44b6c0760ab4694a15032e717f8bd22",
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/10\n",
+      "Training Loss: 0.05115955586403045\n",
+      "Top 1 Acc: 0.9847991313789359\n",
+      "\n",
+      "Running Validation Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5dd2b94a9e9f4eafaeb0e369fc960d7f",
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+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/10\n",
+      "Val Loss: 0.041224136175515014\n",
+      "Top 1 Acc: 0.986\n",
+      "\n",
+      "Running Training Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe703c6001554401a9e9f52836e6ba9d",
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/10\n",
+      "Training Loss: 0.026982061771754516\n",
+      "Top 1 Acc: 0.9927873429502094\n",
+      "\n",
+      "Running Validation Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aa3099cb4a84476cabec49993e7e75ef",
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/10\n",
+      "Val Loss: 0.025956420957299997\n",
+      "Top 1 Acc: 0.992\n",
+      "\n",
+      "Running Training Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b0e9f7c6a514215800184945cab6fe2",
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/10\n",
+      "Training Loss: 0.013596414999022997\n",
+      "Top 1 Acc: 0.9963548937490305\n",
+      "\n",
+      "Running Validation Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd1053c775aa48a29f2862a7130b38ee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/10\n",
+      "Val Loss: 0.05041731969686225\n",
+      "Top 1 Acc: 0.986\n",
+      "\n",
+      "Running Training Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "06661f1e1a85418986208cb5687a37ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/10\n",
+      "Training Loss: 0.010854419509563352\n",
+      "Top 1 Acc: 0.9971304482705134\n",
+      "\n",
+      "Running Validation Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d1a0ced3c2344d2a2b27a03db584bf2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/10\n",
+      "Val Loss: 0.03620227749252081\n",
+      "Top 1 Acc: 0.986\n",
+      "\n",
+      "Running Training Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0a8901bd1b04dd7b20edf84f377295c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/10\n",
+      "Training Loss: 0.0049538016230627376\n",
+      "Top 1 Acc: 0.9988366682177757\n",
+      "\n",
+      "Running Validation Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5efa58b1a4d7404b9dcfc7e3da97dfb5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/10\n",
+      "Val Loss: 0.03431490289449357\n",
+      "Top 1 Acc: 0.984\n",
+      "\n",
+      "Running Training Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ccd0badc672f44c09ab50e18bfe58cee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/10\n",
+      "Training Loss: 0.0029537986178653345\n",
+      "Top 1 Acc: 0.9993795563828137\n",
+      "\n",
+      "Running Validation Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69fe6cbcdace44dc883653a4c63491d6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/10\n",
+      "Val Loss: 0.03794822200688941\n",
+      "Top 1 Acc: 0.988\n",
+      "\n",
+      "Running Training Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d2b868920f54a0c9f042c1f32018711",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/10\n",
+      "Training Loss: 0.002117149980043878\n",
+      "Top 1 Acc: 0.9998448890957035\n",
+      "\n",
+      "Running Validation Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f4efda38b02479ab5148a7f1be85898",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/10\n",
+      "Val Loss: 0.03721619503517104\n",
+      "Top 1 Acc: 0.982\n",
+      "\n",
+      "Running Training Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c14f28492624d97a8fb80466f4e7691",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/10\n",
+      "Training Loss: 0.0014343990947584759\n",
+      "Top 1 Acc: 0.9999224445478517\n",
+      "\n",
+      "Running Validation Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6765c1f60581479fa94407eb2e3085fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/10\n",
+      "Val Loss: 0.028261509883918734\n",
+      "Top 1 Acc: 0.988\n",
+      "\n",
+      "Running Training Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a094b4c0441477f8758f5ec8ea30fd9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/403 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/10\n",
+      "Training Loss: 0.0011834696520765318\n",
+      "Top 1 Acc: 0.9999224445478517\n",
+      "\n",
+      "Running Validation Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "af29998c14de43dcb4e5d337ea1987d5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/16 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/10\n",
+      "Val Loss: 0.03935582631481793\n",
+      "Top 1 Acc: 0.984\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run transfer learning\n",
+    "epoch = manager.min_epochs\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZO9JUL6FD8kq"
+   },
+   "source": [
+    "We can see that the model is sparse. This command prints out the layerwise sparsity structure of the network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 10,
+     "status": "ok",
+     "timestamp": 1674842564680,
+     "user": {
+      "displayName": "Robert Shaw",
+      "userId": "07936720368498181527"
+     },
+     "user_tz": 300
+    },
+    "id": "1cHaXmcu4zGM",
+    "outputId": "426d92df-0aeb-4d61-8774-5f7ede0029b7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input.conv.module.weight: 0.4712\n",
+      "sections.0.0.conv1.module.weight: 0.5867\n",
+      "sections.0.0.conv2.module.weight: 0.8297\n",
+      "sections.0.0.conv3.module.weight: 0.7705\n",
+      "sections.0.0.identity.conv.module.weight: 0.7460\n",
+      "sections.0.1.conv1.module.weight: 0.7665\n",
+      "sections.0.1.conv2.module.weight: 0.8003\n",
+      "sections.0.1.conv3.module.weight: 0.7880\n",
+      "sections.0.2.conv1.module.weight: 0.7333\n",
+      "sections.0.2.conv2.module.weight: 0.7407\n",
+      "sections.0.2.conv3.module.weight: 0.7699\n",
+      "sections.1.0.conv1.module.weight: 0.6927\n",
+      "sections.1.0.conv2.module.weight: 0.8649\n",
+      "sections.1.0.conv3.module.weight: 0.8389\n",
+      "sections.1.0.identity.conv.module.weight: 0.9181\n",
+      "sections.1.1.conv1.module.weight: 0.9392\n",
+      "sections.1.1.conv2.module.weight: 0.9373\n",
+      "sections.1.1.conv3.module.weight: 0.9193\n",
+      "sections.1.2.conv1.module.weight: 0.8705\n",
+      "sections.1.2.conv2.module.weight: 0.8990\n",
+      "sections.1.2.conv3.module.weight: 0.8502\n",
+      "sections.1.3.conv1.module.weight: 0.8534\n",
+      "sections.1.3.conv2.module.weight: 0.8690\n",
+      "sections.1.3.conv3.module.weight: 0.8700\n",
+      "sections.2.0.conv1.module.weight: 0.7587\n",
+      "sections.2.0.conv2.module.weight: 0.9440\n",
+      "sections.2.0.conv3.module.weight: 0.8631\n",
+      "sections.2.0.identity.conv.module.weight: 0.9545\n",
+      "sections.2.1.conv1.module.weight: 0.9713\n",
+      "sections.2.1.conv2.module.weight: 0.9730\n",
+      "sections.2.1.conv3.module.weight: 0.9379\n",
+      "sections.2.2.conv1.module.weight: 0.9570\n",
+      "sections.2.2.conv2.module.weight: 0.9683\n",
+      "sections.2.2.conv3.module.weight: 0.9246\n",
+      "sections.2.3.conv1.module.weight: 0.9422\n",
+      "sections.2.3.conv2.module.weight: 0.9642\n",
+      "sections.2.3.conv3.module.weight: 0.9326\n",
+      "sections.2.4.conv1.module.weight: 0.9386\n",
+      "sections.2.4.conv2.module.weight: 0.9734\n",
+      "sections.2.4.conv3.module.weight: 0.9485\n",
+      "sections.2.5.conv1.module.weight: 0.9259\n",
+      "sections.2.5.conv2.module.weight: 0.9699\n",
+      "sections.2.5.conv3.module.weight: 0.9408\n",
+      "sections.3.0.conv1.module.weight: 0.8109\n",
+      "sections.3.0.conv2.module.weight: 0.9937\n",
+      "sections.3.0.conv3.module.weight: 0.9342\n",
+      "sections.3.0.identity.conv.module.weight: 0.9819\n",
+      "sections.3.1.conv1.module.weight: 0.9795\n",
+      "sections.3.1.conv2.module.weight: 0.9864\n",
+      "sections.3.1.conv3.module.weight: 0.9682\n",
+      "sections.3.2.conv1.module.weight: 0.9218\n",
+      "sections.3.2.conv2.module.weight: 0.9953\n",
+      "sections.3.2.conv3.module.weight: 0.9449\n",
+      "classifier.fc.module.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WOSrElENDKcm"
+   },
+   "source": [
+    "## **Step 7: Export To ONNX**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OJRVB-ZSDJ-6"
+   },
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_classification\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ffudiHTYDRC4"
+   },
+   "source": [
+    "## **Step 8: Run Inference with DeepSparse**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5jqkTY3vFyG9"
+   },
+   "outputs": [],
+   "source": [
+    "!pip3 install deepsparse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AdUKQ-FNF66P"
+   },
+   "outputs": [],
+   "source": [
+    "from deepsparse import Pipeline\n",
+    "ic_pipeline = Pipeline.create(\n",
+    "    \"image_classification\",\n",
+    "    model_path=\"/content/pytorch_classification/model.onnx\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l38TLFevKfpf"
+   },
+   "source": [
+    "Class 6 is `garbage_truck`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 441,
+     "status": "ok",
+     "timestamp": 1674846828912,
+     "user": {
+      "displayName": "Robert Shaw",
+      "userId": "07936720368498181527"
+     },
+     "user_tz": 300
+    },
+    "id": "eJv4S9fkGjdO",
+    "outputId": "df458b06-3ff4-4a0d-c1c2-3689799abaf5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n"
+     ]
+    }
+   ],
+   "source": [
+    "img_path = \"/root/.cache/nm_datasets/imagenette/imagenette-320/val/n03417042/ILSVRC2012_val_00001144.JPEG\" \n",
+    "images = [img_path]\n",
+    "print(ic_pipeline(images=images).labels[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 293
+    },
+    "executionInfo": {
+     "elapsed": 2,
+     "status": "ok",
+     "timestamp": 1674846829838,
+     "user": {
+      "displayName": "Robert Shaw",
+      "userId": "07936720368498181527"
+     },
+     "user_tz": 300
+    },
+    "id": "79Xj9eqvJXaD",
+    "outputId": "2bdfbe4d-ab3c-4cc4-c54f-a6be6cbb2b2b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7fd544213940>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 431.1x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img = np.array(Image.open(img_path))\n",
+    "plt.matshow(img)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "authorship_tag": "ABX9TyM5OB17UuJsla11foDdM/QW",
+   "machine_shape": "hm",
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0168b617288942379053f80fcf791606": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
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+      "layout": "IPY_MODEL_7f51aa6075e14485bbb532b374247855"
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/integrations/torchvision/tutorials/sparse-transfer-learning.md b/integrations/torchvision/tutorials/sparse-transfer-learning.md
new file mode 100644
index 00000000000..937579925b1
--- /dev/null
+++ b/integrations/torchvision/tutorials/sparse-transfer-learning.md
@@ -0,0 +1,168 @@
+# Sparsifying Image Classification Models with the CLI
+
+This page explains how to fine-tune a pre-sparsified Torchvision image classification models like ResNet-50 with SparseML's CLI.
+
+## Overview
+
+Sparse transfer learning is quite similiar to the typical transfer learning training, where we fine-tune a checkpoint pretrained on ImageNet onto a smaller downstream dataset. However, with Sparse Transfer Learning, we simply start the fine-tuning process from a pre-sparsified checkpoint and maintain sparsity while the training process occurs.
+
+[SparseZoo](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=classification&page=1) contains pre-sparsified checkpoints for common image classification models like ResNet-50 and EfficientNet, which can be used as the starting checkpoints for the training process.
+
+## Installation
+
+Install SparseML via `pip`.
+
+```bash
+pip install sparseml[torchvision]
+```
+
+## Sparse Transfer Learning with ResNet-50
+
+Let's try a simple example of Sparse Transfer Learning ResNet-50 onto [FastAI's Imagenette dataset](https://github.com/fastai/imagenette) (a subset of 10 easily classified classes from ImageNet).
+
+Download Imagnette to your local directory:
+```bash
+wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
+tar -xvf imagenette2-320.tgz
+```
+
+Kick off transfer learning.
+```bash
+sparseml.image_classification.train \
+    --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --arch-key resnet50 \
+    --dataset-path ./imagenette2-320\
+    --batch-size 32
+```
+
+Let's discuss the key arguments:
+- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub. Here, we passed a stub that identifies a 95% pruned-quantized version of ResNet-50 in the SparseZoo. The script downloads the PyTorch model to begin training.
+
+- `--arch-key` specifies the torchvision architecture of the checkpoint. For example, `resnet50` or `mobilenet`.
+
+- `--dataset-path` specifies the dataset used for training. We passed the local path to Imagenette. The dataset should be in the ImageFolder format (we will describe the format below).
+
+- `--recipe` specifies the sparsity related parameters of the training process. Recipes are YAML files that declare the sparsity related algorithms that SparseML should apply. For transfer learning, the recipe instructs SparseML to maintain sparsity during training and to apply quantization over the final epochs. In this case, we passed a SparseZoo stub, which instructs SparseML to download a premade ResNet-50 transfer learning recipe. In addition to SparseZoo stubs, you can also pass a local path to a YAML recipe. See below for more details on what the transfer learning recipe looks like.
+
+Run the help command to inspect the full list of arguments and configurations.
+```bash
+sparseml.image_classification.train --help
+```
+
+Here's what the transfer learning recipe looks like:
+```yaml
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.0005
+
+# quantization variables
+quantization_epochs: 6.0
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+# Phase 2 Apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(num_epochs - quantization_epochs)
+```
+
+The "Modifiers" encode how SparseML should modify the training process for Sparse Transfer Learning.
+- `ConstantPruningModifier` tells SparseML to pin weights at 0 over all epochs, maintaining the sparsity structure of the network
+- `QuantizationModifier` tells SparseML to quanitze the weights with quantization aware training over the last 6 epochs
+
+SparseML parses the instructions declared in the recipe and modifies the training loop accordingly before running the fine-tuning. 
+
+As a result, we end up with a 75% pruned and quantized ResNet-50 trained on Imagenette!
+
+#### Export to ONNX
+
+To deploy with DeepSparse, we need to export the model to ONNX.
+
+Run the following to do so:
+```bash
+sparseml.image_classification.export_onnx \
+  --arch_key resnet50 \
+  --checkpoint_path ./checkpoint.pth \
+  --dataset-path ./imagenette2-320
+```
+
+#### Modify a Recipe
+
+To transfer learn this sparsified model to other datasets you may have to adjust certain hyperparameters in this recipe and/or training script. Some considerations:
+
+- For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
+- Adding more learning rate step milestones can lead to more jumps in accuracy
+- Increase the learning rate when increasing batch size
+- Increase the number of epochs if using SGD instead of the Adam optimizer
+- Update the base learning rate based on the number of steps needed to train your dataset
+
+To update a recipe, you can download the YAML file from SparseZoo, make updates to the YAML directly, and pass the local path to SparseML.
+
+Alternatively, you can use `--recipe_args` to modify a recipe on the fly. The following runs for 15 epochs instead of 10:
+
+```bash
+sparseml.image_classification.train \
+    --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --recipe_args '{"num_epochs": 15"}' \
+    --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --arch-key resnet50 \
+    --dataset-path ./imagenette2-320\
+    --batch-size 32
+```
+
+The `--recipe_args` are a json parsable dictionary of recipe variable names to values to overwrite the YAML-recipe.
+
+#### Dataset Format
+
+SparseML's Torchvision CLI conforms to the [Torchvision ImageFolder dataset format](https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html), where images are arranged into directories representing each class. To use a custom dataset with the SparseML CLI, you will need to arrange your data into this format.
+
+For example, the following downloads Imagenette to your local directory:
+
+```bash
+wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
+tar -xvf imagenette2-320.tgz
+```
+
+The resulting `imagenette-320` directory looks like the following (where each subdirectory like `n01440764` 
+represents one of the 10 classes in the Imagenette dataset) and each `.JPEG` file is a training example.
+```bash
+|-train
+    |-n01440764
+        |-n01440764_10026.JPEG
+        |-n01440764_10027.JPEG
+        |...
+    |-n02979186
+    |-n03028079
+    |-n03417042
+    |-n03445777
+    |-n02102040
+    |-n03000684
+    |-n03394916
+    |-n03425413
+    |-n03888257
+|-val
+    |-n01440764
+    |-n02979186
+    | ...
+```
+
+## Wrapping Up
+
+Checkout the DeepSparse repository for more information on deploying your sparse Image Classification models with DeepSparse for GPU class performance on CPUs.
\ No newline at end of file

From b150d6b0faad77f5f20997c1c559a53c2bfb18df Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Sun, 5 Mar 2023 17:41:07 -0500
Subject: [PATCH 02/29] updated to redo the file structure

---
 integrations/dbolya-yolact/README.md          | 144 ---
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 .../recipes/yolact.pruned_quant.md            | 204 ----
 .../dbolya-yolact/recipes/yolact.quantized.md |  74 --
 .../sparsifying_yolact_using_recipes.md       | 226 ----
 integrations/keras/README.md                  |  94 --
 integrations/keras/classification.py          | 898 ----------------
 .../keras/notebooks/classification.ipynb      | 433 --------
 integrations/keras/prune_resnet20.py          | 241 -----
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 integrations/pytorch/export.py                | 358 -------
 integrations/pytorch/lr_analysis.py           | 408 --------
 .../pytorch/notebooks/classification.ipynb    | 366 -------
 .../pytorch/notebooks/detection.ipynb         | 372 -------
 .../sparse_quantized_transfer_learning.ipynb  | 454 --------
 .../pytorch/notebooks/torchvision.ipynb       | 399 --------
 integrations/pytorch/pr_sensitivity.py        | 416 --------
 integrations/pytorch/recipes/.gitkeep         |   0
 .../classification.transfer_learn_pruned.md   |  98 --
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 .../pytorch/recipes/mobilenet-pruned.md       | 107 --
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 .../recipes/resnet50-imagenette-pruned.md     | 145 ---
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 .../pytorch/torchvision_sparsification.py     | 455 ---------
 integrations/pytorch/train.py                 | 703 -------------
 integrations/pytorch/tutorials/.gitkeep       |   0
 ...ation_sparse_transfer_learning_tutorial.md | 138 ---
 ...parsifying_pytorch_models_using_recipes.md | 132 ---
 integrations/pytorch/utils.py                 | 428 --------
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 integrations/rwightman-timm/README.md         | 116 ---
 .../recipes/vit_base.85.recal.config.yaml     |  83 --
 .../rwightman-timm/setup_integration.sh       |  11 -
 integrations/tensorflow_v1/README.md          |  93 --
 integrations/tensorflow_v1/classification.py  | 965 ------------------
 .../notebooks/classification.ipynb            | 403 --------
 integrations/tensorflow_v1/recipes/.gitkeep   |   0
 integrations/tensorflow_v1/tutorials/.gitkeep |   0
 integrations/ultralytics-yolov3/README.md     | 128 ---
 .../ultralytics-yolov3/data/hyp.pruned.yaml   |  27 -
 .../data/hyp.pruned_quantized.yaml            |  27 -
 .../models/yolov3-spp.lrelu.yaml              |  52 -
 .../recipes/yolov3-spp.pruned.md              | 208 ----
 .../recipes/yolov3-spp.pruned.short.md        | 207 ----
 .../recipes/yolov3-spp.pruned_quantized.md    | 286 ------
 .../yolov3-spp.pruned_quantized.short.md      | 286 ------
 .../recipes/yolov3-spp.test.md                | 285 ------
 .../yolov3-spp.transfer_learn_pruned.md       | 169 ---
 ...ov3-spp.transfer_learn_pruned_quantized.md | 238 -----
 .../ultralytics-yolov3/setup_integration.sh   |  10 -
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 .../images/transfer-learning-wandb-chart.png  | Bin 493088 -> 0 bytes
 .../sparsifying_yolov3_using_recipes.md       | 247 -----
 .../yolov3_sparse_transfer_learning.md        | 217 ----
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diff --git a/integrations/dbolya-yolact/README.md b/integrations/dbolya-yolact/README.md
deleted file mode 100644
index 8d0be24b308..00000000000
--- a/integrations/dbolya-yolact/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML YOLACT Integration
-
-This directory combines the [SparseML](../../) recipe-driven approach with the 
-[dbolya/yolact](https://github.com/dbolya/yolact) repository.
-By integrating the training flows in the `yolact` repository with the SparseML 
-code base,
-we enable model sparsification techniques on the popular 
-[YOLACT architecture](https://arxiv.org/abs/1804.02767)
-creating smaller and faster deployable versions.
-The techniques include, but are not limited to:
-
-- Pruning
-- Quantization
-- Pruning and Quantization
-- Sparse Transfer Learning
-
-## Installation
-
-We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
-A virtual environment can be created using the following commands:
-
-```bash
-python3 -m venv venv # create a venv virtual environment
-source venv/bin/activate # activate venv
-pip install --upgrade pip # upgrade pip
-```
-To begin, install `sparseml[torchvision]>=1.1`
-
-```bash
-pip install "sparseml[torchvision]>=1.1"
-```
-
-Note: This integration requires `python>=3.7,<3.10`
-
-
-## Quick Tour
-
-### Downloading COCO
-
-The `sparseml.yolact.download` utility provides an easy to use interface to download
-the `COCO` dataset.
-
-Simply invoke:
-```bash
-sparseml.yolact.download
-```
-
-To download  a test `COCO` dataset, run `sparseml.yolact.download --test`. For more information
-on this utility append the `--help` option. (Note: by default, the dataset is downloaded to the code execution directory. )
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-`sparseml.yolact.train` adds a `--recipe` argument to the 
-[`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-Example `train` command;
-be sure to provide the correct dataset and recipe path to the utility based on your local setup:
-
-```bash
-sparseml.yolact.train --resume \
-zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---recipe ./recipes/yolact.pruned.md \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
-
-### SparseZoo
-
-| Sparsification Type | Description                                                                       | Zoo Stub                                                                     | COCO mAP@all | Size on Disk | DeepSparse Performance** |
-|---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|--------------|--------------------------|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none           | 0.288        | 170 MB       | -- img/sec               |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none       | 0.286        | 30.1 MB      | -- img/sec               |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned82_quant-none | 0.282        | 9.7 MB       | -- img/sec               |
-
-These models can also be viewed on the [SparseZoo Website](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=segmentation&page=1).
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name                                | Description                                                                                                           |
-|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [recipes](./recipes)                            | Typical recipes for sparsifying YOLACT models along with any downloaded recipes from the SparseZoo.                   |
-| [yolact](./yolact)                              | Integration repository folder used to train and sparsify YOLACT models (`setup_integration.sh` must run first).       |
-| [README.md](./README.md)                        | Readme file.                                                                                                          |
-| [tutorials](./tutorials)                        | Easy to follow sparsification tutorials for YOLACT  models.                                                            |
-
-### Exporting for Inference
-
-After sparsifying a model, the 
-`sparseml.yolact.export_onnx` utility
-converts the model into deployment formats such as [ONNX](https://onnx.ai/).
-The export process is modified such that the quantized and pruned models are 
-corrected and folded properly.
-
-For example, the following command can be run to export a trained/sparsified YOLACT
-model's checkpoint:
-
-```bash
-sparseml.yolact.export_onnx --checkpoint ./quantized-yolact/model.pth \
-    --name quantized-yolact.onnx
-```
-
-
-### DeepSparse
-
-The [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) accepts ONNX 
-formats and is engineered to significantly speed up inference on CPUs for 
-the sparsified models from this integration. [Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact) scripts can be found in the DeepSparse repository.
-
-## Citation
-```bibtex
-@inproceedings{yolact-iccv2019,
-  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  title     = {YOLACT: {Real-time} Instance Segmentation},
-  booktitle = {ICCV},
-  year      = {2019},
-}
-```
diff --git a/integrations/dbolya-yolact/recipes/yolact.pruned.md b/integrations/dbolya-yolact/recipes/yolact.pruned.md
deleted file mode 100644
index b3435b049d2..00000000000
--- a/integrations/dbolya-yolact/recipes/yolact.pruned.md
+++ /dev/null
@@ -1,174 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Hyperparams
-num_epochs: 60
-
-# Pruning Hyperparams
-init_sparsity: 0.35
-pruning_start_target: 0
-pruning_end_target: 0.5
-pruning_update_frequency: 0.2
-mask_type: unstructured
-prune_none_target_sparsity: 0.75
-prune_low_target_sparsity: 0.83
-prune_mid_target_sparsity: 0.87
-prune_high_target_sparsity: 0.92
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0
-    end_epoch: eval(num_epochs)
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-    - backbone.layers.0.1.conv1.0.weight
-    - backbone.layers.0.1.conv2.0.weight
-    - backbone.layers.1.0.0.weight
-    - backbone.layers.1.1.conv1.0.weight
-    - backbone.layers.1.1.conv2.0.weight
-    - backbone.layers.1.2.conv1.0.weight
-    - backbone.layers.1.2.conv2.0.weight
-    - backbone._preconv.0.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_none_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.2.0.0.weight
-      - backbone.layers.2.1.conv1.0.weight
-      - backbone.layers.2.1.conv2.0.weight
-      - backbone.layers.2.2.conv1.0.weight
-      - backbone.layers.2.2.conv2.0.weight
-      - backbone.layers.2.3.conv1.0.weight
-      - backbone.layers.2.3.conv2.0.weight
-      - backbone.layers.2.4.conv1.0.weight
-      - backbone.layers.2.4.conv2.0.weight
-      - backbone.layers.2.5.conv1.0.weight
-      - backbone.layers.2.5.conv2.0.weight
-      - backbone.layers.2.6.conv1.0.weight
-      - backbone.layers.2.6.conv2.0.weight
-      - backbone.layers.2.7.conv1.0.weight
-      - backbone.layers.2.7.conv2.0.weight
-      - backbone.layers.2.8.conv1.0.weight
-      - backbone.layers.2.8.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_low_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - proto_net.0.weight
-      - proto_net.2.weight
-      - proto_net.4.weight
-      - proto_net.8.weight
-      - proto_net.10.weight
-      - fpn.lat_layers.0.weight
-      - fpn.lat_layers.1.weight
-      - fpn.lat_layers.2.weight
-      - fpn.pred_layers.0.weight
-      - fpn.pred_layers.1.weight
-      - fpn.pred_layers.2.weight
-      - fpn.downsample_layers.0.weight
-      - fpn.downsample_layers.1.weight
-      - prediction_layers.0.upfeature.0.weight
-      - prediction_layers.0.bbox_layer.weight
-      - prediction_layers.0.conf_layer.weight
-      - prediction_layers.0.mask_layer.weight
-      - semantic_seg_conv.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_mid_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.3.0.0.weight
-      - backbone.layers.3.1.conv1.0.weight
-      - backbone.layers.3.1.conv2.0.weight
-      - backbone.layers.3.2.conv1.0.weight
-      - backbone.layers.3.2.conv2.0.weight
-      - backbone.layers.3.3.conv1.0.weight
-      - backbone.layers.3.3.conv2.0.weight
-      - backbone.layers.3.4.conv1.0.weight
-      - backbone.layers.3.4.conv2.0.weight
-      - backbone.layers.3.5.conv1.0.weight
-      - backbone.layers.3.5.conv2.0.weight
-      - backbone.layers.3.6.conv1.0.weight
-      - backbone.layers.3.6.conv2.0.weight
-      - backbone.layers.3.7.conv1.0.weight
-      - backbone.layers.3.7.conv2.0.weight
-      - backbone.layers.3.8.conv1.0.weight
-      - backbone.layers.3.8.conv2.0.weight
-      - backbone.layers.4.0.0.weight
-      - backbone.layers.4.1.conv1.0.weight
-      - backbone.layers.4.1.conv2.0.weight
-      - backbone.layers.4.2.conv1.0.weight
-      - backbone.layers.4.2.conv2.0.weight
-      - backbone.layers.4.3.conv1.0.weight
-      - backbone.layers.4.3.conv2.0.weight
-      - backbone.layers.4.4.conv1.0.weight
-      - backbone.layers.4.4.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_high_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
----
-
-# YOLACT Pruned
-
-
-This recipe creates a sparse, [YOLACT](https://github.com/dbolya/yolact) model 
-that recovers to 99.37% of its baseline accuracy on the COCO dataset 
-(50.16, 46.57 mAP@0.5 baseline Vs 49.36, 46.37 mAP@0.5 for this recipe for bounding box, mask).
-Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup. 
-
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
diff --git a/integrations/dbolya-yolact/recipes/yolact.pruned_quant.md b/integrations/dbolya-yolact/recipes/yolact.pruned_quant.md
deleted file mode 100644
index 5b2c365710c..00000000000
--- a/integrations/dbolya-yolact/recipes/yolact.pruned_quant.md
+++ /dev/null
@@ -1,204 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Hyperparams
-num_epochs: 62
-init_lr: 0.001
-num_quantization_epochs: 2
-num_pruning_epochs: eval(num_epochs - num_quantization_epochs)
-quantization_observer_end_target: 0.5
-
-# Pruning Hyperparams
-init_sparsity: 0.2
-pruning_start_target: 0
-pruning_end_target: 0.5
-pruning_update_frequency: 0.2
-mask_type: [1, 4]
-prune_none_target_sparsity: 0.6
-prune_low_target_sparsity: 0.7
-prune_mid_target_sparsity: 0.8
-prune_high_target_sparsity: 0.85
-
-# quantization hyperparams
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0
-    end_epoch: eval(num_epochs)
-
-  # pruning
-  - !SetWeightDecayModifier
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    weight_decay: 0.0005
-
-  - !LearningRateFunctionModifier
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    lr_func: linear
-    init_lr: eval(init_lr)
-    final_lr: eval(0.01 * init_lr)
-  # quantization
-  - !SetWeightDecayModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    weight_decay: 0.0
-
-  - !LearningRateFunctionModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    end_epoch: eval(num_epochs)
-    lr_func: linear
-    init_lr: eval(0.01 * init_lr)
-    final_lr: eval(0.001 * init_lr)
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-    - backbone.layers.0.1.conv1.0.weight
-    - backbone.layers.0.1.conv2.0.weight
-    - backbone.layers.1.0.0.weight
-    - backbone.layers.1.1.conv1.0.weight
-    - backbone.layers.1.1.conv2.0.weight
-    - backbone.layers.1.2.conv1.0.weight
-    - backbone.layers.1.2.conv2.0.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_none_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.2.0.0.weight
-      - backbone.layers.2.1.conv1.0.weight
-      - backbone.layers.2.1.conv2.0.weight
-      - backbone.layers.2.2.conv1.0.weight
-      - backbone.layers.2.2.conv2.0.weight
-      - backbone.layers.2.3.conv1.0.weight
-      - backbone.layers.2.3.conv2.0.weight
-      - backbone.layers.2.4.conv1.0.weight
-      - backbone.layers.2.4.conv2.0.weight
-      - backbone.layers.2.5.conv1.0.weight
-      - backbone.layers.2.5.conv2.0.weight
-      - backbone.layers.2.6.conv1.0.weight
-      - backbone.layers.2.6.conv2.0.weight
-      - backbone.layers.2.7.conv1.0.weight
-      - backbone.layers.2.7.conv2.0.weight
-      - backbone.layers.2.8.conv1.0.weight
-      - backbone.layers.2.8.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_low_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - proto_net.0.weight
-      - proto_net.2.weight
-      - proto_net.4.weight
-      - proto_net.8.weight
-      - proto_net.10.weight
-      - fpn.lat_layers.0.weight
-      - fpn.lat_layers.1.weight
-      - fpn.lat_layers.2.weight
-      - fpn.pred_layers.0.weight
-      - fpn.pred_layers.1.weight
-      - fpn.pred_layers.2.weight
-      - fpn.downsample_layers.0.weight
-      - fpn.downsample_layers.1.weight
-      - prediction_layers.0.upfeature.0.weight
-      - prediction_layers.0.bbox_layer.weight
-      - prediction_layers.0.conf_layer.weight
-      - prediction_layers.0.mask_layer.weight
-      - semantic_seg_conv.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_mid_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.3.0.0.weight
-      - backbone.layers.3.1.conv1.0.weight
-      - backbone.layers.3.1.conv2.0.weight
-      - backbone.layers.3.2.conv1.0.weight
-      - backbone.layers.3.2.conv2.0.weight
-      - backbone.layers.3.3.conv1.0.weight
-      - backbone.layers.3.3.conv2.0.weight
-      - backbone.layers.3.4.conv1.0.weight
-      - backbone.layers.3.4.conv2.0.weight
-      - backbone.layers.3.5.conv1.0.weight
-      - backbone.layers.3.5.conv2.0.weight
-      - backbone.layers.3.6.conv1.0.weight
-      - backbone.layers.3.6.conv2.0.weight
-      - backbone.layers.3.7.conv1.0.weight
-      - backbone.layers.3.7.conv2.0.weight
-      - backbone.layers.3.8.conv1.0.weight
-      - backbone.layers.3.8.conv2.0.weight
-      - backbone.layers.4.0.0.weight
-      - backbone.layers.4.1.conv1.0.weight
-      - backbone.layers.4.1.conv2.0.weight
-      - backbone.layers.4.2.conv1.0.weight
-      - backbone.layers.4.2.conv2.0.weight
-      - backbone.layers.4.3.conv1.0.weight
-      - backbone.layers.4.3.conv2.0.weight
-      - backbone.layers.4.4.conv1.0.weight
-      - backbone.layers.4.4.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_high_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    disable_quantization_observer_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
-    freeze_bn_stats_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
----
-
-# YOLACT Pruned Quantized
-
-This recipe creates a sparse quantized, [YOLACT](https://github.com/dbolya/yolact) model that achieves about 
-98% recovery on the COCO dataset when compared against baseline ( 30.61, 28.80 mAP@all baseline Vs 29.82, 28.26 mAP@all for this recipe for bounding box, mask).
-Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup.
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned_quant.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
diff --git a/integrations/dbolya-yolact/recipes/yolact.quantized.md b/integrations/dbolya-yolact/recipes/yolact.quantized.md
deleted file mode 100644
index f992d0420c1..00000000000
--- a/integrations/dbolya-yolact/recipes/yolact.quantized.md
+++ /dev/null
@@ -1,74 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-num_epochs: 2
-quantization_start_target: 0
-quantization_end_target: 1
-quantization_observer_end_target: 0.5
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-  - !SetWeightDecayModifier
-    start_epoch: eval(quantization_start_target * num_epochs)
-    weight_decay: 0.0
-
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: eval(quantization_end_target * num_epochs)
-    lr_func: linear
-    init_lr: 0.00001
-    final_lr: 0.000001
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: eval(quantization_start_target * num_epochs)
-    disable_quantization_observer_epoch: eval(quantization_observer_end_target * num_epochs)
-    freeze_bn_stats_epoch: eval(quantization_observer_end_target * num_epochs)
----
-
-# YOLACT Quantized
-
-This recipe quantizes a [YOLACT](https://github.com/dbolya/yolact) model.
-Training was done using 4 GPUs with a total batch size of 64 using the 
-[SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup.
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.quantized.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
\ No newline at end of file
diff --git a/integrations/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md b/integrations/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
deleted file mode 100644
index daef9205b9c..00000000000
--- a/integrations/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
+++ /dev/null
@@ -1,226 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying YOLACT Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters 
-and instructions needed to create highly accurate pruned and pruned-quantized [YOLACT](https://arxiv.org/abs/1904.02689) segmentation models.
-
-## Overview
-
-Neural Magic's ML team creates recipes that allow anyone to plug in their data and leverage SparseML's recipe-driven 
-approach on top of [YOLACT](https://github.com/dbolya/yolact) training pipelines.
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and 
-quantization, among others. This sparsification process results in many benefits for deployment environments, 
-including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the 
-complicated process and number of hyper-parameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-
-* Creating a pre-trained model to establish a baseline. You will set up your data and then train the model.
-* Applying a recipe to select the trade-off between the amount of recovery to the baseline training performance with 
-the amount of sparsification for inference performance.
-* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) 
-through a compression algorithm to reduce its deployment size and run it in an inference engine such as 
-[DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-The examples listed in this tutorial are all performed on the COCO dataset.
-
-Before diving in, be sure to go through the setup as listed out in the [README](../README.md) for this integration.
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Creating a Pre-trained Model
-
-Before applying one of the recipes, you must first create the pre-trained model to sparsify further. 
-The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. 
-Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
- 
-Creating a pre-trained model involves three steps: 
-1) Setting up the data.
-2) Fetching a model backbone. 
-3) Training the model.
-
-If training with COCO, skip steps 2 and 3 and use the following baseline SparseZoo stub,
-`zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none` with the `--resume` argument
-in the [`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py)
-
-**Note**: If using your custom data, the [YOLACT](https://github.com/dbolya/yolact) repo mentions a post for [training custom data](https://github.com/dbolya/yolact/issues/70#issuecomment-504283008). 
-Otherwise, setup scripts for [COCO](https://cocodataset.org/#home) can be found under the [yolact/data/scripts path](https://github.com/neuralmagic/yolact/tree/master/data/scripts).
-
-### Setting Up the Data
-
-1. For this tutorial, run the COCO setup script with the following command from the root of the `yolact` repository:
-    ```bash
-    sparseml.yolact.download
-    ```
-   
-Based on your internet connection, downloading and validation of the COCO dataset will take around 10 minutes to complete.
-    The script downloads the COCO dataset into a `coco` folder under the data directory.
-Once completed, the data is ready for training with the folder structure in the following state (only directories are shown for brevity):
-```
-    └─ data
-       └─ coco
-          ├── annotations
-          └── images
- ```
-   
- You are ready to train the model.
-
-### Downloading Model Backbone
-
-1. Training YOLACT from scratch requires a pretrained-backbone model, 
-   currently SparseML supports training with `DarkNet-53` backbone. 
-   - Download ImageNet-pre-trained [`Darknet-53` backbone](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing)
-     and put it in `./weights` directory. The directory structure should look like the following:
-````
-    └── Project_directory
-        ├── data
-        │   └─ coco
-        │      ├── annotations
-        │      └── images
-        │   
-        └── weights
-            └── darknet53.pth
-````
-
-2) Run the following command to kickstart training
-```bash
-sparseml.yolact.train --resume \
-./weights/darknet53.pth \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images \
---backbone
-```
-The weights are stored in the `./weights` directory by default and use the `<config>_<epoch>_<iter>.pth` naming 
-convention
-
-3) Validate that the training command was completed successfully by checking under the `./weights` directory for the trained weights.
-   Upon success, the resulting directory structure should look like the following (a few directories are missing content for brevity):
-```
-└── Project_Directory
-    ├── data
-    │    └── coco
-    │        ├── annotations
-    │        └── images
-    │   
-    └── weights
-        ├── darknet53.pth
-        └── yolact_darknet53_54_800000.pth
- ```
-
-You are ready to use the weights at `./weights/yolact_darknet53_54_800000.pth` with the Neural Magic recipes to create a sparsified model.
-You can also download this baseline, pre-trained checkpoint directly from the [SparseZoo UI](https://sparsezoo.neuralmagic.com/models/cv%2Fsegmentation%2Fyolact-darknet53%2Fpytorch%2Fdbolya%2Fcoco%2Fbase-none),
-or pass its model stub directly to the `--resume` argument while invoking the training script.
-## Applying a Recipe
-
-In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-The [`recipes` folder](../recipes) contains multiple files, each offering certain advantages over others. 
-The table below compares these tradeoffs and shows how to run them on the COCO dataset.
-1. Review this table, which lists recipes, commands, and results.
-
-| Sparsification Type |                                    Description                                    | COCO mAP@all | Size on Disk | DeepSparse Performance** |                                        Commands                                        |
-|:-------------------:|:---------------------------------------------------------------------------------:|:------------:|:------------:|:------------------------:|:--------------------------------------------------------------------------------------:|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.288        | 170 MB       | 29.7 img/sec               | `python train.py`                                                                      |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.286        | 30.1 MB      | 61.6 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned.md`       |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.282        | 9.7 MB       | 144.4 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned_quant.md` |
-
-   \*\* DeepSparse Performance measured on an AWS c5.12xlarge instance with 24 cores, batch size 64, and 550x550 input with version 0.12.0 of the DeepSparse Engine i.e. `deepsparse.benchmark --batch_size 64 --scenario sync [model_path]`
-   
-2. Select a recipe to use on top of the pre-trained model you created.
-
-    - Check your CPU hardware support for quantized networks (VNNI instruction set) using the DeepSparse API:
-      ```bash
-      deepsparse.check_hardware
-      ```
-    - If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using the  `pruned` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
-    - If your hardware does support quantized networks, we recommend using the `pruned quantized` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
-    - When running quantized models, the memory footprint for training will significantly increase (roughly 3x). It is recommended to train at a high batch size at first. This will fail with an out-of-memory exception once quantization starts. Once this happens, use the weights from that run to resume training with lower batch size.
-
-3. To begin applying one of the recipes, use the `--recipe` argument within the YOLACT [train script](https://github.com/neuralmagic/yolact/blob/master/train.py).
-   The recipe argument is combined with our previous training command and COCO pre-trained weights to run the recipes over the model. For example, a command for pruning YOLACT would look like this:
-```bash
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---save_folder=./pruned \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
-After applying a recipe, you are ready to export for inference.
-
-## Exporting for Inference
-
-This step loads a checkpoint file along with the recipe used if any and converts it into the more common inference formats. 
-Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-When you applied a recipe in the previous step, the sparsification run created a new `./pruned` directory under the `yolact` directory:
-
-```
-└── Project_directory
-    ├── data
-    │   ├── coco
-    │   │   ├── annotations
-    │   │   └── images
-    │   └── scripts
-    ├── pruned
-    │    └── yolact_darknet53_3_29316.pth 
-    └── weights
-        └── yolact_darknet53_54_800000.pth
-```
-
-These weights under `./pruned` can be loaded into the `sparseml.yolact.train` and `sparseml.yolact.export_onnx` utilities now. 
-However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-[`sparseml.yolact.export_onnx`](https://github.com/neuralmagic/yolact/blob/master/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-
-1. Enter the following command to load the PyTorch graph, convert to ONNX, and correct any misformatted pieces of the graph for the pruned and quantized models.
-
-    ```bash
-    sparseml.yolact.export_onnx --checkpoint PATH_TO_SPARSIFIED_WEIGHTS
-    ```
-    
-    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-
-2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-   The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
-   An example for benchmarking and deploying YOLACT models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLACT models for image segmentation tasks. 
-In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported it to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/blob/main/src/deepsparse/yolact/README.md) for an example for benchmarking and deploying YOLACT models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Citation
-
-```bibtex
-@inproceedings{yolact-iccv2019,
-  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  title     = {YOLACT: {Real-time} Instance Segmentation},
-  booktitle = {ICCV},
-  year      = {2019},
-}
-```
diff --git a/integrations/keras/README.md b/integrations/keras/README.md
deleted file mode 100644
index c3ffdc4d9b3..00000000000
--- a/integrations/keras/README.md
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML Keras Integration
-
-This directory combines the SparseML recipe-driven approach with standard Keras training flows for models within
-[Keras Applications](https://keras.io/api/applications/) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom Keras training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Coming soon!
-
-## Tutorials
-
-- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/keras/notebooks/classification.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[tf_keras]`
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=keras) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=keras) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| notebooks            | Jupyter notebooks to walk through sparsifiation within standard Keras training flows                                  |
-| recipes              | Typical recipes for sparsifying Keras models along with any downloaded recipes from the SparseZoo.                    |
-| tutorials            | Tutorial walkthroughs for how to sparsify Keras models using recipes.                                                 |
-| classification.py    | Utility training script for sparsifying classification models in Keras (loads SparseML models).                       |
-| prune_resnet20.py    | Simple example walking through pruning a ResNet-20 model on the Cifar dataset.                                        |
-| README.md            | Readme file.                                                                                                          |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/keras/classification.py b/integrations/keras/classification.py
deleted file mode 100644
index 35644f01e36..00000000000
--- a/integrations/keras/classification.py
+++ /dev/null
@@ -1,898 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Perform tasks on image classification models in Keras including:
-* Model training/pruning
-* Model evaluation
-* ONNX export
-
-
-##########
-train command help:
-usage: classification.py train [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                               [--recipe-path RECIPE_PATH]
-                               [--sparse-transfer-learn] [--eval-mode]
-                               --train-batch-size TRAIN_BATCH_SIZE
-                               --test-batch-size TEST_BATCH_SIZE
-                               [--logs-dir LOGS_DIR]
-                               [--save-best-after SAVE_BEST_AFTER]
-                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
-                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
-Train and/or prune an image classification model
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --model-kwargs MODEL_KWARGS
-                        key word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        Key word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --transfer-class-type TRANSFER_CLASS_TYPE
-                        Type of the target class for transfer ("single" for softmax
-                        function or "multi" for sigmoid function); takes effects only
-                        if the specified dataset is different from the pretrained
-                        dataset, implying a transfer learning usecase
-  --transfer-top-layer TRANSFER_TOP_LAYER
-                        Name of the layer which a custom dense layer will be put on;
-                        None by default to imply the layer right under the top of the
-                        original model will be used (in other words, layer[-2])
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --train-dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for train dataset loading
-  --train-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for train dataset loading
-  --train-prefetch-buffer-size PREFETCH_BUFFER_SIZE
-                        Prefetch buffer size for train dataset loading
-  --val-dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for val dataset loading
-  --val-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for val dataset loading
-  --val-prefetch-buffer-size PREFETCH_BUFFER_SIZE
-                        Prefetch buffer size for val dataset loading
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. If set to
-                        'transfer_learning', then will create a schedule to
-                        enable sparse transfer learning. Recipe from SparseZoo
-                        could be specified using model stub starting with "zoo:"
-  --sparse-transfer-learn
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode           Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
-                        epochs to save checkpoints at
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-################
-Evaluate command help:
-usage: classification.py evaluate [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               --test-batch-size TEST_BATCH_SIZE
-Evaluate an image classification model
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while evaluating
-################
-Export command help:
-usage: classification.py export [-h] --arch-key ARCH_KEY
-                                [--pretrained PRETRAINED]
-                                [--pretrained-dataset PRETRAINED_DATASET]
-                                [--checkpoint-path CHECKPOINT_PATH]
-                                --dataset-path DATASET_PATH
-                                [--dataset-kwargs DATASET_KWARGS]
-                                [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                                [--num-samples NUM_SAMPLES]
-                                [--onnx-opset OPSET]
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        key word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --num-samples NUM_SAMPLES
-                        Number of samples to generate, default to 100
-  --onnx-opset OPSET
-                        The ONNX opset to export to, default to 11
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --export-debug-mode EXPORT_DEBUG
-                        The debug mode for ONNX export (passed into keras2onnx).
-                        Default to False.
-
-
-############
-EXAMPLES
-############
-
-#############
-Example command for fine-tuning a ResNet50 model from Keras Application, pretrained on
-Imagenet, for the Imagenette dataset. The recipe could be used to define learning rate
-schedule. Note that there're no pruning performed from this experiment.
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --pretrained-dataset imagenet \
-     --dataset imagenette \
-     --dataset-path <DATASET_DIR> \
-     --train-batch-size 64 \
-     --recipe-path <RECIPE_PATH>
-############
-Example command for pruning a model with a recipe. Assuming that a ResNet50 model
-from Keras Application was fine-tuned for the Imagenette dataset. The following
-command specifies the model checkpoint after fine-tuning, and a recipe that could
-be used next to prune its weights:
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --train-batch-size 64 \
-     --recipe-path <RECIPE_PATH>
-
-In the above command, specifying "zoo" for --checkpoint-path forces loading the
-base model from SparseZoo. The following command prunes a ResNet50 from Keras
-Applications fine-tuned for Imagenette dataset, using a recipe from SparseZoo:
-
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path zoo \
-     --pretrained-dataset imagenette \
-     --dataset imagenette \
-     --dataset-path <DATASET_DIR> \
-     --train-batch-size 64 \
-     --recipe-path zoo:cv/classification/resnet_v1-50/keras/\
-        keras.applications/imagenette/pruned-moderate?recipe_type=original
-############
-Example command for loading a ResNet50 model from a checkpoint, then
-evaluate it on the validation of the Imagenette dataset.
-python scripts/keras_classification.py evaluate \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --test-batch-size 64
-############
-Example command for loading a ResNet50 model from a checkpoint, then export it
-into ONNX format. It also generates a number of inputs, outputs and labels.
-python scripts/keras_classification.py export \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --num-samples <NUM_SAMPLES>
-     --save-dir <EXPORT_DIR>
-"""
-
-import argparse
-import inspect
-import json
-import math
-import os
-from typing import Optional, Tuple
-
-import numpy
-import tensorflow
-
-from sparseml import get_main_logger
-from sparseml.keras.datasets import Dataset, DatasetRegistry
-from sparseml.keras.models import ModelRegistry
-from sparseml.keras.optim import ScheduledModifierManager, remove_pruning_masks
-from sparseml.keras.utils import (
-    LossesAndMetricsLoggingCallback,
-    ModelExporter,
-    TensorBoardLogger,
-    keras,
-)
-from sparseml.utils import create_dirs
-
-
-LOGGER = get_main_logger()
-TRAIN_COMMAND = "train"
-EVAL_COMMAND = "evaluate"
-EXPORT_COMMAND = "export"
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Run tasks on classification models and datasets "
-        "using the sparseml API"
-    )
-
-    subparsers = parser.add_subparsers(dest="command")
-    train_parser = subparsers.add_parser(
-        TRAIN_COMMAND,
-        description="Train and/or prune an image classification model",
-    )
-    eval_parser = subparsers.add_parser(
-        EVAL_COMMAND,
-        description="Evaluate an image classification model on a dataset",
-    )
-    export_parser = subparsers.add_parser(
-        EXPORT_COMMAND,
-        description="Export a model to onnx as well as "
-        "store sample inputs, outputs, and labels",
-    )
-
-    parsers = [
-        train_parser,
-        eval_parser,
-        export_parser,
-    ]
-    for par in parsers:
-        # general arguments
-        # model args
-        par.add_argument(
-            "--arch-key",
-            type=str,
-            required=True,
-            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception with the list)",
-        )
-        par.add_argument(
-            "--pretrained",
-            type=str,
-            default=True,
-            help="The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]",
-        )
-        par.add_argument(
-            "--pretrained-dataset",
-            type=str,
-            default=None,
-            help="The dataset to load pretrained weights for if pretrained is set. "
-            "Default is None which will load the default dataset for the architecture."
-            " Ex can be set to imagenet, cifar10, etc",
-        )
-        par.add_argument(
-            "--checkpoint-path",
-            type=str,
-            default=None,
-            help="A path to a previous checkpoint to load the state from and "
-            "resume the state for. If provided, pretrained will be ignored",
-        )
-        par.add_argument(
-            "--model-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to model constructor, should be "
-            " given as a json object",
-        )
-
-        # dataset args
-        par.add_argument(
-            "--dataset",
-            type=str,
-            required=True,
-            help="The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or loadable by a "
-            "dataset in sparseml.keras.datasets",
-        )
-        par.add_argument(
-            "--dataset-path",
-            type=str,
-            required=True,
-            help="The root path to where the dataset is stored",
-        )
-        par.add_argument(
-            "--dataset-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to dataset constructor, should be "
-            " given as a json object",
-        )
-
-        # logging and saving
-        par.add_argument(
-            "--model-tag",
-            type=str,
-            default=None,
-            help="A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        )
-        par.add_argument(
-            "--save-dir",
-            type=str,
-            default="keras_classification",
-            help="The path to the directory for saving results",
-        )
-
-    # task specific arguments: training
-    train_parser.add_argument(
-        "--dataset-parallel-calls",
-        type=int,
-        default=4,
-        help="the number of parallel workers for dataset loading",
-    )
-    train_parser.add_argument(
-        "--train-shuffle-buffer-size",
-        type=int,
-        default=None,
-        help="Shuffle buffer size for dataset loading",
-    )
-    train_parser.add_argument(
-        "--train-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for train dataset loading",
-    )
-    train_parser.add_argument(
-        "--test-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for test dataset loading",
-    )
-    train_parser.add_argument(
-        "--recipe-path",
-        type=str,
-        default=None,
-        help="The path to the yaml file containing the modifiers and "
-        "schedule to apply them with. If set to 'transfer_learning', "
-        "then will create a schedule to enable sparse transfer learning",
-    )
-    train_parser.add_argument(
-        "--transfer-class-type",
-        type=str,
-        default="single",
-        help="Type of target function: 'single' for softmax, 'multi' for sigmoid "
-        "or 'linear' for linear function. This option takes effects only when the "
-        "specified dataset is different from the pretrained dataset, implying a "
-        "transfer learning usecase.",
-    )
-    train_parser.add_argument(
-        "--transfer-top-layer",
-        type=str,
-        default=None,
-        help="Name of the layer which a custom dense layer will be put on. Default "
-        "to None to imply the layer right under the top of the original model "
-        "will be used (in other words, layer[-2])",
-    )
-    train_parser.add_argument(
-        "--train-batch-size",
-        type=int,
-        required=True,
-        help="The batch size to use while training",
-    )
-    train_parser.add_argument(
-        "--test-batch-size",
-        type=int,
-        required=False,
-        help="The batch size to use while testing; default to the train " "batch size",
-    )
-    train_parser.add_argument(
-        "--log-dir",
-        type=str,
-        default=os.path.join("keras_classification_train", "tensorboard-logs"),
-        help="The path to the directory for saving logs",
-    )
-    train_parser.add_argument(
-        "--log-epoch",
-        type=bool,
-        default=True,
-        help="Whether logging should be performed at the end of each epoch",
-    )
-    train_parser.add_argument(
-        "--log-batch",
-        type=bool,
-        default=False,
-        help="Whether logging should be performed at the end of each training " "batch",
-    )
-    train_parser.add_argument(
-        "--log-steps",
-        type=int,
-        default=-1,
-        help="Whether logging should be performed after every specified number "
-        "of steps",
-    )
-    train_parser.add_argument(
-        "--save-best-only",
-        type=bool,
-        default=True,
-        help="Save model only with better monitored metric",
-    )
-    train_parser.add_argument(
-        "--optim",
-        type=str,
-        default="SGD",
-        help="The optimizer type to use, e.g., 'Adam', 'SGD' etc",
-    )
-    train_parser.add_argument(
-        "--optim-args",
-        type=json.loads,
-        default={"momentum": 0.9, "nesterov": True},
-        # default={},
-        help="Additional args to be passed to the optimizer passed in"
-        " as a json object",
-    )
-    train_parser.add_argument(
-        "--run-eagerly",
-        type=bool,
-        default=True,
-        help="Run training in eager execution mode",
-    )
-
-    # task specific arguments: evaluation
-    eval_parser.add_argument(
-        "--test-batch-size",
-        type=int,
-        required=False,
-        help="The batch size to use while testing; default to the train " "batch size",
-    )
-    eval_parser.add_argument(
-        "--dataset-parallel-calls",
-        type=int,
-        default=4,
-        help="the number of parallel workers for dataset loading",
-    )
-    eval_parser.add_argument(
-        "--test-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for test dataset loading",
-    )
-
-    # task specific arguments: export
-    export_parser.add_argument(
-        "--num-samples",
-        type=int,
-        default=100,
-        help="The number of samples to export along with the model onnx "
-        "and pth files (sample inputs and labels as well as the outputs "
-        "from model execution)",
-    )
-    export_parser.add_argument(
-        "--onnx-opset",
-        type=int,
-        default=11,
-        help="The onnx opset to use for export. Default is 11",
-    )
-    export_parser.add_argument(
-        "--export-debug-mode",
-        type=bool,
-        default=False,
-        help="The debug mode for ONNX export (passed into keras2onnx). "
-        "Default to False.",
-    )
-
-    return parser.parse_args()
-
-
-def setup_save_and_log_dirs(args) -> Tuple[str, Optional[str]]:
-    # Saving dir setup
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-    if not args.model_tag:
-        model_tag = "{}@{}".format(args.arch_key, args.dataset)
-        model_id = model_tag
-        model_inc = 0
-
-        while os.path.exists(os.path.join(save_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-    LOGGER.info("Model directory is set to {}".format(save_dir))
-
-    # log dir setup
-    log_dir = (
-        os.path.abspath(os.path.expanduser(args.log_dir))
-        if args.command == TRAIN_COMMAND
-        else None
-    )
-    if args.command == TRAIN_COMMAND:
-        log_dir = os.path.join(log_dir, model_id)
-        create_dirs(log_dir)
-        LOGGER.info("Logging directory is set to {}".format(log_dir))
-    else:
-        log_dir = None
-    return save_dir, log_dir
-
-
-def create_dataset(
-    args, train: bool, image_size: Tuple[int, int]
-) -> Tuple[Dataset, int]:
-    kwargs = args.dataset_kwargs
-    dataset = DatasetRegistry.create(
-        args.dataset,
-        root=args.dataset_path,
-        train=train,
-        image_size=image_size,
-        **kwargs,
-    )
-    LOGGER.info(
-        "created {} dataset: {}, images to resize to {}".format(
-            "train" if train else "val", dataset, image_size
-        )
-    )
-
-    # get num_classes
-    if args.dataset == "imagefolder":
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-
-    return dataset, num_classes
-
-
-def build_dataset(
-    args, dataset: Dataset, train: bool = True
-) -> tensorflow.data.Dataset:
-    test_batch_size = (
-        args.test_batch_size if args.test_batch_size else args.train_batch_size
-    )
-    batch_size = args.train_batch_size if train else test_batch_size
-    if train:
-        shuffle_buffer_size = (
-            args.train_shuffle_buffer_size
-            if args.train_shuffle_buffer_size
-            else dataset.num_images
-        )
-    else:
-        shuffle_buffer_size = None
-
-    if train:
-        prefetch_buffer_size = (
-            args.train_prefetch_buffer_size
-            if args.train_prefetch_buffer_size
-            else batch_size * 8
-        )
-    else:
-        prefetch_buffer_size = (
-            args.test_prefetch_buffer_size
-            if args.test_prefetch_buffer_size
-            else batch_size * 8
-        )
-    built_dataset = dataset.build(
-        batch_size,
-        repeat_count=1,
-        shuffle_buffer_size=shuffle_buffer_size,
-        prefetch_buffer_size=prefetch_buffer_size,
-        num_parallel_calls=args.dataset_parallel_calls,
-    )
-    return built_dataset
-
-
-def create_model(args, input_shape, num_classes):
-    kwargs = args.model_kwargs
-
-    if args.checkpoint_path == "zoo":
-        zoo_model = ModelRegistry.create_zoo_model(
-            args.arch_key, args.pretrained, args.pretrained_dataset
-        )
-        model_file_paths = zoo_model.download_framework_files(extensions=[".h5"])
-        if not model_file_paths:
-            model_file_paths = zoo_model.download_framework_files(extensions=[".tf"])
-        if not model_file_paths:
-            raise RuntimeError("Error downloading model from SparseZoo")
-        model_file_path = model_file_paths[0]
-        model = keras.models.load_model(model_file_path)
-    else:
-        model = ModelRegistry.create(
-            args.arch_key,
-            args.pretrained,
-            args.checkpoint_path,
-            args.pretrained_dataset,
-            input_shape=input_shape,
-            **kwargs,
-        )
-
-    if (
-        args.pretrained_dataset is not None
-        and args.dataset is not None
-        and args.pretrained_dataset != args.dataset
-    ):
-        # Set up the model for fine-tuning
-        # The trainablility of model's parameters should be controlled
-        # through recipe; we therefore do not set "model.trainable" to False here.
-        if args.transfer_class_type == "single":
-            activation = "softmax"
-        elif args.transfer_class_type == "multi":
-            activation = "sigmoid"
-        elif args.transfer_class_type == "linear":
-            activation = None
-        else:
-            raise ValueError(
-                "Unknown transfer_class_type; expected: 'single', 'softmax' or 'linear'"
-            )
-        if args.transfer_top_layer is not None:
-            transfer_top_layer = None
-            for i in list(range(len(model.layers)))[::-1]:
-                if model.layers[i].name == args.transfer_top_layer:
-                    transfer_top_layer = args.transfer_top_layer
-                    break
-            if transfer_top_layer is None:
-                raise ValueError(
-                    "Top layer for transfer learning {} not found".format(
-                        args.transfer_top_layer
-                    )
-                )
-        else:
-            transfer_top_layer = model.layers[-2]
-        new_output = keras.layers.Dense(num_classes, activation=activation)(
-            transfer_top_layer.output
-        )
-        model = keras.Model(model.input, new_output)
-
-    return model
-
-
-def create_optimizer(args):
-    optim_const = {}
-    for opt_name in dir(keras.optimizers):
-        opt = getattr(keras.optimizers, opt_name)
-        if inspect.isclass(opt) and opt_name != "Optimizer":
-            optim_const[opt_name] = opt
-    if args.optim not in optim_const:
-        raise ValueError(
-            "Unknown optimizer {}. Supported: {}".format(
-                args.optim, list(optim_const.keys())
-            )
-        )
-    optimizer = optim_const[args.optim](**args.optim_args)
-    return optimizer
-
-
-def get_logging_update_freq(args):
-    if args.log_steps > 0:
-        update_freq = args.log_steps
-    elif args.log_batch:
-        update_freq = "batch"
-    elif args.log_epoch:
-        update_freq = "epoch"
-    else:
-        update_freq = None
-    return update_freq
-
-
-def handle_train_command(args):
-    save_dir, log_dir = setup_save_and_log_dirs(args)
-
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    # Load datasets
-    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
-    num_train_images = train_dataset.num_images
-    train_dataset = build_dataset(args, train_dataset, train=True)
-
-    val_dataset, _ = create_dataset(args, train=False, image_size=image_size)
-    val_dataset = build_dataset(args, val_dataset, train=False)
-
-    # Create model
-    model = create_model(args, input_shape, num_classes=num_classes)
-
-    # Create optimizer
-    optimizer = create_optimizer(args)
-
-    # Logging
-    if log_dir:
-        update_freq = get_logging_update_freq(args)
-        if update_freq is None:
-            raise ValueError(
-                "Logging requires update frequency to take effect; use either "
-                "'log-epoch', 'log-batch' or 'log-steps' option."
-            )
-        loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
-    else:
-        loggers = []
-
-    # Model saving
-    checkpoint_filepath = os.path.join(
-        save_dir, "model.{epoch:02d}-{val_accuracy:.2f}.tf"
-    )
-    checkpoint_callback = keras.callbacks.ModelCheckpoint(
-        filepath=checkpoint_filepath,
-        save_weights_only=False,
-        monitor="val_accuracy",
-        mode="max",
-        save_best_only=True,
-        verbose=1,
-    )
-
-    # Manager
-    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
-
-    # Enhance model
-    steps_per_epoch = math.ceil(num_train_images / args.train_batch_size)
-    model, optimizer, callbacks = manager.modify(
-        model, optimizer, steps_per_epoch, loggers=loggers
-    )
-    if loggers:
-        callbacks.append(LossesAndMetricsLoggingCallback(loggers))
-    callbacks.append(checkpoint_callback)
-
-    model.compile(
-        loss=keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-        run_eagerly=args.run_eagerly,
-    )
-    model.fit(
-        train_dataset,
-        epochs=manager.max_epochs,
-        callbacks=callbacks,
-        validation_data=val_dataset,
-    )
-
-
-def handle_eval_command(args):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    # Load dataset
-    val_dataset, num_classes = create_dataset(args, train=False, image_size=image_size)
-    val_dataset = build_dataset(args, val_dataset, train=False)
-
-    # Create model
-    model = create_model(args, input_shape, num_classes=num_classes)
-
-    # Evaluate
-    result = model.evaluate(val_dataset)
-    print(dict(zip(model.metrics_names, result)))
-
-
-def handle_export_command(args):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
-    train_dataset = train_dataset.build(args.num_samples)
-
-    model = create_model(args, input_shape, num_classes=num_classes)
-    model = remove_pruning_masks(model)
-    exporter = ModelExporter(model, args.save_dir)
-    exporter.export_onnx(opset=args.onnx_opset, debug_mode=args.export_debug_mode)
-
-    for img_batch, label_batch in train_dataset.take(1):
-        output_batch = model(img_batch)
-        numpy.save(os.path.join(args.save_dir, "inputs.npy"), img_batch)
-        numpy.save(os.path.join(args.save_dir, "outputs.npy"), output_batch)
-        numpy.save(os.path.join(args.save_dir, "labels.npy"), label_batch)
-
-
-def main(args):
-    if args.command == TRAIN_COMMAND:
-        handle_train_command(args)
-    elif args.command == EVAL_COMMAND:
-        handle_eval_command(args)
-    elif args.command == EXPORT_COMMAND:
-        handle_export_command(args)
-    else:
-        raise ValueError("Unknown command passed in")
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-    main(args_)
diff --git a/integrations/keras/notebooks/classification.ipynb b/integrations/keras/notebooks/classification.ipynb
deleted file mode 100644
index f08a5b0ab50..00000000000
--- a/integrations/keras/notebooks/classification.ipynb
+++ /dev/null
@@ -1,433 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021 Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Keras Classification Model Pruning Using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Inference Engine. You will:\n",
-    "- Set up the model and dataset\n",
-    "- Integrate the Keras training flow with SparseML\n",
-    "- Prune the model using the Keras+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your Keras training flow.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo;\n",
-    "* Tensorflow >=2.1, which includes Keras and TensorBoard;\n",
-    "* keras2onnx.\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "In this notebook, you will prune a simple convolution neural network model trained on the MNIST dataset. The pretrained model's architecture and weights are downloaded from the SparseZoo model repo. The dataset is downloaded directly from  Keras datasets library."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Up the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell defines a procedure to download a model from the SparseZoo; additionally, for convenience it also returns the path to an optimization recipe. You construct a Keras model instance from the pretrained to prune in a later step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from sparseml.keras.utils import keras\n",
-    "from sparsezoo import Model\n",
-    "\n",
-    "# Root directory for the notebook artifacts\n",
-    "root_dir = \"./notebooks/keras\"\n",
-    "\n",
-    "def download_model_and_recipe(root_dir: str):\n",
-    "    \"\"\"\n",
-    "    Download pretrained model and a pruning recipe\n",
-    "    \"\"\"\n",
-    "    model_dir = os.path.join(root_dir, \"mnist\")\n",
-    "    zoo_model = Model(model_dir)\n",
-    "\n",
-    "\n",
-    "    model_file_path = zoo_model.training.default.get_file(\"model.h5\").path\n",
-    "    if not os.path.exists(model_file_path) or not model_file_path.endswith(\".h5\"):\n",
-    "        raise RuntimeError(\"Model file not found: {}\".format(model_file_path))\n",
-    "    recipe_file_path = zoo_model.recipes.default.path\n",
-    "    if not os.path.exists(recipe_file_path):\n",
-    "        raise RuntimeError(\"Recipe file not found: {}\".format(recipe_file_path))\n",
-    "    return model_file_path, recipe_file_path\n",
-    "\n",
-    "model_file_path, recipe_file_path = download_model_and_recipe(root_dir)\n",
-    "\n",
-    "print(\"Loading model {}\".format(model_file_path))\n",
-    "model = keras.models.load_model(model_file_path)\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Up the Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will download the MNIST dataset from Keras datasets library as follows. You will also normalize the data before using it for training and evaluation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "\n",
-    "# Number of classes\n",
-    "num_classes = 10\n",
-    "\n",
-    "# Load MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
-    "\n",
-    "# Normalize data\n",
-    "x_train = x_train.astype('float32') / 255\n",
-    "x_test = x_test.astype('float32') / 255\n",
-    "\n",
-    "# Add batch dimension (for older TF versions)\n",
-    "x_train = numpy.expand_dims(x_train, -1)\n",
-    "x_test = numpy.expand_dims(x_test, -1)\n",
-    "\n",
-    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
-    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
-    "\n",
-    "print(\"Dataset loaded and normalized\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before pruning the model, you could run the cell below to verify the accuracy of the model on the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = model.evaluate(x_test, y_test)\n",
-    "print(\"Test loss, accuracy: \", res)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Pruning the Pretrained Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, you will prune the above pretrained Keras model using the SparseML model optimization library. Recall that a common training workflow in Keras is first to compile the model with the appropriate losses, metrics and an optimizer, then to train the model using the `fit()` method of the `Model` class. The SparseML library makes it easy to extend this training workflow to perform gradual pruning based on weight magnitudes.\n",
-    "\n",
-    "Given a pretrained model, the pruning workflow can be summarized as follows:\n",
-    "1. Create a recipe for pruning, which could be done effectively using the Sparsify toolkit\n",
-    "2. Instantiate a Keras optimizer instance (such as SGD or Adam)\n",
-    "3. Instantiate a `ScheduledModifierManager` object from the recipe\n",
-    "4. Enhance the model and optimizer with pruning data structures by calling the manager's `modify` method. At this step, you have options to define the loggers used during the pruning process. The results of this step are a model to be pruned, an optimizer that should be used and a list of callbacks\n",
-    "5. [Optional] Add to the callback list any additional callbacks such as model checkpoint and the SparseML built-in LossesAndMetricsLogging callback\n",
-    "6. Compile and fit the modified model using Keras built-in APIs, using the optimizer and callback list\n",
-    "7. Erase the pruning information in the enhanced model, and get back the original model with pruned weights\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, you will set up a directory path for logging and the frequency for the logging update."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "# Logging directory\n",
-    "log_dir = \"./tensorboard/mnist:\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
-    "print(\"Logging directory: {}\".format(log_dir))\n",
-    "\n",
-    "# Number of steps before the next logging should take place\n",
-    "# Use \"epoch\" or \"batch\" to log at every training epoch or batch (respectively)\n",
-    "update_freq = 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell contains the few steps required for pruning using the SparseML library, ultimately resulting in a modified model, optimizer and a list of callbacks incoporating the optimization logics. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "import math\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "from sparseml.keras.optim import ScheduledModifierManager\n",
-    "from sparseml.keras.utils import LossesAndMetricsLoggingCallback, TensorBoardLogger\n",
-    "\n",
-    "# Training batch size\n",
-    "batch_size = 32\n",
-    "\n",
-    "# Number of steps per epoch\n",
-    "steps_per_epoch = math.ceil(len(x_train) / batch_size)\n",
-    "\n",
-    "# Create a manager from the recipe\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_file_path)\n",
-    "\n",
-    "# Create an optimizer\n",
-    "optimizer = Adam()\n",
-    "\n",
-    "# Optional: Create a TensorBoardLogger instance\n",
-    "loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)\n",
-    "\n",
-    "# Modify the model and optimizer\n",
-    "model_for_pruning, optimizer, callbacks = manager.modify(model, optimizer, steps_per_epoch, loggers=loggers)\n",
-    "\n",
-    "# Include your own callbacks. Here you will use the built-in LossesAndMetricsLoggingCallback\n",
-    "callbacks.append(LossesAndMetricsLoggingCallback(loggers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you are ready to compile the modified model using the losses and metrics of your choice, and the optimizer enhanced by the mananger. The last step is to train the model using its `fit()` method, passing in (among other information) the list of callbacks constructed above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compile the modified model\n",
-    "model_for_pruning.compile(\n",
-    "    loss=keras.losses.categorical_crossentropy,\n",
-    "    optimizer=optimizer,\n",
-    "    metrics=['accuracy'],\n",
-    "    run_eagerly=True\n",
-    ")\n",
-    "\n",
-    "# Prune the model\n",
-    "model_for_pruning.fit(x_train, y_train, batch_size=batch_size, epochs=manager.max_epochs,\n",
-    "                      validation_data=(x_test, y_test), shuffle=True, callbacks=callbacks)\n",
-    "\n",
-    "print(\"Pruning finished\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is time to verify the accuracy of the pruned model. Later on you can also check the sparsity level of the ONNX version of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Verify the pruned model's accuracy\n",
-    "res = model_for_pruning.evaluate(x_test, y_test)\n",
-    "print(\"Validation loss, accuracy: \", res)\n",
-    "\n",
-    "# Erase the enhanced information used for pruning \n",
-    "pruned_model = manager.finalize(model_for_pruning)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given that you used a TensorBoardLogger, you can now observe the logging information in TensorBoard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext tensorboard\n",
-    "%tensorboard --logdir ./tensorboard"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Examine the Pruned Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can observe the layers of the pruned Keras model using its `layers` property and `get_weights()` method. First, print out the list of layers of this model, and recall that in this optimization recipe we pruned all the Conv2D layers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pruned_model.layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can observe the weights of any Conv2D that you pruned, and notice that the majority of them are zeros."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change the layer index to examine the layers. Choose one among 1, 4, 7, 10 as the layer\n",
-    "# indices of the pruned layers.\n",
-    "layer_index = 10\n",
-    "pruned_model.layers[layer_index].get_weights()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To observe the overall sparsity of the model, as well as the sparsity level of each layer, run the following cell. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.keras.utils import sparsity\n",
-    "from pprint import pprint\n",
-    "\n",
-    "model_sparsity, layer_sparsity_dict = sparsity(pruned_model)\n",
-    "print(\"Model sparsity: {}\".format(model_sparsity))\n",
-    "print(\"Layer sparsities:\")\n",
-    "pprint(layer_sparsity_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For Keras, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to.\n",
-    "\n",
-    "#### Note:\n",
-    "The `keras2onnx` is known to cause issues during the conversion; in particular, if you installed Tensorflow 2.4, you might encounter the `AttributeError: 'KerasTensor' object has no attribute 'graph'`. You might need to downgrade to Tensorflow 2.2 or 2.3 for the ONNX export to work properly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.keras.utils import ModelExporter\n",
-    "\n",
-    "save_dir = \"keras_classification\"\n",
-    "onnx_file_name = \"pruned_mnist.onnx\"\n",
-    "\n",
-    "exporter = ModelExporter(pruned_model, output_dir=save_dir)\n",
-    "exporter.export_onnx(name=onnx_file_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the DeepSparse Engine"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/keras/prune_resnet20.py b/integrations/keras/prune_resnet20.py
deleted file mode 100644
index f4429da738b..00000000000
--- a/integrations/keras/prune_resnet20.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Example of pruning a ResNet20-v1 model pretrained on the Cifar-10 dataset.
-The pretrained model and this pruning script were adapted from:
-https://keras.io/zh/integrations/cifar10_resnet/
-
-Run the following command from the top repo directory:
-
-   python3 integrations/keras/prune_resnet20.py
-
-"""
-
-import math
-import os
-from datetime import datetime
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.callbacks import ModelCheckpoint
-from tensorflow.keras.datasets import cifar10
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-from sparseml.keras.optim import ScheduledModifierManager
-from sparseml.keras.utils.callbacks import LossesAndMetricsLoggingCallback
-from sparseml.keras.utils.exporter import ModelExporter
-from sparseml.keras.utils.logger import TensorBoardLogger
-from sparsezoo import Model
-
-
-# Root directory
-root_dir = "./integrations/keras"
-
-# Logging setup
-log_dir = os.path.join(root_dir, "tensorboard", "resnet20_v1")
-log_dir += ":" + datetime.now().strftime("%Y%m%d-%H%M%S")
-update_freq = 100  # Logging update every this many training steps
-
-# Train/dataset setting
-num_classes = 10
-batch_size = 32
-subtract_pixel_mean = True
-data_augmentation = True
-
-# Pruned model directory
-pruned_model_dir = os.path.join(root_dir, "pruned")
-if not os.path.isdir(pruned_model_dir):
-    os.makedirs(pruned_model_dir)
-
-
-def download_model_and_recipe(root_dir: str):
-    """
-    Download pretrained model and a pruning recipe
-    """
-
-    # Use the recipe stub
-    recipe_file_path = (
-        "zoo:cv/classification/resnet_v1-20/keras/sparseml/cifar_10/pruned-conservative"
-    )
-
-    # Load base model to prune
-    base_zoo_model = Model(recipe_file_path)
-    base_zoo_model.path = os.path.join(root_dir, "resnet20_v1")
-    checkpoint = base_zoo_model.training.default
-    model_file_path = checkpoint.get_file("model.h5").path
-    recipe_file_path = base_zoo_model.recipes.default.path
-    if not os.path.exists(model_file_path) or not model_file_path.endswith(".h5"):
-        raise RuntimeError("Model file not found: {}".format(model_file_path))
-
-    return model_file_path, recipe_file_path
-
-
-def load_and_normalize_cifar10(subtract_pixel_mean: bool = True):
-    """
-    Load and normalize the Cifar-10 dataset
-    """
-    # Load the CIFAR10 data.
-    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
-
-    # Normalize data.
-    X_train = X_train.astype("float32") / 255
-    X_test = X_test.astype("float32") / 255
-
-    # If subtract pixel mean is enabled
-    if subtract_pixel_mean:
-        X_train_mean = np.mean(X_train, axis=0)
-        X_train -= X_train_mean
-        X_test -= X_train_mean
-
-    # Convert class vectors to binary class matrices.
-    y_train = tf.keras.utils.to_categorical(y_train, num_classes)
-    y_test = tf.keras.utils.to_categorical(y_test, num_classes)
-
-    return (X_train, y_train), (X_test, y_test)
-
-
-def model_checkpoint_callback():
-    """
-    Create model checkpoint callback
-    """
-    pruned_model_name = "resnet20_v1.pruned.{epoch:03d}.h5"
-    pruned_filepath = os.path.join(pruned_model_dir, pruned_model_name)
-
-    # Prepare a callback for model saving
-    checkpoint = ModelCheckpoint(
-        filepath=pruned_filepath, monitor="val_accuracy", verbose=1, save_best_only=True
-    )
-    return checkpoint
-
-
-def main():
-    print("Load and normalize Cifar-10 dataset")
-    (X_train, y_train), (X_test, y_test) = load_and_normalize_cifar10()
-
-    model_file_path, recipe_file_path = download_model_and_recipe(root_dir)
-
-    print("Load pretrained model")
-    base_model = tf.keras.models.load_model(model_file_path)
-    base_model.summary()
-
-    scores = base_model.evaluate(X_test, y_test, verbose=1)
-    print("Test loss:", scores[0])
-    print("Test accuracy:", scores[1])
-
-    optimizer = tf.keras.optimizers.Adam()
-
-    steps_per_epoch = math.ceil(len(X_train) / batch_size)
-
-    # Enhance the model and optimizer for pruning using the manager
-    loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
-    manager = ScheduledModifierManager.from_yaml(recipe_file_path)
-    model_for_pruning, optimizer, callbacks = manager.modify(
-        base_model, optimizer, steps_per_epoch, loggers=loggers
-    )
-    callbacks.append(LossesAndMetricsLoggingCallback(loggers))
-    callbacks.append(model_checkpoint_callback())
-
-    # Compile the enhanced model
-    model_for_pruning.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-        run_eagerly=True,
-    )
-
-    # Run training and pruning, with or without data augmentation.
-    if not data_augmentation:
-        print("Not using data augmentation.")
-        model_for_pruning.fit(
-            X_train,
-            y_train,
-            batch_size=batch_size,
-            validation_data=(X_test, y_test),
-            shuffle=True,
-            callbacks=callbacks,
-            epochs=manager.max_epochs,
-        )
-    else:
-        print("Using real-time data augmentation.")
-        # This will do preprocessing and realtime data augmentation:
-        datagen = ImageDataGenerator(
-            # set input mean to 0 over the dataset
-            featurewise_center=False,
-            # set each sample mean to 0
-            samplewise_center=False,
-            # divide inputs by std of dataset
-            featurewise_std_normalization=False,
-            # divide each input by its std
-            samplewise_std_normalization=False,
-            # apply ZCA whitening
-            zca_whitening=False,
-            # epsilon for ZCA whitening
-            zca_epsilon=1e-06,
-            # randomly rotate images in the range (deg 0 to 180)
-            rotation_range=0,
-            # randomly shift images horizontally
-            width_shift_range=0.1,
-            # randomly shift images vertically
-            height_shift_range=0.1,
-            # set range for random shear
-            shear_range=0.0,
-            # set range for random zoom
-            zoom_range=0.0,
-            # set range for random channel shifts
-            channel_shift_range=0.0,
-            # set mode for filling points outside the input boundaries
-            fill_mode="nearest",
-            # value used for fill_mode = "constant"
-            cval=0.0,
-            # randomly flip images
-            horizontal_flip=True,
-            # randomly flip images
-            vertical_flip=False,
-            # set rescaling factor (applied before any other transformation)
-            rescale=None,
-            # set function that will be applied on each input
-            preprocessing_function=None,
-            # image data format, either "channels_first" or "channels_last"
-            data_format=None,
-            # fraction of images reserved for validation (strictly between 0 and 1)
-            validation_split=0.0,
-        )
-
-        # Compute quantities required for featurewise normalization
-        # (std, mean, and principal components if ZCA whitening is applied).
-        datagen.fit(X_train)
-
-        # Fit the model on the batches generated by datagen.flow().
-        model_for_pruning.fit_generator(
-            datagen.flow(X_train, y_train, batch_size=batch_size),
-            validation_data=(X_test, y_test),
-            epochs=manager.max_epochs,
-            verbose=1,
-            workers=4,
-            callbacks=callbacks,
-        )
-
-    # Erase pruning masks and export to ONNX model
-    pruned_model = manager.finalize(model_for_pruning)
-    exporter = ModelExporter(pruned_model, output_dir=pruned_model_dir)
-    onnx_model_name = "pruned_resnet20_v1.onnx"
-    exporter.export_onnx(name=onnx_model_name)
-    print(
-        "Model exported to {}".format(os.path.join(pruned_model_dir, onnx_model_name))
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/keras/recipes/.gitkeep b/integrations/keras/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/keras/tutorials/.gitkeep b/integrations/keras/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/pytorch/README.md b/integrations/pytorch/README.md
deleted file mode 100644
index ca0a38fbb0f..00000000000
--- a/integrations/pytorch/README.md
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML PyTorch Integration
-
-This directory combines the SparseML recipe-driven approach with standard PyTorch training flows for models within
-[torchvision](https://pytorch.org/vision/stable/index.html) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom PyTorch training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Blog: [ResNet-50 on CPUs: Sparsifying for Better Performance on CPUs](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse/)
-
-## Tutorials
-
-- [Classification](./notebooks/classification.ipynb)
-- [Detection](./notebooks/detection.ipynb)
-- [Sparse Quantized Transfer Learning](./notebooks/sparse_quantized_transfer_learning.ipynb)
-- [Torchvision](./notebooks/torchvision.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[torchvision]`
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=pytorch) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=pytorch) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name                                             | Description                                                                                                           |
-|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [notebooks](./notebooks)                                     | Jupyter notebooks to walk through sparsifiation within standard PyTorch training flows                                |
-| [recipes](./recipes)                                         | Typical recipes for sparsifying PyTorch models along with any downloaded recipes from the SparseZoo.                  |
-| [tutorials](./tutorials)                                     | Tutorial walkthroughs for how to sparsify PyTorch models using recipes.                                               |
-| [README.md](./README.md)                                     | Readme file.                                                                                                          |
-| [torchvision_sparsification.py](./torchvision_sparsification.py) | Example training script for sparsifying PyTorch torchvision classification models.                                    |
-| `sparseml.image_classification.train`                        | Utility training script for sparsifying classification and detection models in PyTorch (loads SparseML models).       |
-| `sparseml.image_classification.export`                       | Utility script for exporting pytorch model to ONNX file along with sample inputs, outputs and labels.                 |
-| `sparseml.image_classification.lr_analysis`                  |Utility script to Run learning rate sensitivity analysis for a desired image classification architecture |`sparseml.image_classification.pr_sensitivity`                |Utility script to Run a kernel sparsity (pruning) analysis for a desired image classification architecture |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/pytorch/argparser_/nm_argparser_.py b/integrations/pytorch/argparser_/nm_argparser_.py
deleted file mode 100644
index f460a012054..00000000000
--- a/integrations/pytorch/argparser_/nm_argparser_.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import dataclasses
-import json
-import re
-import sys
-from argparse import ArgumentParser, ArgumentTypeError
-from copy import copy
-from enum import Enum
-from pathlib import Path
-from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
-
-
-DataClass = NewType("DataClass", Any)
-DataClassType = NewType("DataClassType", Any)
-
-
-# From https://stackoverflow.com/questions/15008758
-# /parsing-boolean-values-with-argparse
-def string_to_bool(v):
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise ArgumentTypeError(
-            f"Truthy value expected: got {v} but expected one of yes/no,"
-            f"true/false, t/f, y/n, 1/0 (case insensitive)."
-        )
-
-
-# Inspired from https://huggingface.co/transformers/_modules
-# /transformers/hf_argparser.html
-class NmArgumentParser(ArgumentParser):
-    """
-    This subclass of `argparse.ArgumentParser` uses type hints on dataclasses
-    to generate arguments.
-
-    The class is designed to play well with the native argparse. In particular,
-    you can add more (non-dataclass backed) arguments to the parser after
-    initialization and you'll get the output back after parsing as an additional
-    namespace. Optional: To create sub argument groups use the
-    `_argument_group_name` attribute in the dataclass.
-
-    Note: __post_init__(...) for specific dataclasses passed is executed only
-    when parse_args_into_dataclasses(...) function is called because it needs
-    actual instantiation of the dataclass.
-    """
-
-    dataclass_types: Iterable[DataClassType]
-
-    def __init__(
-        self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs
-    ):
-        """
-        :param dataclass_types: Dataclass type, or list of dataclass types
-            for which we will "fill" instances with the parsed args.
-        :param kwargs: (Optional) Passed to `argparse.ArgumentParser()` in
-            the regular way.
-        """
-        super().__init__(**kwargs)
-        if dataclasses.is_dataclass(dataclass_types):
-            dataclass_types = [dataclass_types]
-        self.dataclass_types = dataclass_types
-        for dataclass_ in self.dataclass_types:
-            self._add_dataclass_arguments(dataclass_)
-
-    def _add_dataclass_arguments(self, dataclass_: DataClassType):
-        if hasattr(dataclass_, "_argument_group_name"):
-            parser = self.add_argument_group(dataclass_._argument_group_name)
-        else:
-            parser = self
-        for field in dataclasses.fields(dataclass_):
-            if not field.init:
-                continue
-
-            name, kwargs = field.name, field.metadata.copy()
-
-            keep_underscores_key = "keep_underscores"
-            keep_underscores = kwargs.get(keep_underscores_key)
-            _field_name = name if keep_underscores else name.replace("_", "-")
-
-            # cleanup
-            if keep_underscores_key in kwargs:
-                del kwargs[keep_underscores_key]
-
-            # field.metadata is not used at all by Data Classes,
-            # it is provided as a third-party extension mechanism.
-            if isinstance(field.type, str):
-                raise ImportError(
-                    "This implementation is not compatible with Postponed "
-                    "Evaluation of Annotations (PEP 563),"
-                    "which can be opted in from Python 3.7 with "
-                    "`from __future__ import annotations`."
-                )
-            typestring = str(field.type)
-            for prim_type in (int, float, str):
-                for collection in (List,):
-                    if (
-                        typestring == f"typing.Union["
-                        f"{collection[prim_type]}, NoneType]"
-                        or typestring == f"typing.Optional" f"[{collection[prim_type]}]"
-                    ):
-                        field.type = collection[prim_type]
-                if (
-                    typestring == f"typing.Union[" f"{prim_type.__name__}, NoneType]"
-                    or typestring == f"typing.Optional[" f"{prim_type.__name__}]"
-                ):
-                    field.type = prim_type
-
-            if isinstance(field.type, type) and issubclass(field.type, Enum):
-                kwargs["choices"] = [x.value for x in field.type]
-                kwargs["type"] = type(kwargs["choices"][0])
-                if field.default is not dataclasses.MISSING:
-                    kwargs["default"] = field.default
-                else:
-                    kwargs["required"] = True
-            elif field.type is bool or field.type == Optional[bool]:
-                if field.default is True:
-                    kwargs_copy = copy(kwargs)
-                    if "help" in kwargs_copy:
-                        kwargs_copy["help"] = f"Do not {kwargs_copy['help'].lower()}"
-                    parser.add_argument(
-                        f"--no-{_field_name}",
-                        action="store_false",
-                        dest=_field_name,
-                        **kwargs_copy,
-                    )
-
-                # Hack because type=bool in argparse does not behave as we want.
-                kwargs["type"] = string_to_bool
-                if field.type is bool or (
-                    field.default is not None
-                    and field.default is not dataclasses.MISSING
-                ):
-                    # Default value is False if we have no default
-                    # when of type bool.
-                    if field.default is dataclasses.MISSING:
-                        default = False
-                    else:
-                        default = field.default
-                    # This is the value that will get picked if
-                    # we don't include --field_name in any way
-                    kwargs["default"] = default
-
-                    # This tells argparse we accept 0 or 1
-                    # value after --field_name
-                    kwargs["nargs"] = "?"
-                    # This is the value that will get picked
-                    # if we do --field_name (without value)
-                    kwargs["const"] = True
-            elif (
-                hasattr(field.type, "__origin__")
-                and re.search(r"^typing\.List\[(.*)\]$", str(field.type)) is not None
-            ):
-                kwargs["nargs"] = "+"
-                kwargs["type"] = field.type.__args__[0]
-                assert all(
-                    x == kwargs["type"] for x in field.type.__args__
-                ), f"{field.name} cannot be a List of mixed types"
-                if field.default_factory is not dataclasses.MISSING:
-                    kwargs["default"] = field.default_factory()
-                elif field.default is dataclasses.MISSING:
-                    kwargs["required"] = True
-            else:
-                kwargs["type"] = field.type
-                if field.default is not dataclasses.MISSING:
-                    kwargs["default"] = field.default
-                elif field.default_factory is not dataclasses.MISSING:
-                    kwargs["default"] = field.default_factory()
-                else:
-                    kwargs["required"] = True
-            parser.add_argument(f"--{_field_name}", **kwargs)
-
-    def parse_args_into_dataclasses(
-        self,
-        args=None,
-        return_remaining_strings=False,
-        look_for_args_file=True,
-        args_filename=None,
-    ) -> Tuple[DataClass, ...]:
-        """
-        Parse command-line args into instances of the specified dataclass types.
-
-        This relies on argparse's `ArgumentParser.parse_known_args`.
-        See the doc at:
-        docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.
-        parse_args
-
-
-        :param args: List of strings to parse. The default is taken from
-            sys.argv. (same as argparse.ArgumentParser)
-        :param return_remaining_strings: If true, also return a list of
-            remaining argument strings.
-        :param look_for_args_file: If true, will look for a ".args" file with
-            the same base name as the entry point script for this process,
-            and will append its potential content to the command line args.
-        :param args_filename: If not None, will uses this file instead of the
-            ".args" file specified in the previous argument.
-        :returns: Tuple consisting of:
-                    - the dataclass instances in the same order as they were
-                    passed to the initializer.abspath
-                    - if applicable, an additional namespace for more
-                    (non-dataclass backed) arguments added to the parser
-                    after initialization.
-                    - The potential list of remaining argument strings.
-                    (same as argparse.ArgumentParser.parse_known_args)
-        """
-        if args_filename or (look_for_args_file and len(sys.argv)):
-            if args_filename:
-                args_file = Path(args_filename)
-            else:
-                args_file = Path(sys.argv[0]).with_suffix(".args")
-
-            if args_file.exists():
-                fargs = args_file.read_text().split()
-                args = fargs + args if args is not None else fargs + sys.argv[1:]
-                # in case of duplicate arguments the first one has precedence
-                # so we append rather than prepend.
-        namespace, remaining_args = self.parse_known_args(args=args)
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in vars(namespace).items() if k in keys}
-            for k in keys:
-                delattr(namespace, k)
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        if len(namespace.__dict__) > 0:
-            # additional namespace.
-            outputs.append(namespace)
-        if return_remaining_strings:
-            return (*outputs, remaining_args)
-        else:
-            if remaining_args:
-                raise ValueError(
-                    f"Some specified arguments are not used by the "
-                    f"NmArgumentParser: {remaining_args}"
-                )
-
-            return (*outputs,)
-
-    def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
-        """
-        Alternative helper method that does not use `argparse` at all,
-        instead loading a json file and populating the dataclass types.
-        """
-        data = json.loads(Path(json_file).read_text())
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in data.items() if k in keys}
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        return (*outputs,)
-
-    def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
-        """
-        Alternative helper method that does not use `argparse` at all,
-        instead uses a dict and populating the dataclass types.
-        """
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in args.items() if k in keys}
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        return (*outputs,)
diff --git a/integrations/pytorch/argparser_/test_nmargparser.py b/integrations/pytorch/argparser_/test_nmargparser.py
deleted file mode 100644
index a7d18a66b79..00000000000
--- a/integrations/pytorch/argparser_/test_nmargparser.py
+++ /dev/null
@@ -1,283 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import unittest
-from argparse import Namespace
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import List, Optional
-
-from nm_argparser_ import NmArgumentParser, string_to_bool
-
-
-def list_field(default=None, metadata=None):
-    return field(default_factory=lambda: default, metadata=metadata)
-
-
-@dataclass
-class BasicExample:
-    foo: int
-    bar: float
-    baz: str
-    flag: bool
-
-
-@dataclass
-class WithDefaultExample:
-    foo: int = 42
-    baz: str = field(default="toto", metadata={"help": "help message"})
-
-
-@dataclass
-class WithDefaultBoolExample:
-    foo: bool = False
-    baz: bool = True
-    opt: Optional[bool] = None
-
-
-class BasicEnum(Enum):
-    titi = "titi"
-    toto = "toto"
-
-
-@dataclass
-class EnumExample:
-    foo: BasicEnum = "toto"
-
-    def __post_init__(self):
-        self.foo = BasicEnum(self.foo)
-
-
-@dataclass
-class OptionalExample:
-    foo: Optional[int] = None
-    bar: Optional[float] = field(default=None, metadata={"help": "help message"})
-    baz: Optional[str] = None
-    ces: Optional[List[str]] = list_field(default=[])
-    des: Optional[List[int]] = list_field(default=[])
-
-
-@dataclass
-class ListExample:
-    foo_int: List[int] = list_field(default=[])
-    bar_int: List[int] = list_field(default=[1, 2, 3])
-    foo_str: List[str] = list_field(default=["Hallo", "Bonjour", "Hello"])
-    foo_float: List[float] = list_field(default=[0.1, 0.2, 0.3])
-
-
-@dataclass
-class RequiredExample:
-    required_list: List[int] = field()
-    required_str: str = field()
-    required_enum: BasicEnum = field()
-
-    def __post_init__(self):
-        self.required_enum = BasicEnum(self.required_enum)
-
-
-@dataclass
-class NonHyphenatedExample:
-    non_hyphenated: int = field(
-        metadata={
-            "keep_underscores": True,
-        }
-    )
-
-
-class NmArgumentParserTest(unittest.TestCase):
-    def argparsersEqual(
-        self, parser: argparse.ArgumentParser, expected_parser: argparse.ArgumentParser
-    ) -> bool:
-        """
-        Small helper to check pseudo-equality of parsed arguments on
-        `ArgumentParser` instances.
-        """
-        self.assertEqual(len(parser._actions), len(expected_parser._actions))
-        for x, y in zip(parser._actions, expected_parser._actions):
-            xx = {k: v for k, v in vars(x).items() if k != "container"}
-            yy = {k: v for k, v in vars(y).items() if k != "container"}
-            self.assertEqual(xx, yy)
-
-    def test_basic(self):
-        parser = NmArgumentParser(BasicExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", type=int, required=True)
-        expected.add_argument("--bar", type=float, required=True)
-        expected.add_argument("--baz", type=str, required=True)
-        expected.add_argument(
-            "--flag", type=string_to_bool, default=False, const=True, nargs="?"
-        )
-        self.argparsersEqual(parser, expected)
-
-        args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
-        (example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False)
-        self.assertFalse(example.flag)
-
-    def test_with_default(self):
-        parser = NmArgumentParser(WithDefaultExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", default=42, type=int)
-        expected.add_argument("--baz", default="toto", type=str, help="help message")
-        self.argparsersEqual(parser, expected)
-
-    def test_with_default_bool(self):
-        parser = NmArgumentParser(WithDefaultBoolExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument(
-            "--foo", type=string_to_bool, default=False, const=True, nargs="?"
-        )
-        expected.add_argument("--no-baz", action="store_false", dest="baz")
-        expected.add_argument(
-            "--baz", type=string_to_bool, default=True, const=True, nargs="?"
-        )
-        expected.add_argument("--opt", type=string_to_bool, default=None)
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args, Namespace(foo=False, baz=True, opt=None))
-
-        args = parser.parse_args(["--foo", "--no-baz"])
-        self.assertEqual(args, Namespace(foo=True, baz=False, opt=None))
-
-        args = parser.parse_args(["--foo", "--baz"])
-        self.assertEqual(args, Namespace(foo=True, baz=True, opt=None))
-
-        args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"])
-        self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
-
-        args = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"])
-        self.assertEqual(args, Namespace(foo=False, baz=False, opt=False))
-
-    def test_with_enum(self):
-        parser = NmArgumentParser(EnumExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument(
-            "--foo", default="toto", choices=["titi", "toto"], type=str
-        )
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args.foo, "toto")
-        enum_ex = parser.parse_args_into_dataclasses([])[0]
-        self.assertEqual(enum_ex.foo, BasicEnum.toto)
-
-        args = parser.parse_args(["--foo", "titi"])
-        self.assertEqual(args.foo, "titi")
-        enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0]
-        self.assertEqual(enum_ex.foo, BasicEnum.titi)
-
-    def test_with_list(self):
-        parser = NmArgumentParser(ListExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo-int", nargs="+", default=[], type=int)
-        expected.add_argument("--bar-int", nargs="+", default=[1, 2, 3], type=int)
-        expected.add_argument(
-            "--foo-str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str
-        )
-        expected.add_argument(
-            "--foo-float", nargs="+", default=[0.1, 0.2, 0.3], type=float
-        )
-
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(
-            args,
-            Namespace(
-                foo_int=[],
-                bar_int=[1, 2, 3],
-                foo_str=["Hallo", "Bonjour", "Hello"],
-                foo_float=[0.1, 0.2, 0.3],
-            ),
-        )
-
-        args = parser.parse_args(
-            "--foo-int 1 --bar-int 2 3 --foo-str parser expected_parser "
-            "c --foo-float 0.1 0.7".split()
-        )
-        self.assertEqual(
-            args,
-            Namespace(
-                foo_int=[1],
-                bar_int=[2, 3],
-                foo_str=["parser", "expected_parser", "c"],
-                foo_float=[0.1, 0.7],
-            ),
-        )
-
-    def test_with_optional(self):
-        parser = NmArgumentParser(OptionalExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", default=None, type=int)
-        expected.add_argument("--bar", default=None, type=float, help="help message")
-        expected.add_argument("--baz", default=None, type=str)
-        expected.add_argument("--ces", nargs="+", default=[], type=str)
-        expected.add_argument("--des", nargs="+", default=[], type=int)
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args, Namespace(foo=None, bar=None, baz=None, ces=[], des=[]))
-
-        args = parser.parse_args(
-            "--foo 12 --bar 3.14 --baz 42 --ces parser expected_parser "
-            "c --des 1 2 3".split()
-        )
-        self.assertEqual(
-            args,
-            Namespace(
-                foo=12,
-                bar=3.14,
-                baz="42",
-                ces=["parser", "expected_parser", "c"],
-                des=[1, 2, 3],
-            ),
-        )
-
-    def test_with_required(self):
-        parser = NmArgumentParser(RequiredExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--required-list", nargs="+", type=int, required=True)
-        expected.add_argument("--required-str", type=str, required=True)
-        expected.add_argument(
-            "--required-enum", type=str, choices=["titi", "toto"], required=True
-        )
-        self.argparsersEqual(parser, expected)
-
-    def test_parse_dict(self):
-        parser = NmArgumentParser(BasicExample)
-
-        args_dict = {
-            "foo": 12,
-            "bar": 3.14,
-            "baz": "42",
-            "flag": True,
-        }
-
-        parsed_args = parser.parse_dict(args_dict)[0]
-        args = BasicExample(**args_dict)
-        self.assertEqual(parsed_args, args)
-
-    def test_non_hyphenated(self):
-        parser = NmArgumentParser(dataclass_types=NonHyphenatedExample)
-        args = parser.parse_args("--non_hyphenated 1".split())
-
-        self.assertEqual(args, Namespace(non_hyphenated=1))
diff --git a/integrations/pytorch/export.py b/integrations/pytorch/export.py
deleted file mode 100644
index 230304b6fc4..00000000000
--- a/integrations/pytorch/export.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-usage: export.py [-h] --arch-key ARCH_KEY --dataset DATASET
-                 --dataset-path DATASET_PATH
-                 [--checkpoint-path CHECKPOINT_PATH]
-                 [--num-samples NUM_SAMPLES] [--onnx-opset ONNX_OPSET]
-                 [--use-zipfile-serialization-if-available
-                 USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE]
-                 [--pretrained PRETRAINED]
-                 [--pretrained-dataset PRETRAINED_DATASET]
-                 [--model-kwargs MODEL_KWARGS]
-                 [--dataset-kwargs DATASET_KWARGS]
-                 [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-
-Utility script to export a model to onnx and also store sample
-inputs/outputs
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list
-                        (will raise an exception with the list)
-  --dataset DATASET     The dataset to use for exporting, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder
-                        for a generic dataset setup with an image
-                        folder structure setup like imagenet or
-                        loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the
-                        state from and resume the state for. If
-                        provided, pretrained will be ignored . If using
-                        a SparseZoo recipe, can also provide 'zoo' to
-                        load the base weights associated with that
-                        recipe
-  --num-samples NUM_SAMPLES
-                        The number of samples to export along with the
-                        model onnx and pth files (sample inputs and
-                        labels as well as the outputs from model
-                        execution)
-  --onnx-opset ONNX_OPSET
-                        The onnx opset to use for export.
-  --use-zipfile-serialization-if-available
-                        USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE
-                        for torch >= 1.6.0 only exports the Module's
-                        state dict using the new zipfile serialization.
-                        Default is True, has no affect on lower torch
-                        versions
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default
-                        is true to load the default pretrained weights
-                        for the model. Otherwise should be set to the
-                        desired weights type: [base, optim, optim-
-                        perf]. To not load any weights set to one of
-                        [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will
-                        load the default dataset for the architecture.
-                        Ex can be set to imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model
-                        constructor, should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results
-                        under save-dir, defaults to the model arch and
-                        dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-##########
-Example command for exporting ResNet50:
-python integrations/pytorch/export.py \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
-"""
-import json
-from dataclasses import dataclass, field
-from typing import Any, Optional, Tuple
-
-from torch.nn import Module
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
-from sparseml.pytorch.utils import ModuleExporter
-from sparseml.utils import convert_to_bool
-
-
-CURRENT_TASK = utils.Tasks.EXPORT
-LOGGER = get_main_logger()
-
-
-@dataclass
-class ExportArgs:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    exporting tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for analysis, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param num_samples: The number of samples to export along with the model
-        onnx and pth files (sample inputs and labels as well as the outputs
-        from model execution). Default is 100.
-    :param onnx_opset: The onnx opset to use for export. Default is 11.
-    :param use_zipfile_serialization_if_available: for torch >= 1.6.0 only
-        exports the Module's state dict using the new zipfile serialization.
-        Default is True, has no affect on lower torch versions.
-    :param pretrained: The type of pretrained weights to use,
-        default is true to load the default pretrained weights for the model.
-        Otherwise should be set to the desired weights type: [base, optim,
-        optim-perf]. To not load any weights set to one of [none, false].
-    :param pretrained_dataset: The dataset to load pretrained weights for if
-        pretrained is set. Default is None which will load the default
-        dataset for the architecture. Ex can be set to imagenet, cifar10, etc.
-    :param model_kwargs: Keyword arguments to be passed to model constructor,
-        should be given as a json object.
-    :param dataset_kwargs: Keyword arguments to be passed to dataset
-        constructor, should be given as a json object.
-    :param model_tag: A tag to use for the model for saving results under
-        save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results.
-    """
-
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception "
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for exporting, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored . If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    num_samples: int = field(
-        default=100,
-        metadata={
-            "help": "The number of samples to export along with the model onnx "
-            "and pth files (sample inputs and labels as well as the "
-            "outputs from model execution)"
-        },
-    )
-
-    onnx_opset: int = field(
-        default=TORCH_DEFAULT_ONNX_OPSET,
-        metadata={
-            "help": "The onnx opset to use for export. "
-            f"Default is {TORCH_DEFAULT_ONNX_OPSET}"
-        },
-    )
-
-    use_zipfile_serialization_if_available: convert_to_bool = field(
-        default=True,
-        metadata={
-            "help": "for torch >= 1.6.0 only exports the Module's state dict "
-            "using the new zipfile serialization. Default is True, "
-            "has no affect on lower torch versions"
-        },
-    )
-
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for "
-            "the model. Otherwise should be set to the desired weights "
-            "type: [base, optim, optim-perf]. To not load any weights "
-            "set to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained "
-            "is set. Default is None which will load the default "
-            "dataset for the architecture. Ex can be set to imagenet, "
-            "cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be  given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.local_rank: int = -1
-        self.is_main_process: bool = True
-
-
-def export(
-    args: ExportArgs, model: Module, val_loader: DataLoader, save_dir: str
-) -> None:
-    """
-    Utility method to export the model and data
-
-    :param args : An ExportArgs object containing config for export task.
-    :param model: loaded model architecture to export
-    :param val_loader: A DataLoader for validation data
-    :param save_dir: Directory to store checkpoints at during exporting process
-    """
-    exporter = ModuleExporter(model, save_dir)
-
-    # export PyTorch state dict
-    LOGGER.info(f"exporting pytorch in {save_dir}")
-
-    exporter.export_pytorch(
-        use_zipfile_serialization_if_available=(
-            args.use_zipfile_serialization_if_available
-        )
-    )
-    onnx_exported = False
-
-    for batch, data in tqdm(
-        enumerate(val_loader),
-        desc="Exporting samples",
-        total=args.num_samples if args.num_samples > 1 else 1,
-    ):
-        if not onnx_exported:
-            # export onnx file using first sample for graph freezing
-            LOGGER.info(f"exporting onnx in {save_dir}")
-            exporter.export_onnx(data[0], opset=args.onnx_opset, convert_qat=True)
-            onnx_exported = True
-
-        if args.num_samples > 0:
-            exporter.export_samples(
-                sample_batches=[data[0]], sample_labels=[data[1]], exp_counter=batch
-            )
-
-
-def export_setup(args_: ExportArgs) -> Tuple[Module, Optional[str], Any]:
-    """
-    Pre-export setup
-
-    :param args_ : An ExportArgs object containing config for export task.
-    """
-    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
-
-    # model creation
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-    model = utils.create_model(args_, num_classes)
-    return model, save_dir, val_loader
-
-
-def main():
-    """
-    Driver function
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=ExportArgs,
-        description="Utility script to export a model to onnx "
-        "and also store sample inputs/outputs",
-    )
-    (args_,) = _parser.parse_args_into_dataclasses()
-    model, save_dir, val_loader = export_setup(args_)
-    export(args_, model, val_loader, save_dir)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/pytorch/lr_analysis.py b/integrations/pytorch/lr_analysis.py
deleted file mode 100644
index a9dc0e94058..00000000000
--- a/integrations/pytorch/lr_analysis.py
+++ /dev/null
@@ -1,408 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-usage: lr_analysis.py [-h] --batch-size BATCH_SIZE --arch-key ARCH_KEY
-                        --dataset DATASET --dataset-path DATASET_PATH [
-                        --pretrained PRETRAINED]
-                      [--pretrained-dataset PRETRAINED_DATASET] [
-                      --model-kwargs MODEL_KWARGS]
-                      [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
-                      [--save-dir SAVE_DIR] [--device DEVICE]
-                      [--loader-num-workers LOADER_NUM_WORKERS] [
-                      --no-loader-pin-memory]
-                      [--loader-pin-memory [LOADER_PIN_MEMORY]]
-                      [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
-                      [--optim-args OPTIM_ARGS] [--final-lr FINAL_LR]
-                      [--steps-per-measurement STEPS_PER_MEASUREMENT]
-
-Utility script to Run a learning rate sensitivity analysis for a desired
-image classification architecture
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --batch-size BATCH_SIZE
-                        The batch size to use for analysis
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16, mobilenet
-                        put as help to see the full list (will raise an
-                        exception with the list)
-  --dataset DATASET     The dataset to use for analysis, ex: imagenet,
-                        imagenette, cifar10,
-                        etc. Set to imagefolder for a generic dataset setup
-                        with an imagefolder structure setup like imagenet or
-                        loadable by a dataset in sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is
-                        true to load the default pretrained weights for the
-                        model. Otherwise should be set to the desired weights
-                        type: [base, optim, optim-perf]. To not load any
-                        weights set to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if pretrained
-                        is set. Default is None which will load the default
-                        dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for
-                        data parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained
-                        will be ignored. If using a SparseZoo recipe,
-                        can also provide 'zoo' to load the base weights
-                        associated with that recipe
-  --init-lr INIT_LR     The initial learning rate to use for the sensitivity
-                        analysis
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed in
-                        as a json object
-  --final-lr FINAL_LR   The final learning rate to use for the sensitivity
-                        analysis
-  --steps-per-measurement STEPS_PER_MEASUREMENT The number of steps (batches)
-                        to run for each measurement
-#########
-EXAMPLES
-#########
-
-##########
-Example command for running LR sensitivity analysis on mobilenet:
-python integrations/pytorch/lr_analysis.py \
-    --arch-key mobilenet --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012 --batch-size 2
-
-Example command for running LR sensitivity analysis on mobilenet using
-imagenette:
-python integrations/pytorch/lr_analysis.py \
-    --arch-key mobilenet --dataset imagenette \
-    --dataset-path ~/datasets/ --batch-size 2
-"""
-
-import json
-import os
-from dataclasses import dataclass, field
-
-from torch.nn import Module
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import default_exponential_check_lrs, lr_loss_sensitivity
-from sparseml.pytorch.utils import PythonLogger, default_device, model_to_device
-
-
-CURRENT_TASK = utils.Tasks.LR_ANALYSIS
-LOGGER = get_main_logger()
-
-
-@dataclass
-class LRAnalysisArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    learning rate analysis.
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`
-    arguments that can be specified on the command line.
-
-    :param batch_size: The batch size to use for analysis.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for analysis, ex imagenet,
-        imagenette, etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param pretrained: The type of pretrained weights to use default is true to
-        load the default pretrained weights for the model. Otherwise should be
-        set to the desired weights type: [base, optim, optim-perf];
-        To not load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load pretrained
-        weights for; if pretrained is set; Default is None which will load the
-        default dataset for the architecture; Ex can be set to imagenet,
-        cifar10, etc.
-    :param model_kwargs: json object containing keyword arguments to be passed
-        to the model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be passed
-        to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results under
-        save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on (can also include ids
-        for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data loading,
-        default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param init_lr: float representing the initial learning for analysis,
-        default=1e-5.
-    :param optim_args: Additional arguments to be passed in to the optimizer as
-        a json object.
-    :param final_lr: The final learning rate to use for the sensitivity
-        analysis.
-    :param steps_per_measurement: The number of steps (batches) to run for each
-        measurement.
-    """
-
-    batch_size: int = field(metadata={"help": "The batch size to use for analysis"})
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list"
-            "(will raise an exception with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for analysis, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights "
-            "for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained is"
-            "set. Default is None which will load the default dataset for "
-            "the architecture. Ex can be set to imagenet, cifar10, etc.",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor,"
-            " should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for "
-            "data parallel), ex:cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will be"
-            "ignored. If using a SparseZoo recipe, can also provide "
-            "'zoo' to load the base weights associated with that recipe"
-        },
-    )
-
-    init_lr: float = field(
-        default=1e-5,
-        metadata={
-            "help": "The initial learning rate to use for the sensitivity analysis"
-        },
-    )
-
-    optim_args: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Additional args to be passed to the optimizer passed in"
-            " as a json object"
-        },
-    )
-
-    final_lr: float = field(
-        default=0.5,
-        metadata={
-            "help": "The final learning rate to use for the sensitivity analysis",
-        },
-    )
-
-    steps_per_measurement: int = field(
-        default=20,
-        metadata={"help": "The number of steps (batches) to run for each measurement"},
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.is_main_process = True
-        self.local_rank = -1
-        self.rank = -1
-
-
-def lr_sensitivity(
-    args,
-    model: Module,
-    train_loader: DataLoader,
-    save_dir: str,
-) -> None:
-    """
-    Utility function to run learning rate sensitivity analysis
-
-    :param args: An LRAnalysisArguments object containing config for current
-        LR analysis task.
-    :param model: loaded model architecture to analyse
-    :param train_loader: A DataLoader for training data
-    :param save_dir: Directory to save results
-    """
-    # optimizer setup
-    optim = SGD(model.parameters(), lr=args.init_lr, **args.optim_args)
-    LOGGER.info(f"created optimizer: {optim}")
-
-    # loss setup
-    loss = utils.get_loss_wrapper(args.arch_key)
-    LOGGER.info(f"created loss: {loss}")
-
-    # device setup
-    model, device, device_ids = model_to_device(model, args.device)
-
-    # learning rate analysis
-    LOGGER.info(f"running analysis: {loss}")
-    analysis = lr_loss_sensitivity(
-        model,
-        train_loader,
-        loss,
-        optim,
-        device,
-        args.steps_per_measurement,
-        check_lrs=default_exponential_check_lrs(args.init_lr, args.final_lr),
-        trainer_loggers=[PythonLogger()],
-    )
-
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info(f"Saving results in {save_dir}")
-    analysis.save_json(os.path.join(save_dir, "lr_sensitivity.json"))
-    analysis.plot(os.path.join(save_dir, "lr_sensitivity.png"))
-    analysis.print_res()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=LRAnalysisArguments,
-        description="Utility script to Run a "
-        "learning rate sensitivity analysis "
-        "for a desired image classification architecture",
-    )
-    args_, _ = _parser.parse_args_into_dataclasses()
-    save_dir, loggers = utils.get_save_dir_and_loggers(
-        args_,
-        task=CURRENT_TASK,
-    )
-
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    # assume shape [C, S, S] where S is the image size
-    image_size = input_shape[1]
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(
-        args_,
-        image_size,
-        task=CURRENT_TASK,
-    )
-
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-    model = utils.create_model(args_, num_classes)
-    lr_sensitivity(args_, model, train_loader, save_dir)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/pytorch/notebooks/classification.ipynb b/integrations/pytorch/notebooks/classification.ipynb
deleted file mode 100644
index 6bdac17c274..00000000000
--- a/integrations/pytorch/notebooks/classification.ipynb
+++ /dev/null
@@ -1,366 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# PyTorch Classification Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML\n",
-    "- Prune the model using the PyTorch+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 15 minutes on a GPU\n",
-    "- 45 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = ModelRegistry.create(...)\n",
-    "- train_dataset = ImagenetteDataset(...)\n",
-    "- val_dataset = ImagenetteDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"resnet50\",\n",
-    "    pretrained=True,\n",
-    "    pretrained_dataset=\"imagenette\",\n",
-    "    num_classes=10,\n",
-    ")\n",
-    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "    total_correct = 0\n",
-    "    total_predictions = 0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
-    "        loss = criterion(outputs, labels)\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "        predictions = outputs.argmax(dim=1)\n",
-    "        total_correct += torch.sum(predictions == labels).item()\n",
-    "        total_predictions += inputs.size(0)\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    accuracy = total_correct / total_predictions\n",
-    "    return loss, accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up PyTorch Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 128\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"classification\",\n",
-    "    architecture=\"resnet_v1\",\n",
-    "    sub_architecture=\"50\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"sparseml\",\n",
-    "    dataset=\"imagenette\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Run model pruning\n",
-    "epoch = manager.min_epochs\n",
-    "while epoch < manager.max_epochs:\n",
-    "    # run training loop\n",
-    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
-    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        \"Training Epoch: {}\\nTraining Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
-    "            epoch_name, train_loss, train_acc\n",
-    "        )\n",
-    "    )\n",
-    "    \n",
-    "    # run validation loop\n",
-    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
-    "    val_loss, val_acc = run_model_one_epoch(\n",
-    "        model, val_loader, criterion, device\n",
-    "    )\n",
-    "    print(\n",
-    "        \"Validation Epoch: {}\\nVal Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
-    "            epoch_name, val_loss, val_acc\n",
-    "        )\n",
-    "    )\n",
-    "    \n",
-    "    epoch += 1\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_classification\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/pytorch/notebooks/detection.ipynb b/integrations/pytorch/notebooks/detection.ipynb
deleted file mode 100644
index 7a76aa223dd..00000000000
--- a/integrations/pytorch/notebooks/detection.ipynb
+++ /dev/null
@@ -1,372 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# PyTorch Detection Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML\n",
-    "- Prune the model using the PyTorch+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 30 minutes on a GPU\n",
-    "- 90 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [SSD](https://arxiv.org/abs/1512.02325) model trained on the [VOC detection dataset](http://host.robots.ox.ac.uk/pascal/VOC/). The model's pretrained weights are downloaded from the SparseZoo model repo. The VOC detection dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "Note, for this notebook, you will use a ResNet18 backbone for the object detector.  This is to save training time and demonstrate the general pruning flow. For better accuracy, you can use a different backbone or model.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = ModelRegistry.create(...)\n",
-    "- train_dataset = VOCDetectionDataset(...)\n",
-    "- val_dataset = VOCDetectionDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import VOCDetectionDataset\n",
-    "from sparseml.pytorch.utils import get_default_boxes_300\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"ssd300_resnet18\",\n",
-    "    pretrained=True,\n",
-    "    pretrained_dataset=\"voc\",\n",
-    "    pretrained_backbone=False,  # no need to download initial weights\n",
-    "    num_classes=21,\n",
-    ")\n",
-    "model_name = model.__class__.__name__\n",
-    "input_shape = ModelRegistry.input_shape(\"ssd300_resnet18\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "default_boxes = get_default_boxes_300(\"voc\")\n",
-    "train_dataset = VOCDetectionDataset(\n",
-    "    train=True, rand_trans=True, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = VOCDetectionDataset(\n",
-    "    train=False, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) to work with our detection model and loss function.  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "from sparseml.pytorch.utils import DEFAULT_LOSS_KEY\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(\n",
-    "    model, data_loader, criterion, device, train=False, optimizer=None\n",
-    "):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = [\n",
-    "            label.to(device) if isinstance(label, torch.Tensor) else label\n",
-    "            for label in labels\n",
-    "        ]\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs = model(inputs)\n",
-    "        loss = criterion((inputs, labels), outputs)[DEFAULT_LOSS_KEY]\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up Detection Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow.  The loss function and collate function are standard for SSD training and are defined in the sparseml API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "from sparseml.pytorch.datasets import ssd_collate_fn\n",
-    "from sparseml.pytorch.utils import SSDLossWrapper\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 64\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    batch_size,\n",
-    "    shuffle=True,\n",
-    "    pin_memory=True,\n",
-    "    num_workers=12,\n",
-    "    collate_fn=ssd_collate_fn,\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset,\n",
-    "    batch_size,\n",
-    "    shuffle=False,\n",
-    "    pin_memory=True,\n",
-    "    num_workers=12,\n",
-    "    collate_fn=ssd_collate_fn,\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer\n",
-    "criterion = SSDLossWrapper()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning SSD-ResNet18 on the VOC dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"detection\",\n",
-    "    architecture=\"ssd\",\n",
-    "    sub_architecture=\"resnet18_300\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"sparseml\",\n",
-    "    dataset=\"voc\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "epoch = manager.min_epochs\n",
-    "while epoch < manager.max_epochs:\n",
-    "    # run training loop\n",
-    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
-    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
-    "    train_loss = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\"Training Epoch: {}\\nTraining Loss: {}\\n\".format(epoch_name, train_loss))\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
-    "    val_loss = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\"Validation Epoch: {}\\nVal Loss: {}\\n\".format(epoch_name, val_loss))\n",
-    "\n",
-    "    epoch += 1\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_detection\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"ssd_resnet18_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 300, 300), name=\"ssd_resnet18_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb b/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
deleted file mode 100644
index d5d50f63a23..00000000000
--- a/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
+++ /dev/null
@@ -1,454 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Sparse-Quantized Transfer Learning in PyTorch using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for creating a performant sparse-quantized model\n",
-    "by transfer learning the pruned structure from an already sparse-quantized model.\n",
-    "\n",
-    "Sparse-quantized models combine [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to reduce both the number of parameters and the precision of the remaining parameters to significantly increase the performance of neural networks. Using these optimizations, your model will obtain significantly better (around 7x vs. unoptimized) performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse).\n",
-    "\n",
-    "Sparse-quantized transfer learning takes two steps. [SparseML](https://github.com/neuralmagic/sparseml) recipes make it easy to perform these optimizations:\n",
-    "- First, fine-tune a pre-trained sparse model for the transfer dataset while maintaining the pre-trained sparsity structure.\n",
-    "- Second, perform [quantization-aware training (QAT)](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) to quantize the now sparse model while still holding the same sparsity structure.\n",
-    "\n",
-    "In this notebook, you will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML for transfer learning\n",
-    "- Perform sparse transfer learning and quantization-aware training using the PyTorch and SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/) and convert the model from a QAT\n",
-    "- [Optional] Compare DeepSparse Engine benchmarks of the final sparse-quantized model to an unoptimized model\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow for transfer learning and generically. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 30 minutes on a GPU\n",
-    "- 90 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML, SparseZoo\n",
-    "* PyTorch (>= 1.7.0) and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision\n",
-    "\n",
-    "assert torch.__version__ >= \"1.7\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will transfer learn from a sparse-quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [ImageNet dataset](http://www.image-net.org/) to the much smaller [Imagenette dataset](https://github.com/fastai/imagenette). The transfer learning weights are downloaded from the [SparseZoo](https://github.com/neuralmagic/sparsezoo) model repository.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "When loading weights for transfer learning classification models, it is standard to override the final classifier layer to fit the output shape for the new dataset.  In the example below, this is done by specifying `ignore_error_tensors` as the weights that will be initialzed for the new model.  In other flows this could be accomplished by setting `model.classifier.fc = torch.nn.Linear(...)`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "from sparsezoo import Model\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
-    "zoo_stub_path = (\n",
-    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate\"\n",
-    "    \"?recipe=transfer_learn\"\n",
-    ")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"resnet50\",\n",
-    "    pretrained_path=zoo_stub_path,\n",
-    "    pretrained_dataset=\"imagenette\",\n",
-    "    num_classes=10,\n",
-    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
-    ")\n",
-    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Creating a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune and quantize your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "    total_correct = 0\n",
-    "    total_predictions = 0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
-    "        loss = criterion(outputs, labels)\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "        predictions = outputs.argmax(dim=1)\n",
-    "        total_correct += torch.sum(predictions == labels).item()\n",
-    "        total_predictions += inputs.size(0)\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    accuracy = total_correct / total_predictions\n",
-    "    return loss, accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Building PyTorch Training Objects\n",
-    "In this step, you will select hyperparameters, a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import Adam\n",
-    "\n",
-    "# hyperparameters\n",
-    "BATCH_SIZE = 32\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(f\"Using device: {device}\")\n",
-    "\n",
-    "# setup data loaders\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Running Sparse-Quantized Transfer Learning with a SparseML Recipe\n",
-    "\n",
-    "To run sparse-quantized transfer learning with SparseML, you will download a transfer learning recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to maintain the pre-optimized model's sparsity structure while learning weights for the new dataset as well as performing quantization-aware training (QAT).\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, QAT, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe  file to match the new target's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error; to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides preconfigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet-50 on the Imagenette dataset and record its saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model\n",
-    "\n",
-    "zoo_model = Model(zoo_stub_path)\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "# Run model pruning\n",
-    "epoch = manager.min_epochs\n",
-    "for epoch in range(manager.max_epochs):\n",
-    "    # run training loop\n",
-    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
-    "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
-    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - Viewing Model Sparsity\n",
-    "To see the effects of sparse-quantized transfer learning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the sparse-quantized transfer learning is complete, it should be prepped for inference.  A common next step for inference is exporting the model to ONNX.  This is also the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) to achieve the sparse-quantized speedups.\n",
-    "\n",
-    "For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Additionally, PyTorch, exports a graph setup for quantization-aware training (QAT) to ONNX. To run a fully quantized graph, you will need to convert these QAT operations to fully quantized INT8 operations.  SparseML provides the `quantize_torch_qat_export` helper function to perform this conversion.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.\n",
-    "\n",
-    "Normally, exporting a QAT model from PyTorch to ONNX will create a graph with \"fake quantized\" operations that represent the QAT graph.  By setting `convert_qat=True` in our exporter, a function will automatically be called to convert this exported model to a fully quantized graph that will contain desired quantized structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_sparse_quantized_transfer_learning\"\n",
-    "quant_onnx_graph_name = \"resnet50_imagenette_pruned_quant.onnx\"\n",
-    "quantized_onnx_path = os.path.join(save_dir, quant_onnx_graph_name)\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned_qat.pth\")\n",
-    "exporter.export_onnx(\n",
-    "    torch.randn(1, 3, 224, 224), name=quant_onnx_graph_name, convert_qat=True\n",
-    ")\n",
-    "\n",
-    "print(f\"Sparse-Quantized ONNX model saved to {quantized_onnx_path}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Optional] Step 8 - Benchmarking\n",
-    "\n",
-    "Finally, to see the total effect of these optimizations, you will benchmark an unoptimized, dense ResNet-50 model from SparseZoo against your sparse-quantized model using the `deepsparse` API.\n",
-    "\n",
-    "To run this step `deepsparse` must be installed in your python environment. You can install it with `pip install deepsparse`.\n",
-    "\n",
-    "Note, in order to view speedup from quantization, your CPU must run VNNI instructions.  The benchmarking cell below contains a check for VNNI instructions and will log a warning if they are not detected.  You can learn more about DeepSparse hardware compatibility [here](https://docs.neuralmagic.com/deepsparse/hardware.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "from deepsparse import benchmark_model\n",
-    "from deepsparse.cpu import cpu_architecture\n",
-    "\n",
-    "\n",
-    "# check VNNI\n",
-    "if cpu_architecture()[\"vnni\"]:\n",
-    "    print(\"VNNI extensions detected, model will run with quantized speedups\\n\")\n",
-    "else:\n",
-    "    print(\n",
-    "        \"WARNING: No VNNI extensions detected. Your model will not run with \"\n",
-    "        \"quantized speedups which will affect benchmarking\\n\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "BATCH_SIZE = 64\n",
-    "NUM_CORES = None  # maximum number of cores available\n",
-    "NUM_ITERATIONS = 100\n",
-    "NUM_WARMUP_ITERATIONS = 20\n",
-    "\n",
-    "\n",
-    "def benchmark_imagenette_model(model_name, model_path):\n",
-    "    print(\n",
-    "        f\"Benchmarking {model_name} for {NUM_ITERATIONS} iterations at batch \"\n",
-    "        f\"size {BATCH_SIZE} with {NUM_CORES} CPU cores\"\n",
-    "    )\n",
-    "    sample_input = [\n",
-    "        numpy.ascontiguousarray(\n",
-    "            numpy.random.randn(BATCH_SIZE, 3, 224, 224).astype(numpy.float32)\n",
-    "        )\n",
-    "    ]\n",
-    "\n",
-    "    results = benchmark_model(\n",
-    "        model=model_path,\n",
-    "        inp=sample_input,\n",
-    "        batch_size=BATCH_SIZE,\n",
-    "        num_cores=NUM_CORES,\n",
-    "        num_iterations=NUM_ITERATIONS,\n",
-    "        num_warmup_iterations=NUM_WARMUP_ITERATIONS,\n",
-    "        show_progress=True,\n",
-    "    )\n",
-    "    print(f\"results:\\n{results}\")\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "# base ResNet-50 Imagenette model downloaded from SparseZoo\n",
-    "base_results = benchmark_imagenette_model(\n",
-    "    \"ResNet-50 Imagenette Base\",\n",
-    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none\"\n",
-    ")\n",
-    "\n",
-    "optimized_results = benchmark_imagenette_model(\n",
-    "    \"ResNet-50 Imagenette pruned-quantized\", quantized_onnx_path\n",
-    ")\n",
-    "\n",
-    "speed_up = base_results.ms_per_batch / optimized_results.ms_per_batch\n",
-    "print(f\"Speed-up from sparse-quantized transfer learning: {speed_up}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have created a sparse-quantized model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Transfer learning, pruning, or quantizing different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Benchmarking other models on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/pytorch/notebooks/torchvision.ipynb b/integrations/pytorch/notebooks/torchvision.ipynb
deleted file mode 100644
index 82c2f82147b..00000000000
--- a/integrations/pytorch/notebooks/torchvision.ipynb
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Torchvision Classification Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning a [torchvision model](https://pytorch.org/docs/stable/torchvision/models.html) using SparseML. You will:\n",
-    "- Download a pre-trained torchvision model and generic dataset\n",
-    "- Define a generic torchvision finetuning flow\n",
-    "- Integrate the torchvision flow with SparseML\n",
-    "- Prune the model using the torchvision+SparseML flow\n",
-    "- Save the model and export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to integrate SparseML with torchvision or more generically a PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 15 minutes on a GPU\n",
-    "- 45 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model while finetuning it on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from torchvision. The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "Additionally, we will override the FC layer in the ResNet50 model to have 10 output classes instead of the ImageNet standard 1000.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = resnet50(pretrained=True)\n",
-    "- train_dataset = ImagenetteDataset(...)\n",
-    "- val_dataset = ImagenetteDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision.models import resnet50\n",
-    "from torch.nn import Linear\n",
-    "\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = resnet50(pretrained=True)\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=224\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=224\n",
-    ")\n",
-    "print(val_dataset)\n",
-    "\n",
-    "# Overriding number of classes\n",
-    "NUM_CLASSES = 10  # number of imagenette classes\n",
-    "model.fc = Linear(in_features=model.fc.in_features, out_features=NUM_CLASSES, bias=True)\n",
-    "print(model.fc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a Torchvision Finetuning Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop taken from the [torchvision finetuning example](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import copy\n",
-    "import torch\n",
-    "\n",
-    "def train_model(\n",
-    "    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False\n",
-    "):\n",
-    "    since = time.time()\n",
-    "\n",
-    "    val_acc_history = []\n",
-    "\n",
-    "    best_acc = 0.0\n",
-    "\n",
-    "    for epoch in range(num_epochs):\n",
-    "        print(\"Epoch {}/{}\".format(epoch, num_epochs - 1))\n",
-    "        print(\"-\" * 10)\n",
-    "\n",
-    "        # Each epoch has a training and validation phase\n",
-    "        for phase in [\"train\", \"val\"]:\n",
-    "            if phase == \"train\":\n",
-    "                model.train()  # Set model to training mode\n",
-    "            else:\n",
-    "                model.eval()  # Set model to evaluate mode\n",
-    "\n",
-    "            running_loss = 0.0\n",
-    "            running_corrects = 0\n",
-    "\n",
-    "            # Iterate over data.\n",
-    "            for inputs, labels in dataloaders[phase]:\n",
-    "                inputs = inputs.to(device)\n",
-    "                labels = labels.to(device)\n",
-    "\n",
-    "                # zero the parameter gradients\n",
-    "                optimizer.zero_grad()\n",
-    "\n",
-    "                # forward\n",
-    "                # track history if only in train\n",
-    "                with torch.set_grad_enabled(phase == \"train\"):\n",
-    "                    # Get model outputs and calculate loss\n",
-    "                    # Special case for inception because in training it has an auxiliary output. In train\n",
-    "                    #   mode we calculate the loss by summing the final output and the auxiliary output\n",
-    "                    #   but in testing we only consider the final output.\n",
-    "                    if is_inception and phase == \"train\":\n",
-    "                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958\n",
-    "                        outputs, aux_outputs = model(inputs)\n",
-    "                        loss1 = criterion(outputs, labels)\n",
-    "                        loss2 = criterion(aux_outputs, labels)\n",
-    "                        loss = loss1 + 0.4 * loss2\n",
-    "                    else:\n",
-    "                        outputs = model(inputs)\n",
-    "                        loss = criterion(outputs, labels)\n",
-    "\n",
-    "                    _, preds = torch.max(outputs, 1)\n",
-    "\n",
-    "                    # backward + optimize only if in training phase\n",
-    "                    if phase == \"train\":\n",
-    "                        loss.backward()\n",
-    "                        optimizer.step()\n",
-    "\n",
-    "                # statistics\n",
-    "                running_loss += loss.item() * inputs.size(0)\n",
-    "                running_corrects += torch.sum(preds == labels.data)\n",
-    "\n",
-    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
-    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
-    "\n",
-    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
-    "\n",
-    "            # deep copy the model\n",
-    "            if phase == \"val\" and epoch_acc > best_acc:\n",
-    "                best_acc = epoch_acc\n",
-    "            if phase == \"val\":\n",
-    "                val_acc_history.append(epoch_acc)\n",
-    "\n",
-    "        print()\n",
-    "\n",
-    "    time_elapsed = time.time() - since\n",
-    "    print(\n",
-    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
-    "            time_elapsed // 60, time_elapsed % 60\n",
-    "        )\n",
-    "    )\n",
-    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
-    "\n",
-    "    # load best model weights\n",
-    "    return model, val_acc_history"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up PyTorch Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 128\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "dataloaders = {\"train\": train_loader, \"val\": val_loader}\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"classification\",\n",
-    "    architecture=\"resnet_v1\",\n",
-    "    sub_architecture=\"50\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"torchvision\",\n",
-    "    dataset=\"imagenette\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "train_model(\n",
-    "    model,\n",
-    "    dataloaders,\n",
-    "    criterion,\n",
-    "    optimizer,\n",
-    "    device,\n",
-    "    num_epochs=manager.max_epochs,\n",
-    "    is_inception=False,\n",
-    ")\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - Save Model and Export to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"torchvision_models\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/pytorch/pr_sensitivity.py b/integrations/pytorch/pr_sensitivity.py
deleted file mode 100644
index fad574bd440..00000000000
--- a/integrations/pytorch/pr_sensitivity.py
+++ /dev/null
@@ -1,416 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-python integrations/pytorch/pr_sensitivity.py --help
-usage: pr_sensitivity.py [-h] --arch-key ARCH_KEY --dataset DATASET
-                         --dataset-path DATASET_PATH
-                         [--pretrained PRETRAINED]
-                         [--pretrained-dataset PRETRAINED_DATASET]
-                         [--model-kwargs MODEL_KWARGS]
-                         [--dataset-kwargs DATASET_KWARGS]
-                         [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                         [--device DEVICE]
-                         [--loader-num-workers LOADER_NUM_WORKERS]
-                         [--no-loader-pin-memory]
-                         [--loader-pin-memory [LOADER_PIN_MEMORY]]
-                         [--checkpoint-path CHECKPOINT_PATH]
-                         [--steps-per-measurement STEPS_PER_MEASUREMENT]
-                         [--batch-size BATCH_SIZE]
-                         [--approximate [APPROXIMATE]]
-
-Utility script to Run a kernel sparsity (pruning) analysis for a
-desired image classification architecture
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list
-                        (will raise an exception with the list)
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder
-                        for a generic dataset setup with an image
-                        folder structure setup like imagenet or
-                        loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default
-                        is true to load the default pretrained weights
-                        for the model. Otherwise should be set to the
-                        desired weights type: [base, optim, optim-
-                        perf]. To not load any weights set to one of
-                        [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will
-                        load the default dataset for the architecture.
-                        Ex can be set to imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model
-                        constructor, should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results
-                        under save-dir, defaults to the model arch and
-                        dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for
-                        data parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the
-                        state from and resume the state for. If
-                        provided, pretrained will be ignored. If using
-                        a SparseZoo recipe, can also provide 'zoo' to
-                        load the base weights associated with that
-                        recipe
-  --steps-per-measurement STEPS_PER_MEASUREMENT
-                        The number of steps (batches) to run for each
-                        measurement
-  --batch-size BATCH_SIZE
-                        The batch size to use for analysis
-  --approximate [APPROXIMATE]
-                        approximate without running data through the
-                        model(uses one shot analysis if --approximate
-                        not passed)
-#########
-EXAMPLES
-#########
-
-##########
-Example command for running one shot KS sens analysis on ssd300_resnet50 for
-coco:
-python integrations/pytorch/lr_analysis.py pr_sensitivity \
-    --arch-key ssd300_resnet50 --dataset coco \
-    --dataset-path ~/datasets/coco-detection
-
-Note: Might need to install pycocotools using pip
-"""
-import json
-import os
-from dataclasses import dataclass, field
-from typing import Any, List, Optional
-
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import (
-    pruning_loss_sens_magnitude,
-    pruning_loss_sens_one_shot,
-)
-from sparseml.pytorch.utils import default_device, model_to_device
-
-
-CURRENT_TASK = utils.Tasks.PR_SENSITIVITY
-LOGGER = get_main_logger()
-
-
-@dataclass
-class PRAnalysisArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    kernel sparsity (pruning) analysis tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for training, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param pretrained: The type of pretrained weights to use default is true to
-        load the default pretrained weights for the model Otherwise should be
-        set to the desired weights type: [base, optim, optim-perf]; To not
-        load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load
-        pretrained weights for if pretrained is set; Default is None which will
-        load the default dataset for the architecture; Ex can be set to
-        imagenet, cifar10, etc".
-    :param model_kwargs: json object containing keyword arguments to be
-        passed to model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be
-        passed to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results
-        under save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on
-        (can also include ids for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data
-        loading, default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with
-        a recipe.
-    :param steps_per_measurement: The number of steps (batches) to run for
-        each measurement
-    :param batch_size: The batch size to use for analysis
-    :param approximate: approximate without running data through the model
-        (uses one shot analysis if --approximate not passed)
-    """
-
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception "
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights "
-            "for the model. Otherwise should be set to the desired "
-            "weights type: [base, optim, optim-perf]. To not load any"
-            " weights set  to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained"
-            "is set. Default is None which will load the default "
-            "dataset for the architecture. Ex can be set to imagenet,"
-            "cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, "
-            "should be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under "
-            "save-dir, defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for "
-            "data parallel), ex: cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored. If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    steps_per_measurement: int = field(
-        default=15,
-        metadata={"help": "The number of steps (batches) to run for each measurement"},
-    )
-    batch_size: int = field(
-        default=64, metadata={"help": "The batch size to use for analysis"}
-    )
-
-    approximate: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "approximate without running data through the model"
-            "(uses one shot analysis if --approximate not passed)",
-        },
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.is_main_process = True
-        self.local_rank = -1
-        self.rank = -1
-
-
-def pruning_loss_sensitivity(
-    args: PRAnalysisArguments,
-    model,
-    train_loader: DataLoader,
-    save_dir: str,
-    loggers: List[Any],
-) -> None:
-    """
-    Utility function for pruning sensitivity analysis
-
-    :param args : A PRAnalysisArguments object containing config for current
-        analysis
-    :param model: loaded model architecture to analyse
-    :param train_loader: A DataLoader for training data
-    :param save_dir: Directory to save results
-    :param loggers: List of loggers to use during analysis
-    """
-    # loss setup
-    if not args.approximate:
-        loss = utils.get_loss_wrapper(args)
-        LOGGER.info(f"created loss: {loss}")
-    else:
-        loss = None
-
-    # device setup
-    if not args.approximate:
-        module, device, device_ids = model_to_device(model, args.device)
-    else:
-        device = None
-
-    # kernel sparsity analysis
-    if args.approximate:
-        analysis = pruning_loss_sens_magnitude(model)
-    else:
-        analysis = pruning_loss_sens_one_shot(
-            model,
-            train_loader,
-            loss,
-            device,
-            args.steps_per_measurement,
-            tester_loggers=loggers,
-        )
-
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info(f"Saving results in {save_dir}")
-    analysis.save_json(
-        os.path.join(
-            save_dir,
-            "ks_approx_sensitivity.json"
-            if args.approximate
-            else "ks_one_shot_sensitivity.json",
-        )
-    )
-    analysis.plot(
-        os.path.join(
-            save_dir,
-            os.path.join(
-                save_dir,
-                "ks_approx_sensitivity.png"
-                if args.approximate
-                else "ks_one_shot_sensitivity.png",
-            ),
-        ),
-        plot_integral=True,
-    )
-    analysis.print_res()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=PRAnalysisArguments,
-        description="Utility script to Run a "
-        "kernel sparsity (pruning) analysis "
-        "for a desired image classification architecture",
-    )
-
-    args_, _ = _parser.parse_args_into_dataclasses()
-
-    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
-
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    # assume shape [C, S, S] where S is the image size
-    image_size = input_shape[1]
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
-
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-
-    model = utils.create_model(args_, num_classes)
-
-    pruning_loss_sensitivity(args_, model, train_loader, save_dir, loggers)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/pytorch/recipes/.gitkeep b/integrations/pytorch/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/pytorch/recipes/classification.transfer_learn_pruned.md b/integrations/pytorch/recipes/classification.transfer_learn_pruned.md
deleted file mode 100644
index 29479dcdc1f..00000000000
--- a/integrations/pytorch/recipes/classification.transfer_learn_pruned.md
+++ /dev/null
@@ -1,98 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# Epoch and Learning-Rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-    
----
-
-# Transfer Learning
-
-This recipe provides a framework for performing sparse transfer learning on
-SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
-
-
-To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned
-```
-
-To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned.md\
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag mobilenet-imagenette-sparse-transfer-learned
-```
-## Other Datasets
-To transfer learn this sparsified model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increase the learning rate when increasing batch size
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md b/integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
deleted file mode 100644
index 5820fcf05f3..00000000000
--- a/integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
+++ /dev/null
@@ -1,106 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# Epoch and Learning-Rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-# quantization variables
-quantization_start_epoch: &quantization_start_epoch 4.0
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-
-# phase 2 apply quantization
-sparse_quantized_transfer_learning_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-
----
-# Transfer Learning
-
-This recipe provides a framework for performing sparse transfer learning on
-SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
-
-
-To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned-quantized
-```
-
-To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag mobilenet-imagenette-sparse-transfer-learned-quantized
-```
-## Other Datasets
-To transfer learn this sparsified model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increase the learning rate when increasing batch size
-* Increasing the number of epochs before starting quantization can give a higher baseline
-* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/pytorch/recipes/mobilenet-pruned.md b/integrations/pytorch/recipes/mobilenet-pruned.md
deleted file mode 100644
index 03dd40b9811..00000000000
--- a/integrations/pytorch/recipes/mobilenet-pruned.md
+++ /dev/null
@@ -1,107 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 90
-init_lr: &init_lr 0.0028
-lr_step_epochs: &lr_step_epochs [43, 60]
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 0.0
-pruning_end_epoch: &pruning_end_epoch 40.0
-pruning_update_frequency: &pruning_update_frequency 0.4
-init_sparsity: &init_sparsity 0.05
-pruning_mask_type: &pruning_mask_type unstructured
-prune_low_target_sparsity: &prune_low_target_sparsity 0.7
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.9
-
-
-training_modifiers:
-  - !EpochRangeModifier
-    end_epoch: *num_epochs
-    start_epoch: 0.0
-
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: -1.0
-    init_lr: *init_lr
-    lr_class: MultiStepLR
-    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
-    start_epoch: 0.0
-    update_frequency: -1.0
-    
-pruning_modifiers:
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_low_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.1.0.point.conv.weight', 'sections.1.1.point.conv.weight', 'sections.2.0.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_mid_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.2.1.point.conv.weight', 'sections.3.0.point.conv.weight', 'sections.3.1.point.conv.weight', 'sections.3.5.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_high_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.3.2.point.conv.weight', 'sections.3.3.point.conv.weight', 'sections.3.4.point.conv.weight', 'sections.4.0.point.conv.weight', 'sections.4.1.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# MobileNet-V1 ImageNet Moderate Sparse
-
-This recipe creates a sparse [MobileNet-V1](https://arxiv.org/abs/1704.04861) model that
-achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
-Training was done using 4 GPUs using a total training batch size of 1024
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key mobilenet \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 1024 --test-batch-size 2056 \
-    --loader-num-workers 16 \
-    --model-tag mobilenet-imagenet-pruned-moderate
-```
diff --git a/integrations/pytorch/recipes/mobilenet-pruned_quant.md b/integrations/pytorch/recipes/mobilenet-pruned_quant.md
deleted file mode 100644
index 94f71cf07d8..00000000000
--- a/integrations/pytorch/recipes/mobilenet-pruned_quant.md
+++ /dev/null
@@ -1,160 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 100.0
-init_lr: &init_lr 0.005
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
-pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 90
-disable_quantization_observer_epoch: &disable_quantization_observer_epoch 92.0
-freeze_bn_stats_epoch: &freeze_bn_stats_epoch 92.0
-quantization_init_lr: &quantization_init_lr 0.00001
-quantization_lr_step_milestones: &quantization_lr_step_milestones [91]
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 36.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.55
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.7
-prune_high_target_sparsity: &prune_high_target_sparsity 0.8
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    start_epoch: *pruning_recovery_start_epoch
-    end_epoch: *quantization_start_epoch
-    lr_class: CosineAnnealingWarmRestarts
-    lr_kwargs:
-      lr_min: *pruning_recovery_end_lr
-      cycle_epochs: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_recovery_start_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !LearningRateModifier
-    start_epoch: *quantization_start_epoch
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *quantization_lr_step_milestones
-      gamma: 0.1
-    init_lr: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.point.conv.weight
-      - sections.1.0.point.conv.weight
-      - sections.1.1.point.conv.weight
-      - sections.2.0.point.conv.weight
-      - classifier.fc.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.2.1.point.conv.weight
-      - sections.3.0.point.conv.weight
-      - sections.3.1.point.conv.weight
-      - sections.3.5.point.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.3.2.point.conv.weight
-      - sections.3.3.point.conv.weight
-      - sections.3.4.point.conv.weight
-      - sections.4.0.point.conv.weight
-      - sections.4.1.point.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
-    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
-
-    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
-    # but are unnecessary due to these already being set during the training phase
----
-
-# MobileNet-V1 ImageNet Moderate Sparse Quantization
-
-This recipe creates a sparse quantized [MobileNet-V1](https://arxiv.org/abs/1704.04861) model
-that achieves 99% of its baseline accuracy on the ImageNet dataset.  Training was done using
-4 GPUs using a total training batch size of 96 using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-python -m torch.distributed.launch \
-  --nproc_per_node 4 \
-  src/sparseml/pytorch/image_classification/train.py \
-    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch-sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key mobilenet \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 96 --test-batch-size 256 \
-    --loader-num-workers 16 \
-    --model-tag mobilenet-imagenet-pruned_quant-moderate
-```
diff --git a/integrations/pytorch/recipes/resnet50-imagenette-pruned.md b/integrations/pytorch/recipes/resnet50-imagenette-pruned.md
deleted file mode 100644
index 16318a27b99..00000000000
--- a/integrations/pytorch/recipes/resnet50-imagenette-pruned.md
+++ /dev/null
@@ -1,145 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 10
-lr: &lr 0.008
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 8.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.8
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.85
-prune_high_target_sparsity: &prune_high_target_sparsity 0.9
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.conv1.weight
-      - sections.0.0.conv2.weight
-      - sections.0.0.conv3.weight
-      - sections.0.0.identity.conv.weight
-      - sections.0.1.conv1.weight
-      - sections.0.1.conv3.weight
-      - sections.0.2.conv1.weight
-      - sections.0.2.conv3.weight
-      - sections.1.0.conv1.weight
-      - sections.1.0.conv3.weight
-      - sections.1.2.conv3.weight
-      - sections.1.3.conv1.weight
-      - sections.2.0.conv1.weight
-      - sections.3.0.conv1.weight
-      - classifier.fc.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.0.1.conv2.weight
-      - sections.0.2.conv2.weight
-      - sections.1.0.identity.conv.weight
-      - sections.1.1.conv1.weight
-      - sections.1.1.conv2.weight
-      - sections.1.1.conv3.weight
-      - sections.1.2.conv1.weight
-      - sections.1.2.conv2.weight
-      - sections.1.3.conv2.weight
-      - sections.1.3.conv3.weight
-      - sections.2.0.conv3.weight
-      - sections.2.0.identity.conv.weight
-      - sections.2.1.conv1.weight
-      - sections.2.1.conv3.weight
-      - sections.2.2.conv1.weight
-      - sections.2.2.conv3.weight
-      - sections.2.3.conv1.weight
-      - sections.2.3.conv3.weight
-      - sections.2.4.conv1.weight
-      - sections.2.4.conv3.weight
-      - sections.2.5.conv1.weight
-      - sections.2.5.conv3.weight
-      - sections.3.1.conv1.weight
-      - sections.3.2.conv1.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.1.0.conv2.weight
-      - sections.2.0.conv2.weight
-      - sections.2.1.conv2.weight
-      - sections.2.2.conv2.weight
-      - sections.2.3.conv2.weight
-      - sections.2.4.conv2.weight
-      - sections.2.5.conv2.weight
-      - sections.3.0.conv2.weight
-      - sections.3.0.conv3.weight
-      - sections.3.0.identity.conv.weight
-      - sections.3.1.conv2.weight
-      - sections.3.1.conv3.weight
-      - sections.3.2.conv2.weight
-      - sections.3.2.conv3.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# ResNet-50 Imagenette Conservative Sparse
-
-This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
-achieves full recovery of its baseline accuracy on the Imagenette dataset.
-Training was done using 1 GPU with a total training batch size of 128
-using a SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training. 
-Alternatively, a full walk-through notebook is located at `sparseml/integrations/pytorch/notebooks/classification.ipynb`.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 128 --test-batch-size 256 \
-    --loader-num-workers 8 \
-    --model-tag resnet50-imagenette-pruned-conservative
-```
diff --git a/integrations/pytorch/recipes/resnet50-pruned.md b/integrations/pytorch/recipes/resnet50-pruned.md
deleted file mode 100644
index 27fe718867b..00000000000
--- a/integrations/pytorch/recipes/resnet50-pruned.md
+++ /dev/null
@@ -1,83 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 90
-init_lr: &init_lr 0.0028
-lr_step_epochs: &lr_step_epochs [43, 60]
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 0.0
-pruning_end_epoch: &pruning_end_epoch 40.0
-pruning_update_frequency: &pruning_update_frequency 0.4
-init_sparsity: &init_sparsity 0.05
-final_sparsity: &final_sparsity 0.88
-pruning_mask_type: &pruning_mask_type unstructured
-
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    end_epoch: *num_epochs
-    start_epoch: 0.0
-
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: -1.0
-    init_lr: *init_lr
-    lr_class: MultiStepLR
-    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
-    start_epoch: 0.0
-    update_frequency: -1.0
-        
-pruning_modifiers:
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *final_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.0.0.conv1.weight', 'sections.0.0.conv2.weight', 'sections.0.0.conv3.weight', 'sections.0.0.identity.conv.weight', 'sections.0.1.conv1.weight', 'sections.0.1.conv2.weight', 'sections.0.1.conv3.weight', 'sections.0.2.conv1.weight', 'sections.0.2.conv2.weight', 'sections.0.2.conv3.weight', 'sections.1.0.conv1.weight', 'sections.1.0.conv2.weight', 'sections.1.0.conv3.weight', 'sections.1.0.identity.conv.weight', 'sections.1.1.conv1.weight', 'sections.1.1.conv2.weight', 'sections.1.1.conv3.weight', 'sections.1.2.conv1.weight', 'sections.1.2.conv2.weight', 'sections.1.2.conv3.weight', 'sections.1.3.conv1.weight', 'sections.1.3.conv2.weight', 'sections.1.3.conv3.weight', 'sections.2.0.conv1.weight', 'sections.2.0.conv2.weight', 'sections.2.0.conv3.weight', 'sections.2.0.identity.conv.weight', 'sections.2.1.conv1.weight', 'sections.2.1.conv2.weight', 'sections.2.1.conv3.weight', 'sections.2.2.conv1.weight', 'sections.2.2.conv2.weight', 'sections.2.2.conv3.weight', 'sections.2.3.conv1.weight', 'sections.2.3.conv2.weight', 'sections.2.3.conv3.weight', 'sections.2.4.conv1.weight', 'sections.2.4.conv2.weight', 'sections.2.4.conv3.weight', 'sections.2.5.conv1.weight', 'sections.2.5.conv2.weight', 'sections.2.5.conv3.weight', 'sections.3.0.conv1.weight', 'sections.3.0.conv2.weight', 'sections.3.0.conv3.weight', 'sections.3.0.identity.conv.weight', 'sections.3.1.conv1.weight', 'sections.3.1.conv2.weight', 'sections.3.1.conv3.weight', 'sections.3.2.conv1.weight', 'sections.3.2.conv2.weight', 'sections.3.2.conv3.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# ResNet-50 ImageNet Moderate Sparse
-
-This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
-achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
-Training was done using 4 GPUs using a total training batch size of 1024
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 1024 --test-batch-size 2056 \
-    --loader-num-workers 16 \
-    --model-tag resnet50-imagenet-pruned-moderate
-```
diff --git a/integrations/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md b/integrations/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
deleted file mode 100644
index 49fba746f53..00000000000
--- a/integrations/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
+++ /dev/null
@@ -1,100 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# epoch and learning rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-# quantization variables
-quantization_start_epoch: &quantization_start_epoch 4.0
-quantize_submodules: &quantize_submodules
-  - input
-  - sections
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# phase 1 sparse transfer learning / recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL__
-
-# phase 2 apply quantization
-sparse_quantized_transfer_learning_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-
-  - !SetWeightDecayModifier
-    start_epoch: 0.0
-    weight_decay: 0.0
----
-
-# Transfer Learning ResNet50 ImageNet Pruned-Quantized
-
-This recipe provides a framework for performing pruned-quantized transfer learning on
-SparseML's `pruned_quantized` ResNet50 model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-On the default transfer learning dataset, Imagenette, this recipe fully recovered to 99.9% Top-1 accuracy.
-To run this recipe, you may run the example notebook
-`sparseml/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb`
-or can run the example script command below.
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn \
-    --checkpoint-path zoo \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 8 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-pruned_quant-transfer_learned
-```
-
-## Other Datasets
-To transfer learn this ResNet 50 Pruned-Quantized model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increate the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increasing the number of epochs before starting quantization can give a higher baseline
-* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
-* Increase the learning rate when increasing batch size
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/pytorch/recipes/resnet50-pruned_quant.md b/integrations/pytorch/recipes/resnet50-pruned_quant.md
deleted file mode 100644
index fe83cb8a3fc..00000000000
--- a/integrations/pytorch/recipes/resnet50-pruned_quant.md
+++ /dev/null
@@ -1,198 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 135.0
-init_lr: &init_lr 0.008
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
-pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
-pruning_recovery_end_lr: &pruning_recovery_end_lr 0.00001
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 100
-disable_quantization_observer_epoch: &disable_quantization_observer_epoch 115.0
-freeze_bn_stats_epoch: &freeze_bn_stats_epoch 116.0
-quantization_init_lr: &quantization_init_lr 0.01
-quantization_lr_step_milestones: &quantization_lr_step_milestones [107, 114, 125]
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 36.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.7
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.75
-prune_high_target_sparsity: &prune_high_target_sparsity 0.8
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    start_epoch: *pruning_recovery_start_epoch
-    end_epoch: *quantization_start_epoch
-    lr_class: CosineAnnealingWarmRestarts
-    lr_kwargs:
-      lr_min: *pruning_recovery_end_lr
-      cycle_epochs: *pruning_recovery_num_epochs
-    init_lr: *pruning_recovery_start_lr
-
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_recovery_start_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !LearningRateModifier
-    start_epoch: *quantization_start_epoch
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *quantization_lr_step_milestones
-      gamma: 0.1
-    init_lr: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.identity.conv.weight
-      - sections.0.0.conv1.weight
-      - sections.0.0.conv2.weight
-      - sections.0.0.conv3.weight
-      - sections.0.1.conv1.weight
-      - sections.0.1.conv3.weight
-      - sections.0.2.conv1.weight
-      - sections.0.2.conv3.weight
-      - sections.1.0.conv1.weight
-      - sections.1.0.conv3.weight
-      - sections.1.2.conv3.weight
-      - sections.1.3.conv1.weight
-      - sections.2.0.conv1.weight
-      - sections.3.0.conv1.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.0.1.conv2.weight
-      - sections.0.2.conv2.weight
-      - sections.1.0.identity.conv.weight
-      - sections.1.1.conv1.weight
-      - sections.1.1.conv2.weight
-      - sections.1.1.conv3.weight
-      - sections.1.2.conv1.weight
-      - sections.1.2.conv2.weight
-      - sections.1.3.conv2.weight
-      - sections.1.3.conv3.weight
-      - sections.2.0.conv3.weight
-      - sections.2.0.identity.conv.weight
-      - sections.2.1.conv1.weight
-      - sections.2.1.conv3.weight
-      - sections.2.2.conv1.weight
-      - sections.2.3.conv1.weight
-      - sections.2.3.conv3.weight
-      - sections.2.4.conv1.weight
-      - sections.2.4.conv3.weight
-      - sections.2.5.conv1.weight
-      - sections.2.5.conv3.weight
-      - sections.3.1.conv1.weight
-      - sections.3.2.conv1.weight
-      - sections.1.0.conv2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.2.0.conv2.weight
-      - sections.2.1.conv2.weight
-      - sections.2.2.conv2.weight
-      - sections.2.3.conv2.weight
-      - sections.2.4.conv2.weight
-      - sections.2.5.conv2.weight
-      - sections.3.0.conv2.weight
-      - sections.3.0.conv3.weight
-      - sections.3.0.identity.conv.weight
-      - sections.3.1.conv2.weight
-      - sections.3.1.conv3.weight
-      - sections.3.2.conv2.weight
-      - sections.3.2.conv3.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
-    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
-
-    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
-    # but are unnecessary due to these already being set during the training phase
----
-
-# ResNet-50 ImageNet Moderate Sparse Quantization
-
-This recipe creates a sparse quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of its baseline accuracy
-on the ImageNet dataset.  Training was done using 4 GPUs using a total training batch size of 96
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-python -m torch.distributed.launch \
-  --nproc_per_node 4 \
-  src/sparseml/pytorch/image_classification/train.py \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 96 --test-batch-size 256 \
-    --loader-num-workers 16 \
-    --model-tag resnet50-imagenet-pruned_quant-moderate
-```
diff --git a/integrations/pytorch/torchvision_sparsification.py b/integrations/pytorch/torchvision_sparsification.py
deleted file mode 100644
index 07acd698b19..00000000000
--- a/integrations/pytorch/torchvision_sparsification.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Example script for running training and optimization tasks on torchvision
-classification models with an imagefolder based dataset using SparseML.
-Example tasks include:
-* Model pruning
-* Quantization aware training
-* Sparse transfer learning
-
-More information about torchvision models can be found here:
-https://pytorch.org/docs/stable/torchvision/models.html
-
-##########
-Command help:
-usage: torchvision_sparseml.py [-h] --model MODEL [--recipe-path RECIPE_PATH]
-                               [--image-size IMAGE_SIZE]
-                               [--batch-size BATCH_SIZE]
-                               [--pretrained PRETRAINED]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               --imagefolder-path IMAGEFOLDER_PATH
-                               [--loader-num-workers LOADER_NUM_WORKERS]
-                               [--loader-pin-memory LOADER_PIN_MEMORY]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-
-Train or finetune an image classification model from torchvision.models
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --model MODEL         The torchvision model class to use, ex: inception_v3,
-                        resnet50, mobilenet_v2 model name is fed directly to
-                        torchvision.models, more information can be found here
-                        https://pytorch.org/docs/stable/torchvision/models.htm
-                        l
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the sparseml
-                        modifiers and schedule to apply them with
-  --image-size IMAGE_SIZE
-                        Size of image to use for model input. Default is 224
-                        unless pytorch documentation specifies otherwise
-  --batch-size BATCH_SIZE
-                        Batch size to use when training model. Default is 32
-  --pretrained PRETRAINED
-                        Set True to use torchvisions pretrained weights, to
-                        not set weights, set False. default is true.
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --imagefolder-path IMAGEFOLDER_PATH
-                        Path to root of dataset's generic 'image folder' path.
-                        Should have an image folder structure like imagenet
-                        with subdirectories 'train' and 'val' see https://pyto
-                        rch.org/docs/stable/torchvision/datasets.html#imagefol
-                        der
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --loader-pin-memory LOADER_PIN_MEMORY
-                        Use pinned memory for data loading
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-
-##########
-Example command for pruning resnet50 on an imagefolder dataset:
-python integrations/pytorch/torchvision_sparsification.py \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --model resnet50 \
-    --imagefolder-path ~/datasets/ILSVRC2012 \
-    --batch-size 256
-"""
-
-import argparse
-import os
-import time
-from types import ModuleType
-
-import torch
-from torch.nn import CrossEntropyLoss
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-from torchvision import models
-
-from sparseml.pytorch.datasets.classification import ImageFolderDataset
-from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
-from sparseml.pytorch.utils import ModuleExporter, PythonLogger, load_model
-from sparseml.utils import create_dirs
-
-
-MODEL_IMAGE_SIZES = {
-    "inception_v3": 299,
-}
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Train or finetune an image classification model from "
-        "torchvision.models"
-    )
-
-    # model args
-    parser.add_argument(
-        "--model",
-        type=str,
-        required=True,
-        help=(
-            "The torchvision model class to use, ex: inception_v3, resnet50, "
-            "mobilenet_v2 model name is fed directly to torchvision.models, "
-            "more information can be found here "
-            "https://pytorch.org/docs/stable/torchvision/models.html"
-        ),
-    )
-    parser.add_argument(
-        "--recipe-path",
-        type=str,
-        required=True,
-        help="The path to the yaml file containing the sparseml modifiers and "
-        "schedule to apply them with",
-    )
-    parser.add_argument(
-        "--image-size",
-        type=int,
-        required=False,
-        default=None,
-        help=(
-            "Size of image to use for model input. Default is 224 unless pytorch "
-            "documentation specifies otherwise"
-        ),
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        required=False,
-        default=32,
-        help="Batch size to use when training model. Default is 32",
-    )
-    parser.add_argument(
-        "--pretrained",
-        type=bool,
-        default=True,
-        help="Set True to use torchvisions pretrained weights,"
-        " to not set weights, set False. default is true.",
-    )
-    parser.add_argument(
-        "--checkpoint-path",
-        type=str,
-        default=None,
-        help="A path to a previous checkpoint to load the state from and "
-        "resume the state for. If provided, pretrained will be ignored",
-    )
-
-    # dataset args
-    parser.add_argument(
-        "--imagefolder-path",
-        type=str,
-        required=True,
-        help="Path to root of dataset's generic 'image folder' path. Should have "
-        "an image folder structure like imagenet with subdirectories 'train' and 'val'"
-        " see https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder",
-    )
-    parser.add_argument(
-        "--loader-num-workers",
-        type=int,
-        default=4,
-        help="The number of workers to use for data loading",
-    )
-    parser.add_argument(
-        "--loader-pin-memory",
-        type=bool,
-        default=True,
-        help="Use pinned memory for data loading",
-    )
-
-    # logging and saving
-    parser.add_argument(
-        "--model-tag",
-        type=str,
-        default=None,
-        help="A tag to use for the model for saving results under save-dir, "
-        "defaults to the model arch and dataset used",
-    )
-    parser.add_argument(
-        "--save-dir",
-        type=str,
-        default="torchvision_sparseml_export",
-        help="The path to the directory for saving results",
-    )
-
-    args = parser.parse_args()
-    if args.image_size is None:
-        args.image_size = (
-            MODEL_IMAGE_SIZES[args.model] if args.model in MODEL_IMAGE_SIZES else 224
-        )
-    return args
-
-
-def _get_torchvision_model(name, num_classes, pretrained=True, checkpoint_path=None):
-    model_constructor = getattr(models, name, None)
-    if model_constructor is None or isinstance(model_constructor, ModuleType):
-        # constructor doesn't exist or is a submodule instead of function in torchvision
-        raise ValueError("Torchvision model {} not found".format(name))
-    # build model
-    model = model_constructor(pretrained=False, num_classes=num_classes)
-    if pretrained and not checkpoint_path:
-        pretrained_model = model_constructor(pretrained=True, num_classes=1000)
-        # fix num classes mismatch
-        if num_classes == 1000:
-            model = pretrained_model
-        else:
-            _load_matched_weights(model, pretrained_model)
-        del pretrained_model
-
-    if checkpoint_path is not None:
-        load_model(checkpoint_path, model)
-    return model
-
-
-def _load_matched_weights(base_model, pretrained_model):
-    base_dict = base_model.state_dict()
-    pretrained_dict = pretrained_model.state_dict()
-    for key in base_dict:
-        if (
-            key in pretrained_dict
-            and base_dict[key].shape == pretrained_dict[key].shape
-        ):
-            base_dict[key] = pretrained_dict[key]
-    base_model.load_state_dict(base_dict)
-
-
-def _create_imagefolder_dataloader(args, train=True):
-    dataset = ImageFolderDataset(
-        root=args.imagefolder_path,
-        train=train,
-        rand_trans=train,
-        image_size=args.image_size,
-    )
-    loader = DataLoader(
-        dataset,
-        batch_size=args.batch_size,
-        shuffle=True,
-        num_workers=args.loader_num_workers,
-        pin_memory=args.loader_pin_memory,
-    )
-
-    # return dataloader, number of classes, and input image shape
-    return loader, dataset.num_classes, dataset[0][0].shape
-
-
-######################################################################################
-# torchvision finetuning function from:                                              #
-# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html #
-######################################################################################
-def train_model(
-    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False
-):
-    since = time.time()
-
-    val_acc_history = []
-
-    # not loading best intermediate weights due to sparsity changing
-    # best_model_wts = copy.deepcopy(model.state_dict())
-    best_acc = 0.0
-
-    for epoch in range(num_epochs):
-        print("Epoch {}/{}".format(epoch, num_epochs - 1))
-        print("-" * 10)
-
-        # Each epoch has a training and validation phase
-        for phase in ["train", "val"]:
-            if phase == "train":
-                model.train()  # Set model to training mode
-            else:
-                model.eval()  # Set model to evaluate mode
-
-            running_loss = 0.0
-            running_corrects = 0
-
-            # Iterate over data.
-            for inputs, labels in dataloaders[phase]:
-                inputs = inputs.to(device)
-                labels = labels.to(device)
-
-                # zero the parameter gradients
-                optimizer.zero_grad()
-
-                # forward
-                # track history if only in train
-                with torch.set_grad_enabled(phase == "train"):
-                    # Get model outputs and calculate loss
-                    # Special case for inception because in training it has an
-                    # auxiliary output. In train mode we calculate the loss by summing
-                    # the final output and the auxiliary output but in testing we
-                    # only consider the final output.
-                    if is_inception and phase == "train":
-                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958  # noqa
-                        outputs, aux_outputs = model(inputs)
-                        loss1 = criterion(outputs, labels)
-                        loss2 = criterion(aux_outputs, labels)
-                        loss = loss1 + 0.4 * loss2
-                    else:
-                        outputs = model(inputs)
-                        loss = criterion(outputs, labels)
-
-                    _, preds = torch.max(outputs, 1)
-
-                    # backward + optimize only if in training phase
-                    if phase == "train":
-                        loss.backward()
-                        optimizer.step()
-
-                # statistics
-                running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
-
-            epoch_loss = running_loss / len(dataloaders[phase].dataset)
-            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
-
-            print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc))
-
-            # deep copy the model
-            if phase == "val" and epoch_acc > best_acc:
-                best_acc = epoch_acc
-                # not loading best intermediate weights due to sparsity changing
-                # best_model_wts = copy.deepcopy(model.state_dict())
-            if phase == "val":
-                val_acc_history.append(epoch_acc)
-
-        print()
-
-    time_elapsed = time.time() - since
-    print(
-        "Training complete in {:.0f}m {:.0f}s".format(
-            time_elapsed // 60, time_elapsed % 60
-        )
-    )
-    print("Best val Acc: {:4f}".format(best_acc))
-
-    # load best model weights
-    # not loading best intermediate weights due to sparsity changing
-    # model.load_state_dict(best_model_wts)
-    return model, val_acc_history
-
-
-def _save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    print(f"Saved recipe to {recipe_save_path}")
-
-
-def main(args):
-    ############################
-    # logging and saving setup #
-    ############################
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-
-    # get unique model tag, defaults to '{model_name}'
-    if not args.model_tag:
-        model_tag = args.model.replace("/", ".")
-        model_id = model_tag
-        model_inc = 0
-
-        while os.path.exists(os.path.join(args.save_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-    print("Model id is set to {}".format(model_id))
-
-    ###########################
-    # standard training setup #
-    ###########################
-
-    # create data loaders
-    train_loader, _, _ = _create_imagefolder_dataloader(args, train=True)
-    val_loader, num_classes, image_shape = _create_imagefolder_dataloader(
-        args, train=False
-    )
-    dataloaders = {"train": train_loader, "val": val_loader}
-
-    # create model
-    model = _get_torchvision_model(
-        args.model,
-        num_classes,
-        args.pretrained,
-        args.checkpoint_path,
-    )
-    print("created model: {}".format(model))
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-    print("using device: {}".format(device))
-
-    # create standard SGD optimizer and cross entropy loss function
-    criterion = CrossEntropyLoss()
-    optimizer = SGD(
-        model.parameters(), lr=0.001, momentum=0.9
-    )  # lr will be overridden by recipe
-
-    ##########################
-    # add sparseml modifiers #
-    ##########################
-    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
-    optimizer = ScheduledOptimizer(
-        optimizer,
-        model,
-        manager,
-        steps_per_epoch=len(train_loader),
-        loggers=[PythonLogger()],
-    )
-
-    ########################
-    # torchvision training #
-    ########################
-    _save_recipe(recipe_manager=manager, save_dir=save_dir)
-    model, val_acc_history = train_model(
-        model,
-        dataloaders,
-        criterion,
-        optimizer,
-        device,
-        num_epochs=manager.max_epochs,
-        is_inception="inception" in args.model,
-    )
-
-    ########################
-    # export trained model #
-    ########################
-    exporter = ModuleExporter(model, save_dir)
-    sample_input = torch.randn(image_shape).unsqueeze(0)  # sample batch for ONNX export
-    exporter.export_onnx(sample_input, convert_qat=True)
-    exporter.export_pytorch()
-    print("Model ONNX export and PyTorch weights saved to {}".format(save_dir))
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-    main(args_)
diff --git a/integrations/pytorch/train.py b/integrations/pytorch/train.py
deleted file mode 100644
index 0dc86e934af..00000000000
--- a/integrations/pytorch/train.py
+++ /dev/null
@@ -1,703 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-######
-Command help:
-usage: train.py [-h] --train-batch-size TRAIN_BATCH_SIZE --test-batch-size
-                TEST_BATCH_SIZE --arch-key ARCH_KEY --dataset DATASET
-                --dataset-path DATASET_PATH
-                [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
-                [--optim-args OPTIM_ARGS] [--recipe-path RECIPE_PATH]
-                [--sparse-transfer-learn [SPARSE_TRANSFER_LEARN]]
-                [--eval-mode [EVAL_MODE]] [--optim OPTIM]
-                [--logs-dir LOGS_DIR] [--save-best-after SAVE_BEST_AFTER]
-                [--save-epochs SAVE_EPOCHS]
-                [--use-mixed-precision [USE_MIXED_PRECISION]]
-                [--debug-steps DEBUG_STEPS] [--pretrained PRETRAINED]
-                [--pretrained-dataset PRETRAINED_DATASET]
-                [--model-kwargs MODEL_KWARGS]
-                [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
-                [--save-dir SAVE_DIR] [--device DEVICE]
-                [--loader-num-workers LOADER_NUM_WORKERS]
-                [--no-loader-pin-memory]
-                [--loader-pin-memory [LOADER_PIN_MEMORY]]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. If using a SparseZoo recipe, can also
-                        provide 'zoo' to load the base weights associated with
-                        that recipe
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. Can also provide a
-                        SparseZoo stub prefixed with 'zoo:' with an optional
-                        '?recipe_type=' argument
-  --sparse-transfer-learn [SPARSE_TRANSFER_LEARN]
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode [EVAL_MODE]
-                        Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --optim OPTIM         The optimizer type to use, one of [SGD, Adam, RMSprop]
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS
-                        epochs to save checkpoints at
-  --use-mixed-precision [USE_MIXED_PRECISION]
-                        Trains model using mixed precision. Supported
-                        environments are single GPU and multiple GPUs using
-                        DistributedDataParallel with one GPU per process
-  --debug-steps DEBUG_STEPS
-                        Amount of steps to run for training and testing for a
-                        debug mode
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset constructor,
-                        should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for data
-                        parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-#########
-EXAMPLES
-#########
-
-##########
-Example command for pruning resnet50 on imagenet dataset:
-python integrations/pytorch/train.py \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
-python integrations/pytorch/train.py \
-    --sparse-transfer-learn \
-    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
-    --arch-key mobilenet_v1 --pretrained pruned-moderate \
-    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Template command for running training with this script on multiple GPUs using
-DistributedDataParallel using mixed precision. Note - DDP support in this script
-only tested for torch==1.7.0.
-python -m torch.distributed.launch \
---nproc_per_node <NUM GPUs> \
-integrations/pytorch/train.py \
---use-mixed-precision \
-<TRAIN.PY ARGUMENTS>
-"""
-import argparse
-import json
-import os
-from dataclasses import dataclass, field
-from typing import Any, List, Optional, Tuple
-
-import torch
-from torch.nn import Module
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.utils import (
-    DEFAULT_LOSS_KEY,
-    ModuleDeviceContext,
-    ModuleTester,
-    ModuleTrainer,
-    default_device,
-    get_prunable_layers,
-    model_to_device,
-    set_deterministic_seeds,
-    tensor_sparsity,
-)
-
-
-CURRENT_TASK = utils.Tasks.TRAIN
-LOGGER = get_main_logger()
-
-
-@dataclass
-class TrainingArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    training tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-
-    :param train_batch_size: An int representing the training batch size.
-    :param test_batch_size: An int representing the test batch size.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for training, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param local_rank: DDP argument set by PyTorch in DDP mode, default -1
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param init_lr: float representing the initial learning for training,
-        default=1e-9 .
-    :param optim_args: Additional arguments to be passed in to the optimizer as
-        a json object
-    :param recipe_path: The path to the yaml file containing the modifiers and
-        schedule to apply them with; Can also provide a SparseZoo stub prefixed
-        with 'zoo:'.
-    :param sparse_transfer_learn: Boolean to enable sparse transfer learning
-        modifiers to enforce
-        the sparsity for already sparse layers. The modifiers are added to
-        the ones to be loaded from the recipe-path.
-    :param eval_mode: bool to start evaluation mode so that the model can be
-        evaluated on the desired dataset.
-    :param optim: str respresnting the optimizer type to use, one of
-        [SGD, Adam, RMSprop].
-    :param logs_dir: The path to the directory for saving logs.
-    :param save_best_after: int epoch number to start saving the best
-        validation result after until the end of training.
-    :param save_epochs: int epochs to save checkpoints at.
-    :param use_mixed_precision: bool to train model using mixed precision.
-        Supported environments are single GPU and multiple GPUs using
-        DistributedDataParallel with one GPU per process.
-    :param debug_steps: int represnting amount of steps to run for training and
-        testing for debug mode default=-1.
-    :param pretrained: The type of pretrained weights to use default is true
-        to load the default pretrained weights for the model Otherwise should
-        be set to the desired weights type: [base, optim, optim-perf];
-        To not load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load pretrained
-        weights for if pretrained is set; Default is None which will load the
-        default dataset for the architecture; Ex can be set to imagenet,
-        cifar10, etc".
-    :param model_kwargs: json object containing keyword arguments to be
-        passed to model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be passed
-        to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results
-        under save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on (can also include ids
-        for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data loading,
-        default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    """
-
-    train_batch_size: int = field(
-        metadata={"help": "The batch size to use while training"}
-    )
-
-    test_batch_size: int = field(
-        metadata={"help": "The batch size to use while testing"}
-    )
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception"
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or"
-            " loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    local_rank: int = field(
-        default=-1,
-        metadata={
-            "keep_underscores": True,
-            "help": argparse.SUPPRESS,
-        },
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored . If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    init_lr: float = field(
-        default=1e-9,
-        metadata={
-            "help": "The initial learning rate to use while training, "
-            "the actual initial value used should be set by the"
-            " sparseml recipe"
-        },
-    )
-
-    optim_args: json.loads = field(
-        default_factory=lambda: {
-            "momentum": 0.9,
-            "nesterov": True,
-            "weight_decay": 0.0001,
-        },
-        metadata={
-            "help": "Additional args to be passed to the optimizer passed in"
-            " as a json object",
-        },
-    )
-
-    recipe_path: str = field(
-        default=None,
-        metadata={
-            "help": "The path to the yaml file containing the modifiers and "
-            "schedule to apply them with. Can also provide a "
-            "SparseZoo stub prefixed with 'zoo:' with an optional "
-            "'?recipe_type=' argument"
-        },
-    )
-
-    sparse_transfer_learn: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Enable sparse transfer learning modifiers to enforce the "
-            "sparsity for already sparse layers. The modifiers are "
-            "added to the ones to be loaded from the recipe-path"
-        },
-    )
-
-    eval_mode: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Puts into evaluation mode so that the model can be "
-            "evaluated on the desired dataset"
-        },
-    )
-
-    optim: str = field(
-        default="SGD",
-        metadata={"help": "The optimizer type to use, one of [SGD, Adam, RMSprop]"},
-    )
-
-    logs_dir: str = field(
-        default=os.path.join("pytorch_vision_train", "tensorboard-logs"),
-        metadata={
-            "help": "The path to the directory for saving logs",
-        },
-    )
-
-    save_best_after: int = field(
-        default=-1,
-        metadata={
-            "help": "start saving the best validation result after the given "
-            "epoch completes until the end of training"
-        },
-    )
-    save_epochs: List[int] = field(
-        default_factory=lambda: [], metadata={"help": "epochs to save checkpoints at"}
-    )
-
-    use_mixed_precision: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Trains model using mixed precision. Supported "
-            "environments are single GPU and multiple GPUs using "
-            "DistributedDataParallel with one GPU per process"
-        },
-    )
-
-    debug_steps: int = field(
-        default=-1,
-        metadata={
-            "help": "Amount of steps to run for training and testing for a "
-            "debug mode"
-        },
-    )
-
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for "
-            "the model. Otherwise should be set to the desired weights "
-            "type: [base, optim, optim-perf]. To not load any weights set"
-            "to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained is "
-            "set. Default is None which will load the default dataset for "
-            "the architecture. Ex can be set to imagenet, cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for data "
-            "parallel), ex: cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    def __post_init__(self):
-        # add ddp args
-        env_world_size = int(os.environ.get("WORLD_SIZE", 1))
-        self.world_size = env_world_size
-
-        env_rank = int(os.environ.get("RANK", -1))
-        self.rank = env_rank
-
-        self.is_main_process = self.rank in [
-            -1,
-            0,
-        ]  # non DDP execution or 0th DDP process
-
-        # modify training batch size for give world size
-        assert self.train_batch_size % self.world_size == 0, (
-            f"Invalid training batch size for world size {self.world_size} "
-            f"given batch size {self.train_batch_size}. "
-            f"world size must divide training batch size evenly."
-        )
-
-        self.train_batch_size = self.train_batch_size // self.world_size
-
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        if self.local_rank != -1:
-            torch.distributed.init_process_group(backend="nccl", init_method="env://")
-            set_deterministic_seeds(0)
-
-        self.approximate = False
-
-
-def train(
-    train_args: TrainingArguments,
-    model: Module,
-    train_loader: DataLoader,
-    val_loader: DataLoader,
-    input_shape: Tuple[int, ...],
-    save_dir: str,
-    loggers: List[Any],
-) -> None:
-    """
-    Utility function to drive the training processing
-
-    :param train_args: A TrainingArguments object with
-        arguments for current training task
-    :param model: model architecture to train
-    :param train_loader: A DataLoader for training data
-    :param val_loader: A DataLoader for validation data
-    :param input_shape: A tuple of integers representing the shape of inputs
-    :param save_dir: Directory to store checkpoints at during training process
-    :param loggers: List of loggers to use during training process
-    """
-    # loss setup
-    val_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
-    LOGGER.info(f"created loss for validation: {val_loss}")
-
-    train_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
-    LOGGER.info(f"created loss for training: {train_loss}")
-
-    # training setup
-    if not train_args.eval_mode:
-        epoch, optim, manager = utils.create_scheduled_optimizer(
-            train_args,
-            model,
-            train_loader,
-            loggers,
-        )
-    else:
-        epoch = 0
-        train_loss = None
-        optim = None
-        manager = None
-
-    # device setup
-    if train_args.rank == -1:
-        device = train_args.device
-        ddp = False
-    else:
-        torch.cuda.set_device(train_args.local_rank)
-        device = train_args.local_rank
-        ddp = True
-
-    model, device, device_ids = model_to_device(model, device, ddp=ddp)
-    LOGGER.info(f"running on device {device} for ids {device_ids}")
-
-    trainer = (
-        ModuleTrainer(
-            model,
-            device,
-            train_loss,
-            optim,
-            loggers=loggers,
-            device_context=ModuleDeviceContext(
-                use_mixed_precision=train_args.use_mixed_precision,
-                world_size=train_args.world_size,
-            ),
-        )
-        if not train_args.eval_mode
-        else None
-    )
-
-    if train_args.is_main_process:  # only test on one DDP process if using DDP
-        tester = ModuleTester(model, device, val_loss, loggers=loggers, log_steps=-1)
-
-        # initial baseline eval run
-        tester.run_epoch(
-            val_loader, epoch=epoch - 1, max_epochs=-1, max_steps=train_args.debug_steps
-        )
-
-    if not train_args.eval_mode:
-        utils.save_recipe(recipe_manager=manager, save_dir=save_dir)
-        LOGGER.info(f"starting training from epoch {epoch}")
-
-        if epoch > 0:
-            LOGGER.info("adjusting ScheduledOptimizer to restore point")
-            optim.adjust_current_step(epoch, 0)
-
-        target_metric = (
-            "top1acc" if "top1acc" in tester.loss.available_losses else DEFAULT_LOSS_KEY
-        )
-        best_metric = None
-        val_res = None
-
-        while epoch < manager.max_epochs:
-            if train_args.debug_steps > 0:
-                # correct since all optimizer steps are not
-                # taken in the epochs for debug mode
-                optim.adjust_current_step(epoch, 0)
-
-            if train_args.rank != -1:  # sync DDP dataloaders
-                train_loader.sampler.set_epoch(epoch)
-
-            trainer.run_epoch(
-                train_loader,
-                epoch,
-                max_epochs=manager.max_epochs,
-                max_steps=train_args.debug_steps,
-                show_progress=train_args.is_main_process,
-            )
-
-            # testing steps
-            if train_args.is_main_process:
-                # only test and save on main process
-                val_res = tester.run_epoch(
-                    val_loader,
-                    epoch,
-                    max_epochs=manager.max_epochs,
-                    max_steps=train_args.debug_steps,
-                )
-                val_metric = val_res.result_mean(target_metric).item()
-
-                if epoch >= train_args.save_best_after and (
-                    best_metric is None
-                    or (
-                        val_metric <= best_metric
-                        if target_metric != "top1acc"
-                        else val_metric >= best_metric
-                    )
-                ):
-                    utils.save_model_training(
-                        model,
-                        optim,
-                        input_shape,
-                        "checkpoint-best",
-                        save_dir,
-                        epoch,
-                        val_res,
-                    )
-                    best_metric = val_metric
-
-            # save checkpoints
-            _save_epoch = (
-                train_args.is_main_process
-                and train_args.save_epochs
-                and epoch in train_args.save_epochs
-            )
-            if _save_epoch:
-                utils.save_model_training(
-                    model,
-                    optim,
-                    input_shape,
-                    f"checkpoint-{epoch:04d}-{val_metric:.04f}",
-                    save_dir,
-                    epoch,
-                    val_res,
-                )
-
-            epoch += 1
-
-        # export the final model
-        LOGGER.info("completed...")
-        if train_args.is_main_process:
-            # only convert qat -> quantized ONNX graph for finalized model
-            # TODO: change this to all checkpoints when conversion times improve
-            utils.save_model_training(
-                model, optim, input_shape, "model", save_dir, epoch - 1, val_res, True
-            )
-
-            LOGGER.info("layer sparsities:")
-            for (name, layer) in get_prunable_layers(model):
-                LOGGER.info(
-                    f"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}"
-                )
-
-    # close DDP
-    if train_args.rank != -1:
-        torch.distributed.destroy_process_group()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(dataclass_types=TrainingArguments)
-    training_args, _ = _parser.parse_args_into_dataclasses()
-
-    save_dir, loggers = utils.get_save_dir_and_loggers(training_args, task=CURRENT_TASK)
-
-    input_shape = ModelRegistry.input_shape(training_args.arch_key)
-    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(
-        training_args, image_size, task=CURRENT_TASK
-    )
-
-    num_classes = utils.infer_num_classes(training_args, train_dataset, val_dataset)
-
-    # # model creation
-    model = utils.create_model(training_args, num_classes)
-    train(
-        training_args, model, train_loader, val_loader, input_shape, save_dir, loggers
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/pytorch/tutorials/.gitkeep b/integrations/pytorch/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md b/integrations/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
deleted file mode 100644
index a7962a06f46..00000000000
--- a/integrations/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
+++ /dev/null
@@ -1,138 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparse Transfer Learning for Image Classification
-
-This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
-
-## Overview
-
-Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo. 
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
-
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process. In this tutorial you will:
-- Download and prepare a pre-sparsified image classification model.
-- Apply a sparse transfer learning recipe on the pre-sparsified model.
-
-The examples listed in this tutorial are all performed on the [Imagenette](https://github.com/fastai/imagenette) dataset.
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues) 
-
-## Setting Up
-
-This tutorial can be run by cloning and installing the `sparseml` repository which contains scripts and recipes for
-this example:
-
-```bash
-pip install "sparseml[torchvision, dev]"
-```
-Note:  make sure to upgrade `pip` using `python -m pip install -U pip`
-## Downloading and Preparing a Pre-Sparsified Model
-
-First, you need to download the sparsified models from the [SparseZoo](https://sparsezoo.neuralmagic.com/). A few image classification models with [SparseZoo](https://sparsezoo.neuralmagic.com/) stubs:
-
-| Model Name     |      Stub      | Description |
-|----------|-------------|-------------|
-| resnet-pruned-moderate | zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate |This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the accuracy of the original baseline model (76.1% top1). Pruned layers achieve 88% sparsity.|
-|resnet-pruned-conservative|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves full recovery original baseline model accuracy (76.1% top1). Pruned layers achieve 80% sparsity.|
-|resnet-pruned-moderate-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate|This model is a sparse, INT8 quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the original baseline accuracy. The average model sparsity is about 72% with pruned layers achieving between 70-80% sparsity. This pruned quantized model achieves 75.46% top1 accuracy on the ImageNet dataset.|
-|mobilenetv1-pruned-moderate|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves 99% of the accuracy of the original baseline model (70.9% top1). Pruned layers achieve between 70-90% sparsity.|
-|mobilenetv1-pruned-conservative|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves the same accuracy as the original baseline model. Pruned layers achieve between 60-86% sparsity. This pruned quantized model achieves 70.9% top1 accuracy on the ImageNet dataset.|
-|mobilenetv1-pruned-moderate-quantized|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate|Pruned [MobileNetV1](https://arxiv.org/abs/1704.04861) architecture with width=1.0 trained on the ImageNet dataset. Recalibrated performance model: within 99% of baseline validation top1 accuracy (70.9%)|
-
-
-A few recipe stubs:
-
-|Recipe Name|Stub|Description|
-|----------|-------------|-------------|
-|resnet50.transfer-learning-pruned-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn|This recipe provides a framework for performing pruned-quantized transfer learning on SparseML's pruned_quantized ResNet50 model. This recipe was written for transfer learning onto the Imagenette dataset using a single GPU, Adam optimizer, and batch size 32. Adjust the hyperparameters at the top of the file accordingly for your dataset and training environment|
-
-
-Note: The models above were originally trained and sparsified on the [ImageNet](https://image-net.org/) dataset.
-
-- After deciding on which model meets your performance requirements for both speed and accuracy, select the SparseZoo stub associated with that model.
-   The stub will be used later with the training script to automatically pull down the desired pre-trained weights.
-
-## Applying a Sparse Transfer Learning Recipe
-
-- After noting respective SparseZoo model stub, [train.py](../train.py) script can be used to download checkpoint and [Imagenette](https://github.com/fastai/imagenette) and kick-start transfer learning. 
-   The transfer learning process itself is guided using recipes; We include example [recipes](../recipes) for classification along with others in the SparseML [GitHub repository](https://github.com/neuralmagic/sparseml). 
-[Learn more about recipes and modifiers](../../../docs/source/recipes.md).
-
-- Run the following example command to kick off transfer learning for [ResNet-50](https://arxiv.org/abs/1512.03385) starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/):
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate" \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned
-```
-
-To transfer learn [MobileNet](https://arxiv.org/abs/1704.04861) on [Imagenette](https://github.com/fastai/imagenette), starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/), run the following example command:
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path "zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate" \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --save-dir sparse-transfer-models
-    --model-tag mobilenet-imagenette-sparse-transfer-learned
-```
-The `--checkpoint-path` argument can take in path to a model checkpoint, or a SpareZoo model stub,
-if a SparseZoo recipe stub is provided in `--recipe-path`, 
-the `checkpoint-path` can be set to `zoo` for auto-downloading corresponding checkpoints from SparseZoo. 
-To learn more about the usage, run script with `-h` option to see help. 
-
-The script automatically saves model checkpoints after each epoch and reports the validation loss along with layer sparsities. 
-
-### ONNX Export
-The model can be saved to [ONNX](https://onnx.ai/) format to be loaded later for inference or other experiments using
-the `sparseml.image_classification.export_onnx` script.
-
-```
-sparseml.image_classification.export_onnx \
-    --arch-key mobilenet \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE \
-    --checkpoint-path sparse-transfer-models/mobilenet-imagenette-sparse-transfer-learned/pytorch/model.pth
-```
-
-## Wrap-Up
-
-Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned image classification models. 
-In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and saved the model for future use.
-
-An example for benchmarking and deploying image classification models with DeepSparse [is also available](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md b/integrations/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
deleted file mode 100644
index b67881dc79f..00000000000
--- a/integrations/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
+++ /dev/null
@@ -1,132 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying PyTorch Models Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized PyTorch models.
-
-## Overview
-
-Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of their existing training pipelines.
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others.
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes.
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by pruning a PyTorch [ResNet-50](https://arxiv.org/abs/1512.03385) model.
-
-1. Setting Up
-2. Inspecting a Recipe
-3. Applying a Recipe
-3. Exporting for Inference
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Setting Up
-
-This tutorial can be run by installing `sparseml` which contains scripts for this pruning example:
-
-```bash
-pip install "sparseml[torchvision, dev]"
-```
-
-## Inspecting a Recipe
-
-Recipes are YAML or Markdown files that SparseML uses to easily define and control the sparsification of a model.
-Recipes consist of a series of `Modifiers` that can influence the training process in different ways. A list of
-common modifiers and their uses is provided
-[here](../../../docs/source/recipes.md#modifiers-intro).
-
-SparseML provides a recipe for sparsifying a ResNet-50 model trained on the tiny Imagenette dataset. The recipe can
-be viewed in the browser
-[here](../recipes/resnet50-imagenette-pruned.md)
-or in the command line from the file `sparseml/integrations/pytorch/recipes/resnet50-imagenette-pruned.md`.
-
-The recipe contains three kinds of modifiers:
-
-- an `EpochRangeModifier` that sets the number of epochs used for sparsification
-- a `SetLearningRateModifier` that sets the sparsification learning rate
-- three `GMPruningModifier`s that set various layers of the model to be pruned to different levels.
-
-Notice that each `GMPruningModifier` sets specific parameters of the ResNet-50 model to be pruned. To
-prune a different model, the parameter names should be adjusted to match the new model.  To avoid naming
-individual parameter names, all parameters can be set for pruning by passing `__ALL_PRUNABLE__` instead
-of the parameters list for a single `GMPruningModifier`.
-
-### Applying a Recipe
-
-Recipes can integrated into training flows with a couple of lines of code by using a `ScheduledModifierManager`
-that wraps the PyTorch `Optimizer` step.  An example of how this is done can be found
-[here](../../../docs/source/code.md#pytorch-sparsification).
-
-For this example, we can use the `sparseml.image_classification.train` utility.  This utility runs a
-PyTorch training flow that is modified by a `ScheduledModifierManager` and takes a recipe as an input.
-It natively supports ResNet-50 and other popular image classification models.
-
-To prune a ResNet-50 model trained on the Imagenette dataset with the recipe above, run the following command.
-If the Imagenette dataset has not been downloaded yet, the script will automatically download it to the given
-`--dataset-path`. If you have a local copy of the dataset, `--dataset-path` may be updated.
-
-*Note:* If an out of memory exception occurs, the train batch size should be lowered to fit on your device
-
-```bash
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/resnet50-imagenette-pruned.md \
-    --dataset-path ./data \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenette \
-    --train-batch-size 128 \
-    --test-batch-size 256 \
-    --loader-num-workers 8 \
-    --save-dir sparsification_example \
-    --logs-dir sparsification_example \
-    --model-tag resnet50-imagenette-pruned \
-    --save-best-after 8
-```
-
-A full list of arguments and their descriptions can be found by running 
-
-```bash
-sparseml.image_classification.train --help
-```
-
-
-## Exporting for Inference
-
-After, the training pruned model and training log files will be found under 
-`sparsification_example/resnet50-imagenette-pruned`. To run the sparsified model for accelerated inference with
-an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse), the pruned model must be
-exported to the ONNX format.
-
-This step can be completed using `sparseml.image_classification.export_onnx` script which uses the
-`sparseml.pytorch.utils.ModuleExporter` class. Run the script with `--help` option to see usage.
-
-The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in
-inference performance. An example for benchmarking and deploying image classification models with DeepSparse can
-be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate sparsified models.
-In this tutorial, you installed SparseML, downloaded and inspected a pruning recipe, applied it to a ResNet-50 model,
-and exported the sparse model for inference.
-
-Next steps will include creating new recipes and applying them to different models. Check out other
-[SparseML tutorials](https://github.com/neuralmagic/sparseml/tree/main/integrations) to learn more.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/pytorch/utils.py b/integrations/pytorch/utils.py
deleted file mode 100644
index b5dd57c3c41..00000000000
--- a/integrations/pytorch/utils.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Helper methods for image classification/detection based tasks
-"""
-import os
-from enum import Enum, auto, unique
-from typing import Any, List, Optional, Tuple, Union
-
-import torch
-from torch.nn import Module
-from torch.nn import functional as torch_functional
-from torch.optim import Optimizer
-from torch.utils.data import DataLoader
-
-from sparseml.pytorch.datasets import DatasetRegistry, ssd_collate_fn, yolo_collate_fn
-from sparseml.pytorch.image_classification.utils.helpers import (
-    download_framework_model_by_recipe_type,
-)
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
-from sparseml.pytorch.sparsification import ConstantPruningModifier
-from sparseml.pytorch.utils import (
-    DEFAULT_LOSS_KEY,
-    CrossEntropyLossWrapper,
-    InceptionCrossEntropyLossWrapper,
-    LossWrapper,
-    ModuleExporter,
-    ModuleRunResults,
-    PythonLogger,
-    SSDLossWrapper,
-    TensorBoardLogger,
-    TopKAccuracy,
-    YoloLossWrapper,
-    early_stop_data_loader,
-    torch_distributed_zero_first,
-)
-from sparseml.utils import create_dirs
-from sparsezoo import Model
-
-
-@unique
-class Tasks(Enum):
-    """
-    A class representing supported image classification/detection tasks
-    """
-
-    TRAIN = auto()
-    EXPORT = auto()
-    ANALYSIS = auto()
-    LR_ANALYSIS = auto()
-    PR_SENSITIVITY = auto()
-
-
-# loggers
-def get_save_dir_and_loggers(
-    args: Any, task: Optional[Tasks] = None
-) -> Tuple[Union[str, None], List]:
-    if args.is_main_process:
-        save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-        logs_dir = (
-            os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
-            if task == Tasks.TRAIN
-            else None
-        )
-
-        if not args.model_tag:
-            dataset_name = (
-                f"{args.dataset}-{args.dataset_kwargs['year']}"
-                if "year" in args.dataset_kwargs
-                else args.dataset
-            )
-            model_tag = f"{args.arch_key.replace('/', '.')}_{dataset_name}"
-            model_id = model_tag
-            model_inc = 0
-            # set location to check for models with same name
-            model_main_dir = logs_dir or save_dir
-
-            while os.path.exists(os.path.join(model_main_dir, model_id)):
-                model_inc += 1
-                model_id = f"{model_tag}__{model_inc:02d}"
-        else:
-            model_id = args.model_tag
-
-        save_dir = os.path.join(save_dir, model_id)
-        create_dirs(save_dir)
-
-        # loggers setup
-        loggers = [PythonLogger()]
-        if task == Tasks.TRAIN:
-            logs_dir = os.path.join(logs_dir, model_id)
-            create_dirs(logs_dir)
-            loggers.append(TensorBoardLogger(log_path=logs_dir))
-        print(f"Model id is set to {model_id}")
-    else:
-        # do not log for non main processes
-        save_dir = None
-        loggers = []
-    return save_dir, loggers
-
-
-# data helpers_
-def _get_collate_fn(arch_key: str, task: Optional[Tasks] = None):
-    is_ssd = "ssd" in arch_key.lower()
-    need_collate_function = (
-        is_ssd or "yolo" in arch_key.lower()
-    ) and task != Tasks.EXPORT
-    if need_collate_function:
-        return ssd_collate_fn if is_ssd else yolo_collate_fn
-    return None
-
-
-def _create_train_dataset_and_loader(
-    args: Any,
-    image_size: Tuple[int, ...],
-    task: Optional[Tasks] = None,
-) -> Tuple[Any, Any]:
-    need_train_data = not (
-        task == Tasks.EXPORT
-        or task == Tasks.PR_SENSITIVITY
-        and args.approximate
-        or task == Tasks.TRAIN
-        and args.eval_mode
-    )
-
-    if need_train_data:
-        with torch_distributed_zero_first(
-            args.local_rank,
-        ):  # only download once locally
-            train_dataset = DatasetRegistry.create(
-                args.dataset,
-                root=args.dataset_path,
-                train=True,
-                rand_trans=True,
-                image_size=image_size,
-                **args.dataset_kwargs,
-            )
-        sampler = (
-            torch.utils.data.distributed.DistributedSampler(train_dataset)
-            if args.rank != -1
-            else None
-        )
-        shuffle = True if sampler is None else False
-        batch_size = args.train_batch_size if task == Tasks.TRAIN else args.batch_size
-
-        train_loader = DataLoader(
-            train_dataset,
-            batch_size=batch_size,
-            shuffle=shuffle,
-            num_workers=args.loader_num_workers,
-            pin_memory=args.loader_pin_memory,
-            sampler=sampler,
-            collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
-        )
-        print(f"created train_dataset: {train_dataset}")
-        return train_dataset, train_loader
-
-    return None, None
-
-
-def _create_val_dataset_and_loader(
-    args, image_size: Tuple[int, ...], task: Optional[Tasks] = None
-) -> Tuple[Any, Any]:
-    need_val_data = not (
-        task == Tasks.PR_SENSITIVITY
-        or task == Tasks.LR_ANALYSIS
-        or not (args.is_main_process and args.dataset != "imagefolder")
-    )
-
-    if need_val_data:
-        val_dataset = DatasetRegistry.create(
-            args.dataset,
-            root=args.dataset_path,
-            train=False,
-            rand_trans=False,
-            image_size=image_size,
-            **args.dataset_kwargs,
-        )
-        if args.is_main_process:
-            is_training = task == Tasks.TRAIN
-            val_loader = DataLoader(
-                val_dataset,
-                batch_size=args.test_batch_size if is_training else 1,
-                shuffle=False,
-                num_workers=args.loader_num_workers if is_training else 1,
-                pin_memory=args.loader_pin_memory if is_training else False,
-                collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
-            )
-            if task == Tasks.EXPORT:
-                val_loader = early_stop_data_loader(
-                    val_loader, args.num_samples if args.num_samples > 1 else 1
-                )
-            print(f"created val_dataset: {val_dataset}")
-        else:
-            val_loader = None  # only val dataset needed to get the number of classes
-        return val_dataset, val_loader
-    return None, None  # val dataset not needed
-
-
-def get_train_and_validation_loaders(
-    args: Any, image_size: Tuple[int, ...], task: Optional[Tasks] = None
-):
-    """
-    :param args: Object containing relevant configuration for the task
-    :param image_size: A Tuple of integers representing the shape of input image
-    :param task: The current task being performed
-    :return: 4 element tuple with the following format (train_dataset,
-        train_loader, val_dataset, val_loader)
-    """
-    train_dataset, train_loader = _create_train_dataset_and_loader(
-        args, image_size, task=task
-    )
-    val_dataset, val_loader = _create_val_dataset_and_loader(
-        args, image_size, task=task
-    )
-    return train_dataset, train_loader, val_dataset, val_loader
-
-
-# Model creation Helpers
-
-
-def create_model(args: Any, num_classes: int) -> Module:
-    """
-    :param args: object with configuration for model classes
-    :param num_classes: Integer representing the number of output classes
-    :returns: A Module object representing the created model
-    """
-    with torch_distributed_zero_first(args.local_rank):  # only download once locally
-        if args.checkpoint_path == "zoo":
-            if args.recipe_path and args.recipe_path.startswith("zoo:"):
-                zoo_model = Model(args.recipe_path)
-                args.checkpoint_path = download_framework_model_by_recipe_type(
-                    zoo_model
-                )
-            else:
-                raise ValueError(
-                    "'zoo' provided as --checkpoint-path but a SparseZoo stub"
-                    " prefixed by 'zoo:' not provided as --recipe-path"
-                )
-
-        model = ModelRegistry.create(
-            args.arch_key,
-            args.pretrained,
-            args.checkpoint_path,
-            args.pretrained_dataset,
-            num_classes=num_classes,
-            **args.model_kwargs,
-        )
-    print(f"created model: {model}")
-    return model
-
-
-def infer_num_classes(args: Any, train_dataset, val_dataset):
-    """
-    :param args: Object with configuration settings
-    :param train_dataset: dataset representing training data
-    :param val_dataset: dataset representing validation data
-    :return: An integer representing the number of classes
-    """
-    if "num_classes" in args.model_kwargs:
-        # handle manually overriden num classes
-        num_classes = args.model_kwargs["num_classes"]
-        del args.model_kwargs["num_classes"]
-    elif args.dataset == "imagefolder":
-        dataset = val_dataset or train_dataset  # get non None dataset
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-    return num_classes
-
-
-def get_loss_wrapper(
-    arch_key: str, training: bool = False, task: Optional[Tasks] = None
-):
-    """
-    :param arch_key: The model architecture
-    :param training: True if training task started else False
-    :param task: current task being executed
-    """
-    if "ssd" in arch_key.lower():
-        return SSDLossWrapper()
-    if "yolo" in arch_key.lower():
-        return YoloLossWrapper()
-
-    extras = {"top1acc": TopKAccuracy(1), "top5acc": TopKAccuracy(5)}
-    if task == Tasks.TRAIN:
-        return (
-            CrossEntropyLossWrapper(extras)
-            if training and "inception" not in arch_key
-            else InceptionCrossEntropyLossWrapper(extras)
-        )
-    return LossWrapper(loss_fn=torch_functional.cross_entropy, extras=extras)
-
-
-#  optimizer helper
-def create_scheduled_optimizer(
-    train_args: Any,
-    model: Module,
-    train_loader: DataLoader,
-    loggers: List[Any],
-) -> Tuple[int, ScheduledOptimizer, ScheduledModifierManager]:
-    """
-    :param train_args : An object with task specific config
-    :param model: model architecture to train
-    :param train_loader: A DataLoader for training data
-    :param loggers: List of loggers to use during training process
-    :type train_args: TrainingArguments
-    """
-    # # optimizer setup
-    optim_const = torch.optim.__dict__[train_args.optim]
-    optim = optim_const(
-        model.parameters(), lr=train_args.init_lr, **train_args.optim_args
-    )
-    print(f"created optimizer: {optim}")
-    print(
-        "note, the lr for the optimizer may not reflect the manager yet until "
-        "the recipe config is created and run"
-    )
-
-    # restore from previous check point
-    if train_args.checkpoint_path:
-        # currently optimizer restoring is unsupported
-        # mapping of the restored params to the correct device is not working
-        # load_optimizer(args.checkpoint_path, optim)
-        epoch = 0  # load_epoch(args.checkpoint_path) + 1
-        print(
-            f"restored checkpoint from {train_args.checkpoint_path} for "
-            f"epoch {epoch - 1}"
-        )
-    else:
-        epoch = 0
-
-    # modifier setup
-    add_mods = (
-        ConstantPruningModifier.from_sparse_model(model)
-        if train_args.sparse_transfer_learn
-        else None
-    )
-    manager = ScheduledModifierManager.from_yaml(
-        file_path=train_args.recipe_path, add_modifiers=add_mods
-    )
-    optim = ScheduledOptimizer(
-        optim,
-        model,
-        manager,
-        steps_per_epoch=len(train_loader),
-        loggers=loggers,
-    )
-    print(f"created manager: {manager}")
-    return epoch, optim, manager
-
-
-# saving helpers
-
-
-def save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-    """
-    :param recipe_manager: The ScheduleModified manager to save recipes
-    :param save_dir: The directory to save the recipe
-    """
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    print(f"Saved recipe to {recipe_save_path}")
-
-
-def save_model_training(
-    model: Module,
-    optim: Optimizer,
-    input_shape: Tuple[int, ...],
-    save_name: str,
-    save_dir: str,
-    epoch: int,
-    val_res: Union[ModuleRunResults, None],
-    convert_qat: bool = False,
-):
-    """
-    :param model: model architecture
-    :param optim: The optimizer used
-    :param input_shape: A tuple of integers representing the input shape
-    :param save_name: name to save model to
-    :param save_dir: directory to save results in
-    :param epoch: integer representing umber of epochs to
-    :param val_res: results from validation run
-    :param convert_qat: True if model is to be quantized before saving
-    """
-    has_top1 = "top1acc" in val_res.results
-    metric_name = "top-1 accuracy" if has_top1 else "val_loss"
-    metric = val_res.result_mean("top1acc" if has_top1 else DEFAULT_LOSS_KEY).item()
-    print(
-        f"Saving model for epoch {epoch} and {metric_name} "
-        f"{metric} to {save_dir} for {save_name}"
-    )
-    exporter = ModuleExporter(model, save_dir)
-    exporter.export_pytorch(optim, epoch, f"{save_name}.pth")
-    exporter.export_onnx(
-        torch.randn(1, *input_shape),
-        f"{save_name}.onnx",
-        convert_qat=convert_qat,
-    )
-
-    info_path = os.path.join(save_dir, f"{save_name}.txt")
-
-    with open(info_path, "w") as info_file:
-        info_lines = [
-            f"epoch: {epoch}",
-        ]
-
-        if val_res is not None:
-            for loss in val_res.results.keys():
-                info_lines.append(f"{loss}: {val_res.result_mean(loss).item()}")
-
-        info_file.write("\n".join(info_lines))
diff --git a/integrations/pytorch/vision.py b/integrations/pytorch/vision.py
deleted file mode 100644
index 6ffeb78869c..00000000000
--- a/integrations/pytorch/vision.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Deprecated script kindly use the following task specific scripts for
- optimization tasks on image classification and object detection models:
-
-* Model pruning - sparseml.image_classification.train
-* Quantization aware training - sparseml.image_classification.train
-* Sparse transfer learning - sparseml.image_classification.train
-* pruning sensitivity analysis - sparseml.image_classification.pr_sensitivity
-* learning rate sensitivity analysis - sparseml.image_classification.lr_analysis
-* ONNX export - sparseml.image_classification.export_onnx
-"""
-
-
-if __name__ == "__main__":
-    print(__doc__)
diff --git a/integrations/rwightman-timm/README.md b/integrations/rwightman-timm/README.md
deleted file mode 100644
index 773c10116d5..00000000000
--- a/integrations/rwightman-timm/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML-rwightman/pytorch-image-models integration
-This directory provides a SparseML integrated training script for the popular
-[rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models)
-repository also known as [timm](https://pypi.org/project/timm/).
-
-Using this integration, you will be able to apply SparseML optimizations
-to the powerful training flows of the pytorch-image-models repository.
-
-Some of the tasks you can perform using this integration include, but are not limited to:
-* Pruning
-* Quantization
-* Pruning and Quantization
-* Sparse Transfer Learning
-
-## Installation
-We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
-A virtual environment can be created using the following commands:
-
-```bash
-python3 -m venv venv # create a venv virtual environment
-source venv/bin/activate # activate venv
-pip install --upgrade pip # upgrade pip
-```
-
-To begin, run the following command in the root directory of this integration (`cd integrations/rwightman-timm`):
-```bash
-bash setup_integration.sh
-```
-
-The `setup_integration.sh` file will clone the [pytorch-image-models](https://github.com/neuralmagic/pytorch-image-models.git) repository with the SparseML integration as a subfolder.
-After the repo has successfully cloned, pytorch-image-models will be installed along with any necessary dependencies.
-
-This integration requires `python>=3.7,<3.10`
-
-## Quick Tour
-### Recipes
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration adds a `--recipe` argument to the 
-[`train.py` script](https://github.com/neuralmagic/pytorch-image-models/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-You can learn how to build or download a recipe using the
-[SparseML](https://github.com/neuralmagic/sparseml)
-or [SparseZoo](https://github.com/neuralmagic/sparsezoo)
-documentation, or export one with [Sparsify](https://github.com/neuralmagic/sparsify).
-
-ViT recipes are not yet available on SparseZoo, but are in progress.
-
-Using a local recipe and checkpoint, pruning a model can be done by running the following command from within the root of this integration's folder
-```bash
-python train.py \
-  /PATH/TO/DATASET/imagenet/ \
-  --recipe ../recipes/vit_base.85.recal.config.yaml \
-  --dataset imagenet \
-  --batch-size 64 \
-  --remode pixel --reprob 0.0 --smoothing 0.1 --mixup 0.5 --mixup 0.5 \
-  --output models/optimized \
-  --model vit_base_patch16_224 \
-  --workers 8 
-```  
-
-Documentation on the original script can be found
-[here](https://rwightman.github.io/pytorch-image-models/scripts/).
-The latest commit hash that `train.py` is based on is included in the docstring.
-
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [recipes](./recipes)              | Typical recipes for sparsifying ViT models along with any downloaded recipes from the SparseZoo.                      |
-| tutorials            | Tutorial walkthroughs for how to sparsify ViT models using recipes (Coming Soon).                                                   |          |
-| [README.md](./README.md)            | Readme file.                                                                                                        |
-| [setup_integration.sh](./setup_integration.sh) | Setup file for the integration run from the command line.   
-
-### Exporting for Inference
-
-After sparsifying a model, to convert the model into an [ONNX](https://onnx.ai/) deployment format run the `export.py script`(https://github.com/neuralmagic/pytorch-image-models/blob/master/export.py).
-
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-For example, the following command can be run from within the integration's folder to export a trained/sparsified model's checkpoint:
-```bash
-python export.py \
-    --checkpoint ./path/to/checkpoint/model.pth.tar \
-    --recipe ../recipes/vit_base.85.recal.config.yaml \
-    --save-dir ./exported-models \
-    --filename vit_base_patch16_224 \
-    --batch-size 1 \
-    --image-shape 3 224 224 \
-    --config ./path/to/checkpoint/args.yaml
-```
-
-The DeepSparse Engine [accepts ONNX formats](https://docs.neuralmagic.com/sparseml/source/onnx_export.html) and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse).
\ No newline at end of file
diff --git a/integrations/rwightman-timm/recipes/vit_base.85.recal.config.yaml b/integrations/rwightman-timm/recipes/vit_base.85.recal.config.yaml
deleted file mode 100644
index 148060b57dd..00000000000
--- a/integrations/rwightman-timm/recipes/vit_base.85.recal.config.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-version: 1.1.0
-
-modifiers:
-    - !GMPruningModifier
-        end_epoch: 36.0
-        final_sparsity: 0.03
-        global_sparsity: False
-        init_sparsity: 0.25
-        inter_func: cubic
-        leave_enabled: True
-        mask_type: unstructured
-        params: 
-            - blocks.0.attn.qkv.weight
-            - blocks.0.attn.proj.weight
-            - blocks.0.mlp.fc1.weight
-            - blocks.0.mlp.fc2.weight
-            - blocks.1.attn.qkv.weight
-            - blocks.1.attn.proj.weight
-            - blocks.1.mlp.fc1.weight
-            - blocks.1.mlp.fc2.weight
-            - blocks.2.attn.qkv.weight
-            - blocks.2.attn.proj.weight
-            - blocks.2.mlp.fc1.weight
-            - blocks.2.mlp.fc2.weight
-            - blocks.3.attn.qkv.weight
-            - blocks.3.attn.proj.weight
-            - blocks.3.mlp.fc1.weight
-            - blocks.3.mlp.fc2.weight
-            - blocks.4.attn.qkv.weight
-            - blocks.4.attn.proj.weight
-            - blocks.4.mlp.fc1.weight
-            - blocks.4.mlp.fc2.weight
-            - blocks.5.attn.qkv.weight
-            - blocks.5.attn.proj.weight
-            - blocks.5.mlp.fc1.weight
-            - blocks.5.mlp.fc2.weight
-            - blocks.6.attn.qkv.weight
-            - blocks.6.attn.proj.weight
-            - blocks.6.mlp.fc1.weight
-            - blocks.6.mlp.fc2.weight
-            - blocks.7.attn.qkv.weight
-            - blocks.7.attn.proj.weight
-            - blocks.7.mlp.fc1.weight
-            - blocks.7.mlp.fc2.weight
-            - blocks.8.attn.qkv.weight
-            - blocks.8.attn.proj.weight
-            - blocks.8.mlp.fc1.weight
-            - blocks.8.mlp.fc2.weight
-            - blocks.9.attn.qkv.weight
-            - blocks.9.attn.proj.weight
-            - blocks.9.mlp.fc1.weight
-            - blocks.9.mlp.fc2.weight
-            - blocks.10.attn.qkv.weight
-            - blocks.10.attn.proj.weight
-            - blocks.10.mlp.fc1.weight
-            - blocks.10.mlp.fc2.weight
-            - blocks.11.attn.qkv.weight
-            - blocks.11.attn.proj.weight
-            - blocks.11.mlp.fc1.weight
-            - blocks.11.mlp.fc2.weight
-        phased: False
-        score_type: magnitude
-        start_epoch: 1.0
-        update_frequency: 0.5
-
-    - !EpochRangeModifier
-        end_epoch: 100.0
-        start_epoch: 0.0
-
-    - !SetLearningRateModifier
-        constant_logging: False
-        end_epoch: -1
-        learning_rate: 0.002505
-        start_epoch: 0.0
-
-    - !LearningRateModifier
-        constant_logging: False
-        end_epoch: -1.0
-        init_lr: 0.00062625
-        lr_class: StepLR
-        lr_kwargs: {'step_size': 10, 'gamma': 0.25}
-        start_epoch: 40.0
-        update_frequency: -1.0
diff --git a/integrations/rwightman-timm/setup_integration.sh b/integrations/rwightman-timm/setup_integration.sh
deleted file mode 100644
index ed6c1068339..00000000000
--- a/integrations/rwightman-timm/setup_integration.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-
-# Integration setup command to setup the folder so it is ready to train and sparsify models.
-# Creates a pytorch-image-models folder next to this script with all required dependencies from the rwightman/pytorch-image-models repository.
-# Command: `bash setup_integration.sh`
-
-git clone https://github.com/neuralmagic/pytorch-image-models.git
-cd pytorch-image-models
-git checkout release/1.1
-pip install -r requirements.txt
-pip install sparseml[torch]
diff --git a/integrations/tensorflow_v1/README.md b/integrations/tensorflow_v1/README.md
deleted file mode 100644
index 0471fa009d2..00000000000
--- a/integrations/tensorflow_v1/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML TensorFlow V1 Integration
-
-This directory combines the SparseML recipe-driven approach with standard TensorFlow V1 training flows for models within
-[TF Slim](https://github.com/google-research/tf-slim) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom TensorFlow V1 training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Coming soon!
-
-## Tutorials
-
-- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/tensorflow_v1/notebooks/classification.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[tf_v1]` or `pip install sparseml[tf_v1_gpu]` if training on a GPU.
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=tensorflow_v1) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=tensorflow_v1) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| notebooks            | Jupyter notebooks to walk through sparsifiation within standard TensorFlow V1 training flows                          |
-| recipes              | Typical recipes for sparsifying TensorFlow V1 models along with any downloaded recipes from the SparseZoo.            |
-| tutorials            | Tutorial walkthroughs for how to sparsify TensorFlow V1 models using recipes.                                         |
-| classification.py    | Utility training script for sparsifying classification models in TensorFlow V1 (loads SparseML models).               |
-| README.md            | Readme file.                                                                                                          |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/tensorflow_v1/classification.py b/integrations/tensorflow_v1/classification.py
deleted file mode 100644
index ea544a0429e..00000000000
--- a/integrations/tensorflow_v1/classification.py
+++ /dev/null
@@ -1,965 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Perform optimization tasks on image classification tensorflow_v1 including:
-* Model training
-* Model pruning
-* Sparse transfer learning
-* pruning sensitivity analysis
-* ONNX export
-
-
-##########
-Command help:
-usage: classification.py [-h] {train,export,pruning_sensitivity} ...
-
-Run tasks on classification models and datasets using the sparseml API
-
-positional arguments:
-  {train,export,pruning_sensitivity}
-
-optional arguments:
-  -h, --help            show this help message and exit
-
-
-##########
-train command help:
-usage: classification.py train [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                               [--recipe-path RECIPE_PATH]
-                               [--sparse-transfer-learn] [--eval-mode]
-                               --train-batch-size TRAIN_BATCH_SIZE
-                               --test-batch-size TEST_BATCH_SIZE
-                               [--logs-dir LOGS_DIR]
-                               [--save-best-after SAVE_BEST_AFTER]
-                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
-                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
-
-Train and/or prune an image classification model
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
---dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for dataset loading
-  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for dataset loading
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. If set to
-                        'transfer_learning', then will create a schedule to
-                        enable sparse transfer learning
-  --sparse-transfer-learn
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode           Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
-                        epochs to save checkpoints at
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-
-
-##########
-export command help:
-usage: classification.py export [-h] --arch-key ARCH_KEY
-                                [--pretrained PRETRAINED]
-                                [--pretrained-dataset PRETRAINED_DATASET]
-                                [--checkpoint-path CHECKPOINT_PATH]
-                                [--model-kwargs MODEL_KWARGS] --dataset
-                                DATASET --dataset-path DATASET_PATH
-                                [--dataset-kwargs DATASET_KWARGS]
-                                [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                                [--num-samples NUM_SAMPLES]
-                                [--onnx-opset ONNX_OPSET]
-
-Export a model to onnx as well as store sample inputs, outputs, and labels
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --num-samples NUM_SAMPLES
-                        The number of samples to export along with the model
-                        onnx and pth files (sample inputs and labels as well
-                        as the outputs from model execution)
-  --onnx-opset ONNX_OPSET
-                        The onnx opset to use for export. Default is 11
-
-
-##########
-pruning_sensitivity command help:
-usage: classification.py pruning_sensitivity [-h] --arch-key ARCH_KEY
-                                             [--pretrained PRETRAINED]
-                                             [--pretrained-dataset PRETRAINED_DATASET]
-                                             [--checkpoint-path CHECKPOINT_PATH]
-                                             [--model-kwargs MODEL_KWARGS]
-                                             --dataset DATASET --dataset-path
-                                             DATASET_PATH
-                                             [--dataset-kwargs DATASET_KWARGS]
-                                             [--model-tag MODEL_TAG]
-                                             [--save-dir SAVE_DIR]
-                                             [--dataset-parallel-calls
-                                                DATASET_PARALLEL_CALLS]
-                                             [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                                             [--approximate]
-                                             [--steps-per-measurement
-                                                STEPS_PER_MEASUREMENT]
-                                             [--batch-size BATCH_SIZE]
-
-Run a kernel sparsity (pruning) analysis for a given model
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for dataset loading
-  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for dataset loading
-  --approximate         True to approximate without running data through the
-                        model, otherwise will run a one shot analysis
-  --steps-per-measurement STEPS_PER_MEASUREMENT
-                        The number of steps (batches) to run for each
-                        measurement
-  --batch-size BATCH_SIZE
-                        The batch size to use while performing analysis
-
-
-#########
-EXAMPLES
-#########
-
-##########
-Example command for pruning resnet50 on imagenet dataset:
-python scripts/tensorflow_v1/classification.py train \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
-python scripts/tensorflow_v1/classification.py train \
-    --sparse-transfer-learn \
-    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
-    --arch-key mobilenet_v1 --pretrained optim \
-    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for exporting ResNet50:
-python scripts/tensorflow_v1/classification.py export \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
-
-##########
-Example command for running approximated KS sensitivity analysis on mobilenet:
-python scripts/tensorflow_v1/classification.py pruning_sensitivity \
-    --approximate \
-    --arch-key mobilenet --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012
-
-##########
-Example command for running one shot KS sensitivity analysis on resnet50 for coco:
-python scripts/tensorflow_v1/classification.py pruning_sensitivity \
-    --arch-key resnet50 --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012
-"""
-
-import argparse
-import json
-import math
-import os
-from typing import Dict, Optional, Tuple
-
-import numpy
-
-from sparseml import get_main_logger
-from sparseml.tensorflow_v1.datasets import (
-    Dataset,
-    DatasetRegistry,
-    create_split_iterators_handle,
-)
-from sparseml.tensorflow_v1.models import ModelRegistry
-from sparseml.tensorflow_v1.optim import (
-    ConstantPruningModifier,
-    ScheduledModifierManager,
-    pruning_loss_sens_magnitude,
-    pruning_loss_sens_one_shot,
-    pruning_loss_sens_op_vars,
-)
-from sparseml.tensorflow_v1.utils import (
-    GraphExporter,
-    accuracy,
-    batch_cross_entropy_loss,
-    tf_compat,
-    write_simple_summary,
-)
-from sparseml.utils import create_dirs
-
-
-LOGGER = get_main_logger()
-TRAIN_COMMAND = "train"
-EXPORT_COMMAND = "export"
-PRUNING_SENSITVITY_COMMAND = "pruning_sensitivity"
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Run tasks on classification models and datasets "
-        "using the sparseml API"
-    )
-
-    subparsers = parser.add_subparsers(dest="command")
-    train_parser = subparsers.add_parser(
-        TRAIN_COMMAND,
-        description="Train and/or prune an image classification model",
-    )
-    export_parser = subparsers.add_parser(
-        EXPORT_COMMAND,
-        description="Export a model to onnx as well as "
-        "store sample inputs, outputs, and labels",
-    )
-    pruning_sensitivity_parser = subparsers.add_parser(
-        PRUNING_SENSITVITY_COMMAND,
-        description="Run a kernel sparsity (pruning) analysis for a given model",
-    )
-
-    parsers = [
-        train_parser,
-        export_parser,
-        pruning_sensitivity_parser,
-    ]
-    for par in parsers:
-        # general arguments
-        # model args
-        par.add_argument(
-            "--arch-key",
-            type=str,
-            required=True,
-            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception with the list)",
-        )
-        par.add_argument(
-            "--pretrained",
-            type=str,
-            default=True,
-            help="The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]",
-        )
-        par.add_argument(
-            "--pretrained-dataset",
-            type=str,
-            default=None,
-            help="The dataset to load pretrained weights for if pretrained is set. "
-            "Default is None which will load the default dataset for the architecture."
-            " Ex can be set to imagenet, cifar10, etc",
-        )
-        par.add_argument(
-            "--checkpoint-path",
-            type=str,
-            default=None,
-            help="A path to a previous checkpoint to load the state from and "
-            "resume the state for. If provided, pretrained will be ignored",
-        )
-        par.add_argument(
-            "--model-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to model constructor, should be "
-            " given as a json object",
-        )
-
-        # dataset args
-        par.add_argument(
-            "--dataset",
-            type=str,
-            required=True,
-            help="The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or loadable by a "
-            "dataset in sparseml.tensorflow_v1.datasets",
-        )
-        par.add_argument(
-            "--dataset-path",
-            type=str,
-            required=True,
-            help="The root path to where the dataset is stored",
-        )
-        par.add_argument(
-            "--dataset-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to dataset constructor, should be "
-            " given as a json object",
-        )
-
-        # logging and saving
-        par.add_argument(
-            "--model-tag",
-            type=str,
-            default=None,
-            help="A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        )
-        par.add_argument(
-            "--save-dir",
-            type=str,
-            default="tensorflow_v1_classification",
-            help="The path to the directory for saving results",
-        )
-
-        # task specific arguments
-        if par in [train_parser, pruning_sensitivity_parser]:
-            par.add_argument(
-                "--dataset-parallel-calls",
-                type=int,
-                default=4,
-                help="the number of parallel workers for dataset loading",
-            )
-            par.add_argument(
-                "--shuffle-buffer-size",
-                type=int,
-                default=1000,
-                help="Shuffle buffer size for dataset loading",
-            )
-
-        if par == train_parser:
-            par.add_argument(
-                "--recipe-path",
-                type=str,
-                default=None,
-                help="The path to the yaml file containing the modifiers and "
-                "schedule to apply them with. If set to 'transfer_learning', "
-                "then will create a schedule to enable sparse transfer learning",
-            )
-            par.add_argument(
-                "--sparse-transfer-learn",
-                action="store_true",
-                help=(
-                    "Enable sparse transfer learning modifiers to enforce the sparsity "
-                    "for already sparse layers. The modifiers are added to the "
-                    "ones to be loaded from the recipe-path"
-                ),
-            )
-            par.add_argument(
-                "--eval-mode",
-                action="store_true",
-                help="Puts into evaluation mode so that the model can be "
-                "evaluated on the desired dataset",
-            )
-            par.add_argument(
-                "--train-batch-size",
-                type=int,
-                required=True,
-                help="The batch size to use while training",
-            )
-            par.add_argument(
-                "--test-batch-size",
-                type=int,
-                required=True,
-                help="The batch size to use while testing",
-            )
-            par.add_argument(
-                "--logs-dir",
-                type=str,
-                default=os.path.join(
-                    "tensorflow_v1_classification_train", "tensorboard-logs"
-                ),
-                help="The path to the directory for saving logs",
-            )
-            par.add_argument(
-                "--save-best-after",
-                type=int,
-                default=-1,
-                help="start saving the best validation result after the given "
-                "epoch completes until the end of training",
-            )
-            par.add_argument(
-                "--save-epochs",
-                type=int,
-                default=[],
-                nargs="+",
-                help="epochs to save checkpoints at",
-            )
-            par.add_argument(
-                "--init-lr",
-                type=float,
-                default=1e-9,
-                help="The initial learning rate to use while training, "
-                "the actual initial value used should be set by the sparseml recipe",
-            )
-            par.add_argument(
-                "--optim-args",
-                type=json.loads,
-                default={},
-                help="Additional args to be passed to the optimizer passed in"
-                " as a json object",
-            )
-
-        if par == export_parser:
-            par.add_argument(
-                "--num-samples",
-                type=int,
-                default=100,
-                help="The number of samples to export along with the model onnx "
-                "and pth files (sample inputs and labels as well as the outputs "
-                "from model execution)",
-            )
-            par.add_argument(
-                "--onnx-opset",
-                type=int,
-                default=11,
-                help="The onnx opset to use for export. Default is 11",
-            )
-
-        if par == pruning_sensitivity_parser:
-            par.add_argument(
-                "--approximate",
-                action="store_true",
-                help="True to approximate without running data through the model, "
-                "otherwise will run a one shot analysis",
-            )
-            par.add_argument(
-                "--steps-per-measurement",
-                type=int,
-                default=15,
-                help="The number of steps (batches) to run for each measurement",
-            )
-            par.add_argument(
-                "--batch-size",
-                type=int,
-                default=64,
-                help="The batch size to use while performing analysis",
-            )
-
-    return parser.parse_args()
-
-
-def _setup_save_dirs(args) -> Tuple[str, Optional[str]]:
-    # logging and saving setup
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-    logs_dir = (
-        os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
-        if args.command == TRAIN_COMMAND
-        else None
-    )
-
-    if not args.model_tag:
-        model_tag = "{}_{}".format(args.arch_key.replace("/", "."), args.dataset)
-        model_id = model_tag
-        model_inc = 0
-        # set location to check for models with same name
-        model_main_dir = logs_dir or save_dir
-
-        while os.path.exists(os.path.join(model_main_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-
-    # logs dir setup
-    if args.command == TRAIN_COMMAND:
-        logs_dir = os.path.join(logs_dir, model_id)
-        create_dirs(logs_dir)
-    else:
-        logs_dir = None
-    LOGGER.info("Model id is set to {}".format(model_id))
-    return save_dir, logs_dir
-
-
-def _create_dataset(args, train=True, image_size=None) -> Tuple[Dataset, int]:
-    kwargs = args.dataset_kwargs
-    if "image_size" in kwargs:
-        image_size = kwargs["image_size"]
-        del kwargs["image_size"]
-
-    dataset = DatasetRegistry.create(
-        args.dataset,
-        root=args.dataset_path,
-        train=train,
-        image_size=image_size,
-        **kwargs,
-    )
-    LOGGER.info("created {} dataset: {}".format("train" if train else "val", dataset))
-
-    # get num_classes
-    if args.dataset == "imagefolder":
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-
-    return dataset, num_classes
-
-
-def _build_dataset(args, dataset: Dataset, batch_size: int) -> Dataset:
-    return dataset.build(
-        batch_size,
-        shuffle_buffer_size=args.shuffle_buffer_size,
-        prefetch_buffer_size=batch_size,
-        num_parallel_calls=args.dataset_parallel_calls,
-    )
-
-
-def _create_model(args, num_classes, inputs, training=False):
-    outputs = ModelRegistry.create(
-        args.arch_key,
-        inputs,
-        training=training,
-        num_classes=num_classes,
-        **args.model_kwargs,
-    )
-    LOGGER.info("created model {}".format(args.arch_key))
-    return outputs
-
-
-def _load_model(args, sess, checkpoint_path=None):
-    sess.run(
-        [
-            tf_compat.global_variables_initializer(),
-            tf_compat.local_variables_initializer(),
-        ]
-    )
-    checkpoint_path = checkpoint_path or args.checkpoint_path
-    ModelRegistry.load_pretrained(
-        args.arch_key,
-        pretrained=args.pretrained,
-        pretrained_dataset=args.pretrained_dataset,
-        pretrained_path=checkpoint_path,
-        sess=sess,
-    )
-    if checkpoint_path:
-        LOGGER.info("Loaded model weights from checkpoint: {}".format(checkpoint_path))
-
-
-def _save_checkpoint(args, sess, save_dir, checkpoint_name) -> str:
-    checkpoint_path = os.path.join(os.path.join(save_dir, checkpoint_name, "model"))
-    create_dirs(checkpoint_path)
-    saver = ModelRegistry.saver(args.arch_key)
-    saved_name = saver.save(sess, checkpoint_path)
-    checkpoint_path = os.path.join(checkpoint_path, saved_name)
-    LOGGER.info("Checkpoint saved to {}".format(checkpoint_path))
-    return checkpoint_path
-
-
-def _save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    LOGGER.info(f"Saved recipe to {recipe_save_path}")
-
-
-def train(args, save_dir, logs_dir):
-    # setup dataset
-    with tf_compat.device("/cpu:0"):
-        train_dataset, _ = _create_dataset(args, train=True)
-        val_dataset, num_classes = _create_dataset(args, train=False)
-        # calc steps
-        train_steps = math.ceil(len(train_dataset) / args.train_batch_size)
-        val_steps = math.ceil(len(val_dataset) / args.test_batch_size)
-        # build datasets
-        train_dataset = _build_dataset(args, train_dataset, args.train_batch_size)
-        val_dataset = _build_dataset(args, val_dataset, args.test_batch_size)
-    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(
-        [train_dataset, val_dataset]
-    )
-
-    # set up model graph
-    images, labels = iterator.get_next()
-    training = tf_compat.placeholder(dtype=tf_compat.bool, shape=[])
-    outputs = _create_model(args, num_classes, images, training)
-
-    # set up training objects
-    loss = batch_cross_entropy_loss(outputs, labels)
-    acc = accuracy(outputs, labels)
-    global_step = tf_compat.train.get_or_create_global_step()
-    train_op = tf_compat.train.AdamOptimizer(
-        learning_rate=args.init_lr, **args.optim_args
-    ).minimize(loss, global_step=global_step)
-    update_ops = tf_compat.get_collection(tf_compat.GraphKeys.UPDATE_OPS)
-    LOGGER.info("Created update ops for training")
-
-    # set up sparseml modifier ops
-    add_mods = (
-        ConstantPruningModifier(params="__ALL__")
-        if args.sparse_transfer_learn
-        else None
-    )
-    manager = ScheduledModifierManager.from_yaml(
-        file_path=args.recipe_path, add_modifiers=add_mods
-    )
-    mod_ops, mod_extras = manager.create_ops(train_steps, global_step)
-    _save_recipe(recipe_manager=manager, save_dir=save_dir)
-    with tf_compat.Session() as sess:
-        # set up tensorboard logging
-        summary_writer = tf_compat.summary.FileWriter(logs_dir, sess.graph)
-        summaries = tf_compat.summary.merge_all()
-        LOGGER.info("Logging to tensorboard at {}".format(logs_dir))
-
-        # initialize variables, load pretrained weights, initialize modifiers
-        train_iter_handle, val_iter_handle = sess.run(
-            [train_iter.string_handle(), val_iter.string_handle()]
-        )
-        LOGGER.info("Initialized graph variables")
-        _load_model(args, sess)
-        manager.initialize_session()
-        LOGGER.info("Initialized SparseML modifiers")
-
-        best_loss = None
-        for epoch in range(manager.max_epochs):
-            # train
-            LOGGER.info("Training for epoch {}...".format(epoch))
-            sess.run(train_iter.initializer)
-            train_acc, train_loss = [], []
-            for step in range(train_steps):
-                _, __, meas_step, meas_loss, meas_acc, meas_summ = sess.run(
-                    [train_op, update_ops, global_step, loss, acc, summaries],
-                    feed_dict={handle: train_iter_handle, training: True},
-                )
-                if step >= train_steps - 1:
-                    # log the general summaries on the last training step
-                    summary_writer.add_summary(meas_summ, meas_step)
-                # run modifier ops
-                sess.run(mod_ops)
-                # summarize
-                write_simple_summary(summary_writer, "Train/Loss", meas_loss, meas_step)
-                write_simple_summary(
-                    summary_writer, "Train/Acc", meas_acc * 100.0, meas_step
-                )
-                train_acc.append(meas_acc)
-                train_loss.append(meas_loss)
-            LOGGER.info(
-                "Epoch {} - Train Loss: {}, Train Acc: {}".format(
-                    epoch, numpy.mean(train_loss).item(), numpy.mean(train_acc).item()
-                )
-            )
-
-            # val
-            LOGGER.info("Validating for epoch {}...".format(epoch))
-            sess.run(val_iter.initializer)
-            val_acc, val_loss = [], []
-            for step in range(val_steps):
-                meas_loss, meas_acc = sess.run(
-                    [loss, acc],
-                    feed_dict={handle: val_iter_handle, training: False},
-                )
-                val_acc.append(meas_acc)
-                val_loss.append(meas_loss)
-                write_simple_summary(
-                    summary_writer, "Val/Loss", numpy.mean(val_loss).item(), epoch
-                )
-                write_simple_summary(
-                    summary_writer, "Val/Acc", numpy.mean(val_acc).item(), epoch
-                )
-            val_loss = numpy.mean(val_loss).item()
-            LOGGER.info(
-                "Epoch {} - Val Loss: {}, Val Acc: {}".format(
-                    epoch, val_loss, numpy.mean(train_acc).item()
-                )
-            )
-            if epoch >= args.save_best_after and (
-                best_loss is None or val_loss <= best_loss
-            ):
-                _save_checkpoint(args, sess, save_dir, "checkpoint-best")
-                best_loss = val_loss
-            if args.save_epochs and epoch in args.save_epochs:
-                _save_checkpoint(
-                    args, sess, save_dir, "checkpoint-epoch-{}".format(epoch)
-                )
-
-        # cleanup graph and save final checkpoint
-        manager.complete_graph()
-        checkpoint_path = _save_checkpoint(args, sess, save_dir, "final-checkpoint")
-    LOGGER.info("Running ONNX export flow")
-    export(
-        args,
-        save_dir,
-        checkpoint_path=checkpoint_path,
-        skip_samples=True,
-        num_classes=num_classes,
-        opset=11,
-    )
-
-
-def export(
-    args,
-    save_dir,
-    checkpoint_path=None,
-    skip_samples=False,
-    num_classes=None,
-    opset=None,
-):
-    assert not skip_samples or num_classes
-    # dataset creation
-    if not skip_samples:
-        val_dataset, num_classes = _create_dataset(args, train=False)
-
-    with tf_compat.Graph().as_default():
-        input_shape = ModelRegistry.input_shape(args.arch_key)
-        inputs = tf_compat.placeholder(
-            tf_compat.float32, [None] + list(input_shape), name="inputs"
-        )
-        outputs = _create_model(args, num_classes, inputs)
-
-        with tf_compat.Session() as sess:
-            _load_model(
-                args, sess, checkpoint_path=checkpoint_path or args.checkpoint_path
-            )
-
-            exporter = GraphExporter(save_dir)
-
-            if not skip_samples:
-                # Export a batch of samples and expected outputs
-                tf_dataset = val_dataset.build(
-                    args.num_samples, repeat_count=1, num_parallel_calls=1
-                )
-                tf_iter = tf_compat.data.make_one_shot_iterator(tf_dataset)
-                features, _ = tf_iter.get_next()
-                inputs_val = sess.run(features)
-                exporter.export_samples([inputs], [inputs_val], [outputs], sess)
-
-            # Export model to tensorflow checkpoint format
-            LOGGER.info("exporting tensorflow in {}".format(save_dir))
-            exporter.export_checkpoint(sess=sess)
-
-            # Export model to pb format
-            LOGGER.info("exporting pb in {}".format(exporter.pb_path))
-            exporter.export_pb(outputs=[outputs])
-
-    # Export model to onnx format
-    LOGGER.info("exporting onnx in {}".format(exporter.onnx_path))
-    exporter.export_onnx([inputs], [outputs], opset=opset or args.onnx_opset)
-
-
-def pruning_loss_sensitivity(args, save_dir):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    train_dataset, num_classes = _create_dataset(
-        args, train=True, image_size=input_shape[1]
-    )
-    with tf_compat.Graph().as_default() as graph:
-        # create model graph
-        inputs = tf_compat.placeholder(
-            tf_compat.float32, [None] + list(input_shape), name="inputs"
-        )
-        outputs = _create_model(args, num_classes, inputs)
-
-        with tf_compat.Session() as sess:
-            _load_model(args, sess, checkpoint_path=args.checkpoint_path)
-            if args.approximate:
-                LOGGER.info("Running weight magnitude loss sensitivity analysis...")
-                analysis = pruning_loss_sens_magnitude(graph, sess)
-            else:
-                op_vars = pruning_loss_sens_op_vars(graph)
-                train_steps = math.ceil(len(train_dataset) / args.batch_size)
-                train_dataset = _build_dataset(args, train_dataset, args.batch_size)
-                handle, iterator, dataset_iter = create_split_iterators_handle(
-                    [train_dataset]
-                )
-                dataset_iter = dataset_iter[0]
-                images, labels = iterator.get_next()
-                loss = batch_cross_entropy_loss(outputs, labels)
-                tensor_names = ["inputs:0", labels.name]
-                sess.run(dataset_iter.initializer)
-
-                def feed_dict_creator(step: int) -> Dict[str, tf_compat.Tensor]:
-                    assert step < train_steps
-                    batch_data = [
-                        tens.eval(session=sess) for tens in dataset_iter.get_next()
-                    ]
-                    return dict(zip(tensor_names, batch_data))
-
-                LOGGER.info("Running one shot loss sensitivity analysis...")
-                analysis = pruning_loss_sens_one_shot(
-                    op_vars=op_vars,
-                    loss_tensor=loss,
-                    steps_per_measurement=args.steps_per_measurement,
-                    feed_dict_creator=feed_dict_creator,
-                    sess=sess,
-                )
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info("Saving results in {}".format(save_dir))
-    analysis.save_json(
-        os.path.join(
-            save_dir,
-            "ks_approx_sensitivity.json"
-            if args.approximate
-            else "ks_one_shot_sensitivity.json",
-        )
-    )
-    analysis.plot(
-        os.path.join(
-            save_dir,
-            os.path.join(
-                save_dir,
-                "ks_approx_sensitivity.png"
-                if args.approximate
-                else "ks_one_shot_sensitivity.png",
-            ),
-        ),
-        plot_integral=True,
-    )
-    analysis.print_res()
-
-
-def main(args):
-    # set up saving and logging dirs
-    save_dir, logs_dir = _setup_save_dirs(args)
-
-    # RUN COMMAND SPECIFIC TASTS
-    if args.command == TRAIN_COMMAND:
-        train(args, save_dir, logs_dir)
-    if args.command == EXPORT_COMMAND:
-        export(args, save_dir)
-    if args.command == PRUNING_SENSITVITY_COMMAND:
-        pruning_loss_sensitivity(args, save_dir)
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-
-    main(args_)
diff --git a/integrations/tensorflow_v1/notebooks/classification.ipynb b/integrations/tensorflow_v1/notebooks/classification.ipynb
deleted file mode 100644
index 158d12aaef0..00000000000
--- a/integrations/tensorflow_v1/notebooks/classification.ipynb
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# TensorFlow v1 Classification Model Pruning Using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Engine. You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a TensorFlow training flow with a simple SparseML integration\n",
-    "- Prune the model using the TensorFlow+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your TensorFlow training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 20 minutes on a GPU\n",
-    "- 60 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* TensorFlow v1 and tf2onnx\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import tensorflow as tf\n",
-    "import tf2onnx\n",
-    "\n",
-    "assert tf.__version__ < \"2\"\n",
-    "\n",
-    "# suppress warnings\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "In the cells below, functions are defined to load the dataset, model, and training objects to be called during training from within the `Graph` context.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate function for to load your model or dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "from sparseml.tensorflow_v1.models import ModelRegistry\n",
-    "from sparseml.tensorflow_v1.datasets import (\n",
-    "    ImagenetteDataset,\n",
-    "    ImagenetteSize,\n",
-    ")\n",
-    "from sparseml.tensorflow_v1.utils import (\n",
-    "    batch_cross_entropy_loss,\n",
-    "    accuracy,\n",
-    ")\n",
-    "\n",
-    "MODEL_NAME = \"resnet50\"\n",
-    "BATCH_SIZE = 128\n",
-    "INPUT_SIZE = 224\n",
-    "NUM_CLASSES = 10\n",
-    "SAVE_DIR = \"tensorflow_v1_classification_pruning\"\n",
-    "\n",
-    "\n",
-    "def load_dataset():\n",
-    "    with tf.device(\"/cpu:0\"):\n",
-    "        print(\"loading datasets\")\n",
-    "        train_dataset = ImagenetteDataset(\n",
-    "            train=True, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
-    "        )\n",
-    "        train_len = len(train_dataset)\n",
-    "        train_steps = math.ceil(train_len / float(BATCH_SIZE))\n",
-    "        train_dataset = train_dataset.build(\n",
-    "            BATCH_SIZE,\n",
-    "            shuffle_buffer_size=1000,\n",
-    "            prefetch_buffer_size=BATCH_SIZE,\n",
-    "            num_parallel_calls=4,\n",
-    "        )\n",
-    "\n",
-    "        val_dataset = ImagenetteDataset(\n",
-    "            train=False, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
-    "        )\n",
-    "        val_len = len(val_dataset)\n",
-    "        val_steps = math.ceil(val_len / float(BATCH_SIZE))\n",
-    "        val_dataset = val_dataset.build(\n",
-    "            BATCH_SIZE,\n",
-    "            shuffle_buffer_size=1000,\n",
-    "            prefetch_buffer_size=BATCH_SIZE,\n",
-    "            num_parallel_calls=4,\n",
-    "        )\n",
-    "\n",
-    "    return train_dataset, val_dataset, (train_steps, val_steps)\n",
-    "\n",
-    "\n",
-    "def create_model(sample_input, training):\n",
-    "    print(\"Creating model graph for {}\".format(MODEL_NAME))\n",
-    "    logits = ModelRegistry.create(\n",
-    "        MODEL_NAME,\n",
-    "        inputs=sample_input,\n",
-    "        training=training,\n",
-    "        num_classes=NUM_CLASSES,\n",
-    "    )\n",
-    "    return logits\n",
-    "\n",
-    "\n",
-    "def create_training_objects(sample_labels):\n",
-    "    print(\"Creating loss, accuracy, and optimizer in graph\")\n",
-    "    loss = batch_cross_entropy_loss(logits, labels)\n",
-    "    acc = accuracy(logits, labels)\n",
-    "    global_step = tf.train.get_or_create_global_step()\n",
-    "    train_op = tf.train.AdamOptimizer(learning_rate=0.00008).minimize(\n",
-    "        loss, global_step=global_step\n",
-    "    )\n",
-    "    return loss, acc, global_step, train_op\n",
-    "\n",
-    "\n",
-    "def load_pretrained():\n",
-    "    print(\"loading pre-trained model weights\")\n",
-    "    ModelRegistry.load_pretrained(\n",
-    "        MODEL_NAME, pretrained=\"base\", remove_dynamic_tl_vars=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Create a SparseML Modifier Manager\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to create operations that modify the training process.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the operators generated from this manager object, you will be able to prune your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.tensorflow_v1.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "\n",
-    "def create_sparseml_manager():\n",
-    "    model = search_models(\n",
-    "        domain=\"cv\",\n",
-    "        sub_domain=\"classification\",\n",
-    "        architecture=\"resnet_v1\",\n",
-    "        sub_architecture=\"50\",\n",
-    "        framework=\"tensorflow_v1\",\n",
-    "        repo=\"sparseml\",\n",
-    "        dataset=\"imagenette\",\n",
-    "        sparse_name=\"pruned\",\n",
-    "    )[0]  # unwrap search result\n",
-    "\n",
-    "    recipe_path = model.recipes.default.path\n",
-    "    print(f\"Recipe downloaded to: {recipe_path}\")\n",
-    "\n",
-    "    return ScheduledModifierManager.from_yaml(recipe_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Prune your model using a TensorFlow training loop\n",
-    "SparseML can plug directly into your existing TensorFlow training flow by creating additional operators to run. To demonstrate this, in the cell below, prune the model using a standard TensorFlow training loop while also running the operators created by the manager object.  To prune your existing models using SparseML, you can use your own training flow with the additional operators added.\n",
-    "\n",
-    "For your convienence the lines needed for integrating with SparseML are preceeded by large comment blocks.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `BATCH_SIZE` in Step 2 above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "from sparseml.utils import create_unique_dir, create_dirs\n",
-    "from sparseml.tensorflow_v1.datasets import create_split_iterators_handle\n",
-    "\n",
-    "with tf.Graph().as_default() as graph:\n",
-    "    # create dataset\n",
-    "    train_dataset, val_dataset, (train_steps, val_steps) = load_dataset()\n",
-    "    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(\n",
-    "        [train_dataset, val_dataset]\n",
-    "    )\n",
-    "    images, labels = iterator.get_next()\n",
-    "\n",
-    "    # create base training objects\n",
-    "    training = tf.placeholder(dtype=tf.bool, shape=[])\n",
-    "    logits = create_model(images, training)\n",
-    "    loss, acc, global_step, train_op = create_training_objects(labels)\n",
-    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
-    "\n",
-    "    #######################################################\n",
-    "    # create sparseml training manager\n",
-    "    #######################################################\n",
-    "    manager = create_sparseml_manager()\n",
-    "    mod_ops, mod_extras = manager.create_ops(train_steps, global_step, graph=graph)\n",
-    "\n",
-    "    with tf.Session() as sess:\n",
-    "        print(\"initializing session\")\n",
-    "        sess.run(\n",
-    "            [\n",
-    "                tf.global_variables_initializer(),\n",
-    "                tf.local_variables_initializer(),\n",
-    "            ]\n",
-    "        )\n",
-    "        train_iter_handle, val_iter_handle = sess.run(\n",
-    "            [train_iter.string_handle(), val_iter.string_handle()]\n",
-    "        )\n",
-    "\n",
-    "        # initialize sparseml manager after pretrained weights loaded\n",
-    "        load_pretrained()\n",
-    "        manager.initialize_session()\n",
-    "\n",
-    "        num_epochs = manager.max_epochs\n",
-    "        for epoch in tqdm(range(num_epochs), desc=\"pruning\"):\n",
-    "            print(\"training for epoch {}...\".format(epoch))\n",
-    "            sess.run(train_iter.initializer)\n",
-    "            train_losses = []\n",
-    "            train_acc = []\n",
-    "\n",
-    "            for step in range(train_steps):\n",
-    "                _, __, meas_step, meas_loss, meas_acc = sess.run(\n",
-    "                    [train_op, update_ops, global_step, loss, acc],\n",
-    "                    feed_dict={handle: train_iter_handle, training: True},\n",
-    "                )\n",
-    "                train_losses.append(meas_loss)\n",
-    "                train_acc.append(meas_acc)\n",
-    "\n",
-    "                #######################################################\n",
-    "                # Modifier update ops line for transfer learning from a sparse model in TensorFlow\n",
-    "                #######################################################\n",
-    "                sess.run(mod_ops)\n",
-    "            print(\n",
-    "                \"completed epoch {} training with: loss {} / acc {}\".format(\n",
-    "                    epoch,\n",
-    "                    numpy.mean(train_losses).item(),\n",
-    "                    numpy.mean(train_acc).item() * 100,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "            print(\"validating for epoch {}...\".format(epoch))\n",
-    "            sess.run(val_iter.initializer)\n",
-    "            val_losses = []\n",
-    "            val_acc = []\n",
-    "\n",
-    "            for step in range(val_steps):\n",
-    "                meas_loss, meas_acc = sess.run(\n",
-    "                    [loss, acc],\n",
-    "                    feed_dict={handle: val_iter_handle, training: False},\n",
-    "                )\n",
-    "                val_losses.append(meas_loss)\n",
-    "                val_acc.append(meas_acc)\n",
-    "\n",
-    "            print(\n",
-    "                \"completed epoch {} validation with: loss {} / acc {}\".format(\n",
-    "                    epoch,\n",
-    "                    numpy.mean(val_losses).item(),\n",
-    "                    numpy.mean(val_acc).item() * 100,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "        #######################################################\n",
-    "        # Final line for sparseml training in TensorFlow, complete the graph\n",
-    "        #######################################################\n",
-    "        manager.complete_graph()\n",
-    "\n",
-    "        NAME = \"resnet50-imagenette-pruned\"\n",
-    "        checkpoint_path = create_unique_dir(\n",
-    "            os.path.join(\".\", SAVE_DIR, NAME, \"checkpoint\")\n",
-    "        )\n",
-    "        checkpoint_path = os.path.join(checkpoint_path, \"model\")\n",
-    "        create_dirs(checkpoint_path)\n",
-    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
-    "        saver.save(sess, checkpoint_path)\n",
-    "        print(\"saved model checkpoint to {}\".format(checkpoint_path))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For TensorFlow, exporting to ONNX is not natively supported. To add support, you will use the `tf2onnx` Python package. In the cell block below, a convenience class, `GraphExporter()`, is used to handle exporting. It wraps the somewhat complicated API for `tf2onnx` into an easy to use interface.\n",
-    "\n",
-    "Note, for some configurations, the tf2onnx code does not work properly in a Jupyter Notebook. To remedy this, you should run the `exporter.export_onnx()` function call in a Python console or script.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.utils import clean_path\n",
-    "from sparseml.tensorflow_v1.utils import GraphExporter\n",
-    "\n",
-    "\n",
-    "export_path = clean_path(os.path.join(\".\", SAVE_DIR, NAME))\n",
-    "exporter = GraphExporter(export_path)\n",
-    "\n",
-    "with tf.Graph().as_default() as graph:\n",
-    "    print(\"Recreating graph...\", flush=True)\n",
-    "\n",
-    "    input_placeholder = tf.placeholder(\n",
-    "        tf.float32, [None, INPUT_SIZE, INPUT_SIZE, 3], name=\"inputs\"\n",
-    "    )\n",
-    "    logits = create_model(input_placeholder, training=False)\n",
-    "\n",
-    "    input_names = [input_placeholder.name]\n",
-    "    output_names = [logits.name]\n",
-    "\n",
-    "    with tf.Session() as sess:\n",
-    "        sess.run(tf.global_variables_initializer())\n",
-    "        print(\"Restoring previous weights...\", flush=True)\n",
-    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
-    "        saver.restore(sess, checkpoint_path)\n",
-    "\n",
-    "        print(\"Exporting to pb...\", flush=True)\n",
-    "        exporter.export_pb(outputs=[logits])\n",
-    "        print(\"Exported pb file to {}\".format(exporter.pb_path), flush=True)\n",
-    "\n",
-    "print(\"Exporting to onnx...\", flush=True)\n",
-    "exporter.export_onnx(inputs=input_names, outputs=output_names)\n",
-    "print(\"Exported onnx file to {}\".format(exporter.onnx_path))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the DeepSparse Engine"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/tensorflow_v1/recipes/.gitkeep b/integrations/tensorflow_v1/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/tensorflow_v1/tutorials/.gitkeep b/integrations/tensorflow_v1/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/ultralytics-yolov3/README.md b/integrations/ultralytics-yolov3/README.md
deleted file mode 100644
index 1dae432bd4f..00000000000
--- a/integrations/ultralytics-yolov3/README.md
+++ /dev/null
@@ -1,128 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML Ultralytics YOLOv3 Integration
-
-This directory combines the SparseML recipe-driven approach with the 
-[ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository.
-By integrating the robust training flows in the `yolov3` repository with the SparseML code base,
-we enable model sparsification techniques on the popular [YOLOv3 architecture](https://arxiv.org/abs/1804.02767)
-creating smaller and faster deployable versions.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Blog: [YOLOv3 on CPUs: Sparsifying to Achieve GPU-Level Performance](https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-deepsparse/)
-- Example: [DeepSparse YOLOv3 Inference Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo)
-- Video: [DeepSparse YOLOv3 Pruned Quantized Performance](https://youtu.be/o5qIYs47MPw)
-
-## Tutorials
-
-- [Sparsifying YOLOv3 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md)
-- [Sparse Transfer Learning With YOLOv3](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md)
-
-## Installation
-
-To begin, run the following command in the root directory of this integration (`cd integrations/ultralytics-yolov3`):
-```bash
-bash setup_integration.sh
-```
-
-The setup_integration.sh file will clone the yolov3 repository with the SparseML integration as a subfolder.
-After the repo has successfully cloned,  all dependencies from the `yolov3/requirements.txt` file will install in your current environment.
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration adds a `--recipe` argument to the [`train.py` script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-For example, pruning and quantizing a model on the VOC dataset can be done by running the following command from within the yolov3 repository folder:
-```bash
-python train.py --batch 16 --img 512 --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml --hyp ../data/hyp.pruned_quantized.yaml \
-    --recipe ../recipes/yolov3-spp.pruned_quantized.short.md
-```
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-The following popular stubs can be used to download these models and recipes through the SparseZoo API directly.
-
-| Model                       | Dataset | mAP@50 | Description                                                    | SparseZoo Stub                                                                   |
-|-----------------------------|---------|--------|----------------------------------------------------------------|----------------------------------------------------------------------------------|
-| YOLOv3-SPP Pruned Quantized | COCO    | 60.4   | 83% sparse and INT8 quantized model with LeakyReLU activations | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 |
-| YOLOv3-SPP Pruned           | COCO    | 62.4   | 88% sparse model with LeakyReLU activations                    | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97       |
-| YOLOv3-SPP                  | COCO    | 64.2   | Baseline model with LeakyReLU activations                      | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none                  |
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/detection?repo=ultralytics) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/detection?repo=ultralytics) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| data                 | The hyperparameters to use and files to use for training on data.                                                     |
-| models               | Model architecture definitions along with any downloaded checkpoints from the SparseZoo.                              |
-| recipes              | Typical recipes for sparsifying YOLOv3 models along with any downloaded recipes from the SparseZoo.                   |
-| tutorials            | Tutorial walkthroughs for how to sparsify YOLOv3 models using recipes.                                                |
-| yolov3               | Integration repository folder used to train and sparsify YOLOv3 models (setup_integration.sh must run first).         |
-| README.md            | Readme file.                                                                                                          |
-| setup_integration.sh | Setup file for the integration run from the command line.                                                             |
-
-### Exporting for Inference
-
-After sparsifying a model, the [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) 
-converts the model into deployment formats such as [ONNX](https://onnx.ai/).
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-For example, the following command can be run from within the yolov3 repository folder to export a trained/sparsified model's checkpoint:
-```bash
-python models/export.py --weights PATH/TO/weights.pt --img-size 512 512
-```
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
diff --git a/integrations/ultralytics-yolov3/data/hyp.pruned.yaml b/integrations/ultralytics-yolov3/data/hyp.pruned.yaml
deleted file mode 100644
index 69a20b0f57b..00000000000
--- a/integrations/ultralytics-yolov3/data/hyp.pruned.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/data/hyp.pruned_quantized.yaml b/integrations/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
deleted file mode 100644
index a146f379e9c..00000000000
--- a/integrations/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml b/integrations/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
deleted file mode 100644
index 57edb510de2..00000000000
--- a/integrations/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
+++ /dev/null
@@ -1,52 +0,0 @@
-# parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
-act: nn.LeakyReLU  # activations
-
-# anchors
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# darknet53 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [32, 3, 1]],  # 0
-   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
-   [-1, 1, Bottleneck, [64]],
-   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
-   [-1, 2, Bottleneck, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
-   [-1, 8, Bottleneck, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
-   [-1, 8, Bottleneck, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
-   [-1, 4, Bottleneck, [1024]],  # 10
-  ]
-
-# YOLOv3-SPP head
-head:
-  [[-1, 1, Bottleneck, [1024, False]],
-   [-1, 1, SPP, [512, [5, 9, 13]]],
-   [-1, 1, Conv, [1024, 3, 1]],
-   [-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
-
-   [-2, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
-
-   [-2, 1, Conv, [128, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
-   [-1, 1, Bottleneck, [256, False]],
-   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
-
-   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
-  ]
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
deleted file mode 100644
index d5fc87fb2b3..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
+++ /dev/null
@@ -1,208 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 300
-
-# pruning hyperparameters
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 80
-update_frequency: &pruning_update_frequency 0.5
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned
-
-This recipe creates a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 97% recovery of its baseline accuracy on the COCO detection dataset.
-Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
-[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned.yaml \
-    --name yolov3-spp-lrelu-pruned
-```
-
-hyp.pruned.yaml:
-```yaml
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
deleted file mode 100644
index c04ef0a581d..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
+++ /dev/null
@@ -1,207 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 80
-
-# pruning hyperparameters
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 0.5
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned Short
-
-This recipe creates a sparse [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened schedule as compared to the original pruned recipe.
-It will train faster, but will recover slightly worse.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.short.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned.yaml \
-    --name yolov3-spp-lrelu-pruned
-```
-
-hyp.pruned.yaml:
-```yaml
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
deleted file mode 100644
index e9108a49440..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
+++ /dev/null
@@ -1,286 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 242
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 240
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 100
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized
-
-This recipe creates a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 94% recovery of its baseline accuracy on the COCO detection dataset.
-Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
-[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
deleted file mode 100644
index c25340e5a41..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
+++ /dev/null
@@ -1,286 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 52
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 50
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 23
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized
-
-This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened shcedule as compared to the original pruned recipe.
-It will train faster, but will recover slightly worse.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.short.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md
deleted file mode 100644
index 29b26e134eb..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md
+++ /dev/null
@@ -1,285 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 5
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 4
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 2
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Test Recipe
-
-This is a test recipe useful for quickly evaluating the time and resources needed for pruning and quantizing a model.
-In addition, it offers a quick integration tests pathway.
-This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that will not be accurate.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.test.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.short.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
deleted file mode 100644
index 2a21412a138..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
+++ /dev/null
@@ -1,169 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR Hyperparams
-num_epochs: &num_epochs 50
-
-# Pruning Hyperparams
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 5
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 1
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.92
-
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params:
-      - model.1.conv.weight
-      - model.2.cv1.conv.weight
-      - model.2.cv2.conv.weight
-      - model.3.conv.weight
-      - model.4.0.cv1.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.3.cv2.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv1.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv1.conv.weight
-      - model.11.cv2.conv.weight
-      - model.12.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.13.conv.weight
-      - model.14.conv.weight
-      - model.15.conv.weight
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.19.cv2.conv.weight
-      - model.20.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.21.conv.weight
-      - model.22.conv.weight
-      - model.23.conv.weight
-      - model.26.cv1.conv.weight
-      - model.26.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.27.1.cv2.conv.weight
-
-  - !GMPruningModifier
-    params:
-      - model.28.m.0.weight
-      - model.28.m.1.weight
-      - model.28.m.2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned Transfer Learning
-
-This recipe transfer learns from a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
-It was originally tested on the VOC dataset and achieved 0.84 mAP@0.5.
-
-Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-The training results for this recipe are made available through Weights and Biases for easy viewing.
-
-- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/3kvb4neh)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
- ```python
-from sparsezoo import Model
-
-stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
-model = Model(stub)
-downloaded_path = model.path
-print(f'Model with stub {stub} downloaded to {downloaded_path}.')
-```
-
-*script command:*
-
-```bash
-python train.py \
-    --data voc.yaml \
-    --cfg ../models/yolov3-spp.lrelu.yaml \
-    --weights DOWNLOADED_PATH \
-    --hyp data/hyp.finetune.yaml \
-    --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
-```
diff --git a/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md b/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
deleted file mode 100644
index 8cf05b9d59c..00000000000
--- a/integrations/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+++ /dev/null
@@ -1,238 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR Hyperparams
-num_epochs: &num_epochs 52
-warmup_lr: &warmup_lr 0.0
-warmup_lr_bias: &warmup_lr_bias 0.05
-init_lr: &init_lr 0.0032
-final_lr: &final_lr 0.00038
-
-# Pruning Hyperparams
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 5
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 1
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.92
-
-# Quantization Hyperparams
-quantization_start_epoch: &quantization_start_epoch 50
-quantization_init_lr: &quantization_init_lr 0.0038
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 100
-    end_epoch: 160
-    lr_func: cosine
-    init_lr: *init_lr
-    final_lr: *final_lr
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *warmup_lr
-    final_lr: *init_lr
-    param_groups: [0, 1]
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *warmup_lr_bias
-    final_lr: *init_lr
-    param_groups: [2]
-    
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params:
-      - model.1.conv.weight
-      - model.2.cv1.conv.weight
-      - model.2.cv2.conv.weight
-      - model.3.conv.weight
-      - model.4.0.cv1.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.3.cv2.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv1.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv1.conv.weight
-      - model.11.cv2.conv.weight
-      - model.12.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.13.conv.weight
-      - model.14.conv.weight
-      - model.15.conv.weight
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.19.cv2.conv.weight
-      - model.20.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.21.conv.weight
-      - model.22.conv.weight
-      - model.23.conv.weight
-      - model.26.cv1.conv.weight
-      - model.26.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.27.1.cv2.conv.weight
-
-  - !GMPruningModifier
-    params:
-      - model.28.m.0.weight
-      - model.28.m.1.weight
-      - model.28.m.2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-    mask_type: *mask_type
-        
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized Transfer Learning
-
-This recipe transfer learns from a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
-It was originally tested on the VOC dataset and achieved 0.838 mAP@0.5.
-
-Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-The training results for this recipe are made available through Weights and Biases for easy viewing.
-
-- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/2hbvu7w2)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
- ```python
-from sparsezoo import Model
-
-stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
-model = Model(stub)
-downloded_path = model.path
-print(f"Model with stub {stub} downloaded to {downloaded_path}.")
-```
-
-*script command:*
-
-```bash
-python train.py \
-    --data voc.yaml \
-    --cfg ../models/yolov3-spp.lrelu.yaml \
-    --weights DOWNLOADED_PATH \
-    --hyp data/hyp.finetune.yaml \
-    --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
-```
diff --git a/integrations/ultralytics-yolov3/setup_integration.sh b/integrations/ultralytics-yolov3/setup_integration.sh
deleted file mode 100644
index b14482e2979..00000000000
--- a/integrations/ultralytics-yolov3/setup_integration.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-
-# Integration setup command to setup the folder so it is ready to train and sparsify models.
-# Creates a yolov3 folder next to this script with all required dependencies from the ultralytics/yolov3 repository.
-# Command: `bash setup_integration.sh`
-
-git clone https://github.com/neuralmagic/yolov3.git
-cd yolov3
-git checkout release/1.1
-pip install -r requirements.txt
diff --git a/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg b/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg
deleted file mode 100644
index 0d2c3adbaf49fb4ecc101718d124891141d0ecac..0000000000000000000000000000000000000000
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diff --git a/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md b/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
deleted file mode 100644
index cd9ecbee7df..00000000000
--- a/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
+++ /dev/null
@@ -1,247 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying YOLOv3 Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models.
-
-## Overview
-
-Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of Ultralytics’ robust training pipelines. Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-
-* Creating a pre-trained model to establish a baseline. You will set up your custom data and then train the model.
-* Applying a recipe to select the trade off between the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-The examples listed in this tutorial are all performed on the VOC dataset. Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc).
-
-<div style="width: 100%; display: flex; justify-content: center;">
-    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
-        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
-    </a>
-</div>
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Creating a Pre-trained Model
-
-Before applying one of the recipes, you must first create the pre-trained model to sparsify further. The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
-
-Creating a pre-trained model involves two steps: 1) setting up the data and 2) training the model.
-
-**Note**: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
-
-
-### Setting Up the Data
-
-1. For this tutorial, we run the VOC setup script with the following command from the root of the yolov3 repository:
-```bash
-bash data/scripts/get_voc.sh
-```
-2. Download and validation of the VOC dataset will begin and takes around 10 minutes to finish.
-The script downloads the VOC dataset into a `VOC` folder under the parent directory.
-Notie that, once completed, the data is ready for training with the folder structure in the following state:
-```
-|-- VOC
-|   |-- images
-|   |   |-- train
-|   |   `-- val
-|   `-- labels
-|       |-- train
-|       `-- val
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- utils
-|   |-- weights
-|   |-- Dockerfile
-|   |-- LICENSE
-|   |-- README.md
-|   |-- detect.py
-|   |-- hubconf.py
-|   |-- requirements.txt
-|   |-- test.py
-|   |-- train.py
-|   `-- tutorial.ipynb
-```
-
-You are ready to train the model.
-
-### Training the Model
-
-The training command will take a few hours to complete (~3 hours for a Titan RTX). Afterward, you will have a model that achieves roughly 0.85 mAP on the VOC dataset ready for sparsifying.
-
-1. To expedite the training process, you will transfer learn from weights initially trained on the COCO dataset. These are stored in the SparseZoo and accessed with the following Python code. Enter:
-
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none")
-checkpoint_path = model.training.default.path
-print(checkpoint_path)
-```
-
-After running, the code has downloaded the checkpoint file to the local system:
-```bash
-downloading...: 100%|██████████| 126342273/126342273 [00:04<00:00, 28306365.97it/s]
-/Users/markkurtz/.cache/sparsezoo/16fe8358-2e91-4b81-a1f2-df85bd1a9ac3/pytorch/model.pth
-```
-
-2. Next, the checkpoint file provides the source weights for training on VOC. Run the following train command from within the yolov3 repository folder:
-```bash
-python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50
-```
-
-The training command creates a `runs` directory under the yolov3 repository directory.
-This directory will contain the outputs from the training run, including experimental results along with the trained model:
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-You are ready to use the weights at `yolov3/runs/train/exp/weights/best.pt` with the Neural Magic recipes to create a sparsified model.
-
-## Applying a Recipe
-
-In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-The [`recipes` folder](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes) contains multiple files, each offering certain advantages over others. The table below compares these tradeoffs and shows how to run them on the VOC dataset.
-1. Review this table, which lists recipes, commands, and results.
-
-| Recipe Name                                                                                                                                              | Description                                                                                                                     | Train Command                                                                                                                                                                               | VOC mAP@0.5 | Size on Disk | DeepSparse Performance** | Training Epochs (time) | Weights and Biases                                                       |
-|----------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------------|--------------------------|------------------------|--------------------------------------------------------------------------|
-| Baseline                                                                                                                                                 | The baseline, pretrained model originally transfer learned onto the VOC dataset.                                                | ``` python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 ```                                                                                     | 0.857       | 194 MB       | 19.5 img/sec             | 50 (3.21 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/261llnb1) |
-| [Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md)                                 | Creates a highly sparse, FP32 model that recovers close to the baseline model.                                                  | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.md ```                           | 0.858       | 33.6 MB      | 34.0 img/sec             | 300 (20.45 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts) |
-| [Pruned Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md)                     | Creates a highly sparse, FP32 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.short.md ```                     | 0.854       | 33.6 MB      | 34.0 img/sec             | 80 (5.53 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n) |
-| [Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md)             | Creates a highly sparse, INT8 model that recovers reasonably close to the baseline model.                                       | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.md ```       | 0.827       | 13.4 MB      | 90.4 img/sec             | 242 (18.32 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs) |
-| [Pruned Quantized Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md) | Creates a highly sparse, INT8 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.short.md ``` | 0.808       | 13.4 MB      | 90.4 img/sec             | 52 (4.23 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy) |
-| [Test](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md)                                     | A test recipe to test the training pipeline and device for both pruning and quantization in 5 epochs.                           | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.test.md ```                   | 0.702       | 13.4 MB      | 90.4 img/sec             | 5 (17 minutes)         | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60) |
-
-** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
-
-2. Notice the Weights and Biases information in the table, which is very useful for comparing across these runs. The epochs versus mAP@0.5 graph is:
-
-<img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
-
-**Notes About Recipe Selection**
-
-If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using either the  `pruned` or `pruned short` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
-
-If your hardware does support quantized networks (VNNI instruction set on CPUs, for example), we recommend using the `pruned quantized` or `pruned quantized short` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
-
-When running quantized models, the memory footprint for training will significantly increase (roughly 3x). Therefore it is essential to take this into account when selecting a batch size to train at. To ensure no issues with the longer quantized runs, run the quicker test recipe first to ensure your configurations are correct and the training process will complete successfully.
-
-3. To begin applying one of the recipes, use the `--recipe` argument within the Ultralytics [train script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
-In addition, the hyperparameters are changed slightly to better work with the recipe.
-These hyperparameters are stored in appropriately named files under the [`data` directory](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/data) and are passed into the training script using the `--hyp` argument.
-Both of the arguments are combined with our previous training command and VOC pre-trained weights to run the recipes over the model. For example:
-```bash
-python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 --recipe PATH_TO_SPARSIFICATION_RECIPE
-```
-After applying a recipe, you are ready to export for inference.
-
-## Exporting for Inference
-
-This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-When you applied a recipe in the previous step, the sparsification run created a new `exp` directory under the yolov3 runs directory:
-
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |-- exp1
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-The `best.pt` file contains a checkpoint of the best weights measured on the validation set.
-These weights can be loaded into the `train.py` and `test.py` scripts now. However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-The [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-
-1. Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
-
-```bash
-python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
-```
-
-The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-```
-|-- exp
-|-- exp1
-|-- weights
-|   |-- best.pt
-|   |-- best.onnx
-|   `-- last.pt
-`-- ...
-```
-
-2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
- 
-The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models. In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md b/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
deleted file mode 100644
index fbcdea5b80f..00000000000
--- a/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
+++ /dev/null
@@ -1,217 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparse Transfer Learning With YOLOv3
-
-This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
-
-## Overview
-
-Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo on top of Ultralytics’ robust training pipelines. 
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-- Downloading and preparing a pre-sparsified model.
-- Setting up your custom data.
-- Applying a sparse transfer learning recipe.
-- Exporting an inference graph to reduce its deployment size and run it in an inference engine such as DeepSparse.
-
-The examples listed in this tutorial are all performed on the VOC dataset. 
-Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning).
-
-<div style="width: 100%; display: flex; justify-content: center;">
-    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
-        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
-    </a>
-</div>
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Downloading and Preparing a Pre-pruned Model
-
-First, you need to download the sparsified models from the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-These models were originally pruned on the COCO dataset achieving the following metrics:
-
-| Sparsification Type | Description                                                                       | COCO mAP@0.5 | Size on Disk | DeepSparse Performance** |
-|---------------------|-----------------------------------------------------------------------------------|--------------|--------------|--------------------------|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.642        | 194 MB       | 19.5 img/sec             |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.624        | 33.6 MB      | 34.0 img/sec             |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.605        | 13.4 MB      | 90.4 img/sec             |
-** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
-
-1) After deciding on which model meets your performance requirements for both speed and accuracy, the following code is used to download the PyTorch checkpoints for the desired model from the SparseZoo:
-    ```python
-    from sparsezoo import Model
-    
-    BASELINE_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none'
-    PRUNED_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
-    PRUNED_QUANT_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
-    
-    stub = PRUNED_QUANT_STUB
-    model = Model(stub)
-    downloded_path = model.path
-    print(f'Model with stub {stub} downloaded to {downloded_path}.')
-    ```
-
-2) Once the desired checkpoint has downloaded, it must be reset for training again.
-    The `utility.py` script within YOLOv3 repository is used for this.
-    First change into the `yolov3` directory using `cd yolov3`.
-    Next run the following command to prepare the checkpoint for training:
-    ```bash
-    python utility.py strip /PATH/TO/DOWNLOADED/WEIGHTS.pt
-    ```
-   
-You are now ready to set up the data for training.
-
-## Setting Up the Data
-
-Note: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). 
-Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
-
-1) For this tutorial, run the VOC setup script with the following command from the root of the `yolov3` repository:
-    
-    `bash data/scripts/get_voc.sh`
-2) Download and validation of the VOC dataset will begin and take around 10 minutes to finish. 
-    The script downloads the VOC dataset into a VOC folder under the parent directory. 
-    Notice that, once completed, the data is ready for training with the folder structure in the following state:
-    ```
-    |-- VOC
-    |   |-- images
-    |   |   |-- train
-    |   |   `-- val
-    |   `-- labels
-    |       |-- train
-    |       `-- val
-    |-- yolov3
-    |   |-- data
-    |   |-- models
-    |   |-- utils
-    |   |-- weights
-    |   |-- Dockerfile
-    |   |-- LICENSE
-    |   |-- README.md
-    |   |-- detect.py
-    |   |-- hubconf.py
-    |   |-- requirements.txt
-    |   |-- test.py
-    |   |-- train.py
-    |   `-- tutorial.ipynb
-    ```
-
-You are ready to transfer learn the model.
-
-## Transfer Learning the Model
-
-The training command will take a few hours to complete (~3 hours for a Titan RTX). 
-Afterward, you will have a sparse model transfer learned onto the VOC dataset.
-
-The command uses the `--recipe` argument to encode the proper hyperparams such that SparseML will enforce the sparsity for all layers.
-Without the proper recipes, the zeroed weights will slowly become dense as the model is trained further.
-The recipes are specific to the sparsification type, so the training command will differ based on if you are transfer learning the pruned quantized, pruned, or baseline model.
-
-1) Select the proper command to run based on the sparsification type of the model you downloaded earlier.
-   Change directory into the `yolov3` folder to use the `train.py` script.
-   - Pruned transfer learning, achieves 0.84 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
-        ```
-   - Pruned-Quantized transfer learning, achieves 0.838 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
-        ```
-     Note: Running quantization-aware training (QAT) will consume up to three times the memory as FP16 training. 
-     It is recommended to use this recipe with a batch size that fits in memory for FP16 as normal at the start, though.
-     This will keep the training times the same until the quantization portion.
-     Then it will fail with an out of memory exception, but you can use the `last.pt` checkpoint with the `--weights` argument to resume the run with a lower batch size for QAT.
-   - Baseline transfer learning, achieves 0.86 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml
-        ```
-
-    Weights and Biases is very useful for comparing across the different runs; the epochs vs mAP@0.5 graph is supplied below:
-    
-    <img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
-
-Afterward, you will have a sparse model trained on the VOC dataset almost ready for inference.
-The training command creates a `runs` directory under the `yolov3` repository directory. 
-This directory will contain the outputs from the training run, including experimental results along with the trained model:
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-## Exporting for Inference
-
-This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. 
-Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as DeepSparse.
-
-The `best.pt` file contains a checkpoint of the best weights measured on the validation set. 
-These weights can be loaded into the `train.py` and `test.py` scripts now. 
-However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-The [export.py script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-1) Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
-    ```bash
-    python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
-    ```
-    
-    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-    ```
-    |-- exp
-    |-- exp1
-    |-- weights
-    |   |-- best.pt
-    |   |-- best.onnx
-    |   `-- last.pt
-    `-- ...
-    ```
-2) Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-    The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
-    An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
-
-## Wrap-Up
-
-Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned and pruned-quantized YOLOv3 models. 
-In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and exported to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)

From 72436e1b9aef25e4dce9e64952a34f80999a5baa Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Sun, 5 Mar 2023 17:41:25 -0500
Subject: [PATCH 03/29] added old examples

---
 integrations/examples/README.md               |  20 +
 integrations/examples/dbolya-yolact/README.md | 144 +++
 .../dbolya-yolact/recipes/yolact.pruned.md    | 174 ++++
 .../recipes/yolact.pruned_quant.md            | 204 ++++
 .../dbolya-yolact/recipes/yolact.quantized.md |  74 ++
 .../sparsifying_yolact_using_recipes.md       | 226 ++++
 integrations/examples/keras/README.md         |  94 ++
 integrations/examples/keras/classification.py | 898 ++++++++++++++++
 .../keras/notebooks/classification.ipynb      | 433 ++++++++
 integrations/examples/keras/prune_resnet20.py | 241 +++++
 integrations/examples/keras/recipes/.gitkeep  |   0
 .../examples/keras/tutorials/.gitkeep         |   0
 integrations/examples/pytorch/README.md       |  99 ++
 .../pytorch/argparser_/nm_argparser_.py       | 282 +++++
 .../pytorch/argparser_/test_nmargparser.py    | 283 +++++
 integrations/examples/pytorch/export.py       | 358 +++++++
 integrations/examples/pytorch/lr_analysis.py  | 408 ++++++++
 .../pytorch/notebooks/classification.ipynb    | 366 +++++++
 .../pytorch/notebooks/detection.ipynb         | 372 +++++++
 .../sparse_quantized_transfer_learning.ipynb  | 454 ++++++++
 .../pytorch/notebooks/torchvision.ipynb       | 399 ++++++++
 .../examples/pytorch/pr_sensitivity.py        | 416 ++++++++
 .../examples/pytorch/recipes/.gitkeep         |   0
 .../classification.transfer_learn_pruned.md   |  98 ++
 ...ication.transfer_learn_pruned_quantized.md | 106 ++
 .../pytorch/recipes/mobilenet-pruned.md       | 107 ++
 .../pytorch/recipes/mobilenet-pruned_quant.md | 160 +++
 .../recipes/resnet50-imagenette-pruned.md     | 145 +++
 .../pytorch/recipes/resnet50-pruned.md        |  83 ++
 .../resnet50-pruned_quant-transfer_learn.md   | 100 ++
 .../pytorch/recipes/resnet50-pruned_quant.md  | 198 ++++
 .../pytorch/torchvision_sparsification.py     | 455 +++++++++
 integrations/examples/pytorch/train.py        | 703 +++++++++++++
 .../examples/pytorch/tutorials/.gitkeep       |   0
 ...ation_sparse_transfer_learning_tutorial.md | 138 +++
 ...parsifying_pytorch_models_using_recipes.md | 132 +++
 integrations/examples/pytorch/utils.py        | 428 ++++++++
 integrations/examples/pytorch/vision.py       |  29 +
 .../examples/rwightman-timm/README.md         | 116 +++
 .../recipes/vit_base.85.recal.config.yaml     |  83 ++
 .../rwightman-timm/setup_integration.sh       |  11 +
 integrations/examples/tensorflow_v1/README.md |  93 ++
 .../examples/tensorflow_v1/classification.py  | 965 ++++++++++++++++++
 .../notebooks/classification.ipynb            | 403 ++++++++
 .../examples/tensorflow_v1/recipes/.gitkeep   |   0
 .../examples/tensorflow_v1/tutorials/.gitkeep |   0
 .../examples/ultralytics-yolov3/README.md     | 128 +++
 .../ultralytics-yolov3/data/hyp.pruned.yaml   |  27 +
 .../data/hyp.pruned_quantized.yaml            |  27 +
 .../models/yolov3-spp.lrelu.yaml              |  52 +
 .../recipes/yolov3-spp.pruned.md              | 208 ++++
 .../recipes/yolov3-spp.pruned.short.md        | 207 ++++
 .../recipes/yolov3-spp.pruned_quantized.md    | 286 ++++++
 .../yolov3-spp.pruned_quantized.short.md      | 286 ++++++
 .../recipes/yolov3-spp.test.md                | 285 ++++++
 .../yolov3-spp.transfer_learn_pruned.md       | 169 +++
 ...ov3-spp.transfer_learn_pruned_quantized.md | 238 +++++
 .../ultralytics-yolov3/setup_integration.sh   |  10 +
 .../images/pruned-quantized-result.jpeg       | Bin 0 -> 2071890 bytes
 .../images/pruned-quantized-wandb-chart.png   | Bin 0 -> 915074 bytes
 .../images/transfer-learning-wandb-chart.png  | Bin 0 -> 493088 bytes
 .../sparsifying_yolov3_using_recipes.md       | 247 +++++
 .../yolov3_sparse_transfer_learning.md        | 217 ++++
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diff --git a/integrations/examples/README.md b/integrations/examples/README.md
new file mode 100644
index 00000000000..25f69459dcf
--- /dev/null
+++ b/integrations/examples/README.md
@@ -0,0 +1,20 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Examples
+
+This directory contains old examples of SparseML integrated with other libraries. Note that these pathways
+have not been tested recently, but we have left there here as examples of how to integrate SparseML into a new repository.
\ No newline at end of file
diff --git a/integrations/examples/dbolya-yolact/README.md b/integrations/examples/dbolya-yolact/README.md
new file mode 100644
index 00000000000..8d0be24b308
--- /dev/null
+++ b/integrations/examples/dbolya-yolact/README.md
@@ -0,0 +1,144 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML YOLACT Integration
+
+This directory combines the [SparseML](../../) recipe-driven approach with the 
+[dbolya/yolact](https://github.com/dbolya/yolact) repository.
+By integrating the training flows in the `yolact` repository with the SparseML 
+code base,
+we enable model sparsification techniques on the popular 
+[YOLACT architecture](https://arxiv.org/abs/1804.02767)
+creating smaller and faster deployable versions.
+The techniques include, but are not limited to:
+
+- Pruning
+- Quantization
+- Pruning and Quantization
+- Sparse Transfer Learning
+
+## Installation
+
+We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
+A virtual environment can be created using the following commands:
+
+```bash
+python3 -m venv venv # create a venv virtual environment
+source venv/bin/activate # activate venv
+pip install --upgrade pip # upgrade pip
+```
+To begin, install `sparseml[torchvision]>=1.1`
+
+```bash
+pip install "sparseml[torchvision]>=1.1"
+```
+
+Note: This integration requires `python>=3.7,<3.10`
+
+
+## Quick Tour
+
+### Downloading COCO
+
+The `sparseml.yolact.download` utility provides an easy to use interface to download
+the `COCO` dataset.
+
+Simply invoke:
+```bash
+sparseml.yolact.download
+```
+
+To download  a test `COCO` dataset, run `sparseml.yolact.download --test`. For more information
+on this utility append the `--help` option. (Note: by default, the dataset is downloaded to the code execution directory. )
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+`sparseml.yolact.train` adds a `--recipe` argument to the 
+[`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+Example `train` command;
+be sure to provide the correct dataset and recipe path to the utility based on your local setup:
+
+```bash
+sparseml.yolact.train --resume \
+zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--recipe ./recipes/yolact.pruned.md \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
+
+### SparseZoo
+
+| Sparsification Type | Description                                                                       | Zoo Stub                                                                     | COCO mAP@all | Size on Disk | DeepSparse Performance** |
+|---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|--------------|--------------------------|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none           | 0.288        | 170 MB       | -- img/sec               |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none       | 0.286        | 30.1 MB      | -- img/sec               |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned82_quant-none | 0.282        | 9.7 MB       | -- img/sec               |
+
+These models can also be viewed on the [SparseZoo Website](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=segmentation&page=1).
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name                                | Description                                                                                                           |
+|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [recipes](./recipes)                            | Typical recipes for sparsifying YOLACT models along with any downloaded recipes from the SparseZoo.                   |
+| [yolact](./yolact)                              | Integration repository folder used to train and sparsify YOLACT models (`setup_integration.sh` must run first).       |
+| [README.md](./README.md)                        | Readme file.                                                                                                          |
+| [tutorials](./tutorials)                        | Easy to follow sparsification tutorials for YOLACT  models.                                                            |
+
+### Exporting for Inference
+
+After sparsifying a model, the 
+`sparseml.yolact.export_onnx` utility
+converts the model into deployment formats such as [ONNX](https://onnx.ai/).
+The export process is modified such that the quantized and pruned models are 
+corrected and folded properly.
+
+For example, the following command can be run to export a trained/sparsified YOLACT
+model's checkpoint:
+
+```bash
+sparseml.yolact.export_onnx --checkpoint ./quantized-yolact/model.pth \
+    --name quantized-yolact.onnx
+```
+
+
+### DeepSparse
+
+The [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) accepts ONNX 
+formats and is engineered to significantly speed up inference on CPUs for 
+the sparsified models from this integration. [Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact) scripts can be found in the DeepSparse repository.
+
+## Citation
+```bibtex
+@inproceedings{yolact-iccv2019,
+  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  title     = {YOLACT: {Real-time} Instance Segmentation},
+  booktitle = {ICCV},
+  year      = {2019},
+}
+```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md b/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md
new file mode 100644
index 00000000000..b3435b049d2
--- /dev/null
+++ b/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md
@@ -0,0 +1,174 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Hyperparams
+num_epochs: 60
+
+# Pruning Hyperparams
+init_sparsity: 0.35
+pruning_start_target: 0
+pruning_end_target: 0.5
+pruning_update_frequency: 0.2
+mask_type: unstructured
+prune_none_target_sparsity: 0.75
+prune_low_target_sparsity: 0.83
+prune_mid_target_sparsity: 0.87
+prune_high_target_sparsity: 0.92
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0
+    end_epoch: eval(num_epochs)
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+    - backbone.layers.0.1.conv1.0.weight
+    - backbone.layers.0.1.conv2.0.weight
+    - backbone.layers.1.0.0.weight
+    - backbone.layers.1.1.conv1.0.weight
+    - backbone.layers.1.1.conv2.0.weight
+    - backbone.layers.1.2.conv1.0.weight
+    - backbone.layers.1.2.conv2.0.weight
+    - backbone._preconv.0.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_none_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.2.0.0.weight
+      - backbone.layers.2.1.conv1.0.weight
+      - backbone.layers.2.1.conv2.0.weight
+      - backbone.layers.2.2.conv1.0.weight
+      - backbone.layers.2.2.conv2.0.weight
+      - backbone.layers.2.3.conv1.0.weight
+      - backbone.layers.2.3.conv2.0.weight
+      - backbone.layers.2.4.conv1.0.weight
+      - backbone.layers.2.4.conv2.0.weight
+      - backbone.layers.2.5.conv1.0.weight
+      - backbone.layers.2.5.conv2.0.weight
+      - backbone.layers.2.6.conv1.0.weight
+      - backbone.layers.2.6.conv2.0.weight
+      - backbone.layers.2.7.conv1.0.weight
+      - backbone.layers.2.7.conv2.0.weight
+      - backbone.layers.2.8.conv1.0.weight
+      - backbone.layers.2.8.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_low_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - proto_net.0.weight
+      - proto_net.2.weight
+      - proto_net.4.weight
+      - proto_net.8.weight
+      - proto_net.10.weight
+      - fpn.lat_layers.0.weight
+      - fpn.lat_layers.1.weight
+      - fpn.lat_layers.2.weight
+      - fpn.pred_layers.0.weight
+      - fpn.pred_layers.1.weight
+      - fpn.pred_layers.2.weight
+      - fpn.downsample_layers.0.weight
+      - fpn.downsample_layers.1.weight
+      - prediction_layers.0.upfeature.0.weight
+      - prediction_layers.0.bbox_layer.weight
+      - prediction_layers.0.conf_layer.weight
+      - prediction_layers.0.mask_layer.weight
+      - semantic_seg_conv.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_mid_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.3.0.0.weight
+      - backbone.layers.3.1.conv1.0.weight
+      - backbone.layers.3.1.conv2.0.weight
+      - backbone.layers.3.2.conv1.0.weight
+      - backbone.layers.3.2.conv2.0.weight
+      - backbone.layers.3.3.conv1.0.weight
+      - backbone.layers.3.3.conv2.0.weight
+      - backbone.layers.3.4.conv1.0.weight
+      - backbone.layers.3.4.conv2.0.weight
+      - backbone.layers.3.5.conv1.0.weight
+      - backbone.layers.3.5.conv2.0.weight
+      - backbone.layers.3.6.conv1.0.weight
+      - backbone.layers.3.6.conv2.0.weight
+      - backbone.layers.3.7.conv1.0.weight
+      - backbone.layers.3.7.conv2.0.weight
+      - backbone.layers.3.8.conv1.0.weight
+      - backbone.layers.3.8.conv2.0.weight
+      - backbone.layers.4.0.0.weight
+      - backbone.layers.4.1.conv1.0.weight
+      - backbone.layers.4.1.conv2.0.weight
+      - backbone.layers.4.2.conv1.0.weight
+      - backbone.layers.4.2.conv2.0.weight
+      - backbone.layers.4.3.conv1.0.weight
+      - backbone.layers.4.3.conv2.0.weight
+      - backbone.layers.4.4.conv1.0.weight
+      - backbone.layers.4.4.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_high_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+---
+
+# YOLACT Pruned
+
+
+This recipe creates a sparse, [YOLACT](https://github.com/dbolya/yolact) model 
+that recovers to 99.37% of its baseline accuracy on the COCO dataset 
+(50.16, 46.57 mAP@0.5 baseline Vs 49.36, 46.37 mAP@0.5 for this recipe for bounding box, mask).
+Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup. 
+
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md b/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md
new file mode 100644
index 00000000000..5b2c365710c
--- /dev/null
+++ b/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md
@@ -0,0 +1,204 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Hyperparams
+num_epochs: 62
+init_lr: 0.001
+num_quantization_epochs: 2
+num_pruning_epochs: eval(num_epochs - num_quantization_epochs)
+quantization_observer_end_target: 0.5
+
+# Pruning Hyperparams
+init_sparsity: 0.2
+pruning_start_target: 0
+pruning_end_target: 0.5
+pruning_update_frequency: 0.2
+mask_type: [1, 4]
+prune_none_target_sparsity: 0.6
+prune_low_target_sparsity: 0.7
+prune_mid_target_sparsity: 0.8
+prune_high_target_sparsity: 0.85
+
+# quantization hyperparams
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0
+    end_epoch: eval(num_epochs)
+
+  # pruning
+  - !SetWeightDecayModifier
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    weight_decay: 0.0005
+
+  - !LearningRateFunctionModifier
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    lr_func: linear
+    init_lr: eval(init_lr)
+    final_lr: eval(0.01 * init_lr)
+  # quantization
+  - !SetWeightDecayModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    weight_decay: 0.0
+
+  - !LearningRateFunctionModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    end_epoch: eval(num_epochs)
+    lr_func: linear
+    init_lr: eval(0.01 * init_lr)
+    final_lr: eval(0.001 * init_lr)
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+    - backbone.layers.0.1.conv1.0.weight
+    - backbone.layers.0.1.conv2.0.weight
+    - backbone.layers.1.0.0.weight
+    - backbone.layers.1.1.conv1.0.weight
+    - backbone.layers.1.1.conv2.0.weight
+    - backbone.layers.1.2.conv1.0.weight
+    - backbone.layers.1.2.conv2.0.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_none_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.2.0.0.weight
+      - backbone.layers.2.1.conv1.0.weight
+      - backbone.layers.2.1.conv2.0.weight
+      - backbone.layers.2.2.conv1.0.weight
+      - backbone.layers.2.2.conv2.0.weight
+      - backbone.layers.2.3.conv1.0.weight
+      - backbone.layers.2.3.conv2.0.weight
+      - backbone.layers.2.4.conv1.0.weight
+      - backbone.layers.2.4.conv2.0.weight
+      - backbone.layers.2.5.conv1.0.weight
+      - backbone.layers.2.5.conv2.0.weight
+      - backbone.layers.2.6.conv1.0.weight
+      - backbone.layers.2.6.conv2.0.weight
+      - backbone.layers.2.7.conv1.0.weight
+      - backbone.layers.2.7.conv2.0.weight
+      - backbone.layers.2.8.conv1.0.weight
+      - backbone.layers.2.8.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_low_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - proto_net.0.weight
+      - proto_net.2.weight
+      - proto_net.4.weight
+      - proto_net.8.weight
+      - proto_net.10.weight
+      - fpn.lat_layers.0.weight
+      - fpn.lat_layers.1.weight
+      - fpn.lat_layers.2.weight
+      - fpn.pred_layers.0.weight
+      - fpn.pred_layers.1.weight
+      - fpn.pred_layers.2.weight
+      - fpn.downsample_layers.0.weight
+      - fpn.downsample_layers.1.weight
+      - prediction_layers.0.upfeature.0.weight
+      - prediction_layers.0.bbox_layer.weight
+      - prediction_layers.0.conf_layer.weight
+      - prediction_layers.0.mask_layer.weight
+      - semantic_seg_conv.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_mid_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.3.0.0.weight
+      - backbone.layers.3.1.conv1.0.weight
+      - backbone.layers.3.1.conv2.0.weight
+      - backbone.layers.3.2.conv1.0.weight
+      - backbone.layers.3.2.conv2.0.weight
+      - backbone.layers.3.3.conv1.0.weight
+      - backbone.layers.3.3.conv2.0.weight
+      - backbone.layers.3.4.conv1.0.weight
+      - backbone.layers.3.4.conv2.0.weight
+      - backbone.layers.3.5.conv1.0.weight
+      - backbone.layers.3.5.conv2.0.weight
+      - backbone.layers.3.6.conv1.0.weight
+      - backbone.layers.3.6.conv2.0.weight
+      - backbone.layers.3.7.conv1.0.weight
+      - backbone.layers.3.7.conv2.0.weight
+      - backbone.layers.3.8.conv1.0.weight
+      - backbone.layers.3.8.conv2.0.weight
+      - backbone.layers.4.0.0.weight
+      - backbone.layers.4.1.conv1.0.weight
+      - backbone.layers.4.1.conv2.0.weight
+      - backbone.layers.4.2.conv1.0.weight
+      - backbone.layers.4.2.conv2.0.weight
+      - backbone.layers.4.3.conv1.0.weight
+      - backbone.layers.4.3.conv2.0.weight
+      - backbone.layers.4.4.conv1.0.weight
+      - backbone.layers.4.4.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_high_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    disable_quantization_observer_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
+    freeze_bn_stats_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
+---
+
+# YOLACT Pruned Quantized
+
+This recipe creates a sparse quantized, [YOLACT](https://github.com/dbolya/yolact) model that achieves about 
+98% recovery on the COCO dataset when compared against baseline ( 30.61, 28.80 mAP@all baseline Vs 29.82, 28.26 mAP@all for this recipe for bounding box, mask).
+Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup.
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned_quant.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md b/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md
new file mode 100644
index 00000000000..f992d0420c1
--- /dev/null
+++ b/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md
@@ -0,0 +1,74 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+num_epochs: 2
+quantization_start_target: 0
+quantization_end_target: 1
+quantization_observer_end_target: 0.5
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !SetWeightDecayModifier
+    start_epoch: eval(quantization_start_target * num_epochs)
+    weight_decay: 0.0
+
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: eval(quantization_end_target * num_epochs)
+    lr_func: linear
+    init_lr: 0.00001
+    final_lr: 0.000001
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(quantization_start_target * num_epochs)
+    disable_quantization_observer_epoch: eval(quantization_observer_end_target * num_epochs)
+    freeze_bn_stats_epoch: eval(quantization_observer_end_target * num_epochs)
+---
+
+# YOLACT Quantized
+
+This recipe quantizes a [YOLACT](https://github.com/dbolya/yolact) model.
+Training was done using 4 GPUs with a total batch size of 64 using the 
+[SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup.
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.quantized.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
\ No newline at end of file
diff --git a/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md b/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
new file mode 100644
index 00000000000..daef9205b9c
--- /dev/null
+++ b/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
@@ -0,0 +1,226 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying YOLACT Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters 
+and instructions needed to create highly accurate pruned and pruned-quantized [YOLACT](https://arxiv.org/abs/1904.02689) segmentation models.
+
+## Overview
+
+Neural Magic's ML team creates recipes that allow anyone to plug in their data and leverage SparseML's recipe-driven 
+approach on top of [YOLACT](https://github.com/dbolya/yolact) training pipelines.
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and 
+quantization, among others. This sparsification process results in many benefits for deployment environments, 
+including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the 
+complicated process and number of hyper-parameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+
+* Creating a pre-trained model to establish a baseline. You will set up your data and then train the model.
+* Applying a recipe to select the trade-off between the amount of recovery to the baseline training performance with 
+the amount of sparsification for inference performance.
+* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) 
+through a compression algorithm to reduce its deployment size and run it in an inference engine such as 
+[DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+The examples listed in this tutorial are all performed on the COCO dataset.
+
+Before diving in, be sure to go through the setup as listed out in the [README](../README.md) for this integration.
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Creating a Pre-trained Model
+
+Before applying one of the recipes, you must first create the pre-trained model to sparsify further. 
+The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. 
+Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
+ 
+Creating a pre-trained model involves three steps: 
+1) Setting up the data.
+2) Fetching a model backbone. 
+3) Training the model.
+
+If training with COCO, skip steps 2 and 3 and use the following baseline SparseZoo stub,
+`zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none` with the `--resume` argument
+in the [`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py)
+
+**Note**: If using your custom data, the [YOLACT](https://github.com/dbolya/yolact) repo mentions a post for [training custom data](https://github.com/dbolya/yolact/issues/70#issuecomment-504283008). 
+Otherwise, setup scripts for [COCO](https://cocodataset.org/#home) can be found under the [yolact/data/scripts path](https://github.com/neuralmagic/yolact/tree/master/data/scripts).
+
+### Setting Up the Data
+
+1. For this tutorial, run the COCO setup script with the following command from the root of the `yolact` repository:
+    ```bash
+    sparseml.yolact.download
+    ```
+   
+Based on your internet connection, downloading and validation of the COCO dataset will take around 10 minutes to complete.
+    The script downloads the COCO dataset into a `coco` folder under the data directory.
+Once completed, the data is ready for training with the folder structure in the following state (only directories are shown for brevity):
+```
+    └─ data
+       └─ coco
+          ├── annotations
+          └── images
+ ```
+   
+ You are ready to train the model.
+
+### Downloading Model Backbone
+
+1. Training YOLACT from scratch requires a pretrained-backbone model, 
+   currently SparseML supports training with `DarkNet-53` backbone. 
+   - Download ImageNet-pre-trained [`Darknet-53` backbone](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing)
+     and put it in `./weights` directory. The directory structure should look like the following:
+````
+    └── Project_directory
+        ├── data
+        │   └─ coco
+        │      ├── annotations
+        │      └── images
+        │   
+        └── weights
+            └── darknet53.pth
+````
+
+2) Run the following command to kickstart training
+```bash
+sparseml.yolact.train --resume \
+./weights/darknet53.pth \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images \
+--backbone
+```
+The weights are stored in the `./weights` directory by default and use the `<config>_<epoch>_<iter>.pth` naming 
+convention
+
+3) Validate that the training command was completed successfully by checking under the `./weights` directory for the trained weights.
+   Upon success, the resulting directory structure should look like the following (a few directories are missing content for brevity):
+```
+└── Project_Directory
+    ├── data
+    │    └── coco
+    │        ├── annotations
+    │        └── images
+    │   
+    └── weights
+        ├── darknet53.pth
+        └── yolact_darknet53_54_800000.pth
+ ```
+
+You are ready to use the weights at `./weights/yolact_darknet53_54_800000.pth` with the Neural Magic recipes to create a sparsified model.
+You can also download this baseline, pre-trained checkpoint directly from the [SparseZoo UI](https://sparsezoo.neuralmagic.com/models/cv%2Fsegmentation%2Fyolact-darknet53%2Fpytorch%2Fdbolya%2Fcoco%2Fbase-none),
+or pass its model stub directly to the `--resume` argument while invoking the training script.
+## Applying a Recipe
+
+In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+The [`recipes` folder](../recipes) contains multiple files, each offering certain advantages over others. 
+The table below compares these tradeoffs and shows how to run them on the COCO dataset.
+1. Review this table, which lists recipes, commands, and results.
+
+| Sparsification Type |                                    Description                                    | COCO mAP@all | Size on Disk | DeepSparse Performance** |                                        Commands                                        |
+|:-------------------:|:---------------------------------------------------------------------------------:|:------------:|:------------:|:------------------------:|:--------------------------------------------------------------------------------------:|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.288        | 170 MB       | 29.7 img/sec               | `python train.py`                                                                      |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.286        | 30.1 MB      | 61.6 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned.md`       |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.282        | 9.7 MB       | 144.4 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned_quant.md` |
+
+   \*\* DeepSparse Performance measured on an AWS c5.12xlarge instance with 24 cores, batch size 64, and 550x550 input with version 0.12.0 of the DeepSparse Engine i.e. `deepsparse.benchmark --batch_size 64 --scenario sync [model_path]`
+   
+2. Select a recipe to use on top of the pre-trained model you created.
+
+    - Check your CPU hardware support for quantized networks (VNNI instruction set) using the DeepSparse API:
+      ```bash
+      deepsparse.check_hardware
+      ```
+    - If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using the  `pruned` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
+    - If your hardware does support quantized networks, we recommend using the `pruned quantized` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
+    - When running quantized models, the memory footprint for training will significantly increase (roughly 3x). It is recommended to train at a high batch size at first. This will fail with an out-of-memory exception once quantization starts. Once this happens, use the weights from that run to resume training with lower batch size.
+
+3. To begin applying one of the recipes, use the `--recipe` argument within the YOLACT [train script](https://github.com/neuralmagic/yolact/blob/master/train.py).
+   The recipe argument is combined with our previous training command and COCO pre-trained weights to run the recipes over the model. For example, a command for pruning YOLACT would look like this:
+```bash
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--save_folder=./pruned \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
+After applying a recipe, you are ready to export for inference.
+
+## Exporting for Inference
+
+This step loads a checkpoint file along with the recipe used if any and converts it into the more common inference formats. 
+Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+When you applied a recipe in the previous step, the sparsification run created a new `./pruned` directory under the `yolact` directory:
+
+```
+└── Project_directory
+    ├── data
+    │   ├── coco
+    │   │   ├── annotations
+    │   │   └── images
+    │   └── scripts
+    ├── pruned
+    │    └── yolact_darknet53_3_29316.pth 
+    └── weights
+        └── yolact_darknet53_54_800000.pth
+```
+
+These weights under `./pruned` can be loaded into the `sparseml.yolact.train` and `sparseml.yolact.export_onnx` utilities now. 
+However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+[`sparseml.yolact.export_onnx`](https://github.com/neuralmagic/yolact/blob/master/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+
+1. Enter the following command to load the PyTorch graph, convert to ONNX, and correct any misformatted pieces of the graph for the pruned and quantized models.
+
+    ```bash
+    sparseml.yolact.export_onnx --checkpoint PATH_TO_SPARSIFIED_WEIGHTS
+    ```
+    
+    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+
+2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+   The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
+   An example for benchmarking and deploying YOLACT models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLACT models for image segmentation tasks. 
+In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported it to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/blob/main/src/deepsparse/yolact/README.md) for an example for benchmarking and deploying YOLACT models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Citation
+
+```bibtex
+@inproceedings{yolact-iccv2019,
+  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  title     = {YOLACT: {Real-time} Instance Segmentation},
+  booktitle = {ICCV},
+  year      = {2019},
+}
+```
diff --git a/integrations/examples/keras/README.md b/integrations/examples/keras/README.md
new file mode 100644
index 00000000000..c3ffdc4d9b3
--- /dev/null
+++ b/integrations/examples/keras/README.md
@@ -0,0 +1,94 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML Keras Integration
+
+This directory combines the SparseML recipe-driven approach with standard Keras training flows for models within
+[Keras Applications](https://keras.io/api/applications/) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom Keras training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Coming soon!
+
+## Tutorials
+
+- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/keras/notebooks/classification.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[tf_keras]`
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=keras) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=keras) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| notebooks            | Jupyter notebooks to walk through sparsifiation within standard Keras training flows                                  |
+| recipes              | Typical recipes for sparsifying Keras models along with any downloaded recipes from the SparseZoo.                    |
+| tutorials            | Tutorial walkthroughs for how to sparsify Keras models using recipes.                                                 |
+| classification.py    | Utility training script for sparsifying classification models in Keras (loads SparseML models).                       |
+| prune_resnet20.py    | Simple example walking through pruning a ResNet-20 model on the Cifar dataset.                                        |
+| README.md            | Readme file.                                                                                                          |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/keras/classification.py b/integrations/examples/keras/classification.py
new file mode 100644
index 00000000000..35644f01e36
--- /dev/null
+++ b/integrations/examples/keras/classification.py
@@ -0,0 +1,898 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Perform tasks on image classification models in Keras including:
+* Model training/pruning
+* Model evaluation
+* ONNX export
+
+
+##########
+train command help:
+usage: classification.py train [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                               [--recipe-path RECIPE_PATH]
+                               [--sparse-transfer-learn] [--eval-mode]
+                               --train-batch-size TRAIN_BATCH_SIZE
+                               --test-batch-size TEST_BATCH_SIZE
+                               [--logs-dir LOGS_DIR]
+                               [--save-best-after SAVE_BEST_AFTER]
+                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
+                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
+Train and/or prune an image classification model
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --model-kwargs MODEL_KWARGS
+                        key word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        Key word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --transfer-class-type TRANSFER_CLASS_TYPE
+                        Type of the target class for transfer ("single" for softmax
+                        function or "multi" for sigmoid function); takes effects only
+                        if the specified dataset is different from the pretrained
+                        dataset, implying a transfer learning usecase
+  --transfer-top-layer TRANSFER_TOP_LAYER
+                        Name of the layer which a custom dense layer will be put on;
+                        None by default to imply the layer right under the top of the
+                        original model will be used (in other words, layer[-2])
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --train-dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for train dataset loading
+  --train-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for train dataset loading
+  --train-prefetch-buffer-size PREFETCH_BUFFER_SIZE
+                        Prefetch buffer size for train dataset loading
+  --val-dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for val dataset loading
+  --val-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for val dataset loading
+  --val-prefetch-buffer-size PREFETCH_BUFFER_SIZE
+                        Prefetch buffer size for val dataset loading
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. If set to
+                        'transfer_learning', then will create a schedule to
+                        enable sparse transfer learning. Recipe from SparseZoo
+                        could be specified using model stub starting with "zoo:"
+  --sparse-transfer-learn
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode           Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
+                        epochs to save checkpoints at
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+################
+Evaluate command help:
+usage: classification.py evaluate [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               --test-batch-size TEST_BATCH_SIZE
+Evaluate an image classification model
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while evaluating
+################
+Export command help:
+usage: classification.py export [-h] --arch-key ARCH_KEY
+                                [--pretrained PRETRAINED]
+                                [--pretrained-dataset PRETRAINED_DATASET]
+                                [--checkpoint-path CHECKPOINT_PATH]
+                                --dataset-path DATASET_PATH
+                                [--dataset-kwargs DATASET_KWARGS]
+                                [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                                [--num-samples NUM_SAMPLES]
+                                [--onnx-opset OPSET]
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        key word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --num-samples NUM_SAMPLES
+                        Number of samples to generate, default to 100
+  --onnx-opset OPSET
+                        The ONNX opset to export to, default to 11
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --export-debug-mode EXPORT_DEBUG
+                        The debug mode for ONNX export (passed into keras2onnx).
+                        Default to False.
+
+
+############
+EXAMPLES
+############
+
+#############
+Example command for fine-tuning a ResNet50 model from Keras Application, pretrained on
+Imagenet, for the Imagenette dataset. The recipe could be used to define learning rate
+schedule. Note that there're no pruning performed from this experiment.
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --pretrained-dataset imagenet \
+     --dataset imagenette \
+     --dataset-path <DATASET_DIR> \
+     --train-batch-size 64 \
+     --recipe-path <RECIPE_PATH>
+############
+Example command for pruning a model with a recipe. Assuming that a ResNet50 model
+from Keras Application was fine-tuned for the Imagenette dataset. The following
+command specifies the model checkpoint after fine-tuning, and a recipe that could
+be used next to prune its weights:
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --train-batch-size 64 \
+     --recipe-path <RECIPE_PATH>
+
+In the above command, specifying "zoo" for --checkpoint-path forces loading the
+base model from SparseZoo. The following command prunes a ResNet50 from Keras
+Applications fine-tuned for Imagenette dataset, using a recipe from SparseZoo:
+
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path zoo \
+     --pretrained-dataset imagenette \
+     --dataset imagenette \
+     --dataset-path <DATASET_DIR> \
+     --train-batch-size 64 \
+     --recipe-path zoo:cv/classification/resnet_v1-50/keras/\
+        keras.applications/imagenette/pruned-moderate?recipe_type=original
+############
+Example command for loading a ResNet50 model from a checkpoint, then
+evaluate it on the validation of the Imagenette dataset.
+python scripts/keras_classification.py evaluate \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --test-batch-size 64
+############
+Example command for loading a ResNet50 model from a checkpoint, then export it
+into ONNX format. It also generates a number of inputs, outputs and labels.
+python scripts/keras_classification.py export \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --num-samples <NUM_SAMPLES>
+     --save-dir <EXPORT_DIR>
+"""
+
+import argparse
+import inspect
+import json
+import math
+import os
+from typing import Optional, Tuple
+
+import numpy
+import tensorflow
+
+from sparseml import get_main_logger
+from sparseml.keras.datasets import Dataset, DatasetRegistry
+from sparseml.keras.models import ModelRegistry
+from sparseml.keras.optim import ScheduledModifierManager, remove_pruning_masks
+from sparseml.keras.utils import (
+    LossesAndMetricsLoggingCallback,
+    ModelExporter,
+    TensorBoardLogger,
+    keras,
+)
+from sparseml.utils import create_dirs
+
+
+LOGGER = get_main_logger()
+TRAIN_COMMAND = "train"
+EVAL_COMMAND = "evaluate"
+EXPORT_COMMAND = "export"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run tasks on classification models and datasets "
+        "using the sparseml API"
+    )
+
+    subparsers = parser.add_subparsers(dest="command")
+    train_parser = subparsers.add_parser(
+        TRAIN_COMMAND,
+        description="Train and/or prune an image classification model",
+    )
+    eval_parser = subparsers.add_parser(
+        EVAL_COMMAND,
+        description="Evaluate an image classification model on a dataset",
+    )
+    export_parser = subparsers.add_parser(
+        EXPORT_COMMAND,
+        description="Export a model to onnx as well as "
+        "store sample inputs, outputs, and labels",
+    )
+
+    parsers = [
+        train_parser,
+        eval_parser,
+        export_parser,
+    ]
+    for par in parsers:
+        # general arguments
+        # model args
+        par.add_argument(
+            "--arch-key",
+            type=str,
+            required=True,
+            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception with the list)",
+        )
+        par.add_argument(
+            "--pretrained",
+            type=str,
+            default=True,
+            help="The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]",
+        )
+        par.add_argument(
+            "--pretrained-dataset",
+            type=str,
+            default=None,
+            help="The dataset to load pretrained weights for if pretrained is set. "
+            "Default is None which will load the default dataset for the architecture."
+            " Ex can be set to imagenet, cifar10, etc",
+        )
+        par.add_argument(
+            "--checkpoint-path",
+            type=str,
+            default=None,
+            help="A path to a previous checkpoint to load the state from and "
+            "resume the state for. If provided, pretrained will be ignored",
+        )
+        par.add_argument(
+            "--model-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to model constructor, should be "
+            " given as a json object",
+        )
+
+        # dataset args
+        par.add_argument(
+            "--dataset",
+            type=str,
+            required=True,
+            help="The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or loadable by a "
+            "dataset in sparseml.keras.datasets",
+        )
+        par.add_argument(
+            "--dataset-path",
+            type=str,
+            required=True,
+            help="The root path to where the dataset is stored",
+        )
+        par.add_argument(
+            "--dataset-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to dataset constructor, should be "
+            " given as a json object",
+        )
+
+        # logging and saving
+        par.add_argument(
+            "--model-tag",
+            type=str,
+            default=None,
+            help="A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        )
+        par.add_argument(
+            "--save-dir",
+            type=str,
+            default="keras_classification",
+            help="The path to the directory for saving results",
+        )
+
+    # task specific arguments: training
+    train_parser.add_argument(
+        "--dataset-parallel-calls",
+        type=int,
+        default=4,
+        help="the number of parallel workers for dataset loading",
+    )
+    train_parser.add_argument(
+        "--train-shuffle-buffer-size",
+        type=int,
+        default=None,
+        help="Shuffle buffer size for dataset loading",
+    )
+    train_parser.add_argument(
+        "--train-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for train dataset loading",
+    )
+    train_parser.add_argument(
+        "--test-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for test dataset loading",
+    )
+    train_parser.add_argument(
+        "--recipe-path",
+        type=str,
+        default=None,
+        help="The path to the yaml file containing the modifiers and "
+        "schedule to apply them with. If set to 'transfer_learning', "
+        "then will create a schedule to enable sparse transfer learning",
+    )
+    train_parser.add_argument(
+        "--transfer-class-type",
+        type=str,
+        default="single",
+        help="Type of target function: 'single' for softmax, 'multi' for sigmoid "
+        "or 'linear' for linear function. This option takes effects only when the "
+        "specified dataset is different from the pretrained dataset, implying a "
+        "transfer learning usecase.",
+    )
+    train_parser.add_argument(
+        "--transfer-top-layer",
+        type=str,
+        default=None,
+        help="Name of the layer which a custom dense layer will be put on. Default "
+        "to None to imply the layer right under the top of the original model "
+        "will be used (in other words, layer[-2])",
+    )
+    train_parser.add_argument(
+        "--train-batch-size",
+        type=int,
+        required=True,
+        help="The batch size to use while training",
+    )
+    train_parser.add_argument(
+        "--test-batch-size",
+        type=int,
+        required=False,
+        help="The batch size to use while testing; default to the train " "batch size",
+    )
+    train_parser.add_argument(
+        "--log-dir",
+        type=str,
+        default=os.path.join("keras_classification_train", "tensorboard-logs"),
+        help="The path to the directory for saving logs",
+    )
+    train_parser.add_argument(
+        "--log-epoch",
+        type=bool,
+        default=True,
+        help="Whether logging should be performed at the end of each epoch",
+    )
+    train_parser.add_argument(
+        "--log-batch",
+        type=bool,
+        default=False,
+        help="Whether logging should be performed at the end of each training " "batch",
+    )
+    train_parser.add_argument(
+        "--log-steps",
+        type=int,
+        default=-1,
+        help="Whether logging should be performed after every specified number "
+        "of steps",
+    )
+    train_parser.add_argument(
+        "--save-best-only",
+        type=bool,
+        default=True,
+        help="Save model only with better monitored metric",
+    )
+    train_parser.add_argument(
+        "--optim",
+        type=str,
+        default="SGD",
+        help="The optimizer type to use, e.g., 'Adam', 'SGD' etc",
+    )
+    train_parser.add_argument(
+        "--optim-args",
+        type=json.loads,
+        default={"momentum": 0.9, "nesterov": True},
+        # default={},
+        help="Additional args to be passed to the optimizer passed in"
+        " as a json object",
+    )
+    train_parser.add_argument(
+        "--run-eagerly",
+        type=bool,
+        default=True,
+        help="Run training in eager execution mode",
+    )
+
+    # task specific arguments: evaluation
+    eval_parser.add_argument(
+        "--test-batch-size",
+        type=int,
+        required=False,
+        help="The batch size to use while testing; default to the train " "batch size",
+    )
+    eval_parser.add_argument(
+        "--dataset-parallel-calls",
+        type=int,
+        default=4,
+        help="the number of parallel workers for dataset loading",
+    )
+    eval_parser.add_argument(
+        "--test-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for test dataset loading",
+    )
+
+    # task specific arguments: export
+    export_parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=100,
+        help="The number of samples to export along with the model onnx "
+        "and pth files (sample inputs and labels as well as the outputs "
+        "from model execution)",
+    )
+    export_parser.add_argument(
+        "--onnx-opset",
+        type=int,
+        default=11,
+        help="The onnx opset to use for export. Default is 11",
+    )
+    export_parser.add_argument(
+        "--export-debug-mode",
+        type=bool,
+        default=False,
+        help="The debug mode for ONNX export (passed into keras2onnx). "
+        "Default to False.",
+    )
+
+    return parser.parse_args()
+
+
+def setup_save_and_log_dirs(args) -> Tuple[str, Optional[str]]:
+    # Saving dir setup
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+    if not args.model_tag:
+        model_tag = "{}@{}".format(args.arch_key, args.dataset)
+        model_id = model_tag
+        model_inc = 0
+
+        while os.path.exists(os.path.join(save_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+    LOGGER.info("Model directory is set to {}".format(save_dir))
+
+    # log dir setup
+    log_dir = (
+        os.path.abspath(os.path.expanduser(args.log_dir))
+        if args.command == TRAIN_COMMAND
+        else None
+    )
+    if args.command == TRAIN_COMMAND:
+        log_dir = os.path.join(log_dir, model_id)
+        create_dirs(log_dir)
+        LOGGER.info("Logging directory is set to {}".format(log_dir))
+    else:
+        log_dir = None
+    return save_dir, log_dir
+
+
+def create_dataset(
+    args, train: bool, image_size: Tuple[int, int]
+) -> Tuple[Dataset, int]:
+    kwargs = args.dataset_kwargs
+    dataset = DatasetRegistry.create(
+        args.dataset,
+        root=args.dataset_path,
+        train=train,
+        image_size=image_size,
+        **kwargs,
+    )
+    LOGGER.info(
+        "created {} dataset: {}, images to resize to {}".format(
+            "train" if train else "val", dataset, image_size
+        )
+    )
+
+    # get num_classes
+    if args.dataset == "imagefolder":
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+
+    return dataset, num_classes
+
+
+def build_dataset(
+    args, dataset: Dataset, train: bool = True
+) -> tensorflow.data.Dataset:
+    test_batch_size = (
+        args.test_batch_size if args.test_batch_size else args.train_batch_size
+    )
+    batch_size = args.train_batch_size if train else test_batch_size
+    if train:
+        shuffle_buffer_size = (
+            args.train_shuffle_buffer_size
+            if args.train_shuffle_buffer_size
+            else dataset.num_images
+        )
+    else:
+        shuffle_buffer_size = None
+
+    if train:
+        prefetch_buffer_size = (
+            args.train_prefetch_buffer_size
+            if args.train_prefetch_buffer_size
+            else batch_size * 8
+        )
+    else:
+        prefetch_buffer_size = (
+            args.test_prefetch_buffer_size
+            if args.test_prefetch_buffer_size
+            else batch_size * 8
+        )
+    built_dataset = dataset.build(
+        batch_size,
+        repeat_count=1,
+        shuffle_buffer_size=shuffle_buffer_size,
+        prefetch_buffer_size=prefetch_buffer_size,
+        num_parallel_calls=args.dataset_parallel_calls,
+    )
+    return built_dataset
+
+
+def create_model(args, input_shape, num_classes):
+    kwargs = args.model_kwargs
+
+    if args.checkpoint_path == "zoo":
+        zoo_model = ModelRegistry.create_zoo_model(
+            args.arch_key, args.pretrained, args.pretrained_dataset
+        )
+        model_file_paths = zoo_model.download_framework_files(extensions=[".h5"])
+        if not model_file_paths:
+            model_file_paths = zoo_model.download_framework_files(extensions=[".tf"])
+        if not model_file_paths:
+            raise RuntimeError("Error downloading model from SparseZoo")
+        model_file_path = model_file_paths[0]
+        model = keras.models.load_model(model_file_path)
+    else:
+        model = ModelRegistry.create(
+            args.arch_key,
+            args.pretrained,
+            args.checkpoint_path,
+            args.pretrained_dataset,
+            input_shape=input_shape,
+            **kwargs,
+        )
+
+    if (
+        args.pretrained_dataset is not None
+        and args.dataset is not None
+        and args.pretrained_dataset != args.dataset
+    ):
+        # Set up the model for fine-tuning
+        # The trainablility of model's parameters should be controlled
+        # through recipe; we therefore do not set "model.trainable" to False here.
+        if args.transfer_class_type == "single":
+            activation = "softmax"
+        elif args.transfer_class_type == "multi":
+            activation = "sigmoid"
+        elif args.transfer_class_type == "linear":
+            activation = None
+        else:
+            raise ValueError(
+                "Unknown transfer_class_type; expected: 'single', 'softmax' or 'linear'"
+            )
+        if args.transfer_top_layer is not None:
+            transfer_top_layer = None
+            for i in list(range(len(model.layers)))[::-1]:
+                if model.layers[i].name == args.transfer_top_layer:
+                    transfer_top_layer = args.transfer_top_layer
+                    break
+            if transfer_top_layer is None:
+                raise ValueError(
+                    "Top layer for transfer learning {} not found".format(
+                        args.transfer_top_layer
+                    )
+                )
+        else:
+            transfer_top_layer = model.layers[-2]
+        new_output = keras.layers.Dense(num_classes, activation=activation)(
+            transfer_top_layer.output
+        )
+        model = keras.Model(model.input, new_output)
+
+    return model
+
+
+def create_optimizer(args):
+    optim_const = {}
+    for opt_name in dir(keras.optimizers):
+        opt = getattr(keras.optimizers, opt_name)
+        if inspect.isclass(opt) and opt_name != "Optimizer":
+            optim_const[opt_name] = opt
+    if args.optim not in optim_const:
+        raise ValueError(
+            "Unknown optimizer {}. Supported: {}".format(
+                args.optim, list(optim_const.keys())
+            )
+        )
+    optimizer = optim_const[args.optim](**args.optim_args)
+    return optimizer
+
+
+def get_logging_update_freq(args):
+    if args.log_steps > 0:
+        update_freq = args.log_steps
+    elif args.log_batch:
+        update_freq = "batch"
+    elif args.log_epoch:
+        update_freq = "epoch"
+    else:
+        update_freq = None
+    return update_freq
+
+
+def handle_train_command(args):
+    save_dir, log_dir = setup_save_and_log_dirs(args)
+
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    # Load datasets
+    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
+    num_train_images = train_dataset.num_images
+    train_dataset = build_dataset(args, train_dataset, train=True)
+
+    val_dataset, _ = create_dataset(args, train=False, image_size=image_size)
+    val_dataset = build_dataset(args, val_dataset, train=False)
+
+    # Create model
+    model = create_model(args, input_shape, num_classes=num_classes)
+
+    # Create optimizer
+    optimizer = create_optimizer(args)
+
+    # Logging
+    if log_dir:
+        update_freq = get_logging_update_freq(args)
+        if update_freq is None:
+            raise ValueError(
+                "Logging requires update frequency to take effect; use either "
+                "'log-epoch', 'log-batch' or 'log-steps' option."
+            )
+        loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
+    else:
+        loggers = []
+
+    # Model saving
+    checkpoint_filepath = os.path.join(
+        save_dir, "model.{epoch:02d}-{val_accuracy:.2f}.tf"
+    )
+    checkpoint_callback = keras.callbacks.ModelCheckpoint(
+        filepath=checkpoint_filepath,
+        save_weights_only=False,
+        monitor="val_accuracy",
+        mode="max",
+        save_best_only=True,
+        verbose=1,
+    )
+
+    # Manager
+    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
+
+    # Enhance model
+    steps_per_epoch = math.ceil(num_train_images / args.train_batch_size)
+    model, optimizer, callbacks = manager.modify(
+        model, optimizer, steps_per_epoch, loggers=loggers
+    )
+    if loggers:
+        callbacks.append(LossesAndMetricsLoggingCallback(loggers))
+    callbacks.append(checkpoint_callback)
+
+    model.compile(
+        loss=keras.losses.categorical_crossentropy,
+        optimizer=optimizer,
+        metrics=["accuracy"],
+        run_eagerly=args.run_eagerly,
+    )
+    model.fit(
+        train_dataset,
+        epochs=manager.max_epochs,
+        callbacks=callbacks,
+        validation_data=val_dataset,
+    )
+
+
+def handle_eval_command(args):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    # Load dataset
+    val_dataset, num_classes = create_dataset(args, train=False, image_size=image_size)
+    val_dataset = build_dataset(args, val_dataset, train=False)
+
+    # Create model
+    model = create_model(args, input_shape, num_classes=num_classes)
+
+    # Evaluate
+    result = model.evaluate(val_dataset)
+    print(dict(zip(model.metrics_names, result)))
+
+
+def handle_export_command(args):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
+    train_dataset = train_dataset.build(args.num_samples)
+
+    model = create_model(args, input_shape, num_classes=num_classes)
+    model = remove_pruning_masks(model)
+    exporter = ModelExporter(model, args.save_dir)
+    exporter.export_onnx(opset=args.onnx_opset, debug_mode=args.export_debug_mode)
+
+    for img_batch, label_batch in train_dataset.take(1):
+        output_batch = model(img_batch)
+        numpy.save(os.path.join(args.save_dir, "inputs.npy"), img_batch)
+        numpy.save(os.path.join(args.save_dir, "outputs.npy"), output_batch)
+        numpy.save(os.path.join(args.save_dir, "labels.npy"), label_batch)
+
+
+def main(args):
+    if args.command == TRAIN_COMMAND:
+        handle_train_command(args)
+    elif args.command == EVAL_COMMAND:
+        handle_eval_command(args)
+    elif args.command == EXPORT_COMMAND:
+        handle_export_command(args)
+    else:
+        raise ValueError("Unknown command passed in")
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+    main(args_)
diff --git a/integrations/examples/keras/notebooks/classification.ipynb b/integrations/examples/keras/notebooks/classification.ipynb
new file mode 100644
index 00000000000..f08a5b0ab50
--- /dev/null
+++ b/integrations/examples/keras/notebooks/classification.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021 Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Keras Classification Model Pruning Using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Inference Engine. You will:\n",
+    "- Set up the model and dataset\n",
+    "- Integrate the Keras training flow with SparseML\n",
+    "- Prune the model using the Keras+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your Keras training flow.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo;\n",
+    "* Tensorflow >=2.1, which includes Keras and TensorBoard;\n",
+    "* keras2onnx.\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "In this notebook, you will prune a simple convolution neural network model trained on the MNIST dataset. The pretrained model's architecture and weights are downloaded from the SparseZoo model repo. The dataset is downloaded directly from  Keras datasets library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell defines a procedure to download a model from the SparseZoo; additionally, for convenience it also returns the path to an optimization recipe. You construct a Keras model instance from the pretrained to prune in a later step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from sparseml.keras.utils import keras\n",
+    "from sparsezoo import Model\n",
+    "\n",
+    "# Root directory for the notebook artifacts\n",
+    "root_dir = \"./notebooks/keras\"\n",
+    "\n",
+    "def download_model_and_recipe(root_dir: str):\n",
+    "    \"\"\"\n",
+    "    Download pretrained model and a pruning recipe\n",
+    "    \"\"\"\n",
+    "    model_dir = os.path.join(root_dir, \"mnist\")\n",
+    "    zoo_model = Model(model_dir)\n",
+    "\n",
+    "\n",
+    "    model_file_path = zoo_model.training.default.get_file(\"model.h5\").path\n",
+    "    if not os.path.exists(model_file_path) or not model_file_path.endswith(\".h5\"):\n",
+    "        raise RuntimeError(\"Model file not found: {}\".format(model_file_path))\n",
+    "    recipe_file_path = zoo_model.recipes.default.path\n",
+    "    if not os.path.exists(recipe_file_path):\n",
+    "        raise RuntimeError(\"Recipe file not found: {}\".format(recipe_file_path))\n",
+    "    return model_file_path, recipe_file_path\n",
+    "\n",
+    "model_file_path, recipe_file_path = download_model_and_recipe(root_dir)\n",
+    "\n",
+    "print(\"Loading model {}\".format(model_file_path))\n",
+    "model = keras.models.load_model(model_file_path)\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up the Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will download the MNIST dataset from Keras datasets library as follows. You will also normalize the data before using it for training and evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "# Number of classes\n",
+    "num_classes = 10\n",
+    "\n",
+    "# Load MNIST dataset\n",
+    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "# Normalize data\n",
+    "x_train = x_train.astype('float32') / 255\n",
+    "x_test = x_test.astype('float32') / 255\n",
+    "\n",
+    "# Add batch dimension (for older TF versions)\n",
+    "x_train = numpy.expand_dims(x_train, -1)\n",
+    "x_test = numpy.expand_dims(x_test, -1)\n",
+    "\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print(\"Dataset loaded and normalized\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before pruning the model, you could run the cell below to verify the accuracy of the model on the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = model.evaluate(x_test, y_test)\n",
+    "print(\"Test loss, accuracy: \", res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Pruning the Pretrained Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, you will prune the above pretrained Keras model using the SparseML model optimization library. Recall that a common training workflow in Keras is first to compile the model with the appropriate losses, metrics and an optimizer, then to train the model using the `fit()` method of the `Model` class. The SparseML library makes it easy to extend this training workflow to perform gradual pruning based on weight magnitudes.\n",
+    "\n",
+    "Given a pretrained model, the pruning workflow can be summarized as follows:\n",
+    "1. Create a recipe for pruning, which could be done effectively using the Sparsify toolkit\n",
+    "2. Instantiate a Keras optimizer instance (such as SGD or Adam)\n",
+    "3. Instantiate a `ScheduledModifierManager` object from the recipe\n",
+    "4. Enhance the model and optimizer with pruning data structures by calling the manager's `modify` method. At this step, you have options to define the loggers used during the pruning process. The results of this step are a model to be pruned, an optimizer that should be used and a list of callbacks\n",
+    "5. [Optional] Add to the callback list any additional callbacks such as model checkpoint and the SparseML built-in LossesAndMetricsLogging callback\n",
+    "6. Compile and fit the modified model using Keras built-in APIs, using the optimizer and callback list\n",
+    "7. Erase the pruning information in the enhanced model, and get back the original model with pruned weights\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, you will set up a directory path for logging and the frequency for the logging update."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "# Logging directory\n",
+    "log_dir = \"./tensorboard/mnist:\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
+    "print(\"Logging directory: {}\".format(log_dir))\n",
+    "\n",
+    "# Number of steps before the next logging should take place\n",
+    "# Use \"epoch\" or \"batch\" to log at every training epoch or batch (respectively)\n",
+    "update_freq = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell contains the few steps required for pruning using the SparseML library, ultimately resulting in a modified model, optimizer and a list of callbacks incoporating the optimization logics. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "import math\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "from sparseml.keras.optim import ScheduledModifierManager\n",
+    "from sparseml.keras.utils import LossesAndMetricsLoggingCallback, TensorBoardLogger\n",
+    "\n",
+    "# Training batch size\n",
+    "batch_size = 32\n",
+    "\n",
+    "# Number of steps per epoch\n",
+    "steps_per_epoch = math.ceil(len(x_train) / batch_size)\n",
+    "\n",
+    "# Create a manager from the recipe\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_file_path)\n",
+    "\n",
+    "# Create an optimizer\n",
+    "optimizer = Adam()\n",
+    "\n",
+    "# Optional: Create a TensorBoardLogger instance\n",
+    "loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)\n",
+    "\n",
+    "# Modify the model and optimizer\n",
+    "model_for_pruning, optimizer, callbacks = manager.modify(model, optimizer, steps_per_epoch, loggers=loggers)\n",
+    "\n",
+    "# Include your own callbacks. Here you will use the built-in LossesAndMetricsLoggingCallback\n",
+    "callbacks.append(LossesAndMetricsLoggingCallback(loggers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you are ready to compile the modified model using the losses and metrics of your choice, and the optimizer enhanced by the mananger. The last step is to train the model using its `fit()` method, passing in (among other information) the list of callbacks constructed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the modified model\n",
+    "model_for_pruning.compile(\n",
+    "    loss=keras.losses.categorical_crossentropy,\n",
+    "    optimizer=optimizer,\n",
+    "    metrics=['accuracy'],\n",
+    "    run_eagerly=True\n",
+    ")\n",
+    "\n",
+    "# Prune the model\n",
+    "model_for_pruning.fit(x_train, y_train, batch_size=batch_size, epochs=manager.max_epochs,\n",
+    "                      validation_data=(x_test, y_test), shuffle=True, callbacks=callbacks)\n",
+    "\n",
+    "print(\"Pruning finished\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is time to verify the accuracy of the pruned model. Later on you can also check the sparsity level of the ONNX version of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify the pruned model's accuracy\n",
+    "res = model_for_pruning.evaluate(x_test, y_test)\n",
+    "print(\"Validation loss, accuracy: \", res)\n",
+    "\n",
+    "# Erase the enhanced information used for pruning \n",
+    "pruned_model = manager.finalize(model_for_pruning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given that you used a TensorBoardLogger, you can now observe the logging information in TensorBoard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir ./tensorboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Examine the Pruned Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can observe the layers of the pruned Keras model using its `layers` property and `get_weights()` method. First, print out the list of layers of this model, and recall that in this optimization recipe we pruned all the Conv2D layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pruned_model.layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can observe the weights of any Conv2D that you pruned, and notice that the majority of them are zeros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the layer index to examine the layers. Choose one among 1, 4, 7, 10 as the layer\n",
+    "# indices of the pruned layers.\n",
+    "layer_index = 10\n",
+    "pruned_model.layers[layer_index].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To observe the overall sparsity of the model, as well as the sparsity level of each layer, run the following cell. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.keras.utils import sparsity\n",
+    "from pprint import pprint\n",
+    "\n",
+    "model_sparsity, layer_sparsity_dict = sparsity(pruned_model)\n",
+    "print(\"Model sparsity: {}\".format(model_sparsity))\n",
+    "print(\"Layer sparsities:\")\n",
+    "pprint(layer_sparsity_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For Keras, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to.\n",
+    "\n",
+    "#### Note:\n",
+    "The `keras2onnx` is known to cause issues during the conversion; in particular, if you installed Tensorflow 2.4, you might encounter the `AttributeError: 'KerasTensor' object has no attribute 'graph'`. You might need to downgrade to Tensorflow 2.2 or 2.3 for the ONNX export to work properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.keras.utils import ModelExporter\n",
+    "\n",
+    "save_dir = \"keras_classification\"\n",
+    "onnx_file_name = \"pruned_mnist.onnx\"\n",
+    "\n",
+    "exporter = ModelExporter(pruned_model, output_dir=save_dir)\n",
+    "exporter.export_onnx(name=onnx_file_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the DeepSparse Engine"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/keras/prune_resnet20.py b/integrations/examples/keras/prune_resnet20.py
new file mode 100644
index 00000000000..f4429da738b
--- /dev/null
+++ b/integrations/examples/keras/prune_resnet20.py
@@ -0,0 +1,241 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Example of pruning a ResNet20-v1 model pretrained on the Cifar-10 dataset.
+The pretrained model and this pruning script were adapted from:
+https://keras.io/zh/integrations/cifar10_resnet/
+
+Run the following command from the top repo directory:
+
+   python3 integrations/keras/prune_resnet20.py
+
+"""
+
+import math
+import os
+from datetime import datetime
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.callbacks import ModelCheckpoint
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+from sparseml.keras.optim import ScheduledModifierManager
+from sparseml.keras.utils.callbacks import LossesAndMetricsLoggingCallback
+from sparseml.keras.utils.exporter import ModelExporter
+from sparseml.keras.utils.logger import TensorBoardLogger
+from sparsezoo import Model
+
+
+# Root directory
+root_dir = "./integrations/keras"
+
+# Logging setup
+log_dir = os.path.join(root_dir, "tensorboard", "resnet20_v1")
+log_dir += ":" + datetime.now().strftime("%Y%m%d-%H%M%S")
+update_freq = 100  # Logging update every this many training steps
+
+# Train/dataset setting
+num_classes = 10
+batch_size = 32
+subtract_pixel_mean = True
+data_augmentation = True
+
+# Pruned model directory
+pruned_model_dir = os.path.join(root_dir, "pruned")
+if not os.path.isdir(pruned_model_dir):
+    os.makedirs(pruned_model_dir)
+
+
+def download_model_and_recipe(root_dir: str):
+    """
+    Download pretrained model and a pruning recipe
+    """
+
+    # Use the recipe stub
+    recipe_file_path = (
+        "zoo:cv/classification/resnet_v1-20/keras/sparseml/cifar_10/pruned-conservative"
+    )
+
+    # Load base model to prune
+    base_zoo_model = Model(recipe_file_path)
+    base_zoo_model.path = os.path.join(root_dir, "resnet20_v1")
+    checkpoint = base_zoo_model.training.default
+    model_file_path = checkpoint.get_file("model.h5").path
+    recipe_file_path = base_zoo_model.recipes.default.path
+    if not os.path.exists(model_file_path) or not model_file_path.endswith(".h5"):
+        raise RuntimeError("Model file not found: {}".format(model_file_path))
+
+    return model_file_path, recipe_file_path
+
+
+def load_and_normalize_cifar10(subtract_pixel_mean: bool = True):
+    """
+    Load and normalize the Cifar-10 dataset
+    """
+    # Load the CIFAR10 data.
+    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
+
+    # Normalize data.
+    X_train = X_train.astype("float32") / 255
+    X_test = X_test.astype("float32") / 255
+
+    # If subtract pixel mean is enabled
+    if subtract_pixel_mean:
+        X_train_mean = np.mean(X_train, axis=0)
+        X_train -= X_train_mean
+        X_test -= X_train_mean
+
+    # Convert class vectors to binary class matrices.
+    y_train = tf.keras.utils.to_categorical(y_train, num_classes)
+    y_test = tf.keras.utils.to_categorical(y_test, num_classes)
+
+    return (X_train, y_train), (X_test, y_test)
+
+
+def model_checkpoint_callback():
+    """
+    Create model checkpoint callback
+    """
+    pruned_model_name = "resnet20_v1.pruned.{epoch:03d}.h5"
+    pruned_filepath = os.path.join(pruned_model_dir, pruned_model_name)
+
+    # Prepare a callback for model saving
+    checkpoint = ModelCheckpoint(
+        filepath=pruned_filepath, monitor="val_accuracy", verbose=1, save_best_only=True
+    )
+    return checkpoint
+
+
+def main():
+    print("Load and normalize Cifar-10 dataset")
+    (X_train, y_train), (X_test, y_test) = load_and_normalize_cifar10()
+
+    model_file_path, recipe_file_path = download_model_and_recipe(root_dir)
+
+    print("Load pretrained model")
+    base_model = tf.keras.models.load_model(model_file_path)
+    base_model.summary()
+
+    scores = base_model.evaluate(X_test, y_test, verbose=1)
+    print("Test loss:", scores[0])
+    print("Test accuracy:", scores[1])
+
+    optimizer = tf.keras.optimizers.Adam()
+
+    steps_per_epoch = math.ceil(len(X_train) / batch_size)
+
+    # Enhance the model and optimizer for pruning using the manager
+    loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
+    manager = ScheduledModifierManager.from_yaml(recipe_file_path)
+    model_for_pruning, optimizer, callbacks = manager.modify(
+        base_model, optimizer, steps_per_epoch, loggers=loggers
+    )
+    callbacks.append(LossesAndMetricsLoggingCallback(loggers))
+    callbacks.append(model_checkpoint_callback())
+
+    # Compile the enhanced model
+    model_for_pruning.compile(
+        loss=tf.keras.losses.categorical_crossentropy,
+        optimizer=optimizer,
+        metrics=["accuracy"],
+        run_eagerly=True,
+    )
+
+    # Run training and pruning, with or without data augmentation.
+    if not data_augmentation:
+        print("Not using data augmentation.")
+        model_for_pruning.fit(
+            X_train,
+            y_train,
+            batch_size=batch_size,
+            validation_data=(X_test, y_test),
+            shuffle=True,
+            callbacks=callbacks,
+            epochs=manager.max_epochs,
+        )
+    else:
+        print("Using real-time data augmentation.")
+        # This will do preprocessing and realtime data augmentation:
+        datagen = ImageDataGenerator(
+            # set input mean to 0 over the dataset
+            featurewise_center=False,
+            # set each sample mean to 0
+            samplewise_center=False,
+            # divide inputs by std of dataset
+            featurewise_std_normalization=False,
+            # divide each input by its std
+            samplewise_std_normalization=False,
+            # apply ZCA whitening
+            zca_whitening=False,
+            # epsilon for ZCA whitening
+            zca_epsilon=1e-06,
+            # randomly rotate images in the range (deg 0 to 180)
+            rotation_range=0,
+            # randomly shift images horizontally
+            width_shift_range=0.1,
+            # randomly shift images vertically
+            height_shift_range=0.1,
+            # set range for random shear
+            shear_range=0.0,
+            # set range for random zoom
+            zoom_range=0.0,
+            # set range for random channel shifts
+            channel_shift_range=0.0,
+            # set mode for filling points outside the input boundaries
+            fill_mode="nearest",
+            # value used for fill_mode = "constant"
+            cval=0.0,
+            # randomly flip images
+            horizontal_flip=True,
+            # randomly flip images
+            vertical_flip=False,
+            # set rescaling factor (applied before any other transformation)
+            rescale=None,
+            # set function that will be applied on each input
+            preprocessing_function=None,
+            # image data format, either "channels_first" or "channels_last"
+            data_format=None,
+            # fraction of images reserved for validation (strictly between 0 and 1)
+            validation_split=0.0,
+        )
+
+        # Compute quantities required for featurewise normalization
+        # (std, mean, and principal components if ZCA whitening is applied).
+        datagen.fit(X_train)
+
+        # Fit the model on the batches generated by datagen.flow().
+        model_for_pruning.fit_generator(
+            datagen.flow(X_train, y_train, batch_size=batch_size),
+            validation_data=(X_test, y_test),
+            epochs=manager.max_epochs,
+            verbose=1,
+            workers=4,
+            callbacks=callbacks,
+        )
+
+    # Erase pruning masks and export to ONNX model
+    pruned_model = manager.finalize(model_for_pruning)
+    exporter = ModelExporter(pruned_model, output_dir=pruned_model_dir)
+    onnx_model_name = "pruned_resnet20_v1.onnx"
+    exporter.export_onnx(name=onnx_model_name)
+    print(
+        "Model exported to {}".format(os.path.join(pruned_model_dir, onnx_model_name))
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/examples/keras/recipes/.gitkeep b/integrations/examples/keras/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/keras/tutorials/.gitkeep b/integrations/examples/keras/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/pytorch/README.md b/integrations/examples/pytorch/README.md
new file mode 100644
index 00000000000..ca0a38fbb0f
--- /dev/null
+++ b/integrations/examples/pytorch/README.md
@@ -0,0 +1,99 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML PyTorch Integration
+
+This directory combines the SparseML recipe-driven approach with standard PyTorch training flows for models within
+[torchvision](https://pytorch.org/vision/stable/index.html) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom PyTorch training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Blog: [ResNet-50 on CPUs: Sparsifying for Better Performance on CPUs](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse/)
+
+## Tutorials
+
+- [Classification](./notebooks/classification.ipynb)
+- [Detection](./notebooks/detection.ipynb)
+- [Sparse Quantized Transfer Learning](./notebooks/sparse_quantized_transfer_learning.ipynb)
+- [Torchvision](./notebooks/torchvision.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[torchvision]`
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=pytorch) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=pytorch) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name                                             | Description                                                                                                           |
+|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [notebooks](./notebooks)                                     | Jupyter notebooks to walk through sparsifiation within standard PyTorch training flows                                |
+| [recipes](./recipes)                                         | Typical recipes for sparsifying PyTorch models along with any downloaded recipes from the SparseZoo.                  |
+| [tutorials](./tutorials)                                     | Tutorial walkthroughs for how to sparsify PyTorch models using recipes.                                               |
+| [README.md](./README.md)                                     | Readme file.                                                                                                          |
+| [torchvision_sparsification.py](./torchvision_sparsification.py) | Example training script for sparsifying PyTorch torchvision classification models.                                    |
+| `sparseml.image_classification.train`                        | Utility training script for sparsifying classification and detection models in PyTorch (loads SparseML models).       |
+| `sparseml.image_classification.export`                       | Utility script for exporting pytorch model to ONNX file along with sample inputs, outputs and labels.                 |
+| `sparseml.image_classification.lr_analysis`                  |Utility script to Run learning rate sensitivity analysis for a desired image classification architecture |`sparseml.image_classification.pr_sensitivity`                |Utility script to Run a kernel sparsity (pruning) analysis for a desired image classification architecture |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/pytorch/argparser_/nm_argparser_.py b/integrations/examples/pytorch/argparser_/nm_argparser_.py
new file mode 100644
index 00000000000..f460a012054
--- /dev/null
+++ b/integrations/examples/pytorch/argparser_/nm_argparser_.py
@@ -0,0 +1,282 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import dataclasses
+import json
+import re
+import sys
+from argparse import ArgumentParser, ArgumentTypeError
+from copy import copy
+from enum import Enum
+from pathlib import Path
+from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
+
+
+DataClass = NewType("DataClass", Any)
+DataClassType = NewType("DataClassType", Any)
+
+
+# From https://stackoverflow.com/questions/15008758
+# /parsing-boolean-values-with-argparse
+def string_to_bool(v):
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise ArgumentTypeError(
+            f"Truthy value expected: got {v} but expected one of yes/no,"
+            f"true/false, t/f, y/n, 1/0 (case insensitive)."
+        )
+
+
+# Inspired from https://huggingface.co/transformers/_modules
+# /transformers/hf_argparser.html
+class NmArgumentParser(ArgumentParser):
+    """
+    This subclass of `argparse.ArgumentParser` uses type hints on dataclasses
+    to generate arguments.
+
+    The class is designed to play well with the native argparse. In particular,
+    you can add more (non-dataclass backed) arguments to the parser after
+    initialization and you'll get the output back after parsing as an additional
+    namespace. Optional: To create sub argument groups use the
+    `_argument_group_name` attribute in the dataclass.
+
+    Note: __post_init__(...) for specific dataclasses passed is executed only
+    when parse_args_into_dataclasses(...) function is called because it needs
+    actual instantiation of the dataclass.
+    """
+
+    dataclass_types: Iterable[DataClassType]
+
+    def __init__(
+        self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs
+    ):
+        """
+        :param dataclass_types: Dataclass type, or list of dataclass types
+            for which we will "fill" instances with the parsed args.
+        :param kwargs: (Optional) Passed to `argparse.ArgumentParser()` in
+            the regular way.
+        """
+        super().__init__(**kwargs)
+        if dataclasses.is_dataclass(dataclass_types):
+            dataclass_types = [dataclass_types]
+        self.dataclass_types = dataclass_types
+        for dataclass_ in self.dataclass_types:
+            self._add_dataclass_arguments(dataclass_)
+
+    def _add_dataclass_arguments(self, dataclass_: DataClassType):
+        if hasattr(dataclass_, "_argument_group_name"):
+            parser = self.add_argument_group(dataclass_._argument_group_name)
+        else:
+            parser = self
+        for field in dataclasses.fields(dataclass_):
+            if not field.init:
+                continue
+
+            name, kwargs = field.name, field.metadata.copy()
+
+            keep_underscores_key = "keep_underscores"
+            keep_underscores = kwargs.get(keep_underscores_key)
+            _field_name = name if keep_underscores else name.replace("_", "-")
+
+            # cleanup
+            if keep_underscores_key in kwargs:
+                del kwargs[keep_underscores_key]
+
+            # field.metadata is not used at all by Data Classes,
+            # it is provided as a third-party extension mechanism.
+            if isinstance(field.type, str):
+                raise ImportError(
+                    "This implementation is not compatible with Postponed "
+                    "Evaluation of Annotations (PEP 563),"
+                    "which can be opted in from Python 3.7 with "
+                    "`from __future__ import annotations`."
+                )
+            typestring = str(field.type)
+            for prim_type in (int, float, str):
+                for collection in (List,):
+                    if (
+                        typestring == f"typing.Union["
+                        f"{collection[prim_type]}, NoneType]"
+                        or typestring == f"typing.Optional" f"[{collection[prim_type]}]"
+                    ):
+                        field.type = collection[prim_type]
+                if (
+                    typestring == f"typing.Union[" f"{prim_type.__name__}, NoneType]"
+                    or typestring == f"typing.Optional[" f"{prim_type.__name__}]"
+                ):
+                    field.type = prim_type
+
+            if isinstance(field.type, type) and issubclass(field.type, Enum):
+                kwargs["choices"] = [x.value for x in field.type]
+                kwargs["type"] = type(kwargs["choices"][0])
+                if field.default is not dataclasses.MISSING:
+                    kwargs["default"] = field.default
+                else:
+                    kwargs["required"] = True
+            elif field.type is bool or field.type == Optional[bool]:
+                if field.default is True:
+                    kwargs_copy = copy(kwargs)
+                    if "help" in kwargs_copy:
+                        kwargs_copy["help"] = f"Do not {kwargs_copy['help'].lower()}"
+                    parser.add_argument(
+                        f"--no-{_field_name}",
+                        action="store_false",
+                        dest=_field_name,
+                        **kwargs_copy,
+                    )
+
+                # Hack because type=bool in argparse does not behave as we want.
+                kwargs["type"] = string_to_bool
+                if field.type is bool or (
+                    field.default is not None
+                    and field.default is not dataclasses.MISSING
+                ):
+                    # Default value is False if we have no default
+                    # when of type bool.
+                    if field.default is dataclasses.MISSING:
+                        default = False
+                    else:
+                        default = field.default
+                    # This is the value that will get picked if
+                    # we don't include --field_name in any way
+                    kwargs["default"] = default
+
+                    # This tells argparse we accept 0 or 1
+                    # value after --field_name
+                    kwargs["nargs"] = "?"
+                    # This is the value that will get picked
+                    # if we do --field_name (without value)
+                    kwargs["const"] = True
+            elif (
+                hasattr(field.type, "__origin__")
+                and re.search(r"^typing\.List\[(.*)\]$", str(field.type)) is not None
+            ):
+                kwargs["nargs"] = "+"
+                kwargs["type"] = field.type.__args__[0]
+                assert all(
+                    x == kwargs["type"] for x in field.type.__args__
+                ), f"{field.name} cannot be a List of mixed types"
+                if field.default_factory is not dataclasses.MISSING:
+                    kwargs["default"] = field.default_factory()
+                elif field.default is dataclasses.MISSING:
+                    kwargs["required"] = True
+            else:
+                kwargs["type"] = field.type
+                if field.default is not dataclasses.MISSING:
+                    kwargs["default"] = field.default
+                elif field.default_factory is not dataclasses.MISSING:
+                    kwargs["default"] = field.default_factory()
+                else:
+                    kwargs["required"] = True
+            parser.add_argument(f"--{_field_name}", **kwargs)
+
+    def parse_args_into_dataclasses(
+        self,
+        args=None,
+        return_remaining_strings=False,
+        look_for_args_file=True,
+        args_filename=None,
+    ) -> Tuple[DataClass, ...]:
+        """
+        Parse command-line args into instances of the specified dataclass types.
+
+        This relies on argparse's `ArgumentParser.parse_known_args`.
+        See the doc at:
+        docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.
+        parse_args
+
+
+        :param args: List of strings to parse. The default is taken from
+            sys.argv. (same as argparse.ArgumentParser)
+        :param return_remaining_strings: If true, also return a list of
+            remaining argument strings.
+        :param look_for_args_file: If true, will look for a ".args" file with
+            the same base name as the entry point script for this process,
+            and will append its potential content to the command line args.
+        :param args_filename: If not None, will uses this file instead of the
+            ".args" file specified in the previous argument.
+        :returns: Tuple consisting of:
+                    - the dataclass instances in the same order as they were
+                    passed to the initializer.abspath
+                    - if applicable, an additional namespace for more
+                    (non-dataclass backed) arguments added to the parser
+                    after initialization.
+                    - The potential list of remaining argument strings.
+                    (same as argparse.ArgumentParser.parse_known_args)
+        """
+        if args_filename or (look_for_args_file and len(sys.argv)):
+            if args_filename:
+                args_file = Path(args_filename)
+            else:
+                args_file = Path(sys.argv[0]).with_suffix(".args")
+
+            if args_file.exists():
+                fargs = args_file.read_text().split()
+                args = fargs + args if args is not None else fargs + sys.argv[1:]
+                # in case of duplicate arguments the first one has precedence
+                # so we append rather than prepend.
+        namespace, remaining_args = self.parse_known_args(args=args)
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in vars(namespace).items() if k in keys}
+            for k in keys:
+                delattr(namespace, k)
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        if len(namespace.__dict__) > 0:
+            # additional namespace.
+            outputs.append(namespace)
+        if return_remaining_strings:
+            return (*outputs, remaining_args)
+        else:
+            if remaining_args:
+                raise ValueError(
+                    f"Some specified arguments are not used by the "
+                    f"NmArgumentParser: {remaining_args}"
+                )
+
+            return (*outputs,)
+
+    def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
+        """
+        Alternative helper method that does not use `argparse` at all,
+        instead loading a json file and populating the dataclass types.
+        """
+        data = json.loads(Path(json_file).read_text())
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in data.items() if k in keys}
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        return (*outputs,)
+
+    def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
+        """
+        Alternative helper method that does not use `argparse` at all,
+        instead uses a dict and populating the dataclass types.
+        """
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in args.items() if k in keys}
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        return (*outputs,)
diff --git a/integrations/examples/pytorch/argparser_/test_nmargparser.py b/integrations/examples/pytorch/argparser_/test_nmargparser.py
new file mode 100644
index 00000000000..a7d18a66b79
--- /dev/null
+++ b/integrations/examples/pytorch/argparser_/test_nmargparser.py
@@ -0,0 +1,283 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import unittest
+from argparse import Namespace
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import List, Optional
+
+from nm_argparser_ import NmArgumentParser, string_to_bool
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class BasicExample:
+    foo: int
+    bar: float
+    baz: str
+    flag: bool
+
+
+@dataclass
+class WithDefaultExample:
+    foo: int = 42
+    baz: str = field(default="toto", metadata={"help": "help message"})
+
+
+@dataclass
+class WithDefaultBoolExample:
+    foo: bool = False
+    baz: bool = True
+    opt: Optional[bool] = None
+
+
+class BasicEnum(Enum):
+    titi = "titi"
+    toto = "toto"
+
+
+@dataclass
+class EnumExample:
+    foo: BasicEnum = "toto"
+
+    def __post_init__(self):
+        self.foo = BasicEnum(self.foo)
+
+
+@dataclass
+class OptionalExample:
+    foo: Optional[int] = None
+    bar: Optional[float] = field(default=None, metadata={"help": "help message"})
+    baz: Optional[str] = None
+    ces: Optional[List[str]] = list_field(default=[])
+    des: Optional[List[int]] = list_field(default=[])
+
+
+@dataclass
+class ListExample:
+    foo_int: List[int] = list_field(default=[])
+    bar_int: List[int] = list_field(default=[1, 2, 3])
+    foo_str: List[str] = list_field(default=["Hallo", "Bonjour", "Hello"])
+    foo_float: List[float] = list_field(default=[0.1, 0.2, 0.3])
+
+
+@dataclass
+class RequiredExample:
+    required_list: List[int] = field()
+    required_str: str = field()
+    required_enum: BasicEnum = field()
+
+    def __post_init__(self):
+        self.required_enum = BasicEnum(self.required_enum)
+
+
+@dataclass
+class NonHyphenatedExample:
+    non_hyphenated: int = field(
+        metadata={
+            "keep_underscores": True,
+        }
+    )
+
+
+class NmArgumentParserTest(unittest.TestCase):
+    def argparsersEqual(
+        self, parser: argparse.ArgumentParser, expected_parser: argparse.ArgumentParser
+    ) -> bool:
+        """
+        Small helper to check pseudo-equality of parsed arguments on
+        `ArgumentParser` instances.
+        """
+        self.assertEqual(len(parser._actions), len(expected_parser._actions))
+        for x, y in zip(parser._actions, expected_parser._actions):
+            xx = {k: v for k, v in vars(x).items() if k != "container"}
+            yy = {k: v for k, v in vars(y).items() if k != "container"}
+            self.assertEqual(xx, yy)
+
+    def test_basic(self):
+        parser = NmArgumentParser(BasicExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", type=int, required=True)
+        expected.add_argument("--bar", type=float, required=True)
+        expected.add_argument("--baz", type=str, required=True)
+        expected.add_argument(
+            "--flag", type=string_to_bool, default=False, const=True, nargs="?"
+        )
+        self.argparsersEqual(parser, expected)
+
+        args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
+        (example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False)
+        self.assertFalse(example.flag)
+
+    def test_with_default(self):
+        parser = NmArgumentParser(WithDefaultExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", default=42, type=int)
+        expected.add_argument("--baz", default="toto", type=str, help="help message")
+        self.argparsersEqual(parser, expected)
+
+    def test_with_default_bool(self):
+        parser = NmArgumentParser(WithDefaultBoolExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument(
+            "--foo", type=string_to_bool, default=False, const=True, nargs="?"
+        )
+        expected.add_argument("--no-baz", action="store_false", dest="baz")
+        expected.add_argument(
+            "--baz", type=string_to_bool, default=True, const=True, nargs="?"
+        )
+        expected.add_argument("--opt", type=string_to_bool, default=None)
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args, Namespace(foo=False, baz=True, opt=None))
+
+        args = parser.parse_args(["--foo", "--no-baz"])
+        self.assertEqual(args, Namespace(foo=True, baz=False, opt=None))
+
+        args = parser.parse_args(["--foo", "--baz"])
+        self.assertEqual(args, Namespace(foo=True, baz=True, opt=None))
+
+        args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"])
+        self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
+
+        args = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"])
+        self.assertEqual(args, Namespace(foo=False, baz=False, opt=False))
+
+    def test_with_enum(self):
+        parser = NmArgumentParser(EnumExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument(
+            "--foo", default="toto", choices=["titi", "toto"], type=str
+        )
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args.foo, "toto")
+        enum_ex = parser.parse_args_into_dataclasses([])[0]
+        self.assertEqual(enum_ex.foo, BasicEnum.toto)
+
+        args = parser.parse_args(["--foo", "titi"])
+        self.assertEqual(args.foo, "titi")
+        enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0]
+        self.assertEqual(enum_ex.foo, BasicEnum.titi)
+
+    def test_with_list(self):
+        parser = NmArgumentParser(ListExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo-int", nargs="+", default=[], type=int)
+        expected.add_argument("--bar-int", nargs="+", default=[1, 2, 3], type=int)
+        expected.add_argument(
+            "--foo-str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str
+        )
+        expected.add_argument(
+            "--foo-float", nargs="+", default=[0.1, 0.2, 0.3], type=float
+        )
+
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(
+            args,
+            Namespace(
+                foo_int=[],
+                bar_int=[1, 2, 3],
+                foo_str=["Hallo", "Bonjour", "Hello"],
+                foo_float=[0.1, 0.2, 0.3],
+            ),
+        )
+
+        args = parser.parse_args(
+            "--foo-int 1 --bar-int 2 3 --foo-str parser expected_parser "
+            "c --foo-float 0.1 0.7".split()
+        )
+        self.assertEqual(
+            args,
+            Namespace(
+                foo_int=[1],
+                bar_int=[2, 3],
+                foo_str=["parser", "expected_parser", "c"],
+                foo_float=[0.1, 0.7],
+            ),
+        )
+
+    def test_with_optional(self):
+        parser = NmArgumentParser(OptionalExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", default=None, type=int)
+        expected.add_argument("--bar", default=None, type=float, help="help message")
+        expected.add_argument("--baz", default=None, type=str)
+        expected.add_argument("--ces", nargs="+", default=[], type=str)
+        expected.add_argument("--des", nargs="+", default=[], type=int)
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args, Namespace(foo=None, bar=None, baz=None, ces=[], des=[]))
+
+        args = parser.parse_args(
+            "--foo 12 --bar 3.14 --baz 42 --ces parser expected_parser "
+            "c --des 1 2 3".split()
+        )
+        self.assertEqual(
+            args,
+            Namespace(
+                foo=12,
+                bar=3.14,
+                baz="42",
+                ces=["parser", "expected_parser", "c"],
+                des=[1, 2, 3],
+            ),
+        )
+
+    def test_with_required(self):
+        parser = NmArgumentParser(RequiredExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--required-list", nargs="+", type=int, required=True)
+        expected.add_argument("--required-str", type=str, required=True)
+        expected.add_argument(
+            "--required-enum", type=str, choices=["titi", "toto"], required=True
+        )
+        self.argparsersEqual(parser, expected)
+
+    def test_parse_dict(self):
+        parser = NmArgumentParser(BasicExample)
+
+        args_dict = {
+            "foo": 12,
+            "bar": 3.14,
+            "baz": "42",
+            "flag": True,
+        }
+
+        parsed_args = parser.parse_dict(args_dict)[0]
+        args = BasicExample(**args_dict)
+        self.assertEqual(parsed_args, args)
+
+    def test_non_hyphenated(self):
+        parser = NmArgumentParser(dataclass_types=NonHyphenatedExample)
+        args = parser.parse_args("--non_hyphenated 1".split())
+
+        self.assertEqual(args, Namespace(non_hyphenated=1))
diff --git a/integrations/examples/pytorch/export.py b/integrations/examples/pytorch/export.py
new file mode 100644
index 00000000000..230304b6fc4
--- /dev/null
+++ b/integrations/examples/pytorch/export.py
@@ -0,0 +1,358 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+usage: export.py [-h] --arch-key ARCH_KEY --dataset DATASET
+                 --dataset-path DATASET_PATH
+                 [--checkpoint-path CHECKPOINT_PATH]
+                 [--num-samples NUM_SAMPLES] [--onnx-opset ONNX_OPSET]
+                 [--use-zipfile-serialization-if-available
+                 USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE]
+                 [--pretrained PRETRAINED]
+                 [--pretrained-dataset PRETRAINED_DATASET]
+                 [--model-kwargs MODEL_KWARGS]
+                 [--dataset-kwargs DATASET_KWARGS]
+                 [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+
+Utility script to export a model to onnx and also store sample
+inputs/outputs
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list
+                        (will raise an exception with the list)
+  --dataset DATASET     The dataset to use for exporting, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder
+                        for a generic dataset setup with an image
+                        folder structure setup like imagenet or
+                        loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the
+                        state from and resume the state for. If
+                        provided, pretrained will be ignored . If using
+                        a SparseZoo recipe, can also provide 'zoo' to
+                        load the base weights associated with that
+                        recipe
+  --num-samples NUM_SAMPLES
+                        The number of samples to export along with the
+                        model onnx and pth files (sample inputs and
+                        labels as well as the outputs from model
+                        execution)
+  --onnx-opset ONNX_OPSET
+                        The onnx opset to use for export.
+  --use-zipfile-serialization-if-available
+                        USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE
+                        for torch >= 1.6.0 only exports the Module's
+                        state dict using the new zipfile serialization.
+                        Default is True, has no affect on lower torch
+                        versions
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default
+                        is true to load the default pretrained weights
+                        for the model. Otherwise should be set to the
+                        desired weights type: [base, optim, optim-
+                        perf]. To not load any weights set to one of
+                        [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will
+                        load the default dataset for the architecture.
+                        Ex can be set to imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model
+                        constructor, should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results
+                        under save-dir, defaults to the model arch and
+                        dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+##########
+Example command for exporting ResNet50:
+python integrations/pytorch/export.py \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
+"""
+import json
+from dataclasses import dataclass, field
+from typing import Any, Optional, Tuple
+
+from torch.nn import Module
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
+from sparseml.pytorch.utils import ModuleExporter
+from sparseml.utils import convert_to_bool
+
+
+CURRENT_TASK = utils.Tasks.EXPORT
+LOGGER = get_main_logger()
+
+
+@dataclass
+class ExportArgs:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    exporting tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for analysis, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param num_samples: The number of samples to export along with the model
+        onnx and pth files (sample inputs and labels as well as the outputs
+        from model execution). Default is 100.
+    :param onnx_opset: The onnx opset to use for export. Default is 11.
+    :param use_zipfile_serialization_if_available: for torch >= 1.6.0 only
+        exports the Module's state dict using the new zipfile serialization.
+        Default is True, has no affect on lower torch versions.
+    :param pretrained: The type of pretrained weights to use,
+        default is true to load the default pretrained weights for the model.
+        Otherwise should be set to the desired weights type: [base, optim,
+        optim-perf]. To not load any weights set to one of [none, false].
+    :param pretrained_dataset: The dataset to load pretrained weights for if
+        pretrained is set. Default is None which will load the default
+        dataset for the architecture. Ex can be set to imagenet, cifar10, etc.
+    :param model_kwargs: Keyword arguments to be passed to model constructor,
+        should be given as a json object.
+    :param dataset_kwargs: Keyword arguments to be passed to dataset
+        constructor, should be given as a json object.
+    :param model_tag: A tag to use for the model for saving results under
+        save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results.
+    """
+
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception "
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for exporting, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored . If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    num_samples: int = field(
+        default=100,
+        metadata={
+            "help": "The number of samples to export along with the model onnx "
+            "and pth files (sample inputs and labels as well as the "
+            "outputs from model execution)"
+        },
+    )
+
+    onnx_opset: int = field(
+        default=TORCH_DEFAULT_ONNX_OPSET,
+        metadata={
+            "help": "The onnx opset to use for export. "
+            f"Default is {TORCH_DEFAULT_ONNX_OPSET}"
+        },
+    )
+
+    use_zipfile_serialization_if_available: convert_to_bool = field(
+        default=True,
+        metadata={
+            "help": "for torch >= 1.6.0 only exports the Module's state dict "
+            "using the new zipfile serialization. Default is True, "
+            "has no affect on lower torch versions"
+        },
+    )
+
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for "
+            "the model. Otherwise should be set to the desired weights "
+            "type: [base, optim, optim-perf]. To not load any weights "
+            "set to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained "
+            "is set. Default is None which will load the default "
+            "dataset for the architecture. Ex can be set to imagenet, "
+            "cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be  given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.local_rank: int = -1
+        self.is_main_process: bool = True
+
+
+def export(
+    args: ExportArgs, model: Module, val_loader: DataLoader, save_dir: str
+) -> None:
+    """
+    Utility method to export the model and data
+
+    :param args : An ExportArgs object containing config for export task.
+    :param model: loaded model architecture to export
+    :param val_loader: A DataLoader for validation data
+    :param save_dir: Directory to store checkpoints at during exporting process
+    """
+    exporter = ModuleExporter(model, save_dir)
+
+    # export PyTorch state dict
+    LOGGER.info(f"exporting pytorch in {save_dir}")
+
+    exporter.export_pytorch(
+        use_zipfile_serialization_if_available=(
+            args.use_zipfile_serialization_if_available
+        )
+    )
+    onnx_exported = False
+
+    for batch, data in tqdm(
+        enumerate(val_loader),
+        desc="Exporting samples",
+        total=args.num_samples if args.num_samples > 1 else 1,
+    ):
+        if not onnx_exported:
+            # export onnx file using first sample for graph freezing
+            LOGGER.info(f"exporting onnx in {save_dir}")
+            exporter.export_onnx(data[0], opset=args.onnx_opset, convert_qat=True)
+            onnx_exported = True
+
+        if args.num_samples > 0:
+            exporter.export_samples(
+                sample_batches=[data[0]], sample_labels=[data[1]], exp_counter=batch
+            )
+
+
+def export_setup(args_: ExportArgs) -> Tuple[Module, Optional[str], Any]:
+    """
+    Pre-export setup
+
+    :param args_ : An ExportArgs object containing config for export task.
+    """
+    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
+
+    # model creation
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+    model = utils.create_model(args_, num_classes)
+    return model, save_dir, val_loader
+
+
+def main():
+    """
+    Driver function
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=ExportArgs,
+        description="Utility script to export a model to onnx "
+        "and also store sample inputs/outputs",
+    )
+    (args_,) = _parser.parse_args_into_dataclasses()
+    model, save_dir, val_loader = export_setup(args_)
+    export(args_, model, val_loader, save_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/examples/pytorch/lr_analysis.py b/integrations/examples/pytorch/lr_analysis.py
new file mode 100644
index 00000000000..a9dc0e94058
--- /dev/null
+++ b/integrations/examples/pytorch/lr_analysis.py
@@ -0,0 +1,408 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+usage: lr_analysis.py [-h] --batch-size BATCH_SIZE --arch-key ARCH_KEY
+                        --dataset DATASET --dataset-path DATASET_PATH [
+                        --pretrained PRETRAINED]
+                      [--pretrained-dataset PRETRAINED_DATASET] [
+                      --model-kwargs MODEL_KWARGS]
+                      [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
+                      [--save-dir SAVE_DIR] [--device DEVICE]
+                      [--loader-num-workers LOADER_NUM_WORKERS] [
+                      --no-loader-pin-memory]
+                      [--loader-pin-memory [LOADER_PIN_MEMORY]]
+                      [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
+                      [--optim-args OPTIM_ARGS] [--final-lr FINAL_LR]
+                      [--steps-per-measurement STEPS_PER_MEASUREMENT]
+
+Utility script to Run a learning rate sensitivity analysis for a desired
+image classification architecture
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --batch-size BATCH_SIZE
+                        The batch size to use for analysis
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16, mobilenet
+                        put as help to see the full list (will raise an
+                        exception with the list)
+  --dataset DATASET     The dataset to use for analysis, ex: imagenet,
+                        imagenette, cifar10,
+                        etc. Set to imagefolder for a generic dataset setup
+                        with an imagefolder structure setup like imagenet or
+                        loadable by a dataset in sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is
+                        true to load the default pretrained weights for the
+                        model. Otherwise should be set to the desired weights
+                        type: [base, optim, optim-perf]. To not load any
+                        weights set to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if pretrained
+                        is set. Default is None which will load the default
+                        dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for
+                        data parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained
+                        will be ignored. If using a SparseZoo recipe,
+                        can also provide 'zoo' to load the base weights
+                        associated with that recipe
+  --init-lr INIT_LR     The initial learning rate to use for the sensitivity
+                        analysis
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed in
+                        as a json object
+  --final-lr FINAL_LR   The final learning rate to use for the sensitivity
+                        analysis
+  --steps-per-measurement STEPS_PER_MEASUREMENT The number of steps (batches)
+                        to run for each measurement
+#########
+EXAMPLES
+#########
+
+##########
+Example command for running LR sensitivity analysis on mobilenet:
+python integrations/pytorch/lr_analysis.py \
+    --arch-key mobilenet --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012 --batch-size 2
+
+Example command for running LR sensitivity analysis on mobilenet using
+imagenette:
+python integrations/pytorch/lr_analysis.py \
+    --arch-key mobilenet --dataset imagenette \
+    --dataset-path ~/datasets/ --batch-size 2
+"""
+
+import json
+import os
+from dataclasses import dataclass, field
+
+from torch.nn import Module
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import default_exponential_check_lrs, lr_loss_sensitivity
+from sparseml.pytorch.utils import PythonLogger, default_device, model_to_device
+
+
+CURRENT_TASK = utils.Tasks.LR_ANALYSIS
+LOGGER = get_main_logger()
+
+
+@dataclass
+class LRAnalysisArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    learning rate analysis.
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`
+    arguments that can be specified on the command line.
+
+    :param batch_size: The batch size to use for analysis.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for analysis, ex imagenet,
+        imagenette, etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param pretrained: The type of pretrained weights to use default is true to
+        load the default pretrained weights for the model. Otherwise should be
+        set to the desired weights type: [base, optim, optim-perf];
+        To not load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load pretrained
+        weights for; if pretrained is set; Default is None which will load the
+        default dataset for the architecture; Ex can be set to imagenet,
+        cifar10, etc.
+    :param model_kwargs: json object containing keyword arguments to be passed
+        to the model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be passed
+        to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results under
+        save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on (can also include ids
+        for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data loading,
+        default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param init_lr: float representing the initial learning for analysis,
+        default=1e-5.
+    :param optim_args: Additional arguments to be passed in to the optimizer as
+        a json object.
+    :param final_lr: The final learning rate to use for the sensitivity
+        analysis.
+    :param steps_per_measurement: The number of steps (batches) to run for each
+        measurement.
+    """
+
+    batch_size: int = field(metadata={"help": "The batch size to use for analysis"})
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list"
+            "(will raise an exception with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for analysis, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights "
+            "for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained is"
+            "set. Default is None which will load the default dataset for "
+            "the architecture. Ex can be set to imagenet, cifar10, etc.",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor,"
+            " should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for "
+            "data parallel), ex:cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will be"
+            "ignored. If using a SparseZoo recipe, can also provide "
+            "'zoo' to load the base weights associated with that recipe"
+        },
+    )
+
+    init_lr: float = field(
+        default=1e-5,
+        metadata={
+            "help": "The initial learning rate to use for the sensitivity analysis"
+        },
+    )
+
+    optim_args: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Additional args to be passed to the optimizer passed in"
+            " as a json object"
+        },
+    )
+
+    final_lr: float = field(
+        default=0.5,
+        metadata={
+            "help": "The final learning rate to use for the sensitivity analysis",
+        },
+    )
+
+    steps_per_measurement: int = field(
+        default=20,
+        metadata={"help": "The number of steps (batches) to run for each measurement"},
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.is_main_process = True
+        self.local_rank = -1
+        self.rank = -1
+
+
+def lr_sensitivity(
+    args,
+    model: Module,
+    train_loader: DataLoader,
+    save_dir: str,
+) -> None:
+    """
+    Utility function to run learning rate sensitivity analysis
+
+    :param args: An LRAnalysisArguments object containing config for current
+        LR analysis task.
+    :param model: loaded model architecture to analyse
+    :param train_loader: A DataLoader for training data
+    :param save_dir: Directory to save results
+    """
+    # optimizer setup
+    optim = SGD(model.parameters(), lr=args.init_lr, **args.optim_args)
+    LOGGER.info(f"created optimizer: {optim}")
+
+    # loss setup
+    loss = utils.get_loss_wrapper(args.arch_key)
+    LOGGER.info(f"created loss: {loss}")
+
+    # device setup
+    model, device, device_ids = model_to_device(model, args.device)
+
+    # learning rate analysis
+    LOGGER.info(f"running analysis: {loss}")
+    analysis = lr_loss_sensitivity(
+        model,
+        train_loader,
+        loss,
+        optim,
+        device,
+        args.steps_per_measurement,
+        check_lrs=default_exponential_check_lrs(args.init_lr, args.final_lr),
+        trainer_loggers=[PythonLogger()],
+    )
+
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info(f"Saving results in {save_dir}")
+    analysis.save_json(os.path.join(save_dir, "lr_sensitivity.json"))
+    analysis.plot(os.path.join(save_dir, "lr_sensitivity.png"))
+    analysis.print_res()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=LRAnalysisArguments,
+        description="Utility script to Run a "
+        "learning rate sensitivity analysis "
+        "for a desired image classification architecture",
+    )
+    args_, _ = _parser.parse_args_into_dataclasses()
+    save_dir, loggers = utils.get_save_dir_and_loggers(
+        args_,
+        task=CURRENT_TASK,
+    )
+
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    # assume shape [C, S, S] where S is the image size
+    image_size = input_shape[1]
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(
+        args_,
+        image_size,
+        task=CURRENT_TASK,
+    )
+
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+    model = utils.create_model(args_, num_classes)
+    lr_sensitivity(args_, model, train_loader, save_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/examples/pytorch/notebooks/classification.ipynb b/integrations/examples/pytorch/notebooks/classification.ipynb
new file mode 100644
index 00000000000..6bdac17c274
--- /dev/null
+++ b/integrations/examples/pytorch/notebooks/classification.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# PyTorch Classification Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML\n",
+    "- Prune the model using the PyTorch+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 15 minutes on a GPU\n",
+    "- 45 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = ModelRegistry.create(...)\n",
+    "- train_dataset = ImagenetteDataset(...)\n",
+    "- val_dataset = ImagenetteDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"resnet50\",\n",
+    "    pretrained=True,\n",
+    "    pretrained_dataset=\"imagenette\",\n",
+    "    num_classes=10,\n",
+    ")\n",
+    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
+    "        loss = criterion(outputs, labels)\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up PyTorch Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 128\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"classification\",\n",
+    "    architecture=\"resnet_v1\",\n",
+    "    sub_architecture=\"50\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"sparseml\",\n",
+    "    dataset=\"imagenette\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Run model pruning\n",
+    "epoch = manager.min_epochs\n",
+    "while epoch < manager.max_epochs:\n",
+    "    # run training loop\n",
+    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
+    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Training Epoch: {}\\nTraining Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
+    "            epoch_name, train_loss, train_acc\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    # run validation loop\n",
+    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
+    "    val_loss, val_acc = run_model_one_epoch(\n",
+    "        model, val_loader, criterion, device\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Validation Epoch: {}\\nVal Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
+    "            epoch_name, val_loss, val_acc\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    epoch += 1\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_classification\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/pytorch/notebooks/detection.ipynb b/integrations/examples/pytorch/notebooks/detection.ipynb
new file mode 100644
index 00000000000..7a76aa223dd
--- /dev/null
+++ b/integrations/examples/pytorch/notebooks/detection.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# PyTorch Detection Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML\n",
+    "- Prune the model using the PyTorch+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 30 minutes on a GPU\n",
+    "- 90 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [SSD](https://arxiv.org/abs/1512.02325) model trained on the [VOC detection dataset](http://host.robots.ox.ac.uk/pascal/VOC/). The model's pretrained weights are downloaded from the SparseZoo model repo. The VOC detection dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "Note, for this notebook, you will use a ResNet18 backbone for the object detector.  This is to save training time and demonstrate the general pruning flow. For better accuracy, you can use a different backbone or model.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = ModelRegistry.create(...)\n",
+    "- train_dataset = VOCDetectionDataset(...)\n",
+    "- val_dataset = VOCDetectionDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import VOCDetectionDataset\n",
+    "from sparseml.pytorch.utils import get_default_boxes_300\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"ssd300_resnet18\",\n",
+    "    pretrained=True,\n",
+    "    pretrained_dataset=\"voc\",\n",
+    "    pretrained_backbone=False,  # no need to download initial weights\n",
+    "    num_classes=21,\n",
+    ")\n",
+    "model_name = model.__class__.__name__\n",
+    "input_shape = ModelRegistry.input_shape(\"ssd300_resnet18\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "default_boxes = get_default_boxes_300(\"voc\")\n",
+    "train_dataset = VOCDetectionDataset(\n",
+    "    train=True, rand_trans=True, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = VOCDetectionDataset(\n",
+    "    train=False, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) to work with our detection model and loss function.  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "from sparseml.pytorch.utils import DEFAULT_LOSS_KEY\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(\n",
+    "    model, data_loader, criterion, device, train=False, optimizer=None\n",
+    "):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = [\n",
+    "            label.to(device) if isinstance(label, torch.Tensor) else label\n",
+    "            for label in labels\n",
+    "        ]\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion((inputs, labels), outputs)[DEFAULT_LOSS_KEY]\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up Detection Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow.  The loss function and collate function are standard for SSD training and are defined in the sparseml API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "from sparseml.pytorch.datasets import ssd_collate_fn\n",
+    "from sparseml.pytorch.utils import SSDLossWrapper\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 64\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size,\n",
+    "    shuffle=True,\n",
+    "    pin_memory=True,\n",
+    "    num_workers=12,\n",
+    "    collate_fn=ssd_collate_fn,\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset,\n",
+    "    batch_size,\n",
+    "    shuffle=False,\n",
+    "    pin_memory=True,\n",
+    "    num_workers=12,\n",
+    "    collate_fn=ssd_collate_fn,\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer\n",
+    "criterion = SSDLossWrapper()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning SSD-ResNet18 on the VOC dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"detection\",\n",
+    "    architecture=\"ssd\",\n",
+    "    sub_architecture=\"resnet18_300\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"sparseml\",\n",
+    "    dataset=\"voc\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "epoch = manager.min_epochs\n",
+    "while epoch < manager.max_epochs:\n",
+    "    # run training loop\n",
+    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
+    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
+    "    train_loss = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\"Training Epoch: {}\\nTraining Loss: {}\\n\".format(epoch_name, train_loss))\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
+    "    val_loss = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\"Validation Epoch: {}\\nVal Loss: {}\\n\".format(epoch_name, val_loss))\n",
+    "\n",
+    "    epoch += 1\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_detection\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"ssd_resnet18_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 300, 300), name=\"ssd_resnet18_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb b/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
new file mode 100644
index 00000000000..d5d50f63a23
--- /dev/null
+++ b/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Sparse-Quantized Transfer Learning in PyTorch using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for creating a performant sparse-quantized model\n",
+    "by transfer learning the pruned structure from an already sparse-quantized model.\n",
+    "\n",
+    "Sparse-quantized models combine [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to reduce both the number of parameters and the precision of the remaining parameters to significantly increase the performance of neural networks. Using these optimizations, your model will obtain significantly better (around 7x vs. unoptimized) performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse).\n",
+    "\n",
+    "Sparse-quantized transfer learning takes two steps. [SparseML](https://github.com/neuralmagic/sparseml) recipes make it easy to perform these optimizations:\n",
+    "- First, fine-tune a pre-trained sparse model for the transfer dataset while maintaining the pre-trained sparsity structure.\n",
+    "- Second, perform [quantization-aware training (QAT)](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) to quantize the now sparse model while still holding the same sparsity structure.\n",
+    "\n",
+    "In this notebook, you will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML for transfer learning\n",
+    "- Perform sparse transfer learning and quantization-aware training using the PyTorch and SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/) and convert the model from a QAT\n",
+    "- [Optional] Compare DeepSparse Engine benchmarks of the final sparse-quantized model to an unoptimized model\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow for transfer learning and generically. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 30 minutes on a GPU\n",
+    "- 90 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML, SparseZoo\n",
+    "* PyTorch (>= 1.7.0) and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "assert torch.__version__ >= \"1.7\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will transfer learn from a sparse-quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [ImageNet dataset](http://www.image-net.org/) to the much smaller [Imagenette dataset](https://github.com/fastai/imagenette). The transfer learning weights are downloaded from the [SparseZoo](https://github.com/neuralmagic/sparsezoo) model repository.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "When loading weights for transfer learning classification models, it is standard to override the final classifier layer to fit the output shape for the new dataset.  In the example below, this is done by specifying `ignore_error_tensors` as the weights that will be initialzed for the new model.  In other flows this could be accomplished by setting `model.classifier.fc = torch.nn.Linear(...)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "from sparsezoo import Model\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
+    "zoo_stub_path = (\n",
+    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate\"\n",
+    "    \"?recipe=transfer_learn\"\n",
+    ")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"resnet50\",\n",
+    "    pretrained_path=zoo_stub_path,\n",
+    "    pretrained_dataset=\"imagenette\",\n",
+    "    num_classes=10,\n",
+    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
+    ")\n",
+    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Creating a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune and quantize your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
+    "        loss = criterion(outputs, labels)\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Building PyTorch Training Objects\n",
+    "In this step, you will select hyperparameters, a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "# hyperparameters\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "# setup data loaders\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Running Sparse-Quantized Transfer Learning with a SparseML Recipe\n",
+    "\n",
+    "To run sparse-quantized transfer learning with SparseML, you will download a transfer learning recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to maintain the pre-optimized model's sparsity structure while learning weights for the new dataset as well as performing quantization-aware training (QAT).\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, QAT, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe  file to match the new target's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error; to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides preconfigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet-50 on the Imagenette dataset and record its saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model\n",
+    "\n",
+    "zoo_model = Model(zoo_stub_path)\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "# Run model pruning\n",
+    "epoch = manager.min_epochs\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Viewing Model Sparsity\n",
+    "To see the effects of sparse-quantized transfer learning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the sparse-quantized transfer learning is complete, it should be prepped for inference.  A common next step for inference is exporting the model to ONNX.  This is also the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) to achieve the sparse-quantized speedups.\n",
+    "\n",
+    "For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Additionally, PyTorch, exports a graph setup for quantization-aware training (QAT) to ONNX. To run a fully quantized graph, you will need to convert these QAT operations to fully quantized INT8 operations.  SparseML provides the `quantize_torch_qat_export` helper function to perform this conversion.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.\n",
+    "\n",
+    "Normally, exporting a QAT model from PyTorch to ONNX will create a graph with \"fake quantized\" operations that represent the QAT graph.  By setting `convert_qat=True` in our exporter, a function will automatically be called to convert this exported model to a fully quantized graph that will contain desired quantized structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_sparse_quantized_transfer_learning\"\n",
+    "quant_onnx_graph_name = \"resnet50_imagenette_pruned_quant.onnx\"\n",
+    "quantized_onnx_path = os.path.join(save_dir, quant_onnx_graph_name)\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned_qat.pth\")\n",
+    "exporter.export_onnx(\n",
+    "    torch.randn(1, 3, 224, 224), name=quant_onnx_graph_name, convert_qat=True\n",
+    ")\n",
+    "\n",
+    "print(f\"Sparse-Quantized ONNX model saved to {quantized_onnx_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [Optional] Step 8 - Benchmarking\n",
+    "\n",
+    "Finally, to see the total effect of these optimizations, you will benchmark an unoptimized, dense ResNet-50 model from SparseZoo against your sparse-quantized model using the `deepsparse` API.\n",
+    "\n",
+    "To run this step `deepsparse` must be installed in your python environment. You can install it with `pip install deepsparse`.\n",
+    "\n",
+    "Note, in order to view speedup from quantization, your CPU must run VNNI instructions.  The benchmarking cell below contains a check for VNNI instructions and will log a warning if they are not detected.  You can learn more about DeepSparse hardware compatibility [here](https://docs.neuralmagic.com/deepsparse/hardware.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "from deepsparse import benchmark_model\n",
+    "from deepsparse.cpu import cpu_architecture\n",
+    "\n",
+    "\n",
+    "# check VNNI\n",
+    "if cpu_architecture()[\"vnni\"]:\n",
+    "    print(\"VNNI extensions detected, model will run with quantized speedups\\n\")\n",
+    "else:\n",
+    "    print(\n",
+    "        \"WARNING: No VNNI extensions detected. Your model will not run with \"\n",
+    "        \"quantized speedups which will affect benchmarking\\n\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_CORES = None  # maximum number of cores available\n",
+    "NUM_ITERATIONS = 100\n",
+    "NUM_WARMUP_ITERATIONS = 20\n",
+    "\n",
+    "\n",
+    "def benchmark_imagenette_model(model_name, model_path):\n",
+    "    print(\n",
+    "        f\"Benchmarking {model_name} for {NUM_ITERATIONS} iterations at batch \"\n",
+    "        f\"size {BATCH_SIZE} with {NUM_CORES} CPU cores\"\n",
+    "    )\n",
+    "    sample_input = [\n",
+    "        numpy.ascontiguousarray(\n",
+    "            numpy.random.randn(BATCH_SIZE, 3, 224, 224).astype(numpy.float32)\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    results = benchmark_model(\n",
+    "        model=model_path,\n",
+    "        inp=sample_input,\n",
+    "        batch_size=BATCH_SIZE,\n",
+    "        num_cores=NUM_CORES,\n",
+    "        num_iterations=NUM_ITERATIONS,\n",
+    "        num_warmup_iterations=NUM_WARMUP_ITERATIONS,\n",
+    "        show_progress=True,\n",
+    "    )\n",
+    "    print(f\"results:\\n{results}\")\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "# base ResNet-50 Imagenette model downloaded from SparseZoo\n",
+    "base_results = benchmark_imagenette_model(\n",
+    "    \"ResNet-50 Imagenette Base\",\n",
+    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none\"\n",
+    ")\n",
+    "\n",
+    "optimized_results = benchmark_imagenette_model(\n",
+    "    \"ResNet-50 Imagenette pruned-quantized\", quantized_onnx_path\n",
+    ")\n",
+    "\n",
+    "speed_up = base_results.ms_per_batch / optimized_results.ms_per_batch\n",
+    "print(f\"Speed-up from sparse-quantized transfer learning: {speed_up}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have created a sparse-quantized model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Transfer learning, pruning, or quantizing different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Benchmarking other models on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/pytorch/notebooks/torchvision.ipynb b/integrations/examples/pytorch/notebooks/torchvision.ipynb
new file mode 100644
index 00000000000..82c2f82147b
--- /dev/null
+++ b/integrations/examples/pytorch/notebooks/torchvision.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Torchvision Classification Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning a [torchvision model](https://pytorch.org/docs/stable/torchvision/models.html) using SparseML. You will:\n",
+    "- Download a pre-trained torchvision model and generic dataset\n",
+    "- Define a generic torchvision finetuning flow\n",
+    "- Integrate the torchvision flow with SparseML\n",
+    "- Prune the model using the torchvision+SparseML flow\n",
+    "- Save the model and export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to integrate SparseML with torchvision or more generically a PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 15 minutes on a GPU\n",
+    "- 45 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model while finetuning it on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from torchvision. The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "Additionally, we will override the FC layer in the ResNet50 model to have 10 output classes instead of the ImageNet standard 1000.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = resnet50(pretrained=True)\n",
+    "- train_dataset = ImagenetteDataset(...)\n",
+    "- val_dataset = ImagenetteDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.models import resnet50\n",
+    "from torch.nn import Linear\n",
+    "\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = resnet50(pretrained=True)\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=224\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=224\n",
+    ")\n",
+    "print(val_dataset)\n",
+    "\n",
+    "# Overriding number of classes\n",
+    "NUM_CLASSES = 10  # number of imagenette classes\n",
+    "model.fc = Linear(in_features=model.fc.in_features, out_features=NUM_CLASSES, bias=True)\n",
+    "print(model.fc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a Torchvision Finetuning Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop taken from the [torchvision finetuning example](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import copy\n",
+    "import torch\n",
+    "\n",
+    "def train_model(\n",
+    "    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False\n",
+    "):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    val_acc_history = []\n",
+    "\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        print(\"Epoch {}/{}\".format(epoch, num_epochs - 1))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # forward\n",
+    "                # track history if only in train\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    # Get model outputs and calculate loss\n",
+    "                    # Special case for inception because in training it has an auxiliary output. In train\n",
+    "                    #   mode we calculate the loss by summing the final output and the auxiliary output\n",
+    "                    #   but in testing we only consider the final output.\n",
+    "                    if is_inception and phase == \"train\":\n",
+    "                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958\n",
+    "                        outputs, aux_outputs = model(inputs)\n",
+    "                        loss1 = criterion(outputs, labels)\n",
+    "                        loss2 = criterion(aux_outputs, labels)\n",
+    "                        loss = loss1 + 0.4 * loss2\n",
+    "                    else:\n",
+    "                        outputs = model(inputs)\n",
+    "                        loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # statistics\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
+    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            # deep copy the model\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "            if phase == \"val\":\n",
+    "                val_acc_history.append(epoch_acc)\n",
+    "\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # load best model weights\n",
+    "    return model, val_acc_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up PyTorch Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 128\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "dataloaders = {\"train\": train_loader, \"val\": val_loader}\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"classification\",\n",
+    "    architecture=\"resnet_v1\",\n",
+    "    sub_architecture=\"50\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"torchvision\",\n",
+    "    dataset=\"imagenette\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "train_model(\n",
+    "    model,\n",
+    "    dataloaders,\n",
+    "    criterion,\n",
+    "    optimizer,\n",
+    "    device,\n",
+    "    num_epochs=manager.max_epochs,\n",
+    "    is_inception=False,\n",
+    ")\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Save Model and Export to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"torchvision_models\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/pytorch/pr_sensitivity.py b/integrations/examples/pytorch/pr_sensitivity.py
new file mode 100644
index 00000000000..fad574bd440
--- /dev/null
+++ b/integrations/examples/pytorch/pr_sensitivity.py
@@ -0,0 +1,416 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+python integrations/pytorch/pr_sensitivity.py --help
+usage: pr_sensitivity.py [-h] --arch-key ARCH_KEY --dataset DATASET
+                         --dataset-path DATASET_PATH
+                         [--pretrained PRETRAINED]
+                         [--pretrained-dataset PRETRAINED_DATASET]
+                         [--model-kwargs MODEL_KWARGS]
+                         [--dataset-kwargs DATASET_KWARGS]
+                         [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                         [--device DEVICE]
+                         [--loader-num-workers LOADER_NUM_WORKERS]
+                         [--no-loader-pin-memory]
+                         [--loader-pin-memory [LOADER_PIN_MEMORY]]
+                         [--checkpoint-path CHECKPOINT_PATH]
+                         [--steps-per-measurement STEPS_PER_MEASUREMENT]
+                         [--batch-size BATCH_SIZE]
+                         [--approximate [APPROXIMATE]]
+
+Utility script to Run a kernel sparsity (pruning) analysis for a
+desired image classification architecture
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list
+                        (will raise an exception with the list)
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder
+                        for a generic dataset setup with an image
+                        folder structure setup like imagenet or
+                        loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default
+                        is true to load the default pretrained weights
+                        for the model. Otherwise should be set to the
+                        desired weights type: [base, optim, optim-
+                        perf]. To not load any weights set to one of
+                        [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will
+                        load the default dataset for the architecture.
+                        Ex can be set to imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model
+                        constructor, should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results
+                        under save-dir, defaults to the model arch and
+                        dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for
+                        data parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the
+                        state from and resume the state for. If
+                        provided, pretrained will be ignored. If using
+                        a SparseZoo recipe, can also provide 'zoo' to
+                        load the base weights associated with that
+                        recipe
+  --steps-per-measurement STEPS_PER_MEASUREMENT
+                        The number of steps (batches) to run for each
+                        measurement
+  --batch-size BATCH_SIZE
+                        The batch size to use for analysis
+  --approximate [APPROXIMATE]
+                        approximate without running data through the
+                        model(uses one shot analysis if --approximate
+                        not passed)
+#########
+EXAMPLES
+#########
+
+##########
+Example command for running one shot KS sens analysis on ssd300_resnet50 for
+coco:
+python integrations/pytorch/lr_analysis.py pr_sensitivity \
+    --arch-key ssd300_resnet50 --dataset coco \
+    --dataset-path ~/datasets/coco-detection
+
+Note: Might need to install pycocotools using pip
+"""
+import json
+import os
+from dataclasses import dataclass, field
+from typing import Any, List, Optional
+
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import (
+    pruning_loss_sens_magnitude,
+    pruning_loss_sens_one_shot,
+)
+from sparseml.pytorch.utils import default_device, model_to_device
+
+
+CURRENT_TASK = utils.Tasks.PR_SENSITIVITY
+LOGGER = get_main_logger()
+
+
+@dataclass
+class PRAnalysisArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    kernel sparsity (pruning) analysis tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for training, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param pretrained: The type of pretrained weights to use default is true to
+        load the default pretrained weights for the model Otherwise should be
+        set to the desired weights type: [base, optim, optim-perf]; To not
+        load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load
+        pretrained weights for if pretrained is set; Default is None which will
+        load the default dataset for the architecture; Ex can be set to
+        imagenet, cifar10, etc".
+    :param model_kwargs: json object containing keyword arguments to be
+        passed to model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be
+        passed to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results
+        under save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on
+        (can also include ids for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data
+        loading, default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with
+        a recipe.
+    :param steps_per_measurement: The number of steps (batches) to run for
+        each measurement
+    :param batch_size: The batch size to use for analysis
+    :param approximate: approximate without running data through the model
+        (uses one shot analysis if --approximate not passed)
+    """
+
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception "
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights "
+            "for the model. Otherwise should be set to the desired "
+            "weights type: [base, optim, optim-perf]. To not load any"
+            " weights set  to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained"
+            "is set. Default is None which will load the default "
+            "dataset for the architecture. Ex can be set to imagenet,"
+            "cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, "
+            "should be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under "
+            "save-dir, defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for "
+            "data parallel), ex: cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored. If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    steps_per_measurement: int = field(
+        default=15,
+        metadata={"help": "The number of steps (batches) to run for each measurement"},
+    )
+    batch_size: int = field(
+        default=64, metadata={"help": "The batch size to use for analysis"}
+    )
+
+    approximate: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "approximate without running data through the model"
+            "(uses one shot analysis if --approximate not passed)",
+        },
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.is_main_process = True
+        self.local_rank = -1
+        self.rank = -1
+
+
+def pruning_loss_sensitivity(
+    args: PRAnalysisArguments,
+    model,
+    train_loader: DataLoader,
+    save_dir: str,
+    loggers: List[Any],
+) -> None:
+    """
+    Utility function for pruning sensitivity analysis
+
+    :param args : A PRAnalysisArguments object containing config for current
+        analysis
+    :param model: loaded model architecture to analyse
+    :param train_loader: A DataLoader for training data
+    :param save_dir: Directory to save results
+    :param loggers: List of loggers to use during analysis
+    """
+    # loss setup
+    if not args.approximate:
+        loss = utils.get_loss_wrapper(args)
+        LOGGER.info(f"created loss: {loss}")
+    else:
+        loss = None
+
+    # device setup
+    if not args.approximate:
+        module, device, device_ids = model_to_device(model, args.device)
+    else:
+        device = None
+
+    # kernel sparsity analysis
+    if args.approximate:
+        analysis = pruning_loss_sens_magnitude(model)
+    else:
+        analysis = pruning_loss_sens_one_shot(
+            model,
+            train_loader,
+            loss,
+            device,
+            args.steps_per_measurement,
+            tester_loggers=loggers,
+        )
+
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info(f"Saving results in {save_dir}")
+    analysis.save_json(
+        os.path.join(
+            save_dir,
+            "ks_approx_sensitivity.json"
+            if args.approximate
+            else "ks_one_shot_sensitivity.json",
+        )
+    )
+    analysis.plot(
+        os.path.join(
+            save_dir,
+            os.path.join(
+                save_dir,
+                "ks_approx_sensitivity.png"
+                if args.approximate
+                else "ks_one_shot_sensitivity.png",
+            ),
+        ),
+        plot_integral=True,
+    )
+    analysis.print_res()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=PRAnalysisArguments,
+        description="Utility script to Run a "
+        "kernel sparsity (pruning) analysis "
+        "for a desired image classification architecture",
+    )
+
+    args_, _ = _parser.parse_args_into_dataclasses()
+
+    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
+
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    # assume shape [C, S, S] where S is the image size
+    image_size = input_shape[1]
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
+
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+
+    model = utils.create_model(args_, num_classes)
+
+    pruning_loss_sensitivity(args_, model, train_loader, save_dir, loggers)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/examples/pytorch/recipes/.gitkeep b/integrations/examples/pytorch/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md
new file mode 100644
index 00000000000..29479dcdc1f
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md
@@ -0,0 +1,98 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# Epoch and Learning-Rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+    
+---
+
+# Transfer Learning
+
+This recipe provides a framework for performing sparse transfer learning on
+SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+
+To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned
+```
+
+To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned.md\
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag mobilenet-imagenette-sparse-transfer-learned
+```
+## Other Datasets
+To transfer learn this sparsified model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increase the learning rate when increasing batch size
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
new file mode 100644
index 00000000000..5820fcf05f3
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
@@ -0,0 +1,106 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# Epoch and Learning-Rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+# quantization variables
+quantization_start_epoch: &quantization_start_epoch 4.0
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+# phase 2 apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+
+---
+# Transfer Learning
+
+This recipe provides a framework for performing sparse transfer learning on
+SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+
+To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned-quantized
+```
+
+To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag mobilenet-imagenette-sparse-transfer-learned-quantized
+```
+## Other Datasets
+To transfer learn this sparsified model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increase the learning rate when increasing batch size
+* Increasing the number of epochs before starting quantization can give a higher baseline
+* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/mobilenet-pruned.md b/integrations/examples/pytorch/recipes/mobilenet-pruned.md
new file mode 100644
index 00000000000..03dd40b9811
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/mobilenet-pruned.md
@@ -0,0 +1,107 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 90
+init_lr: &init_lr 0.0028
+lr_step_epochs: &lr_step_epochs [43, 60]
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 0.0
+pruning_end_epoch: &pruning_end_epoch 40.0
+pruning_update_frequency: &pruning_update_frequency 0.4
+init_sparsity: &init_sparsity 0.05
+pruning_mask_type: &pruning_mask_type unstructured
+prune_low_target_sparsity: &prune_low_target_sparsity 0.7
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.9
+
+
+training_modifiers:
+  - !EpochRangeModifier
+    end_epoch: *num_epochs
+    start_epoch: 0.0
+
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: -1.0
+    init_lr: *init_lr
+    lr_class: MultiStepLR
+    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
+    start_epoch: 0.0
+    update_frequency: -1.0
+    
+pruning_modifiers:
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_low_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.1.0.point.conv.weight', 'sections.1.1.point.conv.weight', 'sections.2.0.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_mid_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.2.1.point.conv.weight', 'sections.3.0.point.conv.weight', 'sections.3.1.point.conv.weight', 'sections.3.5.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_high_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.3.2.point.conv.weight', 'sections.3.3.point.conv.weight', 'sections.3.4.point.conv.weight', 'sections.4.0.point.conv.weight', 'sections.4.1.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# MobileNet-V1 ImageNet Moderate Sparse
+
+This recipe creates a sparse [MobileNet-V1](https://arxiv.org/abs/1704.04861) model that
+achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
+Training was done using 4 GPUs using a total training batch size of 1024
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key mobilenet \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 1024 --test-batch-size 2056 \
+    --loader-num-workers 16 \
+    --model-tag mobilenet-imagenet-pruned-moderate
+```
diff --git a/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md b/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md
new file mode 100644
index 00000000000..94f71cf07d8
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md
@@ -0,0 +1,160 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 100.0
+init_lr: &init_lr 0.005
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
+pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 90
+disable_quantization_observer_epoch: &disable_quantization_observer_epoch 92.0
+freeze_bn_stats_epoch: &freeze_bn_stats_epoch 92.0
+quantization_init_lr: &quantization_init_lr 0.00001
+quantization_lr_step_milestones: &quantization_lr_step_milestones [91]
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 36.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.55
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.7
+prune_high_target_sparsity: &prune_high_target_sparsity 0.8
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    start_epoch: *pruning_recovery_start_epoch
+    end_epoch: *quantization_start_epoch
+    lr_class: CosineAnnealingWarmRestarts
+    lr_kwargs:
+      lr_min: *pruning_recovery_end_lr
+      cycle_epochs: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_recovery_start_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !LearningRateModifier
+    start_epoch: *quantization_start_epoch
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *quantization_lr_step_milestones
+      gamma: 0.1
+    init_lr: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.point.conv.weight
+      - sections.1.0.point.conv.weight
+      - sections.1.1.point.conv.weight
+      - sections.2.0.point.conv.weight
+      - classifier.fc.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.2.1.point.conv.weight
+      - sections.3.0.point.conv.weight
+      - sections.3.1.point.conv.weight
+      - sections.3.5.point.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.3.2.point.conv.weight
+      - sections.3.3.point.conv.weight
+      - sections.3.4.point.conv.weight
+      - sections.4.0.point.conv.weight
+      - sections.4.1.point.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
+    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
+
+    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
+    # but are unnecessary due to these already being set during the training phase
+---
+
+# MobileNet-V1 ImageNet Moderate Sparse Quantization
+
+This recipe creates a sparse quantized [MobileNet-V1](https://arxiv.org/abs/1704.04861) model
+that achieves 99% of its baseline accuracy on the ImageNet dataset.  Training was done using
+4 GPUs using a total training batch size of 96 using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+python -m torch.distributed.launch \
+  --nproc_per_node 4 \
+  src/sparseml/pytorch/image_classification/train.py \
+    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch-sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key mobilenet \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 96 --test-batch-size 256 \
+    --loader-num-workers 16 \
+    --model-tag mobilenet-imagenet-pruned_quant-moderate
+```
diff --git a/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md b/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md
new file mode 100644
index 00000000000..16318a27b99
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md
@@ -0,0 +1,145 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 10
+lr: &lr 0.008
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 8.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.8
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.85
+prune_high_target_sparsity: &prune_high_target_sparsity 0.9
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.conv1.weight
+      - sections.0.0.conv2.weight
+      - sections.0.0.conv3.weight
+      - sections.0.0.identity.conv.weight
+      - sections.0.1.conv1.weight
+      - sections.0.1.conv3.weight
+      - sections.0.2.conv1.weight
+      - sections.0.2.conv3.weight
+      - sections.1.0.conv1.weight
+      - sections.1.0.conv3.weight
+      - sections.1.2.conv3.weight
+      - sections.1.3.conv1.weight
+      - sections.2.0.conv1.weight
+      - sections.3.0.conv1.weight
+      - classifier.fc.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.0.1.conv2.weight
+      - sections.0.2.conv2.weight
+      - sections.1.0.identity.conv.weight
+      - sections.1.1.conv1.weight
+      - sections.1.1.conv2.weight
+      - sections.1.1.conv3.weight
+      - sections.1.2.conv1.weight
+      - sections.1.2.conv2.weight
+      - sections.1.3.conv2.weight
+      - sections.1.3.conv3.weight
+      - sections.2.0.conv3.weight
+      - sections.2.0.identity.conv.weight
+      - sections.2.1.conv1.weight
+      - sections.2.1.conv3.weight
+      - sections.2.2.conv1.weight
+      - sections.2.2.conv3.weight
+      - sections.2.3.conv1.weight
+      - sections.2.3.conv3.weight
+      - sections.2.4.conv1.weight
+      - sections.2.4.conv3.weight
+      - sections.2.5.conv1.weight
+      - sections.2.5.conv3.weight
+      - sections.3.1.conv1.weight
+      - sections.3.2.conv1.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.1.0.conv2.weight
+      - sections.2.0.conv2.weight
+      - sections.2.1.conv2.weight
+      - sections.2.2.conv2.weight
+      - sections.2.3.conv2.weight
+      - sections.2.4.conv2.weight
+      - sections.2.5.conv2.weight
+      - sections.3.0.conv2.weight
+      - sections.3.0.conv3.weight
+      - sections.3.0.identity.conv.weight
+      - sections.3.1.conv2.weight
+      - sections.3.1.conv3.weight
+      - sections.3.2.conv2.weight
+      - sections.3.2.conv3.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# ResNet-50 Imagenette Conservative Sparse
+
+This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
+achieves full recovery of its baseline accuracy on the Imagenette dataset.
+Training was done using 1 GPU with a total training batch size of 128
+using a SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training. 
+Alternatively, a full walk-through notebook is located at `sparseml/integrations/pytorch/notebooks/classification.ipynb`.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 128 --test-batch-size 256 \
+    --loader-num-workers 8 \
+    --model-tag resnet50-imagenette-pruned-conservative
+```
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned.md b/integrations/examples/pytorch/recipes/resnet50-pruned.md
new file mode 100644
index 00000000000..27fe718867b
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/resnet50-pruned.md
@@ -0,0 +1,83 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 90
+init_lr: &init_lr 0.0028
+lr_step_epochs: &lr_step_epochs [43, 60]
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 0.0
+pruning_end_epoch: &pruning_end_epoch 40.0
+pruning_update_frequency: &pruning_update_frequency 0.4
+init_sparsity: &init_sparsity 0.05
+final_sparsity: &final_sparsity 0.88
+pruning_mask_type: &pruning_mask_type unstructured
+
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    end_epoch: *num_epochs
+    start_epoch: 0.0
+
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: -1.0
+    init_lr: *init_lr
+    lr_class: MultiStepLR
+    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
+    start_epoch: 0.0
+    update_frequency: -1.0
+        
+pruning_modifiers:
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *final_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.0.0.conv1.weight', 'sections.0.0.conv2.weight', 'sections.0.0.conv3.weight', 'sections.0.0.identity.conv.weight', 'sections.0.1.conv1.weight', 'sections.0.1.conv2.weight', 'sections.0.1.conv3.weight', 'sections.0.2.conv1.weight', 'sections.0.2.conv2.weight', 'sections.0.2.conv3.weight', 'sections.1.0.conv1.weight', 'sections.1.0.conv2.weight', 'sections.1.0.conv3.weight', 'sections.1.0.identity.conv.weight', 'sections.1.1.conv1.weight', 'sections.1.1.conv2.weight', 'sections.1.1.conv3.weight', 'sections.1.2.conv1.weight', 'sections.1.2.conv2.weight', 'sections.1.2.conv3.weight', 'sections.1.3.conv1.weight', 'sections.1.3.conv2.weight', 'sections.1.3.conv3.weight', 'sections.2.0.conv1.weight', 'sections.2.0.conv2.weight', 'sections.2.0.conv3.weight', 'sections.2.0.identity.conv.weight', 'sections.2.1.conv1.weight', 'sections.2.1.conv2.weight', 'sections.2.1.conv3.weight', 'sections.2.2.conv1.weight', 'sections.2.2.conv2.weight', 'sections.2.2.conv3.weight', 'sections.2.3.conv1.weight', 'sections.2.3.conv2.weight', 'sections.2.3.conv3.weight', 'sections.2.4.conv1.weight', 'sections.2.4.conv2.weight', 'sections.2.4.conv3.weight', 'sections.2.5.conv1.weight', 'sections.2.5.conv2.weight', 'sections.2.5.conv3.weight', 'sections.3.0.conv1.weight', 'sections.3.0.conv2.weight', 'sections.3.0.conv3.weight', 'sections.3.0.identity.conv.weight', 'sections.3.1.conv1.weight', 'sections.3.1.conv2.weight', 'sections.3.1.conv3.weight', 'sections.3.2.conv1.weight', 'sections.3.2.conv2.weight', 'sections.3.2.conv3.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# ResNet-50 ImageNet Moderate Sparse
+
+This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
+achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
+Training was done using 4 GPUs using a total training batch size of 1024
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 1024 --test-batch-size 2056 \
+    --loader-num-workers 16 \
+    --model-tag resnet50-imagenet-pruned-moderate
+```
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md b/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
new file mode 100644
index 00000000000..49fba746f53
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
@@ -0,0 +1,100 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# epoch and learning rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+# quantization variables
+quantization_start_epoch: &quantization_start_epoch 4.0
+quantize_submodules: &quantize_submodules
+  - input
+  - sections
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# phase 1 sparse transfer learning / recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL__
+
+# phase 2 apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+
+  - !SetWeightDecayModifier
+    start_epoch: 0.0
+    weight_decay: 0.0
+---
+
+# Transfer Learning ResNet50 ImageNet Pruned-Quantized
+
+This recipe provides a framework for performing pruned-quantized transfer learning on
+SparseML's `pruned_quantized` ResNet50 model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+On the default transfer learning dataset, Imagenette, this recipe fully recovered to 99.9% Top-1 accuracy.
+To run this recipe, you may run the example notebook
+`sparseml/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb`
+or can run the example script command below.
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn \
+    --checkpoint-path zoo \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 8 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-pruned_quant-transfer_learned
+```
+
+## Other Datasets
+To transfer learn this ResNet 50 Pruned-Quantized model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increate the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increasing the number of epochs before starting quantization can give a higher baseline
+* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
+* Increase the learning rate when increasing batch size
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md b/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md
new file mode 100644
index 00000000000..fe83cb8a3fc
--- /dev/null
+++ b/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md
@@ -0,0 +1,198 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 135.0
+init_lr: &init_lr 0.008
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
+pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
+pruning_recovery_end_lr: &pruning_recovery_end_lr 0.00001
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 100
+disable_quantization_observer_epoch: &disable_quantization_observer_epoch 115.0
+freeze_bn_stats_epoch: &freeze_bn_stats_epoch 116.0
+quantization_init_lr: &quantization_init_lr 0.01
+quantization_lr_step_milestones: &quantization_lr_step_milestones [107, 114, 125]
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 36.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.7
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.75
+prune_high_target_sparsity: &prune_high_target_sparsity 0.8
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    start_epoch: *pruning_recovery_start_epoch
+    end_epoch: *quantization_start_epoch
+    lr_class: CosineAnnealingWarmRestarts
+    lr_kwargs:
+      lr_min: *pruning_recovery_end_lr
+      cycle_epochs: *pruning_recovery_num_epochs
+    init_lr: *pruning_recovery_start_lr
+
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_recovery_start_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !LearningRateModifier
+    start_epoch: *quantization_start_epoch
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *quantization_lr_step_milestones
+      gamma: 0.1
+    init_lr: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.identity.conv.weight
+      - sections.0.0.conv1.weight
+      - sections.0.0.conv2.weight
+      - sections.0.0.conv3.weight
+      - sections.0.1.conv1.weight
+      - sections.0.1.conv3.weight
+      - sections.0.2.conv1.weight
+      - sections.0.2.conv3.weight
+      - sections.1.0.conv1.weight
+      - sections.1.0.conv3.weight
+      - sections.1.2.conv3.weight
+      - sections.1.3.conv1.weight
+      - sections.2.0.conv1.weight
+      - sections.3.0.conv1.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.0.1.conv2.weight
+      - sections.0.2.conv2.weight
+      - sections.1.0.identity.conv.weight
+      - sections.1.1.conv1.weight
+      - sections.1.1.conv2.weight
+      - sections.1.1.conv3.weight
+      - sections.1.2.conv1.weight
+      - sections.1.2.conv2.weight
+      - sections.1.3.conv2.weight
+      - sections.1.3.conv3.weight
+      - sections.2.0.conv3.weight
+      - sections.2.0.identity.conv.weight
+      - sections.2.1.conv1.weight
+      - sections.2.1.conv3.weight
+      - sections.2.2.conv1.weight
+      - sections.2.3.conv1.weight
+      - sections.2.3.conv3.weight
+      - sections.2.4.conv1.weight
+      - sections.2.4.conv3.weight
+      - sections.2.5.conv1.weight
+      - sections.2.5.conv3.weight
+      - sections.3.1.conv1.weight
+      - sections.3.2.conv1.weight
+      - sections.1.0.conv2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.2.0.conv2.weight
+      - sections.2.1.conv2.weight
+      - sections.2.2.conv2.weight
+      - sections.2.3.conv2.weight
+      - sections.2.4.conv2.weight
+      - sections.2.5.conv2.weight
+      - sections.3.0.conv2.weight
+      - sections.3.0.conv3.weight
+      - sections.3.0.identity.conv.weight
+      - sections.3.1.conv2.weight
+      - sections.3.1.conv3.weight
+      - sections.3.2.conv2.weight
+      - sections.3.2.conv3.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
+    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
+
+    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
+    # but are unnecessary due to these already being set during the training phase
+---
+
+# ResNet-50 ImageNet Moderate Sparse Quantization
+
+This recipe creates a sparse quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of its baseline accuracy
+on the ImageNet dataset.  Training was done using 4 GPUs using a total training batch size of 96
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+python -m torch.distributed.launch \
+  --nproc_per_node 4 \
+  src/sparseml/pytorch/image_classification/train.py \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 96 --test-batch-size 256 \
+    --loader-num-workers 16 \
+    --model-tag resnet50-imagenet-pruned_quant-moderate
+```
diff --git a/integrations/examples/pytorch/torchvision_sparsification.py b/integrations/examples/pytorch/torchvision_sparsification.py
new file mode 100644
index 00000000000..07acd698b19
--- /dev/null
+++ b/integrations/examples/pytorch/torchvision_sparsification.py
@@ -0,0 +1,455 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Example script for running training and optimization tasks on torchvision
+classification models with an imagefolder based dataset using SparseML.
+Example tasks include:
+* Model pruning
+* Quantization aware training
+* Sparse transfer learning
+
+More information about torchvision models can be found here:
+https://pytorch.org/docs/stable/torchvision/models.html
+
+##########
+Command help:
+usage: torchvision_sparseml.py [-h] --model MODEL [--recipe-path RECIPE_PATH]
+                               [--image-size IMAGE_SIZE]
+                               [--batch-size BATCH_SIZE]
+                               [--pretrained PRETRAINED]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               --imagefolder-path IMAGEFOLDER_PATH
+                               [--loader-num-workers LOADER_NUM_WORKERS]
+                               [--loader-pin-memory LOADER_PIN_MEMORY]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+
+Train or finetune an image classification model from torchvision.models
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model MODEL         The torchvision model class to use, ex: inception_v3,
+                        resnet50, mobilenet_v2 model name is fed directly to
+                        torchvision.models, more information can be found here
+                        https://pytorch.org/docs/stable/torchvision/models.htm
+                        l
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the sparseml
+                        modifiers and schedule to apply them with
+  --image-size IMAGE_SIZE
+                        Size of image to use for model input. Default is 224
+                        unless pytorch documentation specifies otherwise
+  --batch-size BATCH_SIZE
+                        Batch size to use when training model. Default is 32
+  --pretrained PRETRAINED
+                        Set True to use torchvisions pretrained weights, to
+                        not set weights, set False. default is true.
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --imagefolder-path IMAGEFOLDER_PATH
+                        Path to root of dataset's generic 'image folder' path.
+                        Should have an image folder structure like imagenet
+                        with subdirectories 'train' and 'val' see https://pyto
+                        rch.org/docs/stable/torchvision/datasets.html#imagefol
+                        der
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --loader-pin-memory LOADER_PIN_MEMORY
+                        Use pinned memory for data loading
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+
+##########
+Example command for pruning resnet50 on an imagefolder dataset:
+python integrations/pytorch/torchvision_sparsification.py \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --model resnet50 \
+    --imagefolder-path ~/datasets/ILSVRC2012 \
+    --batch-size 256
+"""
+
+import argparse
+import os
+import time
+from types import ModuleType
+
+import torch
+from torch.nn import CrossEntropyLoss
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+from torchvision import models
+
+from sparseml.pytorch.datasets.classification import ImageFolderDataset
+from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
+from sparseml.pytorch.utils import ModuleExporter, PythonLogger, load_model
+from sparseml.utils import create_dirs
+
+
+MODEL_IMAGE_SIZES = {
+    "inception_v3": 299,
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Train or finetune an image classification model from "
+        "torchvision.models"
+    )
+
+    # model args
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help=(
+            "The torchvision model class to use, ex: inception_v3, resnet50, "
+            "mobilenet_v2 model name is fed directly to torchvision.models, "
+            "more information can be found here "
+            "https://pytorch.org/docs/stable/torchvision/models.html"
+        ),
+    )
+    parser.add_argument(
+        "--recipe-path",
+        type=str,
+        required=True,
+        help="The path to the yaml file containing the sparseml modifiers and "
+        "schedule to apply them with",
+    )
+    parser.add_argument(
+        "--image-size",
+        type=int,
+        required=False,
+        default=None,
+        help=(
+            "Size of image to use for model input. Default is 224 unless pytorch "
+            "documentation specifies otherwise"
+        ),
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        required=False,
+        default=32,
+        help="Batch size to use when training model. Default is 32",
+    )
+    parser.add_argument(
+        "--pretrained",
+        type=bool,
+        default=True,
+        help="Set True to use torchvisions pretrained weights,"
+        " to not set weights, set False. default is true.",
+    )
+    parser.add_argument(
+        "--checkpoint-path",
+        type=str,
+        default=None,
+        help="A path to a previous checkpoint to load the state from and "
+        "resume the state for. If provided, pretrained will be ignored",
+    )
+
+    # dataset args
+    parser.add_argument(
+        "--imagefolder-path",
+        type=str,
+        required=True,
+        help="Path to root of dataset's generic 'image folder' path. Should have "
+        "an image folder structure like imagenet with subdirectories 'train' and 'val'"
+        " see https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder",
+    )
+    parser.add_argument(
+        "--loader-num-workers",
+        type=int,
+        default=4,
+        help="The number of workers to use for data loading",
+    )
+    parser.add_argument(
+        "--loader-pin-memory",
+        type=bool,
+        default=True,
+        help="Use pinned memory for data loading",
+    )
+
+    # logging and saving
+    parser.add_argument(
+        "--model-tag",
+        type=str,
+        default=None,
+        help="A tag to use for the model for saving results under save-dir, "
+        "defaults to the model arch and dataset used",
+    )
+    parser.add_argument(
+        "--save-dir",
+        type=str,
+        default="torchvision_sparseml_export",
+        help="The path to the directory for saving results",
+    )
+
+    args = parser.parse_args()
+    if args.image_size is None:
+        args.image_size = (
+            MODEL_IMAGE_SIZES[args.model] if args.model in MODEL_IMAGE_SIZES else 224
+        )
+    return args
+
+
+def _get_torchvision_model(name, num_classes, pretrained=True, checkpoint_path=None):
+    model_constructor = getattr(models, name, None)
+    if model_constructor is None or isinstance(model_constructor, ModuleType):
+        # constructor doesn't exist or is a submodule instead of function in torchvision
+        raise ValueError("Torchvision model {} not found".format(name))
+    # build model
+    model = model_constructor(pretrained=False, num_classes=num_classes)
+    if pretrained and not checkpoint_path:
+        pretrained_model = model_constructor(pretrained=True, num_classes=1000)
+        # fix num classes mismatch
+        if num_classes == 1000:
+            model = pretrained_model
+        else:
+            _load_matched_weights(model, pretrained_model)
+        del pretrained_model
+
+    if checkpoint_path is not None:
+        load_model(checkpoint_path, model)
+    return model
+
+
+def _load_matched_weights(base_model, pretrained_model):
+    base_dict = base_model.state_dict()
+    pretrained_dict = pretrained_model.state_dict()
+    for key in base_dict:
+        if (
+            key in pretrained_dict
+            and base_dict[key].shape == pretrained_dict[key].shape
+        ):
+            base_dict[key] = pretrained_dict[key]
+    base_model.load_state_dict(base_dict)
+
+
+def _create_imagefolder_dataloader(args, train=True):
+    dataset = ImageFolderDataset(
+        root=args.imagefolder_path,
+        train=train,
+        rand_trans=train,
+        image_size=args.image_size,
+    )
+    loader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=args.loader_num_workers,
+        pin_memory=args.loader_pin_memory,
+    )
+
+    # return dataloader, number of classes, and input image shape
+    return loader, dataset.num_classes, dataset[0][0].shape
+
+
+######################################################################################
+# torchvision finetuning function from:                                              #
+# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html #
+######################################################################################
+def train_model(
+    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False
+):
+    since = time.time()
+
+    val_acc_history = []
+
+    # not loading best intermediate weights due to sparsity changing
+    # best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+
+    for epoch in range(num_epochs):
+        print("Epoch {}/{}".format(epoch, num_epochs - 1))
+        print("-" * 10)
+
+        # Each epoch has a training and validation phase
+        for phase in ["train", "val"]:
+            if phase == "train":
+                model.train()  # Set model to training mode
+            else:
+                model.eval()  # Set model to evaluate mode
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            # Iterate over data.
+            for inputs, labels in dataloaders[phase]:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward
+                # track history if only in train
+                with torch.set_grad_enabled(phase == "train"):
+                    # Get model outputs and calculate loss
+                    # Special case for inception because in training it has an
+                    # auxiliary output. In train mode we calculate the loss by summing
+                    # the final output and the auxiliary output but in testing we
+                    # only consider the final output.
+                    if is_inception and phase == "train":
+                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958  # noqa
+                        outputs, aux_outputs = model(inputs)
+                        loss1 = criterion(outputs, labels)
+                        loss2 = criterion(aux_outputs, labels)
+                        loss = loss1 + 0.4 * loss2
+                    else:
+                        outputs = model(inputs)
+                        loss = criterion(outputs, labels)
+
+                    _, preds = torch.max(outputs, 1)
+
+                    # backward + optimize only if in training phase
+                    if phase == "train":
+                        loss.backward()
+                        optimizer.step()
+
+                # statistics
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / len(dataloaders[phase].dataset)
+            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
+
+            print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc))
+
+            # deep copy the model
+            if phase == "val" and epoch_acc > best_acc:
+                best_acc = epoch_acc
+                # not loading best intermediate weights due to sparsity changing
+                # best_model_wts = copy.deepcopy(model.state_dict())
+            if phase == "val":
+                val_acc_history.append(epoch_acc)
+
+        print()
+
+    time_elapsed = time.time() - since
+    print(
+        "Training complete in {:.0f}m {:.0f}s".format(
+            time_elapsed // 60, time_elapsed % 60
+        )
+    )
+    print("Best val Acc: {:4f}".format(best_acc))
+
+    # load best model weights
+    # not loading best intermediate weights due to sparsity changing
+    # model.load_state_dict(best_model_wts)
+    return model, val_acc_history
+
+
+def _save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    print(f"Saved recipe to {recipe_save_path}")
+
+
+def main(args):
+    ############################
+    # logging and saving setup #
+    ############################
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+
+    # get unique model tag, defaults to '{model_name}'
+    if not args.model_tag:
+        model_tag = args.model.replace("/", ".")
+        model_id = model_tag
+        model_inc = 0
+
+        while os.path.exists(os.path.join(args.save_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+    print("Model id is set to {}".format(model_id))
+
+    ###########################
+    # standard training setup #
+    ###########################
+
+    # create data loaders
+    train_loader, _, _ = _create_imagefolder_dataloader(args, train=True)
+    val_loader, num_classes, image_shape = _create_imagefolder_dataloader(
+        args, train=False
+    )
+    dataloaders = {"train": train_loader, "val": val_loader}
+
+    # create model
+    model = _get_torchvision_model(
+        args.model,
+        num_classes,
+        args.pretrained,
+        args.checkpoint_path,
+    )
+    print("created model: {}".format(model))
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    print("using device: {}".format(device))
+
+    # create standard SGD optimizer and cross entropy loss function
+    criterion = CrossEntropyLoss()
+    optimizer = SGD(
+        model.parameters(), lr=0.001, momentum=0.9
+    )  # lr will be overridden by recipe
+
+    ##########################
+    # add sparseml modifiers #
+    ##########################
+    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
+    optimizer = ScheduledOptimizer(
+        optimizer,
+        model,
+        manager,
+        steps_per_epoch=len(train_loader),
+        loggers=[PythonLogger()],
+    )
+
+    ########################
+    # torchvision training #
+    ########################
+    _save_recipe(recipe_manager=manager, save_dir=save_dir)
+    model, val_acc_history = train_model(
+        model,
+        dataloaders,
+        criterion,
+        optimizer,
+        device,
+        num_epochs=manager.max_epochs,
+        is_inception="inception" in args.model,
+    )
+
+    ########################
+    # export trained model #
+    ########################
+    exporter = ModuleExporter(model, save_dir)
+    sample_input = torch.randn(image_shape).unsqueeze(0)  # sample batch for ONNX export
+    exporter.export_onnx(sample_input, convert_qat=True)
+    exporter.export_pytorch()
+    print("Model ONNX export and PyTorch weights saved to {}".format(save_dir))
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+    main(args_)
diff --git a/integrations/examples/pytorch/train.py b/integrations/examples/pytorch/train.py
new file mode 100644
index 00000000000..0dc86e934af
--- /dev/null
+++ b/integrations/examples/pytorch/train.py
@@ -0,0 +1,703 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+######
+Command help:
+usage: train.py [-h] --train-batch-size TRAIN_BATCH_SIZE --test-batch-size
+                TEST_BATCH_SIZE --arch-key ARCH_KEY --dataset DATASET
+                --dataset-path DATASET_PATH
+                [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
+                [--optim-args OPTIM_ARGS] [--recipe-path RECIPE_PATH]
+                [--sparse-transfer-learn [SPARSE_TRANSFER_LEARN]]
+                [--eval-mode [EVAL_MODE]] [--optim OPTIM]
+                [--logs-dir LOGS_DIR] [--save-best-after SAVE_BEST_AFTER]
+                [--save-epochs SAVE_EPOCHS]
+                [--use-mixed-precision [USE_MIXED_PRECISION]]
+                [--debug-steps DEBUG_STEPS] [--pretrained PRETRAINED]
+                [--pretrained-dataset PRETRAINED_DATASET]
+                [--model-kwargs MODEL_KWARGS]
+                [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
+                [--save-dir SAVE_DIR] [--device DEVICE]
+                [--loader-num-workers LOADER_NUM_WORKERS]
+                [--no-loader-pin-memory]
+                [--loader-pin-memory [LOADER_PIN_MEMORY]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. If using a SparseZoo recipe, can also
+                        provide 'zoo' to load the base weights associated with
+                        that recipe
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. Can also provide a
+                        SparseZoo stub prefixed with 'zoo:' with an optional
+                        '?recipe_type=' argument
+  --sparse-transfer-learn [SPARSE_TRANSFER_LEARN]
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode [EVAL_MODE]
+                        Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --optim OPTIM         The optimizer type to use, one of [SGD, Adam, RMSprop]
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS
+                        epochs to save checkpoints at
+  --use-mixed-precision [USE_MIXED_PRECISION]
+                        Trains model using mixed precision. Supported
+                        environments are single GPU and multiple GPUs using
+                        DistributedDataParallel with one GPU per process
+  --debug-steps DEBUG_STEPS
+                        Amount of steps to run for training and testing for a
+                        debug mode
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset constructor,
+                        should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for data
+                        parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+#########
+EXAMPLES
+#########
+
+##########
+Example command for pruning resnet50 on imagenet dataset:
+python integrations/pytorch/train.py \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
+python integrations/pytorch/train.py \
+    --sparse-transfer-learn \
+    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
+    --arch-key mobilenet_v1 --pretrained pruned-moderate \
+    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Template command for running training with this script on multiple GPUs using
+DistributedDataParallel using mixed precision. Note - DDP support in this script
+only tested for torch==1.7.0.
+python -m torch.distributed.launch \
+--nproc_per_node <NUM GPUs> \
+integrations/pytorch/train.py \
+--use-mixed-precision \
+<TRAIN.PY ARGUMENTS>
+"""
+import argparse
+import json
+import os
+from dataclasses import dataclass, field
+from typing import Any, List, Optional, Tuple
+
+import torch
+from torch.nn import Module
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.utils import (
+    DEFAULT_LOSS_KEY,
+    ModuleDeviceContext,
+    ModuleTester,
+    ModuleTrainer,
+    default_device,
+    get_prunable_layers,
+    model_to_device,
+    set_deterministic_seeds,
+    tensor_sparsity,
+)
+
+
+CURRENT_TASK = utils.Tasks.TRAIN
+LOGGER = get_main_logger()
+
+
+@dataclass
+class TrainingArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    training tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+
+    :param train_batch_size: An int representing the training batch size.
+    :param test_batch_size: An int representing the test batch size.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for training, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param local_rank: DDP argument set by PyTorch in DDP mode, default -1
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param init_lr: float representing the initial learning for training,
+        default=1e-9 .
+    :param optim_args: Additional arguments to be passed in to the optimizer as
+        a json object
+    :param recipe_path: The path to the yaml file containing the modifiers and
+        schedule to apply them with; Can also provide a SparseZoo stub prefixed
+        with 'zoo:'.
+    :param sparse_transfer_learn: Boolean to enable sparse transfer learning
+        modifiers to enforce
+        the sparsity for already sparse layers. The modifiers are added to
+        the ones to be loaded from the recipe-path.
+    :param eval_mode: bool to start evaluation mode so that the model can be
+        evaluated on the desired dataset.
+    :param optim: str respresnting the optimizer type to use, one of
+        [SGD, Adam, RMSprop].
+    :param logs_dir: The path to the directory for saving logs.
+    :param save_best_after: int epoch number to start saving the best
+        validation result after until the end of training.
+    :param save_epochs: int epochs to save checkpoints at.
+    :param use_mixed_precision: bool to train model using mixed precision.
+        Supported environments are single GPU and multiple GPUs using
+        DistributedDataParallel with one GPU per process.
+    :param debug_steps: int represnting amount of steps to run for training and
+        testing for debug mode default=-1.
+    :param pretrained: The type of pretrained weights to use default is true
+        to load the default pretrained weights for the model Otherwise should
+        be set to the desired weights type: [base, optim, optim-perf];
+        To not load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load pretrained
+        weights for if pretrained is set; Default is None which will load the
+        default dataset for the architecture; Ex can be set to imagenet,
+        cifar10, etc".
+    :param model_kwargs: json object containing keyword arguments to be
+        passed to model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be passed
+        to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results
+        under save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on (can also include ids
+        for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data loading,
+        default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    """
+
+    train_batch_size: int = field(
+        metadata={"help": "The batch size to use while training"}
+    )
+
+    test_batch_size: int = field(
+        metadata={"help": "The batch size to use while testing"}
+    )
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception"
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or"
+            " loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    local_rank: int = field(
+        default=-1,
+        metadata={
+            "keep_underscores": True,
+            "help": argparse.SUPPRESS,
+        },
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored . If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    init_lr: float = field(
+        default=1e-9,
+        metadata={
+            "help": "The initial learning rate to use while training, "
+            "the actual initial value used should be set by the"
+            " sparseml recipe"
+        },
+    )
+
+    optim_args: json.loads = field(
+        default_factory=lambda: {
+            "momentum": 0.9,
+            "nesterov": True,
+            "weight_decay": 0.0001,
+        },
+        metadata={
+            "help": "Additional args to be passed to the optimizer passed in"
+            " as a json object",
+        },
+    )
+
+    recipe_path: str = field(
+        default=None,
+        metadata={
+            "help": "The path to the yaml file containing the modifiers and "
+            "schedule to apply them with. Can also provide a "
+            "SparseZoo stub prefixed with 'zoo:' with an optional "
+            "'?recipe_type=' argument"
+        },
+    )
+
+    sparse_transfer_learn: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Enable sparse transfer learning modifiers to enforce the "
+            "sparsity for already sparse layers. The modifiers are "
+            "added to the ones to be loaded from the recipe-path"
+        },
+    )
+
+    eval_mode: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Puts into evaluation mode so that the model can be "
+            "evaluated on the desired dataset"
+        },
+    )
+
+    optim: str = field(
+        default="SGD",
+        metadata={"help": "The optimizer type to use, one of [SGD, Adam, RMSprop]"},
+    )
+
+    logs_dir: str = field(
+        default=os.path.join("pytorch_vision_train", "tensorboard-logs"),
+        metadata={
+            "help": "The path to the directory for saving logs",
+        },
+    )
+
+    save_best_after: int = field(
+        default=-1,
+        metadata={
+            "help": "start saving the best validation result after the given "
+            "epoch completes until the end of training"
+        },
+    )
+    save_epochs: List[int] = field(
+        default_factory=lambda: [], metadata={"help": "epochs to save checkpoints at"}
+    )
+
+    use_mixed_precision: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Trains model using mixed precision. Supported "
+            "environments are single GPU and multiple GPUs using "
+            "DistributedDataParallel with one GPU per process"
+        },
+    )
+
+    debug_steps: int = field(
+        default=-1,
+        metadata={
+            "help": "Amount of steps to run for training and testing for a "
+            "debug mode"
+        },
+    )
+
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for "
+            "the model. Otherwise should be set to the desired weights "
+            "type: [base, optim, optim-perf]. To not load any weights set"
+            "to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained is "
+            "set. Default is None which will load the default dataset for "
+            "the architecture. Ex can be set to imagenet, cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for data "
+            "parallel), ex: cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    def __post_init__(self):
+        # add ddp args
+        env_world_size = int(os.environ.get("WORLD_SIZE", 1))
+        self.world_size = env_world_size
+
+        env_rank = int(os.environ.get("RANK", -1))
+        self.rank = env_rank
+
+        self.is_main_process = self.rank in [
+            -1,
+            0,
+        ]  # non DDP execution or 0th DDP process
+
+        # modify training batch size for give world size
+        assert self.train_batch_size % self.world_size == 0, (
+            f"Invalid training batch size for world size {self.world_size} "
+            f"given batch size {self.train_batch_size}. "
+            f"world size must divide training batch size evenly."
+        )
+
+        self.train_batch_size = self.train_batch_size // self.world_size
+
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        if self.local_rank != -1:
+            torch.distributed.init_process_group(backend="nccl", init_method="env://")
+            set_deterministic_seeds(0)
+
+        self.approximate = False
+
+
+def train(
+    train_args: TrainingArguments,
+    model: Module,
+    train_loader: DataLoader,
+    val_loader: DataLoader,
+    input_shape: Tuple[int, ...],
+    save_dir: str,
+    loggers: List[Any],
+) -> None:
+    """
+    Utility function to drive the training processing
+
+    :param train_args: A TrainingArguments object with
+        arguments for current training task
+    :param model: model architecture to train
+    :param train_loader: A DataLoader for training data
+    :param val_loader: A DataLoader for validation data
+    :param input_shape: A tuple of integers representing the shape of inputs
+    :param save_dir: Directory to store checkpoints at during training process
+    :param loggers: List of loggers to use during training process
+    """
+    # loss setup
+    val_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
+    LOGGER.info(f"created loss for validation: {val_loss}")
+
+    train_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
+    LOGGER.info(f"created loss for training: {train_loss}")
+
+    # training setup
+    if not train_args.eval_mode:
+        epoch, optim, manager = utils.create_scheduled_optimizer(
+            train_args,
+            model,
+            train_loader,
+            loggers,
+        )
+    else:
+        epoch = 0
+        train_loss = None
+        optim = None
+        manager = None
+
+    # device setup
+    if train_args.rank == -1:
+        device = train_args.device
+        ddp = False
+    else:
+        torch.cuda.set_device(train_args.local_rank)
+        device = train_args.local_rank
+        ddp = True
+
+    model, device, device_ids = model_to_device(model, device, ddp=ddp)
+    LOGGER.info(f"running on device {device} for ids {device_ids}")
+
+    trainer = (
+        ModuleTrainer(
+            model,
+            device,
+            train_loss,
+            optim,
+            loggers=loggers,
+            device_context=ModuleDeviceContext(
+                use_mixed_precision=train_args.use_mixed_precision,
+                world_size=train_args.world_size,
+            ),
+        )
+        if not train_args.eval_mode
+        else None
+    )
+
+    if train_args.is_main_process:  # only test on one DDP process if using DDP
+        tester = ModuleTester(model, device, val_loss, loggers=loggers, log_steps=-1)
+
+        # initial baseline eval run
+        tester.run_epoch(
+            val_loader, epoch=epoch - 1, max_epochs=-1, max_steps=train_args.debug_steps
+        )
+
+    if not train_args.eval_mode:
+        utils.save_recipe(recipe_manager=manager, save_dir=save_dir)
+        LOGGER.info(f"starting training from epoch {epoch}")
+
+        if epoch > 0:
+            LOGGER.info("adjusting ScheduledOptimizer to restore point")
+            optim.adjust_current_step(epoch, 0)
+
+        target_metric = (
+            "top1acc" if "top1acc" in tester.loss.available_losses else DEFAULT_LOSS_KEY
+        )
+        best_metric = None
+        val_res = None
+
+        while epoch < manager.max_epochs:
+            if train_args.debug_steps > 0:
+                # correct since all optimizer steps are not
+                # taken in the epochs for debug mode
+                optim.adjust_current_step(epoch, 0)
+
+            if train_args.rank != -1:  # sync DDP dataloaders
+                train_loader.sampler.set_epoch(epoch)
+
+            trainer.run_epoch(
+                train_loader,
+                epoch,
+                max_epochs=manager.max_epochs,
+                max_steps=train_args.debug_steps,
+                show_progress=train_args.is_main_process,
+            )
+
+            # testing steps
+            if train_args.is_main_process:
+                # only test and save on main process
+                val_res = tester.run_epoch(
+                    val_loader,
+                    epoch,
+                    max_epochs=manager.max_epochs,
+                    max_steps=train_args.debug_steps,
+                )
+                val_metric = val_res.result_mean(target_metric).item()
+
+                if epoch >= train_args.save_best_after and (
+                    best_metric is None
+                    or (
+                        val_metric <= best_metric
+                        if target_metric != "top1acc"
+                        else val_metric >= best_metric
+                    )
+                ):
+                    utils.save_model_training(
+                        model,
+                        optim,
+                        input_shape,
+                        "checkpoint-best",
+                        save_dir,
+                        epoch,
+                        val_res,
+                    )
+                    best_metric = val_metric
+
+            # save checkpoints
+            _save_epoch = (
+                train_args.is_main_process
+                and train_args.save_epochs
+                and epoch in train_args.save_epochs
+            )
+            if _save_epoch:
+                utils.save_model_training(
+                    model,
+                    optim,
+                    input_shape,
+                    f"checkpoint-{epoch:04d}-{val_metric:.04f}",
+                    save_dir,
+                    epoch,
+                    val_res,
+                )
+
+            epoch += 1
+
+        # export the final model
+        LOGGER.info("completed...")
+        if train_args.is_main_process:
+            # only convert qat -> quantized ONNX graph for finalized model
+            # TODO: change this to all checkpoints when conversion times improve
+            utils.save_model_training(
+                model, optim, input_shape, "model", save_dir, epoch - 1, val_res, True
+            )
+
+            LOGGER.info("layer sparsities:")
+            for (name, layer) in get_prunable_layers(model):
+                LOGGER.info(
+                    f"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}"
+                )
+
+    # close DDP
+    if train_args.rank != -1:
+        torch.distributed.destroy_process_group()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(dataclass_types=TrainingArguments)
+    training_args, _ = _parser.parse_args_into_dataclasses()
+
+    save_dir, loggers = utils.get_save_dir_and_loggers(training_args, task=CURRENT_TASK)
+
+    input_shape = ModelRegistry.input_shape(training_args.arch_key)
+    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(
+        training_args, image_size, task=CURRENT_TASK
+    )
+
+    num_classes = utils.infer_num_classes(training_args, train_dataset, val_dataset)
+
+    # # model creation
+    model = utils.create_model(training_args, num_classes)
+    train(
+        training_args, model, train_loader, val_loader, input_shape, save_dir, loggers
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/examples/pytorch/tutorials/.gitkeep b/integrations/examples/pytorch/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md b/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
new file mode 100644
index 00000000000..a7962a06f46
--- /dev/null
+++ b/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
@@ -0,0 +1,138 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparse Transfer Learning for Image Classification
+
+This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
+
+## Overview
+
+Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo. 
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
+
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process. In this tutorial you will:
+- Download and prepare a pre-sparsified image classification model.
+- Apply a sparse transfer learning recipe on the pre-sparsified model.
+
+The examples listed in this tutorial are all performed on the [Imagenette](https://github.com/fastai/imagenette) dataset.
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues) 
+
+## Setting Up
+
+This tutorial can be run by cloning and installing the `sparseml` repository which contains scripts and recipes for
+this example:
+
+```bash
+pip install "sparseml[torchvision, dev]"
+```
+Note:  make sure to upgrade `pip` using `python -m pip install -U pip`
+## Downloading and Preparing a Pre-Sparsified Model
+
+First, you need to download the sparsified models from the [SparseZoo](https://sparsezoo.neuralmagic.com/). A few image classification models with [SparseZoo](https://sparsezoo.neuralmagic.com/) stubs:
+
+| Model Name     |      Stub      | Description |
+|----------|-------------|-------------|
+| resnet-pruned-moderate | zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate |This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the accuracy of the original baseline model (76.1% top1). Pruned layers achieve 88% sparsity.|
+|resnet-pruned-conservative|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves full recovery original baseline model accuracy (76.1% top1). Pruned layers achieve 80% sparsity.|
+|resnet-pruned-moderate-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate|This model is a sparse, INT8 quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the original baseline accuracy. The average model sparsity is about 72% with pruned layers achieving between 70-80% sparsity. This pruned quantized model achieves 75.46% top1 accuracy on the ImageNet dataset.|
+|mobilenetv1-pruned-moderate|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves 99% of the accuracy of the original baseline model (70.9% top1). Pruned layers achieve between 70-90% sparsity.|
+|mobilenetv1-pruned-conservative|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves the same accuracy as the original baseline model. Pruned layers achieve between 60-86% sparsity. This pruned quantized model achieves 70.9% top1 accuracy on the ImageNet dataset.|
+|mobilenetv1-pruned-moderate-quantized|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate|Pruned [MobileNetV1](https://arxiv.org/abs/1704.04861) architecture with width=1.0 trained on the ImageNet dataset. Recalibrated performance model: within 99% of baseline validation top1 accuracy (70.9%)|
+
+
+A few recipe stubs:
+
+|Recipe Name|Stub|Description|
+|----------|-------------|-------------|
+|resnet50.transfer-learning-pruned-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn|This recipe provides a framework for performing pruned-quantized transfer learning on SparseML's pruned_quantized ResNet50 model. This recipe was written for transfer learning onto the Imagenette dataset using a single GPU, Adam optimizer, and batch size 32. Adjust the hyperparameters at the top of the file accordingly for your dataset and training environment|
+
+
+Note: The models above were originally trained and sparsified on the [ImageNet](https://image-net.org/) dataset.
+
+- After deciding on which model meets your performance requirements for both speed and accuracy, select the SparseZoo stub associated with that model.
+   The stub will be used later with the training script to automatically pull down the desired pre-trained weights.
+
+## Applying a Sparse Transfer Learning Recipe
+
+- After noting respective SparseZoo model stub, [train.py](../train.py) script can be used to download checkpoint and [Imagenette](https://github.com/fastai/imagenette) and kick-start transfer learning. 
+   The transfer learning process itself is guided using recipes; We include example [recipes](../recipes) for classification along with others in the SparseML [GitHub repository](https://github.com/neuralmagic/sparseml). 
+[Learn more about recipes and modifiers](../../../docs/source/recipes.md).
+
+- Run the following example command to kick off transfer learning for [ResNet-50](https://arxiv.org/abs/1512.03385) starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/):
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate" \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned
+```
+
+To transfer learn [MobileNet](https://arxiv.org/abs/1704.04861) on [Imagenette](https://github.com/fastai/imagenette), starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/), run the following example command:
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path "zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate" \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --save-dir sparse-transfer-models
+    --model-tag mobilenet-imagenette-sparse-transfer-learned
+```
+The `--checkpoint-path` argument can take in path to a model checkpoint, or a SpareZoo model stub,
+if a SparseZoo recipe stub is provided in `--recipe-path`, 
+the `checkpoint-path` can be set to `zoo` for auto-downloading corresponding checkpoints from SparseZoo. 
+To learn more about the usage, run script with `-h` option to see help. 
+
+The script automatically saves model checkpoints after each epoch and reports the validation loss along with layer sparsities. 
+
+### ONNX Export
+The model can be saved to [ONNX](https://onnx.ai/) format to be loaded later for inference or other experiments using
+the `sparseml.image_classification.export_onnx` script.
+
+```
+sparseml.image_classification.export_onnx \
+    --arch-key mobilenet \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE \
+    --checkpoint-path sparse-transfer-models/mobilenet-imagenette-sparse-transfer-learned/pytorch/model.pth
+```
+
+## Wrap-Up
+
+Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned image classification models. 
+In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and saved the model for future use.
+
+An example for benchmarking and deploying image classification models with DeepSparse [is also available](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md b/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
new file mode 100644
index 00000000000..b67881dc79f
--- /dev/null
+++ b/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
@@ -0,0 +1,132 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying PyTorch Models Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized PyTorch models.
+
+## Overview
+
+Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of their existing training pipelines.
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others.
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes.
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by pruning a PyTorch [ResNet-50](https://arxiv.org/abs/1512.03385) model.
+
+1. Setting Up
+2. Inspecting a Recipe
+3. Applying a Recipe
+3. Exporting for Inference
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Setting Up
+
+This tutorial can be run by installing `sparseml` which contains scripts for this pruning example:
+
+```bash
+pip install "sparseml[torchvision, dev]"
+```
+
+## Inspecting a Recipe
+
+Recipes are YAML or Markdown files that SparseML uses to easily define and control the sparsification of a model.
+Recipes consist of a series of `Modifiers` that can influence the training process in different ways. A list of
+common modifiers and their uses is provided
+[here](../../../docs/source/recipes.md#modifiers-intro).
+
+SparseML provides a recipe for sparsifying a ResNet-50 model trained on the tiny Imagenette dataset. The recipe can
+be viewed in the browser
+[here](../recipes/resnet50-imagenette-pruned.md)
+or in the command line from the file `sparseml/integrations/pytorch/recipes/resnet50-imagenette-pruned.md`.
+
+The recipe contains three kinds of modifiers:
+
+- an `EpochRangeModifier` that sets the number of epochs used for sparsification
+- a `SetLearningRateModifier` that sets the sparsification learning rate
+- three `GMPruningModifier`s that set various layers of the model to be pruned to different levels.
+
+Notice that each `GMPruningModifier` sets specific parameters of the ResNet-50 model to be pruned. To
+prune a different model, the parameter names should be adjusted to match the new model.  To avoid naming
+individual parameter names, all parameters can be set for pruning by passing `__ALL_PRUNABLE__` instead
+of the parameters list for a single `GMPruningModifier`.
+
+### Applying a Recipe
+
+Recipes can integrated into training flows with a couple of lines of code by using a `ScheduledModifierManager`
+that wraps the PyTorch `Optimizer` step.  An example of how this is done can be found
+[here](../../../docs/source/code.md#pytorch-sparsification).
+
+For this example, we can use the `sparseml.image_classification.train` utility.  This utility runs a
+PyTorch training flow that is modified by a `ScheduledModifierManager` and takes a recipe as an input.
+It natively supports ResNet-50 and other popular image classification models.
+
+To prune a ResNet-50 model trained on the Imagenette dataset with the recipe above, run the following command.
+If the Imagenette dataset has not been downloaded yet, the script will automatically download it to the given
+`--dataset-path`. If you have a local copy of the dataset, `--dataset-path` may be updated.
+
+*Note:* If an out of memory exception occurs, the train batch size should be lowered to fit on your device
+
+```bash
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/resnet50-imagenette-pruned.md \
+    --dataset-path ./data \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenette \
+    --train-batch-size 128 \
+    --test-batch-size 256 \
+    --loader-num-workers 8 \
+    --save-dir sparsification_example \
+    --logs-dir sparsification_example \
+    --model-tag resnet50-imagenette-pruned \
+    --save-best-after 8
+```
+
+A full list of arguments and their descriptions can be found by running 
+
+```bash
+sparseml.image_classification.train --help
+```
+
+
+## Exporting for Inference
+
+After, the training pruned model and training log files will be found under 
+`sparsification_example/resnet50-imagenette-pruned`. To run the sparsified model for accelerated inference with
+an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse), the pruned model must be
+exported to the ONNX format.
+
+This step can be completed using `sparseml.image_classification.export_onnx` script which uses the
+`sparseml.pytorch.utils.ModuleExporter` class. Run the script with `--help` option to see usage.
+
+The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in
+inference performance. An example for benchmarking and deploying image classification models with DeepSparse can
+be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate sparsified models.
+In this tutorial, you installed SparseML, downloaded and inspected a pruning recipe, applied it to a ResNet-50 model,
+and exported the sparse model for inference.
+
+Next steps will include creating new recipes and applying them to different models. Check out other
+[SparseML tutorials](https://github.com/neuralmagic/sparseml/tree/main/integrations) to learn more.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/pytorch/utils.py b/integrations/examples/pytorch/utils.py
new file mode 100644
index 00000000000..b5dd57c3c41
--- /dev/null
+++ b/integrations/examples/pytorch/utils.py
@@ -0,0 +1,428 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Helper methods for image classification/detection based tasks
+"""
+import os
+from enum import Enum, auto, unique
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+from torch.nn import Module
+from torch.nn import functional as torch_functional
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader
+
+from sparseml.pytorch.datasets import DatasetRegistry, ssd_collate_fn, yolo_collate_fn
+from sparseml.pytorch.image_classification.utils.helpers import (
+    download_framework_model_by_recipe_type,
+)
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
+from sparseml.pytorch.sparsification import ConstantPruningModifier
+from sparseml.pytorch.utils import (
+    DEFAULT_LOSS_KEY,
+    CrossEntropyLossWrapper,
+    InceptionCrossEntropyLossWrapper,
+    LossWrapper,
+    ModuleExporter,
+    ModuleRunResults,
+    PythonLogger,
+    SSDLossWrapper,
+    TensorBoardLogger,
+    TopKAccuracy,
+    YoloLossWrapper,
+    early_stop_data_loader,
+    torch_distributed_zero_first,
+)
+from sparseml.utils import create_dirs
+from sparsezoo import Model
+
+
+@unique
+class Tasks(Enum):
+    """
+    A class representing supported image classification/detection tasks
+    """
+
+    TRAIN = auto()
+    EXPORT = auto()
+    ANALYSIS = auto()
+    LR_ANALYSIS = auto()
+    PR_SENSITIVITY = auto()
+
+
+# loggers
+def get_save_dir_and_loggers(
+    args: Any, task: Optional[Tasks] = None
+) -> Tuple[Union[str, None], List]:
+    if args.is_main_process:
+        save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+        logs_dir = (
+            os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
+            if task == Tasks.TRAIN
+            else None
+        )
+
+        if not args.model_tag:
+            dataset_name = (
+                f"{args.dataset}-{args.dataset_kwargs['year']}"
+                if "year" in args.dataset_kwargs
+                else args.dataset
+            )
+            model_tag = f"{args.arch_key.replace('/', '.')}_{dataset_name}"
+            model_id = model_tag
+            model_inc = 0
+            # set location to check for models with same name
+            model_main_dir = logs_dir or save_dir
+
+            while os.path.exists(os.path.join(model_main_dir, model_id)):
+                model_inc += 1
+                model_id = f"{model_tag}__{model_inc:02d}"
+        else:
+            model_id = args.model_tag
+
+        save_dir = os.path.join(save_dir, model_id)
+        create_dirs(save_dir)
+
+        # loggers setup
+        loggers = [PythonLogger()]
+        if task == Tasks.TRAIN:
+            logs_dir = os.path.join(logs_dir, model_id)
+            create_dirs(logs_dir)
+            loggers.append(TensorBoardLogger(log_path=logs_dir))
+        print(f"Model id is set to {model_id}")
+    else:
+        # do not log for non main processes
+        save_dir = None
+        loggers = []
+    return save_dir, loggers
+
+
+# data helpers_
+def _get_collate_fn(arch_key: str, task: Optional[Tasks] = None):
+    is_ssd = "ssd" in arch_key.lower()
+    need_collate_function = (
+        is_ssd or "yolo" in arch_key.lower()
+    ) and task != Tasks.EXPORT
+    if need_collate_function:
+        return ssd_collate_fn if is_ssd else yolo_collate_fn
+    return None
+
+
+def _create_train_dataset_and_loader(
+    args: Any,
+    image_size: Tuple[int, ...],
+    task: Optional[Tasks] = None,
+) -> Tuple[Any, Any]:
+    need_train_data = not (
+        task == Tasks.EXPORT
+        or task == Tasks.PR_SENSITIVITY
+        and args.approximate
+        or task == Tasks.TRAIN
+        and args.eval_mode
+    )
+
+    if need_train_data:
+        with torch_distributed_zero_first(
+            args.local_rank,
+        ):  # only download once locally
+            train_dataset = DatasetRegistry.create(
+                args.dataset,
+                root=args.dataset_path,
+                train=True,
+                rand_trans=True,
+                image_size=image_size,
+                **args.dataset_kwargs,
+            )
+        sampler = (
+            torch.utils.data.distributed.DistributedSampler(train_dataset)
+            if args.rank != -1
+            else None
+        )
+        shuffle = True if sampler is None else False
+        batch_size = args.train_batch_size if task == Tasks.TRAIN else args.batch_size
+
+        train_loader = DataLoader(
+            train_dataset,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            num_workers=args.loader_num_workers,
+            pin_memory=args.loader_pin_memory,
+            sampler=sampler,
+            collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
+        )
+        print(f"created train_dataset: {train_dataset}")
+        return train_dataset, train_loader
+
+    return None, None
+
+
+def _create_val_dataset_and_loader(
+    args, image_size: Tuple[int, ...], task: Optional[Tasks] = None
+) -> Tuple[Any, Any]:
+    need_val_data = not (
+        task == Tasks.PR_SENSITIVITY
+        or task == Tasks.LR_ANALYSIS
+        or not (args.is_main_process and args.dataset != "imagefolder")
+    )
+
+    if need_val_data:
+        val_dataset = DatasetRegistry.create(
+            args.dataset,
+            root=args.dataset_path,
+            train=False,
+            rand_trans=False,
+            image_size=image_size,
+            **args.dataset_kwargs,
+        )
+        if args.is_main_process:
+            is_training = task == Tasks.TRAIN
+            val_loader = DataLoader(
+                val_dataset,
+                batch_size=args.test_batch_size if is_training else 1,
+                shuffle=False,
+                num_workers=args.loader_num_workers if is_training else 1,
+                pin_memory=args.loader_pin_memory if is_training else False,
+                collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
+            )
+            if task == Tasks.EXPORT:
+                val_loader = early_stop_data_loader(
+                    val_loader, args.num_samples if args.num_samples > 1 else 1
+                )
+            print(f"created val_dataset: {val_dataset}")
+        else:
+            val_loader = None  # only val dataset needed to get the number of classes
+        return val_dataset, val_loader
+    return None, None  # val dataset not needed
+
+
+def get_train_and_validation_loaders(
+    args: Any, image_size: Tuple[int, ...], task: Optional[Tasks] = None
+):
+    """
+    :param args: Object containing relevant configuration for the task
+    :param image_size: A Tuple of integers representing the shape of input image
+    :param task: The current task being performed
+    :return: 4 element tuple with the following format (train_dataset,
+        train_loader, val_dataset, val_loader)
+    """
+    train_dataset, train_loader = _create_train_dataset_and_loader(
+        args, image_size, task=task
+    )
+    val_dataset, val_loader = _create_val_dataset_and_loader(
+        args, image_size, task=task
+    )
+    return train_dataset, train_loader, val_dataset, val_loader
+
+
+# Model creation Helpers
+
+
+def create_model(args: Any, num_classes: int) -> Module:
+    """
+    :param args: object with configuration for model classes
+    :param num_classes: Integer representing the number of output classes
+    :returns: A Module object representing the created model
+    """
+    with torch_distributed_zero_first(args.local_rank):  # only download once locally
+        if args.checkpoint_path == "zoo":
+            if args.recipe_path and args.recipe_path.startswith("zoo:"):
+                zoo_model = Model(args.recipe_path)
+                args.checkpoint_path = download_framework_model_by_recipe_type(
+                    zoo_model
+                )
+            else:
+                raise ValueError(
+                    "'zoo' provided as --checkpoint-path but a SparseZoo stub"
+                    " prefixed by 'zoo:' not provided as --recipe-path"
+                )
+
+        model = ModelRegistry.create(
+            args.arch_key,
+            args.pretrained,
+            args.checkpoint_path,
+            args.pretrained_dataset,
+            num_classes=num_classes,
+            **args.model_kwargs,
+        )
+    print(f"created model: {model}")
+    return model
+
+
+def infer_num_classes(args: Any, train_dataset, val_dataset):
+    """
+    :param args: Object with configuration settings
+    :param train_dataset: dataset representing training data
+    :param val_dataset: dataset representing validation data
+    :return: An integer representing the number of classes
+    """
+    if "num_classes" in args.model_kwargs:
+        # handle manually overriden num classes
+        num_classes = args.model_kwargs["num_classes"]
+        del args.model_kwargs["num_classes"]
+    elif args.dataset == "imagefolder":
+        dataset = val_dataset or train_dataset  # get non None dataset
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+    return num_classes
+
+
+def get_loss_wrapper(
+    arch_key: str, training: bool = False, task: Optional[Tasks] = None
+):
+    """
+    :param arch_key: The model architecture
+    :param training: True if training task started else False
+    :param task: current task being executed
+    """
+    if "ssd" in arch_key.lower():
+        return SSDLossWrapper()
+    if "yolo" in arch_key.lower():
+        return YoloLossWrapper()
+
+    extras = {"top1acc": TopKAccuracy(1), "top5acc": TopKAccuracy(5)}
+    if task == Tasks.TRAIN:
+        return (
+            CrossEntropyLossWrapper(extras)
+            if training and "inception" not in arch_key
+            else InceptionCrossEntropyLossWrapper(extras)
+        )
+    return LossWrapper(loss_fn=torch_functional.cross_entropy, extras=extras)
+
+
+#  optimizer helper
+def create_scheduled_optimizer(
+    train_args: Any,
+    model: Module,
+    train_loader: DataLoader,
+    loggers: List[Any],
+) -> Tuple[int, ScheduledOptimizer, ScheduledModifierManager]:
+    """
+    :param train_args : An object with task specific config
+    :param model: model architecture to train
+    :param train_loader: A DataLoader for training data
+    :param loggers: List of loggers to use during training process
+    :type train_args: TrainingArguments
+    """
+    # # optimizer setup
+    optim_const = torch.optim.__dict__[train_args.optim]
+    optim = optim_const(
+        model.parameters(), lr=train_args.init_lr, **train_args.optim_args
+    )
+    print(f"created optimizer: {optim}")
+    print(
+        "note, the lr for the optimizer may not reflect the manager yet until "
+        "the recipe config is created and run"
+    )
+
+    # restore from previous check point
+    if train_args.checkpoint_path:
+        # currently optimizer restoring is unsupported
+        # mapping of the restored params to the correct device is not working
+        # load_optimizer(args.checkpoint_path, optim)
+        epoch = 0  # load_epoch(args.checkpoint_path) + 1
+        print(
+            f"restored checkpoint from {train_args.checkpoint_path} for "
+            f"epoch {epoch - 1}"
+        )
+    else:
+        epoch = 0
+
+    # modifier setup
+    add_mods = (
+        ConstantPruningModifier.from_sparse_model(model)
+        if train_args.sparse_transfer_learn
+        else None
+    )
+    manager = ScheduledModifierManager.from_yaml(
+        file_path=train_args.recipe_path, add_modifiers=add_mods
+    )
+    optim = ScheduledOptimizer(
+        optim,
+        model,
+        manager,
+        steps_per_epoch=len(train_loader),
+        loggers=loggers,
+    )
+    print(f"created manager: {manager}")
+    return epoch, optim, manager
+
+
+# saving helpers
+
+
+def save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+    """
+    :param recipe_manager: The ScheduleModified manager to save recipes
+    :param save_dir: The directory to save the recipe
+    """
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    print(f"Saved recipe to {recipe_save_path}")
+
+
+def save_model_training(
+    model: Module,
+    optim: Optimizer,
+    input_shape: Tuple[int, ...],
+    save_name: str,
+    save_dir: str,
+    epoch: int,
+    val_res: Union[ModuleRunResults, None],
+    convert_qat: bool = False,
+):
+    """
+    :param model: model architecture
+    :param optim: The optimizer used
+    :param input_shape: A tuple of integers representing the input shape
+    :param save_name: name to save model to
+    :param save_dir: directory to save results in
+    :param epoch: integer representing umber of epochs to
+    :param val_res: results from validation run
+    :param convert_qat: True if model is to be quantized before saving
+    """
+    has_top1 = "top1acc" in val_res.results
+    metric_name = "top-1 accuracy" if has_top1 else "val_loss"
+    metric = val_res.result_mean("top1acc" if has_top1 else DEFAULT_LOSS_KEY).item()
+    print(
+        f"Saving model for epoch {epoch} and {metric_name} "
+        f"{metric} to {save_dir} for {save_name}"
+    )
+    exporter = ModuleExporter(model, save_dir)
+    exporter.export_pytorch(optim, epoch, f"{save_name}.pth")
+    exporter.export_onnx(
+        torch.randn(1, *input_shape),
+        f"{save_name}.onnx",
+        convert_qat=convert_qat,
+    )
+
+    info_path = os.path.join(save_dir, f"{save_name}.txt")
+
+    with open(info_path, "w") as info_file:
+        info_lines = [
+            f"epoch: {epoch}",
+        ]
+
+        if val_res is not None:
+            for loss in val_res.results.keys():
+                info_lines.append(f"{loss}: {val_res.result_mean(loss).item()}")
+
+        info_file.write("\n".join(info_lines))
diff --git a/integrations/examples/pytorch/vision.py b/integrations/examples/pytorch/vision.py
new file mode 100644
index 00000000000..6ffeb78869c
--- /dev/null
+++ b/integrations/examples/pytorch/vision.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Deprecated script kindly use the following task specific scripts for
+ optimization tasks on image classification and object detection models:
+
+* Model pruning - sparseml.image_classification.train
+* Quantization aware training - sparseml.image_classification.train
+* Sparse transfer learning - sparseml.image_classification.train
+* pruning sensitivity analysis - sparseml.image_classification.pr_sensitivity
+* learning rate sensitivity analysis - sparseml.image_classification.lr_analysis
+* ONNX export - sparseml.image_classification.export_onnx
+"""
+
+
+if __name__ == "__main__":
+    print(__doc__)
diff --git a/integrations/examples/rwightman-timm/README.md b/integrations/examples/rwightman-timm/README.md
new file mode 100644
index 00000000000..773c10116d5
--- /dev/null
+++ b/integrations/examples/rwightman-timm/README.md
@@ -0,0 +1,116 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML-rwightman/pytorch-image-models integration
+This directory provides a SparseML integrated training script for the popular
+[rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models)
+repository also known as [timm](https://pypi.org/project/timm/).
+
+Using this integration, you will be able to apply SparseML optimizations
+to the powerful training flows of the pytorch-image-models repository.
+
+Some of the tasks you can perform using this integration include, but are not limited to:
+* Pruning
+* Quantization
+* Pruning and Quantization
+* Sparse Transfer Learning
+
+## Installation
+We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
+A virtual environment can be created using the following commands:
+
+```bash
+python3 -m venv venv # create a venv virtual environment
+source venv/bin/activate # activate venv
+pip install --upgrade pip # upgrade pip
+```
+
+To begin, run the following command in the root directory of this integration (`cd integrations/rwightman-timm`):
+```bash
+bash setup_integration.sh
+```
+
+The `setup_integration.sh` file will clone the [pytorch-image-models](https://github.com/neuralmagic/pytorch-image-models.git) repository with the SparseML integration as a subfolder.
+After the repo has successfully cloned, pytorch-image-models will be installed along with any necessary dependencies.
+
+This integration requires `python>=3.7,<3.10`
+
+## Quick Tour
+### Recipes
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration adds a `--recipe` argument to the 
+[`train.py` script](https://github.com/neuralmagic/pytorch-image-models/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+You can learn how to build or download a recipe using the
+[SparseML](https://github.com/neuralmagic/sparseml)
+or [SparseZoo](https://github.com/neuralmagic/sparsezoo)
+documentation, or export one with [Sparsify](https://github.com/neuralmagic/sparsify).
+
+ViT recipes are not yet available on SparseZoo, but are in progress.
+
+Using a local recipe and checkpoint, pruning a model can be done by running the following command from within the root of this integration's folder
+```bash
+python train.py \
+  /PATH/TO/DATASET/imagenet/ \
+  --recipe ../recipes/vit_base.85.recal.config.yaml \
+  --dataset imagenet \
+  --batch-size 64 \
+  --remode pixel --reprob 0.0 --smoothing 0.1 --mixup 0.5 --mixup 0.5 \
+  --output models/optimized \
+  --model vit_base_patch16_224 \
+  --workers 8 
+```  
+
+Documentation on the original script can be found
+[here](https://rwightman.github.io/pytorch-image-models/scripts/).
+The latest commit hash that `train.py` is based on is included in the docstring.
+
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [recipes](./recipes)              | Typical recipes for sparsifying ViT models along with any downloaded recipes from the SparseZoo.                      |
+| tutorials            | Tutorial walkthroughs for how to sparsify ViT models using recipes (Coming Soon).                                                   |          |
+| [README.md](./README.md)            | Readme file.                                                                                                        |
+| [setup_integration.sh](./setup_integration.sh) | Setup file for the integration run from the command line.   
+
+### Exporting for Inference
+
+After sparsifying a model, to convert the model into an [ONNX](https://onnx.ai/) deployment format run the `export.py script`(https://github.com/neuralmagic/pytorch-image-models/blob/master/export.py).
+
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+For example, the following command can be run from within the integration's folder to export a trained/sparsified model's checkpoint:
+```bash
+python export.py \
+    --checkpoint ./path/to/checkpoint/model.pth.tar \
+    --recipe ../recipes/vit_base.85.recal.config.yaml \
+    --save-dir ./exported-models \
+    --filename vit_base_patch16_224 \
+    --batch-size 1 \
+    --image-shape 3 224 224 \
+    --config ./path/to/checkpoint/args.yaml
+```
+
+The DeepSparse Engine [accepts ONNX formats](https://docs.neuralmagic.com/sparseml/source/onnx_export.html) and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse).
\ No newline at end of file
diff --git a/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml b/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
new file mode 100644
index 00000000000..148060b57dd
--- /dev/null
+++ b/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
@@ -0,0 +1,83 @@
+version: 1.1.0
+
+modifiers:
+    - !GMPruningModifier
+        end_epoch: 36.0
+        final_sparsity: 0.03
+        global_sparsity: False
+        init_sparsity: 0.25
+        inter_func: cubic
+        leave_enabled: True
+        mask_type: unstructured
+        params: 
+            - blocks.0.attn.qkv.weight
+            - blocks.0.attn.proj.weight
+            - blocks.0.mlp.fc1.weight
+            - blocks.0.mlp.fc2.weight
+            - blocks.1.attn.qkv.weight
+            - blocks.1.attn.proj.weight
+            - blocks.1.mlp.fc1.weight
+            - blocks.1.mlp.fc2.weight
+            - blocks.2.attn.qkv.weight
+            - blocks.2.attn.proj.weight
+            - blocks.2.mlp.fc1.weight
+            - blocks.2.mlp.fc2.weight
+            - blocks.3.attn.qkv.weight
+            - blocks.3.attn.proj.weight
+            - blocks.3.mlp.fc1.weight
+            - blocks.3.mlp.fc2.weight
+            - blocks.4.attn.qkv.weight
+            - blocks.4.attn.proj.weight
+            - blocks.4.mlp.fc1.weight
+            - blocks.4.mlp.fc2.weight
+            - blocks.5.attn.qkv.weight
+            - blocks.5.attn.proj.weight
+            - blocks.5.mlp.fc1.weight
+            - blocks.5.mlp.fc2.weight
+            - blocks.6.attn.qkv.weight
+            - blocks.6.attn.proj.weight
+            - blocks.6.mlp.fc1.weight
+            - blocks.6.mlp.fc2.weight
+            - blocks.7.attn.qkv.weight
+            - blocks.7.attn.proj.weight
+            - blocks.7.mlp.fc1.weight
+            - blocks.7.mlp.fc2.weight
+            - blocks.8.attn.qkv.weight
+            - blocks.8.attn.proj.weight
+            - blocks.8.mlp.fc1.weight
+            - blocks.8.mlp.fc2.weight
+            - blocks.9.attn.qkv.weight
+            - blocks.9.attn.proj.weight
+            - blocks.9.mlp.fc1.weight
+            - blocks.9.mlp.fc2.weight
+            - blocks.10.attn.qkv.weight
+            - blocks.10.attn.proj.weight
+            - blocks.10.mlp.fc1.weight
+            - blocks.10.mlp.fc2.weight
+            - blocks.11.attn.qkv.weight
+            - blocks.11.attn.proj.weight
+            - blocks.11.mlp.fc1.weight
+            - blocks.11.mlp.fc2.weight
+        phased: False
+        score_type: magnitude
+        start_epoch: 1.0
+        update_frequency: 0.5
+
+    - !EpochRangeModifier
+        end_epoch: 100.0
+        start_epoch: 0.0
+
+    - !SetLearningRateModifier
+        constant_logging: False
+        end_epoch: -1
+        learning_rate: 0.002505
+        start_epoch: 0.0
+
+    - !LearningRateModifier
+        constant_logging: False
+        end_epoch: -1.0
+        init_lr: 0.00062625
+        lr_class: StepLR
+        lr_kwargs: {'step_size': 10, 'gamma': 0.25}
+        start_epoch: 40.0
+        update_frequency: -1.0
diff --git a/integrations/examples/rwightman-timm/setup_integration.sh b/integrations/examples/rwightman-timm/setup_integration.sh
new file mode 100644
index 00000000000..ed6c1068339
--- /dev/null
+++ b/integrations/examples/rwightman-timm/setup_integration.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Integration setup command to setup the folder so it is ready to train and sparsify models.
+# Creates a pytorch-image-models folder next to this script with all required dependencies from the rwightman/pytorch-image-models repository.
+# Command: `bash setup_integration.sh`
+
+git clone https://github.com/neuralmagic/pytorch-image-models.git
+cd pytorch-image-models
+git checkout release/1.1
+pip install -r requirements.txt
+pip install sparseml[torch]
diff --git a/integrations/examples/tensorflow_v1/README.md b/integrations/examples/tensorflow_v1/README.md
new file mode 100644
index 00000000000..0471fa009d2
--- /dev/null
+++ b/integrations/examples/tensorflow_v1/README.md
@@ -0,0 +1,93 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML TensorFlow V1 Integration
+
+This directory combines the SparseML recipe-driven approach with standard TensorFlow V1 training flows for models within
+[TF Slim](https://github.com/google-research/tf-slim) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom TensorFlow V1 training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Coming soon!
+
+## Tutorials
+
+- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/tensorflow_v1/notebooks/classification.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[tf_v1]` or `pip install sparseml[tf_v1_gpu]` if training on a GPU.
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=tensorflow_v1) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=tensorflow_v1) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| notebooks            | Jupyter notebooks to walk through sparsifiation within standard TensorFlow V1 training flows                          |
+| recipes              | Typical recipes for sparsifying TensorFlow V1 models along with any downloaded recipes from the SparseZoo.            |
+| tutorials            | Tutorial walkthroughs for how to sparsify TensorFlow V1 models using recipes.                                         |
+| classification.py    | Utility training script for sparsifying classification models in TensorFlow V1 (loads SparseML models).               |
+| README.md            | Readme file.                                                                                                          |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/tensorflow_v1/classification.py b/integrations/examples/tensorflow_v1/classification.py
new file mode 100644
index 00000000000..ea544a0429e
--- /dev/null
+++ b/integrations/examples/tensorflow_v1/classification.py
@@ -0,0 +1,965 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Perform optimization tasks on image classification tensorflow_v1 including:
+* Model training
+* Model pruning
+* Sparse transfer learning
+* pruning sensitivity analysis
+* ONNX export
+
+
+##########
+Command help:
+usage: classification.py [-h] {train,export,pruning_sensitivity} ...
+
+Run tasks on classification models and datasets using the sparseml API
+
+positional arguments:
+  {train,export,pruning_sensitivity}
+
+optional arguments:
+  -h, --help            show this help message and exit
+
+
+##########
+train command help:
+usage: classification.py train [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                               [--recipe-path RECIPE_PATH]
+                               [--sparse-transfer-learn] [--eval-mode]
+                               --train-batch-size TRAIN_BATCH_SIZE
+                               --test-batch-size TEST_BATCH_SIZE
+                               [--logs-dir LOGS_DIR]
+                               [--save-best-after SAVE_BEST_AFTER]
+                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
+                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
+
+Train and/or prune an image classification model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+--dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for dataset loading
+  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for dataset loading
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. If set to
+                        'transfer_learning', then will create a schedule to
+                        enable sparse transfer learning
+  --sparse-transfer-learn
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode           Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
+                        epochs to save checkpoints at
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+
+
+##########
+export command help:
+usage: classification.py export [-h] --arch-key ARCH_KEY
+                                [--pretrained PRETRAINED]
+                                [--pretrained-dataset PRETRAINED_DATASET]
+                                [--checkpoint-path CHECKPOINT_PATH]
+                                [--model-kwargs MODEL_KWARGS] --dataset
+                                DATASET --dataset-path DATASET_PATH
+                                [--dataset-kwargs DATASET_KWARGS]
+                                [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                                [--num-samples NUM_SAMPLES]
+                                [--onnx-opset ONNX_OPSET]
+
+Export a model to onnx as well as store sample inputs, outputs, and labels
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --num-samples NUM_SAMPLES
+                        The number of samples to export along with the model
+                        onnx and pth files (sample inputs and labels as well
+                        as the outputs from model execution)
+  --onnx-opset ONNX_OPSET
+                        The onnx opset to use for export. Default is 11
+
+
+##########
+pruning_sensitivity command help:
+usage: classification.py pruning_sensitivity [-h] --arch-key ARCH_KEY
+                                             [--pretrained PRETRAINED]
+                                             [--pretrained-dataset PRETRAINED_DATASET]
+                                             [--checkpoint-path CHECKPOINT_PATH]
+                                             [--model-kwargs MODEL_KWARGS]
+                                             --dataset DATASET --dataset-path
+                                             DATASET_PATH
+                                             [--dataset-kwargs DATASET_KWARGS]
+                                             [--model-tag MODEL_TAG]
+                                             [--save-dir SAVE_DIR]
+                                             [--dataset-parallel-calls
+                                                DATASET_PARALLEL_CALLS]
+                                             [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                                             [--approximate]
+                                             [--steps-per-measurement
+                                                STEPS_PER_MEASUREMENT]
+                                             [--batch-size BATCH_SIZE]
+
+Run a kernel sparsity (pruning) analysis for a given model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for dataset loading
+  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for dataset loading
+  --approximate         True to approximate without running data through the
+                        model, otherwise will run a one shot analysis
+  --steps-per-measurement STEPS_PER_MEASUREMENT
+                        The number of steps (batches) to run for each
+                        measurement
+  --batch-size BATCH_SIZE
+                        The batch size to use while performing analysis
+
+
+#########
+EXAMPLES
+#########
+
+##########
+Example command for pruning resnet50 on imagenet dataset:
+python scripts/tensorflow_v1/classification.py train \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
+python scripts/tensorflow_v1/classification.py train \
+    --sparse-transfer-learn \
+    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
+    --arch-key mobilenet_v1 --pretrained optim \
+    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for exporting ResNet50:
+python scripts/tensorflow_v1/classification.py export \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
+
+##########
+Example command for running approximated KS sensitivity analysis on mobilenet:
+python scripts/tensorflow_v1/classification.py pruning_sensitivity \
+    --approximate \
+    --arch-key mobilenet --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012
+
+##########
+Example command for running one shot KS sensitivity analysis on resnet50 for coco:
+python scripts/tensorflow_v1/classification.py pruning_sensitivity \
+    --arch-key resnet50 --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012
+"""
+
+import argparse
+import json
+import math
+import os
+from typing import Dict, Optional, Tuple
+
+import numpy
+
+from sparseml import get_main_logger
+from sparseml.tensorflow_v1.datasets import (
+    Dataset,
+    DatasetRegistry,
+    create_split_iterators_handle,
+)
+from sparseml.tensorflow_v1.models import ModelRegistry
+from sparseml.tensorflow_v1.optim import (
+    ConstantPruningModifier,
+    ScheduledModifierManager,
+    pruning_loss_sens_magnitude,
+    pruning_loss_sens_one_shot,
+    pruning_loss_sens_op_vars,
+)
+from sparseml.tensorflow_v1.utils import (
+    GraphExporter,
+    accuracy,
+    batch_cross_entropy_loss,
+    tf_compat,
+    write_simple_summary,
+)
+from sparseml.utils import create_dirs
+
+
+LOGGER = get_main_logger()
+TRAIN_COMMAND = "train"
+EXPORT_COMMAND = "export"
+PRUNING_SENSITVITY_COMMAND = "pruning_sensitivity"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run tasks on classification models and datasets "
+        "using the sparseml API"
+    )
+
+    subparsers = parser.add_subparsers(dest="command")
+    train_parser = subparsers.add_parser(
+        TRAIN_COMMAND,
+        description="Train and/or prune an image classification model",
+    )
+    export_parser = subparsers.add_parser(
+        EXPORT_COMMAND,
+        description="Export a model to onnx as well as "
+        "store sample inputs, outputs, and labels",
+    )
+    pruning_sensitivity_parser = subparsers.add_parser(
+        PRUNING_SENSITVITY_COMMAND,
+        description="Run a kernel sparsity (pruning) analysis for a given model",
+    )
+
+    parsers = [
+        train_parser,
+        export_parser,
+        pruning_sensitivity_parser,
+    ]
+    for par in parsers:
+        # general arguments
+        # model args
+        par.add_argument(
+            "--arch-key",
+            type=str,
+            required=True,
+            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception with the list)",
+        )
+        par.add_argument(
+            "--pretrained",
+            type=str,
+            default=True,
+            help="The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]",
+        )
+        par.add_argument(
+            "--pretrained-dataset",
+            type=str,
+            default=None,
+            help="The dataset to load pretrained weights for if pretrained is set. "
+            "Default is None which will load the default dataset for the architecture."
+            " Ex can be set to imagenet, cifar10, etc",
+        )
+        par.add_argument(
+            "--checkpoint-path",
+            type=str,
+            default=None,
+            help="A path to a previous checkpoint to load the state from and "
+            "resume the state for. If provided, pretrained will be ignored",
+        )
+        par.add_argument(
+            "--model-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to model constructor, should be "
+            " given as a json object",
+        )
+
+        # dataset args
+        par.add_argument(
+            "--dataset",
+            type=str,
+            required=True,
+            help="The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or loadable by a "
+            "dataset in sparseml.tensorflow_v1.datasets",
+        )
+        par.add_argument(
+            "--dataset-path",
+            type=str,
+            required=True,
+            help="The root path to where the dataset is stored",
+        )
+        par.add_argument(
+            "--dataset-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to dataset constructor, should be "
+            " given as a json object",
+        )
+
+        # logging and saving
+        par.add_argument(
+            "--model-tag",
+            type=str,
+            default=None,
+            help="A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        )
+        par.add_argument(
+            "--save-dir",
+            type=str,
+            default="tensorflow_v1_classification",
+            help="The path to the directory for saving results",
+        )
+
+        # task specific arguments
+        if par in [train_parser, pruning_sensitivity_parser]:
+            par.add_argument(
+                "--dataset-parallel-calls",
+                type=int,
+                default=4,
+                help="the number of parallel workers for dataset loading",
+            )
+            par.add_argument(
+                "--shuffle-buffer-size",
+                type=int,
+                default=1000,
+                help="Shuffle buffer size for dataset loading",
+            )
+
+        if par == train_parser:
+            par.add_argument(
+                "--recipe-path",
+                type=str,
+                default=None,
+                help="The path to the yaml file containing the modifiers and "
+                "schedule to apply them with. If set to 'transfer_learning', "
+                "then will create a schedule to enable sparse transfer learning",
+            )
+            par.add_argument(
+                "--sparse-transfer-learn",
+                action="store_true",
+                help=(
+                    "Enable sparse transfer learning modifiers to enforce the sparsity "
+                    "for already sparse layers. The modifiers are added to the "
+                    "ones to be loaded from the recipe-path"
+                ),
+            )
+            par.add_argument(
+                "--eval-mode",
+                action="store_true",
+                help="Puts into evaluation mode so that the model can be "
+                "evaluated on the desired dataset",
+            )
+            par.add_argument(
+                "--train-batch-size",
+                type=int,
+                required=True,
+                help="The batch size to use while training",
+            )
+            par.add_argument(
+                "--test-batch-size",
+                type=int,
+                required=True,
+                help="The batch size to use while testing",
+            )
+            par.add_argument(
+                "--logs-dir",
+                type=str,
+                default=os.path.join(
+                    "tensorflow_v1_classification_train", "tensorboard-logs"
+                ),
+                help="The path to the directory for saving logs",
+            )
+            par.add_argument(
+                "--save-best-after",
+                type=int,
+                default=-1,
+                help="start saving the best validation result after the given "
+                "epoch completes until the end of training",
+            )
+            par.add_argument(
+                "--save-epochs",
+                type=int,
+                default=[],
+                nargs="+",
+                help="epochs to save checkpoints at",
+            )
+            par.add_argument(
+                "--init-lr",
+                type=float,
+                default=1e-9,
+                help="The initial learning rate to use while training, "
+                "the actual initial value used should be set by the sparseml recipe",
+            )
+            par.add_argument(
+                "--optim-args",
+                type=json.loads,
+                default={},
+                help="Additional args to be passed to the optimizer passed in"
+                " as a json object",
+            )
+
+        if par == export_parser:
+            par.add_argument(
+                "--num-samples",
+                type=int,
+                default=100,
+                help="The number of samples to export along with the model onnx "
+                "and pth files (sample inputs and labels as well as the outputs "
+                "from model execution)",
+            )
+            par.add_argument(
+                "--onnx-opset",
+                type=int,
+                default=11,
+                help="The onnx opset to use for export. Default is 11",
+            )
+
+        if par == pruning_sensitivity_parser:
+            par.add_argument(
+                "--approximate",
+                action="store_true",
+                help="True to approximate without running data through the model, "
+                "otherwise will run a one shot analysis",
+            )
+            par.add_argument(
+                "--steps-per-measurement",
+                type=int,
+                default=15,
+                help="The number of steps (batches) to run for each measurement",
+            )
+            par.add_argument(
+                "--batch-size",
+                type=int,
+                default=64,
+                help="The batch size to use while performing analysis",
+            )
+
+    return parser.parse_args()
+
+
+def _setup_save_dirs(args) -> Tuple[str, Optional[str]]:
+    # logging and saving setup
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+    logs_dir = (
+        os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
+        if args.command == TRAIN_COMMAND
+        else None
+    )
+
+    if not args.model_tag:
+        model_tag = "{}_{}".format(args.arch_key.replace("/", "."), args.dataset)
+        model_id = model_tag
+        model_inc = 0
+        # set location to check for models with same name
+        model_main_dir = logs_dir or save_dir
+
+        while os.path.exists(os.path.join(model_main_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+
+    # logs dir setup
+    if args.command == TRAIN_COMMAND:
+        logs_dir = os.path.join(logs_dir, model_id)
+        create_dirs(logs_dir)
+    else:
+        logs_dir = None
+    LOGGER.info("Model id is set to {}".format(model_id))
+    return save_dir, logs_dir
+
+
+def _create_dataset(args, train=True, image_size=None) -> Tuple[Dataset, int]:
+    kwargs = args.dataset_kwargs
+    if "image_size" in kwargs:
+        image_size = kwargs["image_size"]
+        del kwargs["image_size"]
+
+    dataset = DatasetRegistry.create(
+        args.dataset,
+        root=args.dataset_path,
+        train=train,
+        image_size=image_size,
+        **kwargs,
+    )
+    LOGGER.info("created {} dataset: {}".format("train" if train else "val", dataset))
+
+    # get num_classes
+    if args.dataset == "imagefolder":
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+
+    return dataset, num_classes
+
+
+def _build_dataset(args, dataset: Dataset, batch_size: int) -> Dataset:
+    return dataset.build(
+        batch_size,
+        shuffle_buffer_size=args.shuffle_buffer_size,
+        prefetch_buffer_size=batch_size,
+        num_parallel_calls=args.dataset_parallel_calls,
+    )
+
+
+def _create_model(args, num_classes, inputs, training=False):
+    outputs = ModelRegistry.create(
+        args.arch_key,
+        inputs,
+        training=training,
+        num_classes=num_classes,
+        **args.model_kwargs,
+    )
+    LOGGER.info("created model {}".format(args.arch_key))
+    return outputs
+
+
+def _load_model(args, sess, checkpoint_path=None):
+    sess.run(
+        [
+            tf_compat.global_variables_initializer(),
+            tf_compat.local_variables_initializer(),
+        ]
+    )
+    checkpoint_path = checkpoint_path or args.checkpoint_path
+    ModelRegistry.load_pretrained(
+        args.arch_key,
+        pretrained=args.pretrained,
+        pretrained_dataset=args.pretrained_dataset,
+        pretrained_path=checkpoint_path,
+        sess=sess,
+    )
+    if checkpoint_path:
+        LOGGER.info("Loaded model weights from checkpoint: {}".format(checkpoint_path))
+
+
+def _save_checkpoint(args, sess, save_dir, checkpoint_name) -> str:
+    checkpoint_path = os.path.join(os.path.join(save_dir, checkpoint_name, "model"))
+    create_dirs(checkpoint_path)
+    saver = ModelRegistry.saver(args.arch_key)
+    saved_name = saver.save(sess, checkpoint_path)
+    checkpoint_path = os.path.join(checkpoint_path, saved_name)
+    LOGGER.info("Checkpoint saved to {}".format(checkpoint_path))
+    return checkpoint_path
+
+
+def _save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    LOGGER.info(f"Saved recipe to {recipe_save_path}")
+
+
+def train(args, save_dir, logs_dir):
+    # setup dataset
+    with tf_compat.device("/cpu:0"):
+        train_dataset, _ = _create_dataset(args, train=True)
+        val_dataset, num_classes = _create_dataset(args, train=False)
+        # calc steps
+        train_steps = math.ceil(len(train_dataset) / args.train_batch_size)
+        val_steps = math.ceil(len(val_dataset) / args.test_batch_size)
+        # build datasets
+        train_dataset = _build_dataset(args, train_dataset, args.train_batch_size)
+        val_dataset = _build_dataset(args, val_dataset, args.test_batch_size)
+    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(
+        [train_dataset, val_dataset]
+    )
+
+    # set up model graph
+    images, labels = iterator.get_next()
+    training = tf_compat.placeholder(dtype=tf_compat.bool, shape=[])
+    outputs = _create_model(args, num_classes, images, training)
+
+    # set up training objects
+    loss = batch_cross_entropy_loss(outputs, labels)
+    acc = accuracy(outputs, labels)
+    global_step = tf_compat.train.get_or_create_global_step()
+    train_op = tf_compat.train.AdamOptimizer(
+        learning_rate=args.init_lr, **args.optim_args
+    ).minimize(loss, global_step=global_step)
+    update_ops = tf_compat.get_collection(tf_compat.GraphKeys.UPDATE_OPS)
+    LOGGER.info("Created update ops for training")
+
+    # set up sparseml modifier ops
+    add_mods = (
+        ConstantPruningModifier(params="__ALL__")
+        if args.sparse_transfer_learn
+        else None
+    )
+    manager = ScheduledModifierManager.from_yaml(
+        file_path=args.recipe_path, add_modifiers=add_mods
+    )
+    mod_ops, mod_extras = manager.create_ops(train_steps, global_step)
+    _save_recipe(recipe_manager=manager, save_dir=save_dir)
+    with tf_compat.Session() as sess:
+        # set up tensorboard logging
+        summary_writer = tf_compat.summary.FileWriter(logs_dir, sess.graph)
+        summaries = tf_compat.summary.merge_all()
+        LOGGER.info("Logging to tensorboard at {}".format(logs_dir))
+
+        # initialize variables, load pretrained weights, initialize modifiers
+        train_iter_handle, val_iter_handle = sess.run(
+            [train_iter.string_handle(), val_iter.string_handle()]
+        )
+        LOGGER.info("Initialized graph variables")
+        _load_model(args, sess)
+        manager.initialize_session()
+        LOGGER.info("Initialized SparseML modifiers")
+
+        best_loss = None
+        for epoch in range(manager.max_epochs):
+            # train
+            LOGGER.info("Training for epoch {}...".format(epoch))
+            sess.run(train_iter.initializer)
+            train_acc, train_loss = [], []
+            for step in range(train_steps):
+                _, __, meas_step, meas_loss, meas_acc, meas_summ = sess.run(
+                    [train_op, update_ops, global_step, loss, acc, summaries],
+                    feed_dict={handle: train_iter_handle, training: True},
+                )
+                if step >= train_steps - 1:
+                    # log the general summaries on the last training step
+                    summary_writer.add_summary(meas_summ, meas_step)
+                # run modifier ops
+                sess.run(mod_ops)
+                # summarize
+                write_simple_summary(summary_writer, "Train/Loss", meas_loss, meas_step)
+                write_simple_summary(
+                    summary_writer, "Train/Acc", meas_acc * 100.0, meas_step
+                )
+                train_acc.append(meas_acc)
+                train_loss.append(meas_loss)
+            LOGGER.info(
+                "Epoch {} - Train Loss: {}, Train Acc: {}".format(
+                    epoch, numpy.mean(train_loss).item(), numpy.mean(train_acc).item()
+                )
+            )
+
+            # val
+            LOGGER.info("Validating for epoch {}...".format(epoch))
+            sess.run(val_iter.initializer)
+            val_acc, val_loss = [], []
+            for step in range(val_steps):
+                meas_loss, meas_acc = sess.run(
+                    [loss, acc],
+                    feed_dict={handle: val_iter_handle, training: False},
+                )
+                val_acc.append(meas_acc)
+                val_loss.append(meas_loss)
+                write_simple_summary(
+                    summary_writer, "Val/Loss", numpy.mean(val_loss).item(), epoch
+                )
+                write_simple_summary(
+                    summary_writer, "Val/Acc", numpy.mean(val_acc).item(), epoch
+                )
+            val_loss = numpy.mean(val_loss).item()
+            LOGGER.info(
+                "Epoch {} - Val Loss: {}, Val Acc: {}".format(
+                    epoch, val_loss, numpy.mean(train_acc).item()
+                )
+            )
+            if epoch >= args.save_best_after and (
+                best_loss is None or val_loss <= best_loss
+            ):
+                _save_checkpoint(args, sess, save_dir, "checkpoint-best")
+                best_loss = val_loss
+            if args.save_epochs and epoch in args.save_epochs:
+                _save_checkpoint(
+                    args, sess, save_dir, "checkpoint-epoch-{}".format(epoch)
+                )
+
+        # cleanup graph and save final checkpoint
+        manager.complete_graph()
+        checkpoint_path = _save_checkpoint(args, sess, save_dir, "final-checkpoint")
+    LOGGER.info("Running ONNX export flow")
+    export(
+        args,
+        save_dir,
+        checkpoint_path=checkpoint_path,
+        skip_samples=True,
+        num_classes=num_classes,
+        opset=11,
+    )
+
+
+def export(
+    args,
+    save_dir,
+    checkpoint_path=None,
+    skip_samples=False,
+    num_classes=None,
+    opset=None,
+):
+    assert not skip_samples or num_classes
+    # dataset creation
+    if not skip_samples:
+        val_dataset, num_classes = _create_dataset(args, train=False)
+
+    with tf_compat.Graph().as_default():
+        input_shape = ModelRegistry.input_shape(args.arch_key)
+        inputs = tf_compat.placeholder(
+            tf_compat.float32, [None] + list(input_shape), name="inputs"
+        )
+        outputs = _create_model(args, num_classes, inputs)
+
+        with tf_compat.Session() as sess:
+            _load_model(
+                args, sess, checkpoint_path=checkpoint_path or args.checkpoint_path
+            )
+
+            exporter = GraphExporter(save_dir)
+
+            if not skip_samples:
+                # Export a batch of samples and expected outputs
+                tf_dataset = val_dataset.build(
+                    args.num_samples, repeat_count=1, num_parallel_calls=1
+                )
+                tf_iter = tf_compat.data.make_one_shot_iterator(tf_dataset)
+                features, _ = tf_iter.get_next()
+                inputs_val = sess.run(features)
+                exporter.export_samples([inputs], [inputs_val], [outputs], sess)
+
+            # Export model to tensorflow checkpoint format
+            LOGGER.info("exporting tensorflow in {}".format(save_dir))
+            exporter.export_checkpoint(sess=sess)
+
+            # Export model to pb format
+            LOGGER.info("exporting pb in {}".format(exporter.pb_path))
+            exporter.export_pb(outputs=[outputs])
+
+    # Export model to onnx format
+    LOGGER.info("exporting onnx in {}".format(exporter.onnx_path))
+    exporter.export_onnx([inputs], [outputs], opset=opset or args.onnx_opset)
+
+
+def pruning_loss_sensitivity(args, save_dir):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    train_dataset, num_classes = _create_dataset(
+        args, train=True, image_size=input_shape[1]
+    )
+    with tf_compat.Graph().as_default() as graph:
+        # create model graph
+        inputs = tf_compat.placeholder(
+            tf_compat.float32, [None] + list(input_shape), name="inputs"
+        )
+        outputs = _create_model(args, num_classes, inputs)
+
+        with tf_compat.Session() as sess:
+            _load_model(args, sess, checkpoint_path=args.checkpoint_path)
+            if args.approximate:
+                LOGGER.info("Running weight magnitude loss sensitivity analysis...")
+                analysis = pruning_loss_sens_magnitude(graph, sess)
+            else:
+                op_vars = pruning_loss_sens_op_vars(graph)
+                train_steps = math.ceil(len(train_dataset) / args.batch_size)
+                train_dataset = _build_dataset(args, train_dataset, args.batch_size)
+                handle, iterator, dataset_iter = create_split_iterators_handle(
+                    [train_dataset]
+                )
+                dataset_iter = dataset_iter[0]
+                images, labels = iterator.get_next()
+                loss = batch_cross_entropy_loss(outputs, labels)
+                tensor_names = ["inputs:0", labels.name]
+                sess.run(dataset_iter.initializer)
+
+                def feed_dict_creator(step: int) -> Dict[str, tf_compat.Tensor]:
+                    assert step < train_steps
+                    batch_data = [
+                        tens.eval(session=sess) for tens in dataset_iter.get_next()
+                    ]
+                    return dict(zip(tensor_names, batch_data))
+
+                LOGGER.info("Running one shot loss sensitivity analysis...")
+                analysis = pruning_loss_sens_one_shot(
+                    op_vars=op_vars,
+                    loss_tensor=loss,
+                    steps_per_measurement=args.steps_per_measurement,
+                    feed_dict_creator=feed_dict_creator,
+                    sess=sess,
+                )
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info("Saving results in {}".format(save_dir))
+    analysis.save_json(
+        os.path.join(
+            save_dir,
+            "ks_approx_sensitivity.json"
+            if args.approximate
+            else "ks_one_shot_sensitivity.json",
+        )
+    )
+    analysis.plot(
+        os.path.join(
+            save_dir,
+            os.path.join(
+                save_dir,
+                "ks_approx_sensitivity.png"
+                if args.approximate
+                else "ks_one_shot_sensitivity.png",
+            ),
+        ),
+        plot_integral=True,
+    )
+    analysis.print_res()
+
+
+def main(args):
+    # set up saving and logging dirs
+    save_dir, logs_dir = _setup_save_dirs(args)
+
+    # RUN COMMAND SPECIFIC TASTS
+    if args.command == TRAIN_COMMAND:
+        train(args, save_dir, logs_dir)
+    if args.command == EXPORT_COMMAND:
+        export(args, save_dir)
+    if args.command == PRUNING_SENSITVITY_COMMAND:
+        pruning_loss_sensitivity(args, save_dir)
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+
+    main(args_)
diff --git a/integrations/examples/tensorflow_v1/notebooks/classification.ipynb b/integrations/examples/tensorflow_v1/notebooks/classification.ipynb
new file mode 100644
index 00000000000..158d12aaef0
--- /dev/null
+++ b/integrations/examples/tensorflow_v1/notebooks/classification.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# TensorFlow v1 Classification Model Pruning Using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Engine. You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a TensorFlow training flow with a simple SparseML integration\n",
+    "- Prune the model using the TensorFlow+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your TensorFlow training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 20 minutes on a GPU\n",
+    "- 60 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* TensorFlow v1 and tf2onnx\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import tensorflow as tf\n",
+    "import tf2onnx\n",
+    "\n",
+    "assert tf.__version__ < \"2\"\n",
+    "\n",
+    "# suppress warnings\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "In the cells below, functions are defined to load the dataset, model, and training objects to be called during training from within the `Graph` context.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate function for to load your model or dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "from sparseml.tensorflow_v1.models import ModelRegistry\n",
+    "from sparseml.tensorflow_v1.datasets import (\n",
+    "    ImagenetteDataset,\n",
+    "    ImagenetteSize,\n",
+    ")\n",
+    "from sparseml.tensorflow_v1.utils import (\n",
+    "    batch_cross_entropy_loss,\n",
+    "    accuracy,\n",
+    ")\n",
+    "\n",
+    "MODEL_NAME = \"resnet50\"\n",
+    "BATCH_SIZE = 128\n",
+    "INPUT_SIZE = 224\n",
+    "NUM_CLASSES = 10\n",
+    "SAVE_DIR = \"tensorflow_v1_classification_pruning\"\n",
+    "\n",
+    "\n",
+    "def load_dataset():\n",
+    "    with tf.device(\"/cpu:0\"):\n",
+    "        print(\"loading datasets\")\n",
+    "        train_dataset = ImagenetteDataset(\n",
+    "            train=True, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
+    "        )\n",
+    "        train_len = len(train_dataset)\n",
+    "        train_steps = math.ceil(train_len / float(BATCH_SIZE))\n",
+    "        train_dataset = train_dataset.build(\n",
+    "            BATCH_SIZE,\n",
+    "            shuffle_buffer_size=1000,\n",
+    "            prefetch_buffer_size=BATCH_SIZE,\n",
+    "            num_parallel_calls=4,\n",
+    "        )\n",
+    "\n",
+    "        val_dataset = ImagenetteDataset(\n",
+    "            train=False, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
+    "        )\n",
+    "        val_len = len(val_dataset)\n",
+    "        val_steps = math.ceil(val_len / float(BATCH_SIZE))\n",
+    "        val_dataset = val_dataset.build(\n",
+    "            BATCH_SIZE,\n",
+    "            shuffle_buffer_size=1000,\n",
+    "            prefetch_buffer_size=BATCH_SIZE,\n",
+    "            num_parallel_calls=4,\n",
+    "        )\n",
+    "\n",
+    "    return train_dataset, val_dataset, (train_steps, val_steps)\n",
+    "\n",
+    "\n",
+    "def create_model(sample_input, training):\n",
+    "    print(\"Creating model graph for {}\".format(MODEL_NAME))\n",
+    "    logits = ModelRegistry.create(\n",
+    "        MODEL_NAME,\n",
+    "        inputs=sample_input,\n",
+    "        training=training,\n",
+    "        num_classes=NUM_CLASSES,\n",
+    "    )\n",
+    "    return logits\n",
+    "\n",
+    "\n",
+    "def create_training_objects(sample_labels):\n",
+    "    print(\"Creating loss, accuracy, and optimizer in graph\")\n",
+    "    loss = batch_cross_entropy_loss(logits, labels)\n",
+    "    acc = accuracy(logits, labels)\n",
+    "    global_step = tf.train.get_or_create_global_step()\n",
+    "    train_op = tf.train.AdamOptimizer(learning_rate=0.00008).minimize(\n",
+    "        loss, global_step=global_step\n",
+    "    )\n",
+    "    return loss, acc, global_step, train_op\n",
+    "\n",
+    "\n",
+    "def load_pretrained():\n",
+    "    print(\"loading pre-trained model weights\")\n",
+    "    ModelRegistry.load_pretrained(\n",
+    "        MODEL_NAME, pretrained=\"base\", remove_dynamic_tl_vars=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Create a SparseML Modifier Manager\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to create operations that modify the training process.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the operators generated from this manager object, you will be able to prune your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.tensorflow_v1.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "\n",
+    "def create_sparseml_manager():\n",
+    "    model = search_models(\n",
+    "        domain=\"cv\",\n",
+    "        sub_domain=\"classification\",\n",
+    "        architecture=\"resnet_v1\",\n",
+    "        sub_architecture=\"50\",\n",
+    "        framework=\"tensorflow_v1\",\n",
+    "        repo=\"sparseml\",\n",
+    "        dataset=\"imagenette\",\n",
+    "        sparse_name=\"pruned\",\n",
+    "    )[0]  # unwrap search result\n",
+    "\n",
+    "    recipe_path = model.recipes.default.path\n",
+    "    print(f\"Recipe downloaded to: {recipe_path}\")\n",
+    "\n",
+    "    return ScheduledModifierManager.from_yaml(recipe_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Prune your model using a TensorFlow training loop\n",
+    "SparseML can plug directly into your existing TensorFlow training flow by creating additional operators to run. To demonstrate this, in the cell below, prune the model using a standard TensorFlow training loop while also running the operators created by the manager object.  To prune your existing models using SparseML, you can use your own training flow with the additional operators added.\n",
+    "\n",
+    "For your convienence the lines needed for integrating with SparseML are preceeded by large comment blocks.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `BATCH_SIZE` in Step 2 above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import os\n",
+    "from tqdm.auto import tqdm\n",
+    "from sparseml.utils import create_unique_dir, create_dirs\n",
+    "from sparseml.tensorflow_v1.datasets import create_split_iterators_handle\n",
+    "\n",
+    "with tf.Graph().as_default() as graph:\n",
+    "    # create dataset\n",
+    "    train_dataset, val_dataset, (train_steps, val_steps) = load_dataset()\n",
+    "    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(\n",
+    "        [train_dataset, val_dataset]\n",
+    "    )\n",
+    "    images, labels = iterator.get_next()\n",
+    "\n",
+    "    # create base training objects\n",
+    "    training = tf.placeholder(dtype=tf.bool, shape=[])\n",
+    "    logits = create_model(images, training)\n",
+    "    loss, acc, global_step, train_op = create_training_objects(labels)\n",
+    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
+    "\n",
+    "    #######################################################\n",
+    "    # create sparseml training manager\n",
+    "    #######################################################\n",
+    "    manager = create_sparseml_manager()\n",
+    "    mod_ops, mod_extras = manager.create_ops(train_steps, global_step, graph=graph)\n",
+    "\n",
+    "    with tf.Session() as sess:\n",
+    "        print(\"initializing session\")\n",
+    "        sess.run(\n",
+    "            [\n",
+    "                tf.global_variables_initializer(),\n",
+    "                tf.local_variables_initializer(),\n",
+    "            ]\n",
+    "        )\n",
+    "        train_iter_handle, val_iter_handle = sess.run(\n",
+    "            [train_iter.string_handle(), val_iter.string_handle()]\n",
+    "        )\n",
+    "\n",
+    "        # initialize sparseml manager after pretrained weights loaded\n",
+    "        load_pretrained()\n",
+    "        manager.initialize_session()\n",
+    "\n",
+    "        num_epochs = manager.max_epochs\n",
+    "        for epoch in tqdm(range(num_epochs), desc=\"pruning\"):\n",
+    "            print(\"training for epoch {}...\".format(epoch))\n",
+    "            sess.run(train_iter.initializer)\n",
+    "            train_losses = []\n",
+    "            train_acc = []\n",
+    "\n",
+    "            for step in range(train_steps):\n",
+    "                _, __, meas_step, meas_loss, meas_acc = sess.run(\n",
+    "                    [train_op, update_ops, global_step, loss, acc],\n",
+    "                    feed_dict={handle: train_iter_handle, training: True},\n",
+    "                )\n",
+    "                train_losses.append(meas_loss)\n",
+    "                train_acc.append(meas_acc)\n",
+    "\n",
+    "                #######################################################\n",
+    "                # Modifier update ops line for transfer learning from a sparse model in TensorFlow\n",
+    "                #######################################################\n",
+    "                sess.run(mod_ops)\n",
+    "            print(\n",
+    "                \"completed epoch {} training with: loss {} / acc {}\".format(\n",
+    "                    epoch,\n",
+    "                    numpy.mean(train_losses).item(),\n",
+    "                    numpy.mean(train_acc).item() * 100,\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "            print(\"validating for epoch {}...\".format(epoch))\n",
+    "            sess.run(val_iter.initializer)\n",
+    "            val_losses = []\n",
+    "            val_acc = []\n",
+    "\n",
+    "            for step in range(val_steps):\n",
+    "                meas_loss, meas_acc = sess.run(\n",
+    "                    [loss, acc],\n",
+    "                    feed_dict={handle: val_iter_handle, training: False},\n",
+    "                )\n",
+    "                val_losses.append(meas_loss)\n",
+    "                val_acc.append(meas_acc)\n",
+    "\n",
+    "            print(\n",
+    "                \"completed epoch {} validation with: loss {} / acc {}\".format(\n",
+    "                    epoch,\n",
+    "                    numpy.mean(val_losses).item(),\n",
+    "                    numpy.mean(val_acc).item() * 100,\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "        #######################################################\n",
+    "        # Final line for sparseml training in TensorFlow, complete the graph\n",
+    "        #######################################################\n",
+    "        manager.complete_graph()\n",
+    "\n",
+    "        NAME = \"resnet50-imagenette-pruned\"\n",
+    "        checkpoint_path = create_unique_dir(\n",
+    "            os.path.join(\".\", SAVE_DIR, NAME, \"checkpoint\")\n",
+    "        )\n",
+    "        checkpoint_path = os.path.join(checkpoint_path, \"model\")\n",
+    "        create_dirs(checkpoint_path)\n",
+    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
+    "        saver.save(sess, checkpoint_path)\n",
+    "        print(\"saved model checkpoint to {}\".format(checkpoint_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For TensorFlow, exporting to ONNX is not natively supported. To add support, you will use the `tf2onnx` Python package. In the cell block below, a convenience class, `GraphExporter()`, is used to handle exporting. It wraps the somewhat complicated API for `tf2onnx` into an easy to use interface.\n",
+    "\n",
+    "Note, for some configurations, the tf2onnx code does not work properly in a Jupyter Notebook. To remedy this, you should run the `exporter.export_onnx()` function call in a Python console or script.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.utils import clean_path\n",
+    "from sparseml.tensorflow_v1.utils import GraphExporter\n",
+    "\n",
+    "\n",
+    "export_path = clean_path(os.path.join(\".\", SAVE_DIR, NAME))\n",
+    "exporter = GraphExporter(export_path)\n",
+    "\n",
+    "with tf.Graph().as_default() as graph:\n",
+    "    print(\"Recreating graph...\", flush=True)\n",
+    "\n",
+    "    input_placeholder = tf.placeholder(\n",
+    "        tf.float32, [None, INPUT_SIZE, INPUT_SIZE, 3], name=\"inputs\"\n",
+    "    )\n",
+    "    logits = create_model(input_placeholder, training=False)\n",
+    "\n",
+    "    input_names = [input_placeholder.name]\n",
+    "    output_names = [logits.name]\n",
+    "\n",
+    "    with tf.Session() as sess:\n",
+    "        sess.run(tf.global_variables_initializer())\n",
+    "        print(\"Restoring previous weights...\", flush=True)\n",
+    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
+    "        saver.restore(sess, checkpoint_path)\n",
+    "\n",
+    "        print(\"Exporting to pb...\", flush=True)\n",
+    "        exporter.export_pb(outputs=[logits])\n",
+    "        print(\"Exported pb file to {}\".format(exporter.pb_path), flush=True)\n",
+    "\n",
+    "print(\"Exporting to onnx...\", flush=True)\n",
+    "exporter.export_onnx(inputs=input_names, outputs=output_names)\n",
+    "print(\"Exported onnx file to {}\".format(exporter.onnx_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the DeepSparse Engine"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/examples/tensorflow_v1/recipes/.gitkeep b/integrations/examples/tensorflow_v1/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/tensorflow_v1/tutorials/.gitkeep b/integrations/examples/tensorflow_v1/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/examples/ultralytics-yolov3/README.md b/integrations/examples/ultralytics-yolov3/README.md
new file mode 100644
index 00000000000..1dae432bd4f
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/README.md
@@ -0,0 +1,128 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML Ultralytics YOLOv3 Integration
+
+This directory combines the SparseML recipe-driven approach with the 
+[ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository.
+By integrating the robust training flows in the `yolov3` repository with the SparseML code base,
+we enable model sparsification techniques on the popular [YOLOv3 architecture](https://arxiv.org/abs/1804.02767)
+creating smaller and faster deployable versions.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Blog: [YOLOv3 on CPUs: Sparsifying to Achieve GPU-Level Performance](https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-deepsparse/)
+- Example: [DeepSparse YOLOv3 Inference Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo)
+- Video: [DeepSparse YOLOv3 Pruned Quantized Performance](https://youtu.be/o5qIYs47MPw)
+
+## Tutorials
+
+- [Sparsifying YOLOv3 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md)
+- [Sparse Transfer Learning With YOLOv3](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md)
+
+## Installation
+
+To begin, run the following command in the root directory of this integration (`cd integrations/ultralytics-yolov3`):
+```bash
+bash setup_integration.sh
+```
+
+The setup_integration.sh file will clone the yolov3 repository with the SparseML integration as a subfolder.
+After the repo has successfully cloned,  all dependencies from the `yolov3/requirements.txt` file will install in your current environment.
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration adds a `--recipe` argument to the [`train.py` script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+For example, pruning and quantizing a model on the VOC dataset can be done by running the following command from within the yolov3 repository folder:
+```bash
+python train.py --batch 16 --img 512 --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml --hyp ../data/hyp.pruned_quantized.yaml \
+    --recipe ../recipes/yolov3-spp.pruned_quantized.short.md
+```
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+The following popular stubs can be used to download these models and recipes through the SparseZoo API directly.
+
+| Model                       | Dataset | mAP@50 | Description                                                    | SparseZoo Stub                                                                   |
+|-----------------------------|---------|--------|----------------------------------------------------------------|----------------------------------------------------------------------------------|
+| YOLOv3-SPP Pruned Quantized | COCO    | 60.4   | 83% sparse and INT8 quantized model with LeakyReLU activations | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 |
+| YOLOv3-SPP Pruned           | COCO    | 62.4   | 88% sparse model with LeakyReLU activations                    | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97       |
+| YOLOv3-SPP                  | COCO    | 64.2   | Baseline model with LeakyReLU activations                      | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none                  |
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/detection?repo=ultralytics) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/detection?repo=ultralytics) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| data                 | The hyperparameters to use and files to use for training on data.                                                     |
+| models               | Model architecture definitions along with any downloaded checkpoints from the SparseZoo.                              |
+| recipes              | Typical recipes for sparsifying YOLOv3 models along with any downloaded recipes from the SparseZoo.                   |
+| tutorials            | Tutorial walkthroughs for how to sparsify YOLOv3 models using recipes.                                                |
+| yolov3               | Integration repository folder used to train and sparsify YOLOv3 models (setup_integration.sh must run first).         |
+| README.md            | Readme file.                                                                                                          |
+| setup_integration.sh | Setup file for the integration run from the command line.                                                             |
+
+### Exporting for Inference
+
+After sparsifying a model, the [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) 
+converts the model into deployment formats such as [ONNX](https://onnx.ai/).
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+For example, the following command can be run from within the yolov3 repository folder to export a trained/sparsified model's checkpoint:
+```bash
+python models/export.py --weights PATH/TO/weights.pt --img-size 512 512
+```
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
diff --git a/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml b/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml
new file mode 100644
index 00000000000..69a20b0f57b
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml
@@ -0,0 +1,27 @@
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml b/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
new file mode 100644
index 00000000000..a146f379e9c
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
@@ -0,0 +1,27 @@
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml b/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
new file mode 100644
index 00000000000..57edb510de2
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
@@ -0,0 +1,52 @@
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+act: nn.LeakyReLU  # activations
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# YOLOv3-SPP head
+head:
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, SPP, [512, [5, 9, 13]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
+
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
new file mode 100644
index 00000000000..d5fc87fb2b3
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
@@ -0,0 +1,208 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 300
+
+# pruning hyperparameters
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 80
+update_frequency: &pruning_update_frequency 0.5
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned
+
+This recipe creates a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 97% recovery of its baseline accuracy on the COCO detection dataset.
+Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
+[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned.yaml \
+    --name yolov3-spp-lrelu-pruned
+```
+
+hyp.pruned.yaml:
+```yaml
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
new file mode 100644
index 00000000000..c04ef0a581d
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
@@ -0,0 +1,207 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 80
+
+# pruning hyperparameters
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 0.5
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned Short
+
+This recipe creates a sparse [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened schedule as compared to the original pruned recipe.
+It will train faster, but will recover slightly worse.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.short.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned.yaml \
+    --name yolov3-spp-lrelu-pruned
+```
+
+hyp.pruned.yaml:
+```yaml
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
new file mode 100644
index 00000000000..e9108a49440
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
@@ -0,0 +1,286 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 242
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 240
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 100
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized
+
+This recipe creates a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 94% recovery of its baseline accuracy on the COCO detection dataset.
+Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
+[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
new file mode 100644
index 00000000000..c25340e5a41
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
@@ -0,0 +1,286 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 52
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 50
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 23
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized
+
+This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened shcedule as compared to the original pruned recipe.
+It will train faster, but will recover slightly worse.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.short.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
new file mode 100644
index 00000000000..29b26e134eb
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
@@ -0,0 +1,285 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 5
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 4
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 2
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Test Recipe
+
+This is a test recipe useful for quickly evaluating the time and resources needed for pruning and quantizing a model.
+In addition, it offers a quick integration tests pathway.
+This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that will not be accurate.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.test.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.short.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
new file mode 100644
index 00000000000..2a21412a138
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
@@ -0,0 +1,169 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR Hyperparams
+num_epochs: &num_epochs 50
+
+# Pruning Hyperparams
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 5
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 1
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.92
+
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params:
+      - model.1.conv.weight
+      - model.2.cv1.conv.weight
+      - model.2.cv2.conv.weight
+      - model.3.conv.weight
+      - model.4.0.cv1.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.3.cv2.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv1.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv1.conv.weight
+      - model.11.cv2.conv.weight
+      - model.12.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.13.conv.weight
+      - model.14.conv.weight
+      - model.15.conv.weight
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.19.cv2.conv.weight
+      - model.20.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.21.conv.weight
+      - model.22.conv.weight
+      - model.23.conv.weight
+      - model.26.cv1.conv.weight
+      - model.26.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.27.1.cv2.conv.weight
+
+  - !GMPruningModifier
+    params:
+      - model.28.m.0.weight
+      - model.28.m.1.weight
+      - model.28.m.2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned Transfer Learning
+
+This recipe transfer learns from a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
+It was originally tested on the VOC dataset and achieved 0.84 mAP@0.5.
+
+Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+The training results for this recipe are made available through Weights and Biases for easy viewing.
+
+- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/3kvb4neh)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
+ ```python
+from sparsezoo import Model
+
+stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
+model = Model(stub)
+downloaded_path = model.path
+print(f'Model with stub {stub} downloaded to {downloaded_path}.')
+```
+
+*script command:*
+
+```bash
+python train.py \
+    --data voc.yaml \
+    --cfg ../models/yolov3-spp.lrelu.yaml \
+    --weights DOWNLOADED_PATH \
+    --hyp data/hyp.finetune.yaml \
+    --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
+```
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
new file mode 100644
index 00000000000..8cf05b9d59c
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
@@ -0,0 +1,238 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR Hyperparams
+num_epochs: &num_epochs 52
+warmup_lr: &warmup_lr 0.0
+warmup_lr_bias: &warmup_lr_bias 0.05
+init_lr: &init_lr 0.0032
+final_lr: &final_lr 0.00038
+
+# Pruning Hyperparams
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 5
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 1
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.92
+
+# Quantization Hyperparams
+quantization_start_epoch: &quantization_start_epoch 50
+quantization_init_lr: &quantization_init_lr 0.0038
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 100
+    end_epoch: 160
+    lr_func: cosine
+    init_lr: *init_lr
+    final_lr: *final_lr
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: 3
+    lr_func: linear
+    init_lr: *warmup_lr
+    final_lr: *init_lr
+    param_groups: [0, 1]
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: 3
+    lr_func: linear
+    init_lr: *warmup_lr_bias
+    final_lr: *init_lr
+    param_groups: [2]
+    
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params:
+      - model.1.conv.weight
+      - model.2.cv1.conv.weight
+      - model.2.cv2.conv.weight
+      - model.3.conv.weight
+      - model.4.0.cv1.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.3.cv2.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv1.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv1.conv.weight
+      - model.11.cv2.conv.weight
+      - model.12.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.13.conv.weight
+      - model.14.conv.weight
+      - model.15.conv.weight
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.19.cv2.conv.weight
+      - model.20.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.21.conv.weight
+      - model.22.conv.weight
+      - model.23.conv.weight
+      - model.26.cv1.conv.weight
+      - model.26.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.27.1.cv2.conv.weight
+
+  - !GMPruningModifier
+    params:
+      - model.28.m.0.weight
+      - model.28.m.1.weight
+      - model.28.m.2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+    mask_type: *mask_type
+        
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized Transfer Learning
+
+This recipe transfer learns from a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
+It was originally tested on the VOC dataset and achieved 0.838 mAP@0.5.
+
+Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+The training results for this recipe are made available through Weights and Biases for easy viewing.
+
+- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/2hbvu7w2)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
+ ```python
+from sparsezoo import Model
+
+stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
+model = Model(stub)
+downloded_path = model.path
+print(f"Model with stub {stub} downloaded to {downloaded_path}.")
+```
+
+*script command:*
+
+```bash
+python train.py \
+    --data voc.yaml \
+    --cfg ../models/yolov3-spp.lrelu.yaml \
+    --weights DOWNLOADED_PATH \
+    --hyp data/hyp.finetune.yaml \
+    --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+```
diff --git a/integrations/examples/ultralytics-yolov3/setup_integration.sh b/integrations/examples/ultralytics-yolov3/setup_integration.sh
new file mode 100644
index 00000000000..b14482e2979
--- /dev/null
+++ b/integrations/examples/ultralytics-yolov3/setup_integration.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Integration setup command to setup the folder so it is ready to train and sparsify models.
+# Creates a yolov3 folder next to this script with all required dependencies from the ultralytics/yolov3 repository.
+# Command: `bash setup_integration.sh`
+
+git clone https://github.com/neuralmagic/yolov3.git
+cd yolov3
+git checkout release/1.1
+pip install -r requirements.txt
diff --git a/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg b/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg
new file mode 100644
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diff --git a/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png b/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png
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diff --git a/integrations/examples/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png b/integrations/examples/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png
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diff --git a/integrations/examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md b/integrations/examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
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+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying YOLOv3 Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models.
+
+## Overview
+
+Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of Ultralytics’ robust training pipelines. Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+
+* Creating a pre-trained model to establish a baseline. You will set up your custom data and then train the model.
+* Applying a recipe to select the trade off between the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+The examples listed in this tutorial are all performed on the VOC dataset. Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc).
+
+<div style="width: 100%; display: flex; justify-content: center;">
+    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
+        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
+    </a>
+</div>
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Creating a Pre-trained Model
+
+Before applying one of the recipes, you must first create the pre-trained model to sparsify further. The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
+
+Creating a pre-trained model involves two steps: 1) setting up the data and 2) training the model.
+
+**Note**: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
+
+
+### Setting Up the Data
+
+1. For this tutorial, we run the VOC setup script with the following command from the root of the yolov3 repository:
+```bash
+bash data/scripts/get_voc.sh
+```
+2. Download and validation of the VOC dataset will begin and takes around 10 minutes to finish.
+The script downloads the VOC dataset into a `VOC` folder under the parent directory.
+Notie that, once completed, the data is ready for training with the folder structure in the following state:
+```
+|-- VOC
+|   |-- images
+|   |   |-- train
+|   |   `-- val
+|   `-- labels
+|       |-- train
+|       `-- val
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- utils
+|   |-- weights
+|   |-- Dockerfile
+|   |-- LICENSE
+|   |-- README.md
+|   |-- detect.py
+|   |-- hubconf.py
+|   |-- requirements.txt
+|   |-- test.py
+|   |-- train.py
+|   `-- tutorial.ipynb
+```
+
+You are ready to train the model.
+
+### Training the Model
+
+The training command will take a few hours to complete (~3 hours for a Titan RTX). Afterward, you will have a model that achieves roughly 0.85 mAP on the VOC dataset ready for sparsifying.
+
+1. To expedite the training process, you will transfer learn from weights initially trained on the COCO dataset. These are stored in the SparseZoo and accessed with the following Python code. Enter:
+
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none")
+checkpoint_path = model.training.default.path
+print(checkpoint_path)
+```
+
+After running, the code has downloaded the checkpoint file to the local system:
+```bash
+downloading...: 100%|██████████| 126342273/126342273 [00:04<00:00, 28306365.97it/s]
+/Users/markkurtz/.cache/sparsezoo/16fe8358-2e91-4b81-a1f2-df85bd1a9ac3/pytorch/model.pth
+```
+
+2. Next, the checkpoint file provides the source weights for training on VOC. Run the following train command from within the yolov3 repository folder:
+```bash
+python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50
+```
+
+The training command creates a `runs` directory under the yolov3 repository directory.
+This directory will contain the outputs from the training run, including experimental results along with the trained model:
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+You are ready to use the weights at `yolov3/runs/train/exp/weights/best.pt` with the Neural Magic recipes to create a sparsified model.
+
+## Applying a Recipe
+
+In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+The [`recipes` folder](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes) contains multiple files, each offering certain advantages over others. The table below compares these tradeoffs and shows how to run them on the VOC dataset.
+1. Review this table, which lists recipes, commands, and results.
+
+| Recipe Name                                                                                                                                              | Description                                                                                                                     | Train Command                                                                                                                                                                               | VOC mAP@0.5 | Size on Disk | DeepSparse Performance** | Training Epochs (time) | Weights and Biases                                                       |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------------|--------------------------|------------------------|--------------------------------------------------------------------------|
+| Baseline                                                                                                                                                 | The baseline, pretrained model originally transfer learned onto the VOC dataset.                                                | ``` python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 ```                                                                                     | 0.857       | 194 MB       | 19.5 img/sec             | 50 (3.21 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/261llnb1) |
+| [Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md)                                 | Creates a highly sparse, FP32 model that recovers close to the baseline model.                                                  | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.md ```                           | 0.858       | 33.6 MB      | 34.0 img/sec             | 300 (20.45 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts) |
+| [Pruned Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md)                     | Creates a highly sparse, FP32 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.short.md ```                     | 0.854       | 33.6 MB      | 34.0 img/sec             | 80 (5.53 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n) |
+| [Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md)             | Creates a highly sparse, INT8 model that recovers reasonably close to the baseline model.                                       | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.md ```       | 0.827       | 13.4 MB      | 90.4 img/sec             | 242 (18.32 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs) |
+| [Pruned Quantized Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md) | Creates a highly sparse, INT8 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.short.md ``` | 0.808       | 13.4 MB      | 90.4 img/sec             | 52 (4.23 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy) |
+| [Test](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md)                                     | A test recipe to test the training pipeline and device for both pruning and quantization in 5 epochs.                           | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.test.md ```                   | 0.702       | 13.4 MB      | 90.4 img/sec             | 5 (17 minutes)         | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60) |
+
+** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
+
+2. Notice the Weights and Biases information in the table, which is very useful for comparing across these runs. The epochs versus mAP@0.5 graph is:
+
+<img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
+
+**Notes About Recipe Selection**
+
+If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using either the  `pruned` or `pruned short` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
+
+If your hardware does support quantized networks (VNNI instruction set on CPUs, for example), we recommend using the `pruned quantized` or `pruned quantized short` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
+
+When running quantized models, the memory footprint for training will significantly increase (roughly 3x). Therefore it is essential to take this into account when selecting a batch size to train at. To ensure no issues with the longer quantized runs, run the quicker test recipe first to ensure your configurations are correct and the training process will complete successfully.
+
+3. To begin applying one of the recipes, use the `--recipe` argument within the Ultralytics [train script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
+In addition, the hyperparameters are changed slightly to better work with the recipe.
+These hyperparameters are stored in appropriately named files under the [`data` directory](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/data) and are passed into the training script using the `--hyp` argument.
+Both of the arguments are combined with our previous training command and VOC pre-trained weights to run the recipes over the model. For example:
+```bash
+python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 --recipe PATH_TO_SPARSIFICATION_RECIPE
+```
+After applying a recipe, you are ready to export for inference.
+
+## Exporting for Inference
+
+This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+When you applied a recipe in the previous step, the sparsification run created a new `exp` directory under the yolov3 runs directory:
+
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |-- exp1
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+The `best.pt` file contains a checkpoint of the best weights measured on the validation set.
+These weights can be loaded into the `train.py` and `test.py` scripts now. However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+The [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+
+1. Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
+
+```bash
+python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
+```
+
+The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+```
+|-- exp
+|-- exp1
+|-- weights
+|   |-- best.pt
+|   |-- best.onnx
+|   `-- last.pt
+`-- ...
+```
+
+2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+ 
+The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models. In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md b/integrations/examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
new file mode 100644
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@@ -0,0 +1,217 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparse Transfer Learning With YOLOv3
+
+This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
+
+## Overview
+
+Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo on top of Ultralytics’ robust training pipelines. 
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+- Downloading and preparing a pre-sparsified model.
+- Setting up your custom data.
+- Applying a sparse transfer learning recipe.
+- Exporting an inference graph to reduce its deployment size and run it in an inference engine such as DeepSparse.
+
+The examples listed in this tutorial are all performed on the VOC dataset. 
+Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning).
+
+<div style="width: 100%; display: flex; justify-content: center;">
+    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
+        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
+    </a>
+</div>
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Downloading and Preparing a Pre-pruned Model
+
+First, you need to download the sparsified models from the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+These models were originally pruned on the COCO dataset achieving the following metrics:
+
+| Sparsification Type | Description                                                                       | COCO mAP@0.5 | Size on Disk | DeepSparse Performance** |
+|---------------------|-----------------------------------------------------------------------------------|--------------|--------------|--------------------------|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.642        | 194 MB       | 19.5 img/sec             |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.624        | 33.6 MB      | 34.0 img/sec             |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.605        | 13.4 MB      | 90.4 img/sec             |
+** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
+
+1) After deciding on which model meets your performance requirements for both speed and accuracy, the following code is used to download the PyTorch checkpoints for the desired model from the SparseZoo:
+    ```python
+    from sparsezoo import Model
+    
+    BASELINE_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none'
+    PRUNED_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
+    PRUNED_QUANT_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
+    
+    stub = PRUNED_QUANT_STUB
+    model = Model(stub)
+    downloded_path = model.path
+    print(f'Model with stub {stub} downloaded to {downloded_path}.')
+    ```
+
+2) Once the desired checkpoint has downloaded, it must be reset for training again.
+    The `utility.py` script within YOLOv3 repository is used for this.
+    First change into the `yolov3` directory using `cd yolov3`.
+    Next run the following command to prepare the checkpoint for training:
+    ```bash
+    python utility.py strip /PATH/TO/DOWNLOADED/WEIGHTS.pt
+    ```
+   
+You are now ready to set up the data for training.
+
+## Setting Up the Data
+
+Note: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). 
+Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
+
+1) For this tutorial, run the VOC setup script with the following command from the root of the `yolov3` repository:
+    
+    `bash data/scripts/get_voc.sh`
+2) Download and validation of the VOC dataset will begin and take around 10 minutes to finish. 
+    The script downloads the VOC dataset into a VOC folder under the parent directory. 
+    Notice that, once completed, the data is ready for training with the folder structure in the following state:
+    ```
+    |-- VOC
+    |   |-- images
+    |   |   |-- train
+    |   |   `-- val
+    |   `-- labels
+    |       |-- train
+    |       `-- val
+    |-- yolov3
+    |   |-- data
+    |   |-- models
+    |   |-- utils
+    |   |-- weights
+    |   |-- Dockerfile
+    |   |-- LICENSE
+    |   |-- README.md
+    |   |-- detect.py
+    |   |-- hubconf.py
+    |   |-- requirements.txt
+    |   |-- test.py
+    |   |-- train.py
+    |   `-- tutorial.ipynb
+    ```
+
+You are ready to transfer learn the model.
+
+## Transfer Learning the Model
+
+The training command will take a few hours to complete (~3 hours for a Titan RTX). 
+Afterward, you will have a sparse model transfer learned onto the VOC dataset.
+
+The command uses the `--recipe` argument to encode the proper hyperparams such that SparseML will enforce the sparsity for all layers.
+Without the proper recipes, the zeroed weights will slowly become dense as the model is trained further.
+The recipes are specific to the sparsification type, so the training command will differ based on if you are transfer learning the pruned quantized, pruned, or baseline model.
+
+1) Select the proper command to run based on the sparsification type of the model you downloaded earlier.
+   Change directory into the `yolov3` folder to use the `train.py` script.
+   - Pruned transfer learning, achieves 0.84 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
+        ```
+   - Pruned-Quantized transfer learning, achieves 0.838 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+        ```
+     Note: Running quantization-aware training (QAT) will consume up to three times the memory as FP16 training. 
+     It is recommended to use this recipe with a batch size that fits in memory for FP16 as normal at the start, though.
+     This will keep the training times the same until the quantization portion.
+     Then it will fail with an out of memory exception, but you can use the `last.pt` checkpoint with the `--weights` argument to resume the run with a lower batch size for QAT.
+   - Baseline transfer learning, achieves 0.86 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml
+        ```
+
+    Weights and Biases is very useful for comparing across the different runs; the epochs vs mAP@0.5 graph is supplied below:
+    
+    <img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
+
+Afterward, you will have a sparse model trained on the VOC dataset almost ready for inference.
+The training command creates a `runs` directory under the `yolov3` repository directory. 
+This directory will contain the outputs from the training run, including experimental results along with the trained model:
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+## Exporting for Inference
+
+This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. 
+Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as DeepSparse.
+
+The `best.pt` file contains a checkpoint of the best weights measured on the validation set. 
+These weights can be loaded into the `train.py` and `test.py` scripts now. 
+However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+The [export.py script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+1) Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
+    ```bash
+    python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
+    ```
+    
+    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+    ```
+    |-- exp
+    |-- exp1
+    |-- weights
+    |   |-- best.pt
+    |   |-- best.onnx
+    |   `-- last.pt
+    `-- ...
+    ```
+2) Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+    The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
+    An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
+
+## Wrap-Up
+
+Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned and pruned-quantized YOLOv3 models. 
+In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and exported to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)

From 4da3cf81968f65a8bed366cc340b5858b1ed0be5 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Sun, 5 Mar 2023 18:46:07 -0500
Subject: [PATCH 04/29] Create README.md

---
 integrations/torchvision/README.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index 328b7399b47..28fa1d19b0e 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -16,13 +16,15 @@ limitations under the License.
 
 # SparseML Torchvision Integration
 
-By integrating with Torchvision, SparseML enables you to train inference-optimized sparse versions of popular image classification models like ResNet-50 on your dataset.
+This directory explains how to use SparseML's `torchvision` integration to train inference-optimized sparse NLP models on your dataset.
 
-There are two pathways:
-- **Sparse Transfer Learning**: fine-tune a pre-sparsified checkpoint on your own dataset **[RECOMMENDED]**
-- **Sparsification from Scratch**: Apply state-of-the-art training-aware pruning and quantization algorithms to arbitrary Torchvision models.
+There are two main workflows enabled by SparseML:
 
-Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse for GPU-class performance on the CPU.
+- **Sparse Transfer Learning** - fine-tune a pre-sparsified checkpoint on your own dataset **[RECOMMENDED]**
+
+- **Sparsification from Scratch** - apply pruning and quantization to sparsify `torchvision` models from scratch
+
+Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse.
 
 ## Installation
 
@@ -45,7 +47,7 @@ pip install sparseml[torchvision]
 
 Neural Magic has pre-sparsified versions of common Torchvision models such as ResNet-50. These models can be deployed directly or can be fine-tuned onto custom dataset via sparse transfer learning. This makes it easy to create a sparse image classification model trained on your dataset.
 
-Check out the model cards in the [SparseZoo](https://sparsezoo.neuralmagic.com/?repo=ultralytics&page=1).
+[Check out the available models](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=classification&page=1)
 
 ### Recipes
 

From 8f28f88baf731b423ea9a0676eac35d584ea9c15 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Sun, 5 Mar 2023 19:00:11 -0500
Subject: [PATCH 05/29] Update README.md

---
 integrations/torchvision/README.md | 97 +++++++++++++++++-------------
 1 file changed, 54 insertions(+), 43 deletions(-)

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index 28fa1d19b0e..bc58efbf18d 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -16,7 +16,7 @@ limitations under the License.
 
 # SparseML Torchvision Integration
 
-This directory explains how to use SparseML's `torchvision` integration to train inference-optimized sparse NLP models on your dataset.
+This directory explains how to use SparseML's `torchvision` integration to train inference-optimized sparse image classification models on your dataset.
 
 There are two main workflows enabled by SparseML:
 
@@ -51,38 +51,15 @@ Neural Magic has pre-sparsified versions of common Torchvision models such as Re
 
 ### Recipes
 
-SparseML Recipes are YAML files that encode the instructions for sparsifying a model or sparse transfer learning. The SparseML image classification training script accepts the recipes as inputs, parses the instructions, and applies the specified algorithms and hyperparameters during the training process.
+Recipes are YAML files that encode the instructions for sparsifying a model or sparse transfer learning. SparseML accepts the recipes as inputs, parses the instructions, and applies the specified algorithms and hyperparameters during the training process.
 
-### SparseML CLI
-
-SparseML's CLI enables you to kick-off sparse training workflows with various utilities like creating training pipelines, dataset loading, checkpoint saving, metric reporting, and logging handled for you.
-
-To get started, we just need to a couple key arguments:
-```bash
-sparseml.image_classification.train \
-    --checkpoint-path [CHECKPOINT-PATH] \
-    --recipe [RECIPE-PATH] \
-    --dataset-path [DATASET-PATH]
-```
-
-- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub (which SparseML uses to download a PyTorch checkpoint).
-
-- `--dataset-path` specifies the dataset used for training. It must be a local path to a dataset in the ImageNet format (see CLI tutorials for more details).
-
-- `--recipe` specifies the sparsity related parameters of the training process. It can either be a local path to a YAML recipe file or a SparseZoo stub (which SparseML uses to download a YAML recipe file). The `recipe` is the key to enabling the sparsity-related algorithms implemented by SparseML (see the CLI tutorials for more details on recipes).
-
-For full usage, run:
-```bash
-sparseml.image_classification --help
-```
+In such a way, recipes are the declarative interface for specifying which sparsity-related algorithms to apply.
 
 ### SparseML Python API
 
-For additional flexibility, SparseML has a Python API that also enables you to add sparsification to a native PyTorch training loop.
-
-Just like the CLI, the Python API uses YAML-based recipes to encode the parameters of the sparsification process, allowing youto add SparseML with just a few lines of code.
+Because of the declarative, recipe-based approach, you can add SparseML to your PyTorch training pipelines with just a couple lines of code.
 
-The `ScheduleModifierManager` class is responsible for parsing the YAML recipes and overriding the standard PyTorch model and optimizer objects, 
+SparseML's `ScheduleModifierManager` class is responsible for parsing the YAML recipes and overriding the standard PyTorch model and optimizer objects, 
 encoding the logic of the sparsity algorithms from the recipe. Once you call `manager.modify`, you can then use the model and optimizer as usual, as SparseML abstracts away the complexity of the sparsification algorithms.
 
 The workflow looks like this:
@@ -104,31 +81,57 @@ optimizer = manager.modify(model, optimizer, len(train_data))
 manager.finalize(model)
 ```
 
+Note that the `model`, `optimizer`, and `dataset` are all standard PyTorch objects. We simply pass a recipe to the SparseML `SchedulerModifierManager` and SparseML handles the rest!
+
 See the tutorials for full working examples of the Python API.
 
-## Quick Start: Sparse Transfer Learning
+### SparseML CLI
+
+In addition to the code-level API, SparseML offers pre-made training pipelines for image classification via the CLI interface.
+
+The CLI enables you to kick-off training runs with various utilities like dataset loading and pre-processing, checkpoint saving, metric reporting, and logging handled for you.
+
+To get started, we just need to a couple key arguments:
+```bash
+sparseml.image_classification.train \
+    --checkpoint-path [CHECKPOINT-PATH] \
+    --recipe [RECIPE-PATH] \
+    --dataset-path [DATASET-PATH]
+```
+
+- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub (which SparseML uses to download a PyTorch checkpoint).
+
+- `--dataset-path` specifies the dataset used for training. It must be a local path to a dataset in the ImageNet format (see CLI tutorials for more details).
+
+- `--recipe` specifies the sparsity related parameters of the training process. It can either be a local path to a YAML recipe file or a SparseZoo stub (which SparseML uses to download a YAML recipe file). The `recipe` is the key to enabling the sparsity-related algorithms implemented by SparseML (see the CLI tutorials for more details on recipes).
+
+For full usage, run:
+```bash
+sparseml.image_classification --help
+```
 
-### Overview
+## Quick Start: Sparse Transfer Learning with the CLI
 
-Sparse Transfer is quite similiar to the typical transfer learing process used to train NLP models, where we fine-tune a pretrained checkpoint onto a smaller downstream dataset. With Sparse Transfer Learning, we simply start the fine-tuning process from a pre-sparsified checkpoint and maintain sparsity while the training process occurs.
+### Sparse Transfer Learning Overview
+
+Sparse Transfer is quite similiar to the typical transfer learning process used to train image classification models, where we fine-tune a checkpoint pretrained on ImageNet onto a smaller downstream dataset. With Sparse Transfer Learning, we simply start the fine-tuning process from a pre-sparsified checkpoint and maintain sparsity while the training process occurs.
 
 In this example, we will fine-tune a 95% pruned version of ResNet-50 ([available in SparseZoo](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none)) onto ImageNette.
 
 ### Kick off Training
 
-We can start Sparse Transfer Learning by passing a starting checkpoint and recipe to the training script. For Sparse Transfer, we will use a recipe that instructs SparseML to maintain sparsity during training and to quantize the model. The starting checkpoitn and transfer recipe are specified by the following SparseZoo stub:
+We will use SparseML's `sparseml.torchvision.train` training script.
+
+#### Sparse Transfer Recipe
+
+To run sparse transfer learning, we first need to create/select a sparsification recipe. For sparse transfer, we need a recipe that instructs SparseML to maintain sparsity during training and to quantize the model.
+
+For the Imagenette dataset, there is a transfer learning recipe available in SparseZoo, identified by the following SparseZoo stub:
 ```
 zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification
 ```
 
-<details>
-   <summary>Click to see the recipe</summary>
-
-SparseML parses the `Modifers` in the recipe and updates the training loop with logic encoded therein.
-   
-The key `Modifiers` for sparse transfer learning are the following:
-- `ConstantPruningModifier` instructs SparseML to maintain the sparsity structure of the network during the fine-tuning process
-- `QuantizationModifier` instructs SparseML to apply quantization aware training to quantize the weights over the final epochs
+Here is what the recipe looks like:
    
 ```yaml
 # Epoch and Learning-Rate variables
@@ -161,16 +164,24 @@ sparse_quantized_transfer_learning_modifiers:
   - !QuantizationModifier
     start_epoch: eval(num_epochs - quantization_epochs)
 ```
+   
+The key `Modifiers` for sparse transfer learning are the following:
+- `ConstantPruningModifier` instructs SparseML to maintain the sparsity structure of the network during the fine-tuning process
+- `QuantizationModifier` instructs SparseML to apply quantization aware training to quantize the weights over the final epochs
 
-</details>
+SparseML parses the `Modifers` in the recipe and updates the training loop with logic encoded therein.
+
+#### Download Dataset
 
-First, download and unzip the ImageNette dataset:
+Download and unzip the Imagenette dataset:
 
 ```bash
 wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
 tar -xvf imagenette2-320.tgz
 ```
 
+#### Run the Training Script
+
 Run the following to transfer learn from the 95% pruned-quantized ResNet-50
 onto ImageNette:
 ```bash
@@ -182,7 +193,7 @@ sparseml.image_classification.train \
     --batch-size 32
 ```
 
-The script uses the SparseZoo stubs to identify and download the starting checkpoint and YAML-based recipe file from the SparseZoo. SparseML parses the transfer learning recipe and adjusts the trainign process to maintain sparsity during the fine-tuning process.
+The script uses the SparseZoo stubs to identify and download the starting checkpoint and YAML-based recipe file from the SparseZoo. SparseML parses the transfer learning recipe and adjusts the training logic to maintain sparsity during the fine-tuning process.
 
 The resulting model is 95% pruned and quantized and is trained on ImageNette!
 

From 3c810bf025e2ed60e22318598492e0d24426321d Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Sun, 5 Mar 2023 19:10:47 -0500
Subject: [PATCH 06/29] Update sparse-transfer-learning.md

---
 .../tutorials/sparse-transfer-learning.md     | 78 ++++++++++++-------
 1 file changed, 49 insertions(+), 29 deletions(-)

diff --git a/integrations/torchvision/tutorials/sparse-transfer-learning.md b/integrations/torchvision/tutorials/sparse-transfer-learning.md
index 937579925b1..4914bdba41e 100644
--- a/integrations/torchvision/tutorials/sparse-transfer-learning.md
+++ b/integrations/torchvision/tutorials/sparse-transfer-learning.md
@@ -2,11 +2,13 @@
 
 This page explains how to fine-tune a pre-sparsified Torchvision image classification models like ResNet-50 with SparseML's CLI.
 
-## Overview
+## Sparse Transfer Learning Overview
 
 Sparse transfer learning is quite similiar to the typical transfer learning training, where we fine-tune a checkpoint pretrained on ImageNet onto a smaller downstream dataset. However, with Sparse Transfer Learning, we simply start the fine-tuning process from a pre-sparsified checkpoint and maintain sparsity while the training process occurs.
 
-[SparseZoo](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=classification&page=1) contains pre-sparsified checkpoints for common image classification models like ResNet-50 and EfficientNet, which can be used as the starting checkpoints for the training process.
+SparseZoo contains pre-sparsified checkpoints for common image classification models like ResNet-50 and EfficientNet, which can be used as the starting checkpoints for the training process.
+
+[Check out the full list of pre-sparsified models](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=classification&page=1)
 
 ## Installation
 
@@ -20,34 +22,25 @@ pip install sparseml[torchvision]
 
 Let's try a simple example of Sparse Transfer Learning ResNet-50 onto [FastAI's Imagenette dataset](https://github.com/fastai/imagenette) (a subset of 10 easily classified classes from ImageNet).
 
+### Download Dataset
+
 Download Imagnette to your local directory:
 ```bash
 wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-320.tgz
 tar -xvf imagenette2-320.tgz
 ```
 
-Kick off transfer learning.
-```bash
-sparseml.image_classification.train \
-    --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
-    --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
-    --arch-key resnet50 \
-    --dataset-path ./imagenette2-320\
-    --batch-size 32
-```
+### Kick off Training
 
-Let's discuss the key arguments:
-- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub. Here, we passed a stub that identifies a 95% pruned-quantized version of ResNet-50 in the SparseZoo. The script downloads the PyTorch model to begin training.
+We will use SparseML's `sparseml.image_classification.train` training script.
 
-- `--arch-key` specifies the torchvision architecture of the checkpoint. For example, `resnet50` or `mobilenet`.
+#### Transfer Learning Recipe 
 
-- `--dataset-path` specifies the dataset used for training. We passed the local path to Imagenette. The dataset should be in the ImageFolder format (we will describe the format below).
+To run sparse transfer learning, we first need to create/select a sparsification recipe. For sparse transfer, we need a recipe that instructs SparseML to maintain sparsity during training and to quantize the model over the final epochs.
 
-- `--recipe` specifies the sparsity related parameters of the training process. Recipes are YAML files that declare the sparsity related algorithms that SparseML should apply. For transfer learning, the recipe instructs SparseML to maintain sparsity during training and to apply quantization over the final epochs. In this case, we passed a SparseZoo stub, which instructs SparseML to download a premade ResNet-50 transfer learning recipe. In addition to SparseZoo stubs, you can also pass a local path to a YAML recipe. See below for more details on what the transfer learning recipe looks like.
-
-Run the help command to inspect the full list of arguments and configurations.
+For ResNet-50, there is [premade transfer learning recipe available in SparseZoo](https://sparsezoo.neuralmagic.com/models/cv%2Fclassification%2Fresnet_v1-50%2Fpytorch%2Fsparseml%2Fimagenet%2Fpruned95_quant-none), identified by the following stub:
 ```bash
-sparseml.image_classification.train --help
+zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification
 ```
 
 Here's what the transfer learning recipe looks like:
@@ -83,19 +76,46 @@ sparse_quantized_transfer_learning_modifiers:
     start_epoch: eval(num_epochs - quantization_epochs)
 ```
 
-The "Modifiers" encode how SparseML should modify the training process for Sparse Transfer Learning.
+The "Modifiers" encode how SparseML should modify the training process for Sparse Transfer Learning:
 - `ConstantPruningModifier` tells SparseML to pin weights at 0 over all epochs, maintaining the sparsity structure of the network
-- `QuantizationModifier` tells SparseML to quanitze the weights with quantization aware training over the last 6 epochs
+- `QuantizationModifier` tells SparseML to quantize the weights with quantization aware training over the last 6 epochs
+
+SparseML parses the instructions declared in the recipe and modifies the training loop accordingly before running the fine-tuning.
 
-SparseML parses the instructions declared in the recipe and modifies the training loop accordingly before running the fine-tuning. 
+#### Run the Training Loop
 
-As a result, we end up with a 75% pruned and quantized ResNet-50 trained on Imagenette!
+Run the following to fine-tune the 95% pruned-quantized ResNet-50 on Imagenette.
+```bash
+sparseml.image_classification.train \
+    --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
+    --arch-key resnet50 \
+    --dataset-path ./imagenette2-320\
+    --batch-size 32
+```
+
+Let's discuss the key arguments:
+- `--checkpoint-path` specifies the starting model to use in the training process. It can either be a local path to a PyTorch checkpoint or a SparseZoo stub. Here, we passed a stub that identifies a 95% pruned-quantized version of ResNet-50 in the SparseZoo. The script downloads the PyTorch model to begin training.
+
+- `--arch-key` specifies the torchvision architecture of the checkpoint. For example, `resnet50` or `mobilenet`.
+
+- `--dataset-path` specifies the dataset used for training. We passed the local path to Imagenette. The dataset should be in the ImageFolder format (we will describe the format below).
+
+- `--recipe` specifies the transfer learning recipe. In this case, we passed a SparseZoo stub, which instructs SparseML to download a premade ResNet50 transfer learning recipe. In addition to SparseZoo stubs, you can also pass a local path to a YAML recipe.
+
+The script uses the SparseZoo stubs to identify and download the starting checkpoint and recipe file. SparseML then parses the transfer learning recipe and adjusts the training loop to maintain sparsity during the fine-tuning process. It then kicks off the transfer learning run.
+
+The resulting model is 95% pruned and quantized ResNet-50 trained on Imagenette!
+
+Run the help command to inspect the full list of arguments and configurations.
+```bash
+sparseml.image_classification.train --help
+```
 
-#### Export to ONNX
+### Export to ONNX
 
-To deploy with DeepSparse, we need to export the model to ONNX.
+Run the following to export your model to ONNX for deployment with DeepSparse.
 
-Run the following to do so:
 ```bash
 sparseml.image_classification.export_onnx \
   --arch_key resnet50 \
@@ -103,7 +123,7 @@ sparseml.image_classification.export_onnx \
   --dataset-path ./imagenette2-320
 ```
 
-#### Modify a Recipe
+### Modify a Recipe
 
 To transfer learn this sparsified model to other datasets you may have to adjust certain hyperparameters in this recipe and/or training script. Some considerations:
 
@@ -129,7 +149,7 @@ sparseml.image_classification.train \
 
 The `--recipe_args` are a json parsable dictionary of recipe variable names to values to overwrite the YAML-recipe.
 
-#### Dataset Format
+### Dataset Format
 
 SparseML's Torchvision CLI conforms to the [Torchvision ImageFolder dataset format](https://pytorch.org/vision/main/generated/torchvision.datasets.ImageFolder.html), where images are arranged into directories representing each class. To use a custom dataset with the SparseML CLI, you will need to arrange your data into this format.
 
@@ -165,4 +185,4 @@ represents one of the 10 classes in the Imagenette dataset) and each `.JPEG` fil
 
 ## Wrapping Up
 
-Checkout the DeepSparse repository for more information on deploying your sparse Image Classification models with DeepSparse for GPU class performance on CPUs.
\ No newline at end of file
+Checkout the DeepSparse repository for more information on deploying your sparse Image Classification models with DeepSparse for GPU class performance on CPUs.

From 29b03b84c091e07833baebe6fbe39b907257c201 Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Sun, 5 Mar 2023 19:18:00 -0500
Subject: [PATCH 07/29] cosmetic changes to python notebook

---
 ...rchvision-python-transfer-imagenette.ipynb | 591 +-----------------
 1 file changed, 7 insertions(+), 584 deletions(-)

diff --git a/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb b/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
index 4ebb09561c3..bdf0a4d5685 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
@@ -61,17 +61,6 @@
     "If you are running on Google Colab, restart the runtime after this step."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MVf4UkqT9xXn"
-   },
-   "source": [
-    "## **Code Level APIs**\n",
-    "\n",
-    "If you need to integrate further into a system, you can use the code API directly."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -287,12 +276,13 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "KCMRFLFN8024"
    },
    "source": [
-    "#### Inspecting the Recipe\n",
+    "#### **Inspecting the Recipe**\n",
     "\n",
     "Here is the transfer learning recipe:\n",
     "\n",
@@ -368,6 +358,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "nrgF_ur_B66y"
@@ -375,7 +366,7 @@
    "source": [
     "## **Step 6: Run Training**\n",
     "\n",
-    "We pass the modified `model` and `optimizer` objects to the training loop we build above. Since the `recipe` was applied, there is "
+    "We pass the modified `model` and `optimizer` objects to the training loop we build above. Since we already called `manager.modify` all of the logic from the recipe is encoded in the `optimizer` and `model`. We can use the `model` and `optimizer`, and the SparseML system applies the algorithms specified in the recipe. In this case, since we applied a `ConstantPruningModifier`, the weights which are zero will be pinned to 0 during the fine-tuning, maintaining the sparsity structure as the model fine-tunes."
    ]
   },
   {
@@ -621,514 +612,7 @@
     "id": "xy7JLoTM6RC_",
     "outputId": "7f956859-081b-49fd-9723-e93a55e74c72"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Training Epoch 1/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "365d8cd0a55741b395ee0ba07f8404d0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/403 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 1/10\n",
-      "Training Loss: 0.20641206579269014\n",
-      "Top 1 Acc: 0.9458662944004964\n",
-      "\n",
-      "Running Validation Epoch 1/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5719a6f214c946fc82b46274ee89ba63",
-       "version_major": 2,
-       "version_minor": 0
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-       "  0%|          | 0/16 [00:00<?, ?it/s]"
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 1/10\n",
-      "Val Loss: 0.07535588860628195\n",
-      "Top 1 Acc: 0.978\n",
-      "\n",
-      "Running Training Epoch 2/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b44b6c0760ab4694a15032e717f8bd22",
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-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 2/10\n",
-      "Training Loss: 0.05115955586403045\n",
-      "Top 1 Acc: 0.9847991313789359\n",
-      "\n",
-      "Running Validation Epoch 2/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 2/10\n",
-      "Val Loss: 0.041224136175515014\n",
-      "Top 1 Acc: 0.986\n",
-      "\n",
-      "Running Training Epoch 3/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe703c6001554401a9e9f52836e6ba9d",
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 3/10\n",
-      "Training Loss: 0.026982061771754516\n",
-      "Top 1 Acc: 0.9927873429502094\n",
-      "\n",
-      "Running Validation Epoch 3/10\n"
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-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
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-     "text": [
-      "Validation Epoch: 3/10\n",
-      "Val Loss: 0.025956420957299997\n",
-      "Top 1 Acc: 0.992\n",
-      "\n",
-      "Running Training Epoch 4/10\n"
-     ]
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-    {
-     "data": {
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-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 4/10\n",
-      "Training Loss: 0.013596414999022997\n",
-      "Top 1 Acc: 0.9963548937490305\n",
-      "\n",
-      "Running Validation Epoch 4/10\n"
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-      "Validation Epoch: 4/10\n",
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-      "\n",
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-      "Training Epoch: 5/10\n",
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-      "\n",
-      "Running Validation Epoch 5/10\n"
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-      "Validation Epoch: 5/10\n",
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-      "\n",
-      "Running Training Epoch 6/10\n"
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-      "Training Epoch: 6/10\n",
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-      "Validation Epoch: 6/10\n",
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-      "\n",
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-     "text": [
-      "Training Epoch: 7/10\n",
-      "Training Loss: 0.0029537986178653345\n",
-      "Top 1 Acc: 0.9993795563828137\n",
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-     "text": [
-      "Training Epoch: 8/10\n",
-      "Training Loss: 0.002117149980043878\n",
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-      "\n",
-      "Running Validation Epoch 8/10\n"
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-     "text": [
-      "Validation Epoch: 8/10\n",
-      "Val Loss: 0.03721619503517104\n",
-      "Top 1 Acc: 0.982\n",
-      "\n",
-      "Running Training Epoch 9/10\n"
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-     "text": [
-      "Training Epoch: 9/10\n",
-      "Training Loss: 0.0014343990947584759\n",
-      "Top 1 Acc: 0.9999224445478517\n",
-      "\n",
-      "Running Validation Epoch 9/10\n"
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-     "text": [
-      "Validation Epoch: 9/10\n",
-      "Val Loss: 0.028261509883918734\n",
-      "Top 1 Acc: 0.988\n",
-      "\n",
-      "Running Training Epoch 10/10\n"
-     ]
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 10/10\n",
-      "Training Loss: 0.0011834696520765318\n",
-      "Top 1 Acc: 0.9999224445478517\n",
-      "\n",
-      "Running Validation Epoch 10/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af29998c14de43dcb4e5d337ea1987d5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/16 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 10/10\n",
-      "Val Loss: 0.03935582631481793\n",
-      "Top 1 Acc: 0.984\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# run transfer learning\n",
     "epoch = manager.min_epochs\n",
@@ -1182,68 +666,7 @@
     "id": "1cHaXmcu4zGM",
     "outputId": "426d92df-0aeb-4d61-8774-5f7ede0029b7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input.conv.module.weight: 0.4712\n",
-      "sections.0.0.conv1.module.weight: 0.5867\n",
-      "sections.0.0.conv2.module.weight: 0.8297\n",
-      "sections.0.0.conv3.module.weight: 0.7705\n",
-      "sections.0.0.identity.conv.module.weight: 0.7460\n",
-      "sections.0.1.conv1.module.weight: 0.7665\n",
-      "sections.0.1.conv2.module.weight: 0.8003\n",
-      "sections.0.1.conv3.module.weight: 0.7880\n",
-      "sections.0.2.conv1.module.weight: 0.7333\n",
-      "sections.0.2.conv2.module.weight: 0.7407\n",
-      "sections.0.2.conv3.module.weight: 0.7699\n",
-      "sections.1.0.conv1.module.weight: 0.6927\n",
-      "sections.1.0.conv2.module.weight: 0.8649\n",
-      "sections.1.0.conv3.module.weight: 0.8389\n",
-      "sections.1.0.identity.conv.module.weight: 0.9181\n",
-      "sections.1.1.conv1.module.weight: 0.9392\n",
-      "sections.1.1.conv2.module.weight: 0.9373\n",
-      "sections.1.1.conv3.module.weight: 0.9193\n",
-      "sections.1.2.conv1.module.weight: 0.8705\n",
-      "sections.1.2.conv2.module.weight: 0.8990\n",
-      "sections.1.2.conv3.module.weight: 0.8502\n",
-      "sections.1.3.conv1.module.weight: 0.8534\n",
-      "sections.1.3.conv2.module.weight: 0.8690\n",
-      "sections.1.3.conv3.module.weight: 0.8700\n",
-      "sections.2.0.conv1.module.weight: 0.7587\n",
-      "sections.2.0.conv2.module.weight: 0.9440\n",
-      "sections.2.0.conv3.module.weight: 0.8631\n",
-      "sections.2.0.identity.conv.module.weight: 0.9545\n",
-      "sections.2.1.conv1.module.weight: 0.9713\n",
-      "sections.2.1.conv2.module.weight: 0.9730\n",
-      "sections.2.1.conv3.module.weight: 0.9379\n",
-      "sections.2.2.conv1.module.weight: 0.9570\n",
-      "sections.2.2.conv2.module.weight: 0.9683\n",
-      "sections.2.2.conv3.module.weight: 0.9246\n",
-      "sections.2.3.conv1.module.weight: 0.9422\n",
-      "sections.2.3.conv2.module.weight: 0.9642\n",
-      "sections.2.3.conv3.module.weight: 0.9326\n",
-      "sections.2.4.conv1.module.weight: 0.9386\n",
-      "sections.2.4.conv2.module.weight: 0.9734\n",
-      "sections.2.4.conv3.module.weight: 0.9485\n",
-      "sections.2.5.conv1.module.weight: 0.9259\n",
-      "sections.2.5.conv2.module.weight: 0.9699\n",
-      "sections.2.5.conv3.module.weight: 0.9408\n",
-      "sections.3.0.conv1.module.weight: 0.8109\n",
-      "sections.3.0.conv2.module.weight: 0.9937\n",
-      "sections.3.0.conv3.module.weight: 0.9342\n",
-      "sections.3.0.identity.conv.module.weight: 0.9819\n",
-      "sections.3.1.conv1.module.weight: 0.9795\n",
-      "sections.3.1.conv2.module.weight: 0.9864\n",
-      "sections.3.1.conv3.module.weight: 0.9682\n",
-      "sections.3.2.conv1.module.weight: 0.9218\n",
-      "sections.3.2.conv2.module.weight: 0.9953\n",
-      "sections.3.2.conv3.module.weight: 0.9449\n",
-      "classifier.fc.module.weight: 0.0000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
     "\n",
@@ -1390,7 +813,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 431.1x288 with 1 Axes>"
       ]

From b72953f28a7374663811bb1362030b3b2342abe7 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Sun, 5 Mar 2023 19:30:37 -0500
Subject: [PATCH 08/29] Update sparse-transfer-learning.md

---
 integrations/torchvision/tutorials/sparse-transfer-learning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/integrations/torchvision/tutorials/sparse-transfer-learning.md b/integrations/torchvision/tutorials/sparse-transfer-learning.md
index 4914bdba41e..92e4c82947c 100644
--- a/integrations/torchvision/tutorials/sparse-transfer-learning.md
+++ b/integrations/torchvision/tutorials/sparse-transfer-learning.md
@@ -140,7 +140,7 @@ Alternatively, you can use `--recipe_args` to modify a recipe on the fly. The fo
 ```bash
 sparseml.image_classification.train \
     --recipe zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
-    --recipe_args '{"num_epochs": 15"}' \
+    --recipe_args '{"num_epochs": 15}' \
     --checkpoint-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none?recipe_type=transfer-classification \
     --arch-key resnet50 \
     --dataset-path ./imagenette2-320\

From e04002337ba2f8e270ddbc1a80fb0e7225aeb400 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 13:50:45 +0000
Subject: [PATCH 09/29] added torchvision pruning from scracth

---
 ...arsify-from-scratch-densenet-flowers.ipynb | 2858 +++++++++++++++
 ...-sparsify-from-scatch-resnet50-beans.ipynb | 2589 +++++++++++++
 ...rsify-from-scratch-mobilenetv2-beans.ipynb | 3210 +++++++++++++++++
 .../densenet-flowers-dense-recipe.yaml        |   16 +
 .../densenet-flowers-pruning-recipe.yaml      |   56 +
 .../mobilenetv2-beans-dense-recipe.yaml       |   22 +
 .../mobilenetv2-beans-pruning-recipe.yaml     |   40 +
 .../recipes/resnet50-beans-dense-recipe.yaml  |   22 +
 .../resnet50-beans-pruning-recipe.yaml        |   46 +
 9 files changed, 8859 insertions(+)
 create mode 100644 integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
 create mode 100644 integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
 create mode 100644 integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
 create mode 100644 integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
 create mode 100644 integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
 create mode 100644 integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
 create mode 100644 integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
 create mode 100644 integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
 create mode 100644 integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml

diff --git a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
new file mode 100644
index 00000000000..f999fb24f57
--- /dev/null
+++ b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
@@ -0,0 +1,2858 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1ad80edf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "from sparseml.pytorch.utils import TensorBoardLogger, ModuleExporter, get_prunable_layers, tensor_sparsity\n",
+    "from sparseml.pytorch.utils.helpers import get_optim_learning_rate\n",
+    "\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "import torchvision\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from tqdm.auto import tqdm\n",
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a128dcc",
+   "metadata": {},
+   "source": [
+    "## **Setup Dataset**\n",
+    "\n",
+    "Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.\n",
+    "\n",
+    "The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7d180bbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "NUM_LABELS = 102\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "# imagenet transformers\n",
+    "imagenet_transform = transforms.Compose([\n",
+    "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
+    "   transforms.CenterCrop(size=(224, 224)),\n",
+    "   transforms.ToTensor(),\n",
+    "   transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+    "])\n",
+    "\n",
+    "# datasets\n",
+    "train_dataset = torchvision.datasets.Flowers102(\n",
+    "    root=\"./data\",\n",
+    "    split=\"train\",\n",
+    "    transform=imagenet_transform,\n",
+    "    download=True\n",
+    ")\n",
+    "\n",
+    "val_dataset = torchvision.datasets.Flowers102(\n",
+    "    root=\"./data\",\n",
+    "    split=\"val\",\n",
+    "    transform=imagenet_transform,\n",
+    "    download=True\n",
+    ")\n",
+    "\n",
+    "# dataloaders\n",
+    "train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16)\n",
+    "val_loader = DataLoader(val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "890cca4a",
+   "metadata": {},
+   "source": [
+    "## Setup Training Loop\n",
+    "\n",
+    "We will use this training loop below. This is standard PyTorch functionality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f1b878a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(device)\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    # loop through batches\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # compute loss, run backpropogation\n",
+    "        outputs = model(inputs)  # model returns logits\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        # run evaluation\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    # return loss and evaluation metric\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b385497a",
+   "metadata": {},
+   "source": [
+    "## **Part 1: Train DenseNet121 Model as Usual**\n",
+    "\n",
+    "We download pretrained DenseNet121 from torchvision, setting it to use 102 classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4d554578",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DenseNet(\n",
+      "  (features): Sequential(\n",
+      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (relu0): ReLU(inplace=True)\n",
+      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "    (denseblock1): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition1): _Transition(\n",
+      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock2): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition2): _Transition(\n",
+      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock3): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer13): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer14): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer15): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer16): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer17): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer18): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer19): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer20): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer21): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer22): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer23): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer24): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition3): _Transition(\n",
+      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock4): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer13): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer14): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer15): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer16): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  )\n",
+      "  (classifier): Linear(in_features=1024, out_features=102, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = torchvision.models.densenet121(weights=torchvision.models.DenseNet121_Weights.DEFAULT)\n",
+    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
+    "model.to(device)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b0a623e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d4553beb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 10.0\r\n",
+      "init_lr: 0.0005\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dense_model/dense-recipe.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e132ebe",
+   "metadata": {},
+   "source": [
+    "Update the Optimizer and Model with the logic from the recipe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1749e00d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
+    "logger = TensorBoardLogger(log_path=\"./dense_model/training/tensorboard_outputs\")\n",
+    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee67c917",
+   "metadata": {},
+   "source": [
+    "Kick off the transfer learning loop. We fine-tune onto the Flowers dataset, reaching 91.3% validation accuracy after 10 epochs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d00d175b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c02f2af1dc1c465b870d978a25e456e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/10\n",
+      "Training Loss: 3.618388310074806\n",
+      "Top 1 Acc: 0.3\n",
+      "\n",
+      "Running Validation Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b464cab80ed64170a8d853500e7c7a5c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/10\n",
+      "Val Loss: 2.134434787556529\n",
+      "Top 1 Acc: 0.6274509803921569\n",
+      "\n",
+      "Running Training Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "950e5fda34bf465a94f6ce90474eb17a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/10\n",
+      "Training Loss: 1.3103822488337755\n",
+      "Top 1 Acc: 0.9019607843137255\n",
+      "\n",
+      "Running Validation Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e97bc059e155478d8f9c815740be5d96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/10\n",
+      "Val Loss: 1.1688878536224365\n",
+      "Top 1 Acc: 0.8598039215686275\n",
+      "\n",
+      "Running Training Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "752e6dec2aa94f2984e8930f57cfc127",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/10\n",
+      "Training Loss: 0.3958540456369519\n",
+      "Top 1 Acc: 0.9931372549019608\n",
+      "\n",
+      "Running Validation Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bde611c736264aa4b09225ce3ad1b1d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/10\n",
+      "Val Loss: 0.7494302792474627\n",
+      "Top 1 Acc: 0.8813725490196078\n",
+      "\n",
+      "Running Training Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41cda43bfcf649c99acaff23df2b8477",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/10\n",
+      "Training Loss: 0.12264440068975091\n",
+      "Top 1 Acc: 0.9990196078431373\n",
+      "\n",
+      "Running Validation Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09dd43adbdfd433584e1ae345574a87a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/10\n",
+      "Val Loss: 0.6009533200412989\n",
+      "Top 1 Acc: 0.9068627450980392\n",
+      "\n",
+      "Running Training Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d70b0a06499844f0bd939aa8537dd8c1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/10\n",
+      "Training Loss: 0.06395778199657798\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dea7eca69ba24561b550f0f767eb6042",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/10\n",
+      "Val Loss: 0.5246470430865884\n",
+      "Top 1 Acc: 0.9166666666666666\n",
+      "\n",
+      "Running Training Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "065b397dfa644e2c8ea9a6443299b3df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/10\n",
+      "Training Loss: 0.041063663142267615\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0f04580fc3d44b98caf400499b7698b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/10\n",
+      "Val Loss: 0.5043302299454808\n",
+      "Top 1 Acc: 0.9156862745098039\n",
+      "\n",
+      "Running Training Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "140c927f45994de8b5c947c18b7ce4b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/10\n",
+      "Training Loss: 0.03354773804312572\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41fb0bf664aa4812a841a7c16a9d8a8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/10\n",
+      "Val Loss: 0.490260950056836\n",
+      "Top 1 Acc: 0.9166666666666666\n",
+      "\n",
+      "Running Training Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4c73a393ba74093aef7479554cbf926",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/10\n",
+      "Training Loss: 0.03196021361509338\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "357e2a67205743f89e0c8fb037ea3708",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/10\n",
+      "Val Loss: 0.4794748453423381\n",
+      "Top 1 Acc: 0.9156862745098039\n",
+      "\n",
+      "Running Training Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b8b2118712a482f8828d5705d77e867",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/10\n",
+      "Training Loss: 0.027999908081255853\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e76c672c7cd84a1e8971a34b91dd8548",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/10\n",
+      "Val Loss: 0.4790585918817669\n",
+      "Top 1 Acc: 0.9137254901960784\n",
+      "\n",
+      "Running Training Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cfc3d87fa604e80aea4483bf58da091",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/10\n",
+      "Training Loss: 0.027712288312613964\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c3894447ffec41518ccf903fce69e4b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/10\n",
+      "Val Loss: 0.4804669988807291\n",
+      "Top 1 Acc: 0.9156862745098039\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    \n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
+    "    \n",
+    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1eb3d14e",
+   "metadata": {},
+   "source": [
+    "Export the model in case we want to reload in the future, so we do not have to rerun."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "11fa4cf2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_dir = \"dense_model\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"densenet-121-dense-flowers.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a7d6309",
+   "metadata": {},
+   "source": [
+    "## Part 2: Prune The Model\n",
+    "\n",
+    "We load the model trained in the prior step as the starting point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4532442e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8447d074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint = torch.load(\"./dense_model/training/densenet-121-dense-flowers.pth\")\n",
+    "model = torchvision.models.densenet121()\n",
+    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "model.to(device)\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d367905a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DenseNet(\n",
+      "  (features): Sequential(\n",
+      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    (relu0): ReLU(inplace=True)\n",
+      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "    (denseblock1): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition1): _Transition(\n",
+      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock2): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition2): _Transition(\n",
+      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock3): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer13): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer14): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer15): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer16): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer17): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer18): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer19): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer20): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer21): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer22): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer23): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer24): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (transition3): _Transition(\n",
+      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
+      "    )\n",
+      "    (denseblock4): _DenseBlock(\n",
+      "      (denselayer1): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer2): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer3): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer4): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer5): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer6): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer7): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer8): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer9): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer10): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer11): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer12): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer13): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer14): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer15): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "      (denselayer16): _DenseLayer(\n",
+      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu1): ReLU(inplace=True)\n",
+      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "        (relu2): ReLU(inplace=True)\n",
+      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      )\n",
+      "    )\n",
+      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  )\n",
+      "  (classifier): Linear(in_features=1024, out_features=102, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a05d884",
+   "metadata": {},
+   "source": [
+    "We create a pruning recipe. We will start with the `GlobalMagnitudePruning` algorithm, pruning layers on average to 90%. We target all of the Conv layers (per the regexes in the `params` dictionary below) - which tie to the layer names above. We will run GMP for 10 epochs and then fine-tune for the final 3 epochs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "9b17fc31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 13.0\r\n",
+      "pruning_epochs: 10.0\r\n",
+      "init_lr: 0.0003\r\n",
+      "final_lr: 0.0001\r\n",
+      "inter_func: cubic\r\n",
+      "mask_type: unstructured\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: eval(final_lr)\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(pruning_epochs)\r\n",
+      "    \r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: eval(final_lr)\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: eval(pruning_epochs)\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "# Pruning\r\n",
+      "pruning_modifiers:\r\n",
+      "  - !GlobalMagnitudePruningModifier\r\n",
+      "    init_sparsity: 0.05\r\n",
+      "    final_sparsity: 0.90\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(pruning_epochs)\r\n",
+      "    update_frequency: 0.5\r\n",
+      "    params: \r\n",
+      "        - 'features.conv0.weight'\r\n",
+      "        - 're:features.denseblock1.*.conv1.weight'\r\n",
+      "        - 're:features.denseblock1.*.conv2.weight'\r\n",
+      "        - 're:features.transition1.conv.weight'\r\n",
+      "        - 're:features.denseblock2.*.conv1.weight'\r\n",
+      "        - 're:features.denseblock2.*.conv2.weight'\r\n",
+      "        - 're:features.transition2.conv.weight'\r\n",
+      "        - 're:features.denseblock3.*.conv1.weight'\r\n",
+      "        - 're:features.denseblock3.*.conv2.weight'\r\n",
+      "        - 're:features.transition3.conv.weight'\r\n",
+      "        - 're:features.denseblock4.*.conv1.weight'\r\n",
+      "        - 're:features.denseblock4.*.conv2.weight'\r\n",
+      "    leave_enabled: True\r\n",
+      "    inter_func: eval(inter_func)\r\n",
+      "    mask_type: eval(mask_type)\r\n",
+      "\r\n",
+      "finetuning_modifiers:\r\n",
+      "  - !ConstantPruningModifier\r\n",
+      "    start_epoch: eval(pruning_epochs)\r\n",
+      "    params: __ALL_PRUNABLE__"
+     ]
+    }
+   ],
+   "source": [
+    "!cat recipe-0.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "68646bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
+    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs\")\n",
+    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "601c8c21",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e512276a1b94d328a06e1c5d0497ad9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/13\n",
+      "Training Loss: 0.05704788229195401\n",
+      "Top 1 Acc: 0.9970588235294118\n",
+      "\n",
+      "Running Validation Epoch 1/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6610db1025a4e408e01777550481193",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/13\n",
+      "Val Loss: 0.5254904199391603\n",
+      "Top 1 Acc: 0.8970588235294118\n",
+      "\n",
+      "Running Training Epoch 2/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddead9b1388b47e7856a34c7e38aad28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/13\n",
+      "Training Loss: 0.045509724208386615\n",
+      "Top 1 Acc: 0.996078431372549\n",
+      "\n",
+      "Running Validation Epoch 2/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0137f5c63a3f4a749078a913f0cabcee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/13\n",
+      "Val Loss: 0.49433585489168763\n",
+      "Top 1 Acc: 0.888235294117647\n",
+      "\n",
+      "Running Training Epoch 3/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca13cec88da1496e95c05201d51527b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/13\n",
+      "Training Loss: 0.05249260214623064\n",
+      "Top 1 Acc: 0.9941176470588236\n",
+      "\n",
+      "Running Validation Epoch 3/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9be1acbb68544497b2292273cfaa14e1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/13\n",
+      "Val Loss: 0.4971598202828318\n",
+      "Top 1 Acc: 0.8921568627450981\n",
+      "\n",
+      "Running Training Epoch 4/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1bd3b6304db3419c95a294acd40659c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/13\n",
+      "Training Loss: 0.04789798497222364\n",
+      "Top 1 Acc: 0.9970588235294118\n",
+      "\n",
+      "Running Validation Epoch 4/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c961395abf764ec095859a79fce2bb8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/13\n",
+      "Val Loss: 0.405253738630563\n",
+      "Top 1 Acc: 0.9098039215686274\n",
+      "\n",
+      "Running Training Epoch 5/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "761637d2e00344fab4bda339bc4a0210",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/13\n",
+      "Training Loss: 0.03558145748684183\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 5/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d7b4d1526c547e282f9a75f58ca9cf1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/13\n",
+      "Val Loss: 0.40998171758838\n",
+      "Top 1 Acc: 0.9098039215686274\n",
+      "\n",
+      "Running Training Epoch 6/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "390a8c78d2d2414fa9d09025917048e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/13\n",
+      "Training Loss: 0.043919957941398025\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 6/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0a209b2b76c845428e21b54bb3a6a507",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/13\n",
+      "Val Loss: 0.44851411995477974\n",
+      "Top 1 Acc: 0.9009803921568628\n",
+      "\n",
+      "Running Training Epoch 7/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f661fa461e094e55bc9cffb2d7b343c5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/13\n",
+      "Training Loss: 0.06865048292092979\n",
+      "Top 1 Acc: 0.9990196078431373\n",
+      "\n",
+      "Running Validation Epoch 7/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a81beb9cb8b1419da298f072304c8ae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/13\n",
+      "Val Loss: 0.500796954613179\n",
+      "Top 1 Acc: 0.8813725490196078\n",
+      "\n",
+      "Running Training Epoch 8/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b00986f389b6483c972f952b56cbe06e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/13\n",
+      "Training Loss: 0.08089102717349306\n",
+      "Top 1 Acc: 0.9950980392156863\n",
+      "\n",
+      "Running Validation Epoch 8/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f8dcafdd8604d0faa7b64c5f5e79913",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/13\n",
+      "Val Loss: 0.4999986342154443\n",
+      "Top 1 Acc: 0.8911764705882353\n",
+      "\n",
+      "Running Training Epoch 9/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "72813826e9b44537bc5a538c8bab0d0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/13\n",
+      "Training Loss: 0.05980767833534628\n",
+      "Top 1 Acc: 0.9990196078431373\n",
+      "\n",
+      "Running Validation Epoch 9/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3271f7a2f30246ccb4d0f7d52a5c88e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/13\n",
+      "Val Loss: 0.4825127385556698\n",
+      "Top 1 Acc: 0.8960784313725491\n",
+      "\n",
+      "Running Training Epoch 10/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "484ed067c6494cd2aaa6efebbc429ed1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/13\n",
+      "Training Loss: 0.029828229744452983\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 10/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "494926a49a0d43ddb0388a178d648048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/13\n",
+      "Val Loss: 0.45201362296938896\n",
+      "Top 1 Acc: 0.9009803921568628\n",
+      "\n",
+      "Running Training Epoch 11/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc469c3661154167b2b6a2f8d217f577",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 11/13\n",
+      "Training Loss: 0.017061863880371675\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 11/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d740893d1cef427491c9f1b94dfc97cb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 11/13\n",
+      "Val Loss: 0.40311497752554715\n",
+      "Top 1 Acc: 0.9058823529411765\n",
+      "\n",
+      "Running Training Epoch 12/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6efb89313174a7a8b9509755e7ac5f5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 12/13\n",
+      "Training Loss: 0.01124472642550245\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 12/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b09c5f91fd804f89bd99e83ac865f23f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 12/13\n",
+      "Val Loss: 0.38544022431597114\n",
+      "Top 1 Acc: 0.907843137254902\n",
+      "\n",
+      "Running Training Epoch 13/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8386e7eca94f4335a00b0f3ae5c21e37",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 13/13\n",
+      "Training Loss: 0.009475693295826204\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 13/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "80b3b1e8c68446bb94be4cf4d0ff37e3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/32 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 13/13\n",
+      "Val Loss: 0.38191458326764405\n",
+      "Top 1 Acc: 0.907843137254902\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    \n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
+    "    \n",
+    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "df5afa94",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking Sparsity Level:\n",
+      "features.conv0.weight: 0.4670\n",
+      "features.denseblock1.denselayer1.conv1.weight: 0.7366\n",
+      "features.denseblock1.denselayer1.conv2.weight: 0.8294\n",
+      "features.denseblock1.denselayer2.conv1.weight: 0.7461\n",
+      "features.denseblock1.denselayer2.conv2.weight: 0.8658\n",
+      "features.denseblock1.denselayer3.conv1.weight: 0.7488\n",
+      "features.denseblock1.denselayer3.conv2.weight: 0.8242\n",
+      "features.denseblock1.denselayer4.conv1.weight: 0.8116\n",
+      "features.denseblock1.denselayer4.conv2.weight: 0.8263\n",
+      "features.denseblock1.denselayer5.conv1.weight: 0.8702\n",
+      "features.denseblock1.denselayer5.conv2.weight: 0.8846\n",
+      "features.denseblock1.denselayer6.conv1.weight: 0.8377\n",
+      "features.denseblock1.denselayer6.conv2.weight: 0.8414\n",
+      "features.transition1.conv.weight: 0.7090\n",
+      "features.denseblock2.denselayer1.conv1.weight: 0.9321\n",
+      "features.denseblock2.denselayer1.conv2.weight: 0.8885\n",
+      "features.denseblock2.denselayer2.conv1.weight: 0.8816\n",
+      "features.denseblock2.denselayer2.conv2.weight: 0.8591\n",
+      "features.denseblock2.denselayer3.conv1.weight: 0.8839\n",
+      "features.denseblock2.denselayer3.conv2.weight: 0.8458\n",
+      "features.denseblock2.denselayer4.conv1.weight: 0.8619\n",
+      "features.denseblock2.denselayer4.conv2.weight: 0.8654\n",
+      "features.denseblock2.denselayer5.conv1.weight: 0.8901\n",
+      "features.denseblock2.denselayer5.conv2.weight: 0.8490\n",
+      "features.denseblock2.denselayer6.conv1.weight: 0.9099\n",
+      "features.denseblock2.denselayer6.conv2.weight: 0.8675\n",
+      "features.denseblock2.denselayer7.conv1.weight: 0.8786\n",
+      "features.denseblock2.denselayer7.conv2.weight: 0.8752\n",
+      "features.denseblock2.denselayer8.conv1.weight: 0.8897\n",
+      "features.denseblock2.denselayer8.conv2.weight: 0.8620\n",
+      "features.denseblock2.denselayer9.conv1.weight: 0.9001\n",
+      "features.denseblock2.denselayer9.conv2.weight: 0.8787\n",
+      "features.denseblock2.denselayer10.conv1.weight: 0.8735\n",
+      "features.denseblock2.denselayer10.conv2.weight: 0.8839\n",
+      "features.denseblock2.denselayer11.conv1.weight: 0.8851\n",
+      "features.denseblock2.denselayer11.conv2.weight: 0.8883\n",
+      "features.denseblock2.denselayer12.conv1.weight: 0.8731\n",
+      "features.denseblock2.denselayer12.conv2.weight: 0.9021\n",
+      "features.transition2.conv.weight: 0.7686\n",
+      "features.denseblock3.denselayer1.conv1.weight: 0.9162\n",
+      "features.denseblock3.denselayer1.conv2.weight: 0.9001\n",
+      "features.denseblock3.denselayer2.conv1.weight: 0.9382\n",
+      "features.denseblock3.denselayer2.conv2.weight: 0.8986\n",
+      "features.denseblock3.denselayer3.conv1.weight: 0.9175\n",
+      "features.denseblock3.denselayer3.conv2.weight: 0.9029\n",
+      "features.denseblock3.denselayer4.conv1.weight: 0.9182\n",
+      "features.denseblock3.denselayer4.conv2.weight: 0.8827\n",
+      "features.denseblock3.denselayer5.conv1.weight: 0.9336\n",
+      "features.denseblock3.denselayer5.conv2.weight: 0.8802\n",
+      "features.denseblock3.denselayer6.conv1.weight: 0.9197\n",
+      "features.denseblock3.denselayer6.conv2.weight: 0.8812\n",
+      "features.denseblock3.denselayer7.conv1.weight: 0.9384\n",
+      "features.denseblock3.denselayer7.conv2.weight: 0.8724\n",
+      "features.denseblock3.denselayer8.conv1.weight: 0.9050\n",
+      "features.denseblock3.denselayer8.conv2.weight: 0.8814\n",
+      "features.denseblock3.denselayer9.conv1.weight: 0.9080\n",
+      "features.denseblock3.denselayer9.conv2.weight: 0.8771\n",
+      "features.denseblock3.denselayer10.conv1.weight: 0.9408\n",
+      "features.denseblock3.denselayer10.conv2.weight: 0.8811\n",
+      "features.denseblock3.denselayer11.conv1.weight: 0.9341\n",
+      "features.denseblock3.denselayer11.conv2.weight: 0.8668\n",
+      "features.denseblock3.denselayer12.conv1.weight: 0.9250\n",
+      "features.denseblock3.denselayer12.conv2.weight: 0.8906\n",
+      "features.denseblock3.denselayer13.conv1.weight: 0.9382\n",
+      "features.denseblock3.denselayer13.conv2.weight: 0.8583\n",
+      "features.denseblock3.denselayer14.conv1.weight: 0.9465\n",
+      "features.denseblock3.denselayer14.conv2.weight: 0.8806\n",
+      "features.denseblock3.denselayer15.conv1.weight: 0.9248\n",
+      "features.denseblock3.denselayer15.conv2.weight: 0.8868\n",
+      "features.denseblock3.denselayer16.conv1.weight: 0.9390\n",
+      "features.denseblock3.denselayer16.conv2.weight: 0.9144\n",
+      "features.denseblock3.denselayer17.conv1.weight: 0.9208\n",
+      "features.denseblock3.denselayer17.conv2.weight: 0.9015\n",
+      "features.denseblock3.denselayer18.conv1.weight: 0.9316\n",
+      "features.denseblock3.denselayer18.conv2.weight: 0.8972\n",
+      "features.denseblock3.denselayer19.conv1.weight: 0.9243\n",
+      "features.denseblock3.denselayer19.conv2.weight: 0.8959\n",
+      "features.denseblock3.denselayer20.conv1.weight: 0.9266\n",
+      "features.denseblock3.denselayer20.conv2.weight: 0.9072\n",
+      "features.denseblock3.denselayer21.conv1.weight: 0.9231\n",
+      "features.denseblock3.denselayer21.conv2.weight: 0.8998\n",
+      "features.denseblock3.denselayer22.conv1.weight: 0.9252\n",
+      "features.denseblock3.denselayer22.conv2.weight: 0.8816\n",
+      "features.denseblock3.denselayer23.conv1.weight: 0.9239\n",
+      "features.denseblock3.denselayer23.conv2.weight: 0.9144\n",
+      "features.denseblock3.denselayer24.conv1.weight: 0.9298\n",
+      "features.denseblock3.denselayer24.conv2.weight: 0.9119\n",
+      "features.transition3.conv.weight: 0.8484\n",
+      "features.denseblock4.denselayer1.conv1.weight: 0.8742\n",
+      "features.denseblock4.denselayer1.conv2.weight: 0.9153\n",
+      "features.denseblock4.denselayer2.conv1.weight: 0.8730\n",
+      "features.denseblock4.denselayer2.conv2.weight: 0.9475\n",
+      "features.denseblock4.denselayer3.conv1.weight: 0.8826\n",
+      "features.denseblock4.denselayer3.conv2.weight: 0.9251\n",
+      "features.denseblock4.denselayer4.conv1.weight: 0.9017\n",
+      "features.denseblock4.denselayer4.conv2.weight: 0.9404\n",
+      "features.denseblock4.denselayer5.conv1.weight: 0.9002\n",
+      "features.denseblock4.denselayer5.conv2.weight: 0.9578\n",
+      "features.denseblock4.denselayer6.conv1.weight: 0.8988\n",
+      "features.denseblock4.denselayer6.conv2.weight: 0.9595\n",
+      "features.denseblock4.denselayer7.conv1.weight: 0.9121\n",
+      "features.denseblock4.denselayer7.conv2.weight: 0.9608\n",
+      "features.denseblock4.denselayer8.conv1.weight: 0.9153\n",
+      "features.denseblock4.denselayer8.conv2.weight: 0.9612\n",
+      "features.denseblock4.denselayer9.conv1.weight: 0.9176\n",
+      "features.denseblock4.denselayer9.conv2.weight: 0.9655\n",
+      "features.denseblock4.denselayer10.conv1.weight: 0.9182\n",
+      "features.denseblock4.denselayer10.conv2.weight: 0.9652\n",
+      "features.denseblock4.denselayer11.conv1.weight: 0.9192\n",
+      "features.denseblock4.denselayer11.conv2.weight: 0.9639\n",
+      "features.denseblock4.denselayer12.conv1.weight: 0.9230\n",
+      "features.denseblock4.denselayer12.conv2.weight: 0.9637\n",
+      "features.denseblock4.denselayer13.conv1.weight: 0.9260\n",
+      "features.denseblock4.denselayer13.conv2.weight: 0.9638\n",
+      "features.denseblock4.denselayer14.conv1.weight: 0.9283\n",
+      "features.denseblock4.denselayer14.conv2.weight: 0.9632\n",
+      "features.denseblock4.denselayer15.conv1.weight: 0.9297\n",
+      "features.denseblock4.denselayer15.conv2.weight: 0.9655\n",
+      "features.denseblock4.denselayer16.conv1.weight: 0.9254\n",
+      "features.denseblock4.denselayer16.conv2.weight: 0.9621\n",
+      "classifier.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Checking Sparsity Level:\")\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "417ac105",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_dir = \"experiment-0\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"densenet-121-sparse-flowers-experiment-0.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-gmp-model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22c96232",
+   "metadata": {},
+   "source": [
+    "The resulting model is is 90% sparse and achieves validation accuracy of 90.3% (vs the unoptimized dense model at 91.3%) without much hyperparameter search,\n",
+    "\n",
+    "Key hyperparameter experiments you may want to run include:\n",
+    "- Learning rate\n",
+    "- Learning rate schedule\n",
+    "- Sparsity level\n",
+    "- Skipping layers\n",
+    "- Number of pruning epochs"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
new file mode 100644
index 00000000000..c57b5c7776e
--- /dev/null
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
@@ -0,0 +1,2589 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1ad80edf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.12.1+cu116\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision\n",
+    "print(torch.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4d554578",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResNet(\n",
+      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  (relu): ReLU(inplace=True)\n",
+      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "  (layer1): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (4): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (5): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "NUM_LABELS = 3\n",
+    "model = torchvision.models.resnet50(weights=torchvision.models.ResNet50_Weights.DEFAULT)\n",
+    "model.fc = torch.nn.Linear(model.fc.in_features, NUM_LABELS)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3f49303e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration default\n",
+      "Reusing dataset beans (/home/ubuntu/.cache/huggingface/datasets/beans/default/0.0.0/d5abfbb94de45599d871182cff389bc870bf145b4829c4b0fe20f0cccd637cbd)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "631d7dbbd3b7461da9fdc14f1130f89e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train/bean_rust/bean_rust_train.84.jpg\n",
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/bean_rust/bean_rust_val.36.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "import datasets\n",
+    "beans_dataset = datasets.load_dataset(\"beans\")\n",
+    "print(beans_dataset[\"train\"][0][\"image_file_path\"])\n",
+    "print(beans_dataset[\"validation\"][0][\"image_file_path\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7d180bbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision import transforms\n",
+    "\n",
+    "imagenet_transform = transforms.Compose([\n",
+    "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
+    "   transforms.CenterCrop(size=(224, 224)),\n",
+    "   transforms.ToTensor(),\n",
+    "   transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+    "])\n",
+    "\n",
+    "train_dataset = torchvision.datasets.ImageFolder(\n",
+    "    root=\"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\",\n",
+    "    transform=imagenet_transform\n",
+    ")\n",
+    "\n",
+    "val_dataset = torchvision.datasets.ImageFolder(\n",
+    "    root=\"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation\",\n",
+    "    transform=imagenet_transform\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5f1b878a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    # loop through batches\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # compute loss, run backpropogation\n",
+    "        outputs = model(inputs)  # model returns logits\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        # run evaluation\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    # return loss and evaluation metric\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b0a623e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "# hyperparameters\n",
+    "BATCH_SIZE = 64\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "# setup data loaders\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d4553beb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 10.0\r\n",
+      "init_lr: 0.0005\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "# Phase 1 Sparse Transfer Learning / Recovery\r\n",
+      "sparse_transfer_learning_modifiers:\r\n",
+      "  - !ConstantPruningModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    params: __ALL_PRUNABLE__\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dense_model/dense-recipe.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1749e00d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1217c3e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.0000\n",
+      "layer1.0.conv2.weight: 0.0000\n",
+      "layer1.0.conv3.weight: 0.0000\n",
+      "layer1.0.downsample.0.weight: 0.0000\n",
+      "layer1.1.conv1.weight: 0.0000\n",
+      "layer1.1.conv2.weight: 0.0000\n",
+      "layer1.1.conv3.weight: 0.0000\n",
+      "layer1.2.conv1.weight: 0.0000\n",
+      "layer1.2.conv2.weight: 0.0000\n",
+      "layer1.2.conv3.weight: 0.0000\n",
+      "layer2.0.conv1.weight: 0.0000\n",
+      "layer2.0.conv2.weight: 0.0000\n",
+      "layer2.0.conv3.weight: 0.0000\n",
+      "layer2.0.downsample.0.weight: 0.0000\n",
+      "layer2.1.conv1.weight: 0.0000\n",
+      "layer2.1.conv2.weight: 0.0000\n",
+      "layer2.1.conv3.weight: 0.0000\n",
+      "layer2.2.conv1.weight: 0.0000\n",
+      "layer2.2.conv2.weight: 0.0000\n",
+      "layer2.2.conv3.weight: 0.0000\n",
+      "layer2.3.conv1.weight: 0.0000\n",
+      "layer2.3.conv2.weight: 0.0000\n",
+      "layer2.3.conv3.weight: 0.0000\n",
+      "layer3.0.conv1.weight: 0.0000\n",
+      "layer3.0.conv2.weight: 0.0000\n",
+      "layer3.0.conv3.weight: 0.0000\n",
+      "layer3.0.downsample.0.weight: 0.0000\n",
+      "layer3.1.conv1.weight: 0.0000\n",
+      "layer3.1.conv2.weight: 0.0000\n",
+      "layer3.1.conv3.weight: 0.0000\n",
+      "layer3.2.conv1.weight: 0.0000\n",
+      "layer3.2.conv2.weight: 0.0000\n",
+      "layer3.2.conv3.weight: 0.0000\n",
+      "layer3.3.conv1.weight: 0.0000\n",
+      "layer3.3.conv2.weight: 0.0000\n",
+      "layer3.3.conv3.weight: 0.0000\n",
+      "layer3.4.conv1.weight: 0.0000\n",
+      "layer3.4.conv2.weight: 0.0000\n",
+      "layer3.4.conv3.weight: 0.0000\n",
+      "layer3.5.conv1.weight: 0.0000\n",
+      "layer3.5.conv2.weight: 0.0000\n",
+      "layer3.5.conv3.weight: 0.0000\n",
+      "layer4.0.conv1.weight: 0.0000\n",
+      "layer4.0.conv2.weight: 0.0000\n",
+      "layer4.0.conv3.weight: 0.0000\n",
+      "layer4.0.downsample.0.weight: 0.0000\n",
+      "layer4.1.conv1.weight: 0.0000\n",
+      "layer4.1.conv2.weight: 0.0000\n",
+      "layer4.1.conv3.weight: 0.0000\n",
+      "layer4.2.conv1.weight: 0.0000\n",
+      "layer4.2.conv2.weight: 0.0000\n",
+      "layer4.2.conv3.weight: 0.0000\n",
+      "fc.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d00d175b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ddc6372a986f463ebfd4eb9a87f3a394",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/10\n",
+      "Training Loss: 0.4339083959074581\n",
+      "Top 1 Acc: 0.8404255319148937\n",
+      "\n",
+      "Running Validation Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50459accdf814889a17105987bb67241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/10\n",
+      "Val Loss: 0.248091588417689\n",
+      "Top 1 Acc: 0.9548872180451128\n",
+      "\n",
+      "Running Training Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83df4e5627e44235b934280a8401808a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/10\n",
+      "Training Loss: 0.06769604531719405\n",
+      "Top 1 Acc: 0.9816247582205029\n",
+      "\n",
+      "Running Validation Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4861ebb90907415c96ca05b18eb6fd1f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/10\n",
+      "Val Loss: 0.16374425676379664\n",
+      "Top 1 Acc: 0.924812030075188\n",
+      "\n",
+      "Running Training Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b68fbd6e4e04a21ba278144368a48f4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/10\n",
+      "Training Loss: 0.02384376264971626\n",
+      "Top 1 Acc: 0.9932301740812379\n",
+      "\n",
+      "Running Validation Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c12285e040534bd0b2a1e22649ddc1f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/10\n",
+      "Val Loss: 0.049745342917352296\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "Running Training Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce8896643f1345828a2058e04a497b0f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/10\n",
+      "Training Loss: 0.09208150484653957\n",
+      "Top 1 Acc: 0.9922630560928434\n",
+      "\n",
+      "Running Validation Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1443e8ceaee4563884b43951fadb32d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/10\n",
+      "Val Loss: 0.0414201499883499\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "Running Training Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e158ef40dbb24e1e82dba48a5175094f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/10\n",
+      "Training Loss: 0.05627283230697846\n",
+      "Top 1 Acc: 0.9835589941972921\n",
+      "\n",
+      "Running Validation Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35196861a6e04149b0da473fd551894f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/10\n",
+      "Val Loss: 0.018425415522263695\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "Running Training Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f376300a2992438e987705b61ed3622c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/10\n",
+      "Training Loss: 0.01825398319017361\n",
+      "Top 1 Acc: 0.995164410058027\n",
+      "\n",
+      "Running Validation Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6989019b3844c5a8babb06109624adf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/10\n",
+      "Val Loss: 0.03305983874209536\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "Running Training Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b87fa5de0374b5e835cc9506c001459",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/10\n",
+      "Training Loss: 0.019919913084081867\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "985407c44f8f43c9826e187ce6ee211b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/10\n",
+      "Val Loss: 0.009156467043794692\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Training Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a02f509c95664f7f97826611c5bfe256",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/10\n",
+      "Training Loss: 0.027912832608254737\n",
+      "Top 1 Acc: 0.9990328820116054\n",
+      "\n",
+      "Running Validation Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1fe9ca9b315447d7bd4726d5ad51c3bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/10\n",
+      "Val Loss: 0.008448541047982872\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Training Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0733093fce85461ba8206421e11783c4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/10\n",
+      "Training Loss: 0.006125200964400873\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fe544acaa799482389d56fc964a92a6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/10\n",
+      "Val Loss: 0.006565663294168189\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Training Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d77f15fd932b4bea875da981f61428cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/10\n",
+      "Training Loss: 0.003272765227045645\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45026f95773c4f2799d18ed949360aab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/10\n",
+      "Val Loss: 0.007307609868197081\n",
+      "Top 1 Acc: 1.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run transfer learning\n",
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "11fa4cf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"dense_model\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet-50-dense-beans.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "9b17fc31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 10.0\r\n",
+      "init_lr: 0.00025\r\n",
+      "inter_func: cubic\r\n",
+      "mask_type: unstructured\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "# Pruning\r\n",
+      "pruning_modifiers:\r\n",
+      "   - !GlobalMagnitudePruningModifier\r\n",
+      "     init_sparsity: 0.05\r\n",
+      "     final_sparsity: 0.90\r\n",
+      "     start_epoch: 0.0\r\n",
+      "     end_epoch: eval(num_epochs)\r\n",
+      "     update_frequency: 1.0\r\n",
+      "     params: \r\n",
+      "        - 're:layer1.*.conv1.weight'\r\n",
+      "        - 're:layer1.*.conv2.weight'\r\n",
+      "        - 're:layer1.*.conv3.weight'\r\n",
+      "        - 're:layer1.0.downsample.0.weight'\r\n",
+      "        - 're:layer2.*.conv1.weight'\r\n",
+      "        - 're:layer2.*.conv2.weight'\r\n",
+      "        - 're:layer2.*.conv3.weight'\r\n",
+      "        - 're:layer2.0.downsample.0.weight'\r\n",
+      "        - 're:layer3.*.conv1.weight'\r\n",
+      "        - 're:layer3.*.conv2.weight'\r\n",
+      "        - 're:layer3.*.conv3.weight'\r\n",
+      "        - 're:layer3.0.downsample.0.weight'\r\n",
+      "        - 're:layer4.*.conv1.weight'\r\n",
+      "        - 're:layer4.*.conv2.weight'\r\n",
+      "        - 're:layer4.*.conv3.weight'\r\n",
+      "        - 're:layer4.0.downsample.0.weight'\r\n",
+      "     leave_enabled: True\r\n",
+      "     inter_func: eval(inter_func)\r\n",
+      "     mask_type: eval(mask_type)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cat recipe-0.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8447d074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint = torch.load(\"./dense_model/training/resnet-50-dense-beans.pth\")\n",
+    "model = torchvision.models.resnet50()\n",
+    "model.fc = torch.nn.Linear(model.fc.in_features, NUM_LABELS)\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "model.to(device)\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9154d351",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResNet(\n",
+      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  (relu): ReLU(inplace=True)\n",
+      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "  (layer1): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (4): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (5): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "68646bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "00d26fb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.0000\n",
+      "layer1.0.conv2.weight: 0.0000\n",
+      "layer1.0.conv3.weight: 0.0000\n",
+      "layer1.0.downsample.0.weight: 0.0000\n",
+      "layer1.1.conv1.weight: 0.0000\n",
+      "layer1.1.conv2.weight: 0.0000\n",
+      "layer1.1.conv3.weight: 0.0000\n",
+      "layer1.2.conv1.weight: 0.0000\n",
+      "layer1.2.conv2.weight: 0.0000\n",
+      "layer1.2.conv3.weight: 0.0000\n",
+      "layer2.0.conv1.weight: 0.0000\n",
+      "layer2.0.conv2.weight: 0.0000\n",
+      "layer2.0.conv3.weight: 0.0000\n",
+      "layer2.0.downsample.0.weight: 0.0000\n",
+      "layer2.1.conv1.weight: 0.0000\n",
+      "layer2.1.conv2.weight: 0.0000\n",
+      "layer2.1.conv3.weight: 0.0000\n",
+      "layer2.2.conv1.weight: 0.0000\n",
+      "layer2.2.conv2.weight: 0.0000\n",
+      "layer2.2.conv3.weight: 0.0000\n",
+      "layer2.3.conv1.weight: 0.0000\n",
+      "layer2.3.conv2.weight: 0.0000\n",
+      "layer2.3.conv3.weight: 0.0000\n",
+      "layer3.0.conv1.weight: 0.0000\n",
+      "layer3.0.conv2.weight: 0.0000\n",
+      "layer3.0.conv3.weight: 0.0000\n",
+      "layer3.0.downsample.0.weight: 0.0000\n",
+      "layer3.1.conv1.weight: 0.0000\n",
+      "layer3.1.conv2.weight: 0.0000\n",
+      "layer3.1.conv3.weight: 0.0000\n",
+      "layer3.2.conv1.weight: 0.0000\n",
+      "layer3.2.conv2.weight: 0.0000\n",
+      "layer3.2.conv3.weight: 0.0000\n",
+      "layer3.3.conv1.weight: 0.0000\n",
+      "layer3.3.conv2.weight: 0.0000\n",
+      "layer3.3.conv3.weight: 0.0000\n",
+      "layer3.4.conv1.weight: 0.0000\n",
+      "layer3.4.conv2.weight: 0.0000\n",
+      "layer3.4.conv3.weight: 0.0000\n",
+      "layer3.5.conv1.weight: 0.0000\n",
+      "layer3.5.conv2.weight: 0.0000\n",
+      "layer3.5.conv3.weight: 0.0000\n",
+      "layer4.0.conv1.weight: 0.0000\n",
+      "layer4.0.conv2.weight: 0.0000\n",
+      "layer4.0.conv3.weight: 0.0000\n",
+      "layer4.0.downsample.0.weight: 0.0000\n",
+      "layer4.1.conv1.weight: 0.0000\n",
+      "layer4.1.conv2.weight: 0.0000\n",
+      "layer4.1.conv3.weight: 0.0000\n",
+      "layer4.2.conv1.weight: 0.0000\n",
+      "layer4.2.conv2.weight: 0.0000\n",
+      "layer4.2.conv3.weight: 0.0000\n",
+      "fc.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "601c8c21",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64ff653882a846d382ff70d2c9a9a261",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/10\n",
+      "Training Loss: 0.009745531282319194\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ade404c92c814dc987f0db7b70b101c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/10\n",
+      "Val Loss: 0.03642222502579292\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.0156\n",
+      "layer1.0.conv2.weight: 0.0299\n",
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+      "layer3.5.conv3.weight: 0.0383\n",
+      "layer4.0.conv1.weight: 0.0313\n",
+      "layer4.0.conv2.weight: 0.0505\n",
+      "layer4.0.conv3.weight: 0.0333\n",
+      "layer4.0.downsample.0.weight: 0.0511\n",
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+      "layer4.2.conv2.weight: 0.0707\n",
+      "layer4.2.conv3.weight: 0.0466\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38d0e8566f17400d90237dcc75d62a33",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/10\n",
+      "Training Loss: 0.015113459074414363\n",
+      "Top 1 Acc: 0.9961315280464217\n",
+      "\n",
+      "Running Validation Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/10\n",
+      "Val Loss: 0.09295554442602831\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.1057\n",
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+      "layer4.0.conv1.weight: 0.1792\n",
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+      "layer4.2.conv2.weight: 0.3903\n",
+      "layer4.2.conv3.weight: 0.2633\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cbddc0bfc5148f38735d36f84fb9b90",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/10\n",
+      "Training Loss: 0.02089866736616172\n",
+      "Top 1 Acc: 0.9941972920696325\n",
+      "\n",
+      "Running Validation Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stdout",
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+     "text": [
+      "Validation Epoch: 3/10\n",
+      "Val Loss: 0.08353681061453244\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
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+      "layer4.2.conv2.weight: 0.6232\n",
+      "layer4.2.conv3.weight: 0.4406\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "output_type": "display_data"
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+     "text": [
+      "Training Epoch: 4/10\n",
+      "Training Loss: 0.015480193025295567\n",
+      "Top 1 Acc: 0.9961315280464217\n",
+      "\n",
+      "Running Validation Epoch 4/10\n"
+     ]
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
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+     "text": [
+      "Validation Epoch: 4/10\n",
+      "Val Loss: 0.014964782571648053\n",
+      "Top 1 Acc: 0.9924812030075187\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
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+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
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+     "text": [
+      "Training Epoch: 5/10\n",
+      "Training Loss: 0.006244629046277088\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 5/10\n"
+     ]
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+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+     "text": [
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+      "Val Loss: 0.007931285625090823\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "layer4.0.conv3.weight: 0.5572\n",
+      "layer4.0.downsample.0.weight: 0.7473\n",
+      "layer4.1.conv1.weight: 0.6891\n",
+      "layer4.1.conv2.weight: 0.8100\n",
+      "layer4.1.conv3.weight: 0.6422\n",
+      "layer4.2.conv1.weight: 0.7877\n",
+      "layer4.2.conv2.weight: 0.8729\n",
+      "layer4.2.conv3.weight: 0.6948\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "416b62306b5c44ef9318d2dd93b811d7",
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+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/10\n",
+      "Training Loss: 0.010191820685149115\n",
+      "Top 1 Acc: 0.9970986460348162\n",
+      "\n",
+      "Running Validation Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "62622232c7f8445886e3628039f1e8e2",
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+       "version_minor": 0
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+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/10\n",
+      "Val Loss: 0.013040244715133062\n",
+      "Top 1 Acc: 0.9924812030075187\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.3665\n",
+      "layer1.0.conv2.weight: 0.5532\n",
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+      "layer1.2.conv3.weight: 0.4930\n",
+      "layer2.0.conv1.weight: 0.4214\n",
+      "layer2.0.conv2.weight: 0.6199\n",
+      "layer2.0.conv3.weight: 0.5578\n",
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+      "layer2.1.conv1.weight: 0.6706\n",
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+      "layer2.2.conv2.weight: 0.6889\n",
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+      "layer2.3.conv1.weight: 0.5859\n",
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+      "layer2.3.conv3.weight: 0.6093\n",
+      "layer3.0.conv1.weight: 0.5457\n",
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+      "layer3.1.conv1.weight: 0.7758\n",
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+      "layer3.2.conv1.weight: 0.8025\n",
+      "layer3.2.conv2.weight: 0.8117\n",
+      "layer3.2.conv3.weight: 0.7537\n",
+      "layer3.3.conv1.weight: 0.7438\n",
+      "layer3.3.conv2.weight: 0.8234\n",
+      "layer3.3.conv3.weight: 0.7287\n",
+      "layer3.4.conv1.weight: 0.7485\n",
+      "layer3.4.conv2.weight: 0.8092\n",
+      "layer3.4.conv3.weight: 0.7441\n",
+      "layer3.5.conv1.weight: 0.6878\n",
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+      "layer3.5.conv3.weight: 0.6757\n",
+      "layer4.0.conv1.weight: 0.6091\n",
+      "layer4.0.conv2.weight: 0.8388\n",
+      "layer4.0.conv3.weight: 0.6473\n",
+      "layer4.0.downsample.0.weight: 0.8285\n",
+      "layer4.1.conv1.weight: 0.7774\n",
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+      "layer4.2.conv1.weight: 0.8599\n",
+      "layer4.2.conv2.weight: 0.9266\n",
+      "layer4.2.conv3.weight: 0.7776\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58cc3f7d549141e59efb73fda109a56d",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/10\n",
+      "Training Loss: 0.028277927598751643\n",
+      "Top 1 Acc: 0.9970986460348162\n",
+      "\n",
+      "Running Validation Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/10\n",
+      "Val Loss: 0.027645598476131756\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.4146\n",
+      "layer1.0.conv2.weight: 0.6099\n",
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+      "layer2.0.conv2.weight: 0.6813\n",
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+      "layer3.1.conv1.weight: 0.8290\n",
+      "layer3.1.conv2.weight: 0.8717\n",
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+      "layer3.2.conv2.weight: 0.8624\n",
+      "layer3.2.conv3.weight: 0.8075\n",
+      "layer3.3.conv1.weight: 0.8027\n",
+      "layer3.3.conv2.weight: 0.8722\n",
+      "layer3.3.conv3.weight: 0.7889\n",
+      "layer3.4.conv1.weight: 0.8073\n",
+      "layer3.4.conv2.weight: 0.8622\n",
+      "layer3.4.conv3.weight: 0.7983\n",
+      "layer3.5.conv1.weight: 0.7526\n",
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+      "layer3.5.conv3.weight: 0.7386\n",
+      "layer4.0.conv1.weight: 0.6762\n",
+      "layer4.0.conv2.weight: 0.8933\n",
+      "layer4.0.conv3.weight: 0.7177\n",
+      "layer4.0.downsample.0.weight: 0.8815\n",
+      "layer4.1.conv1.weight: 0.8387\n",
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+      "layer4.2.conv1.weight: 0.9043\n",
+      "layer4.2.conv2.weight: 0.9547\n",
+      "layer4.2.conv3.weight: 0.8350\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "040dac607a08475691f7697cb6d6ab12",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/10\n",
+      "Training Loss: 0.0318604385589852\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e39e2a04a13447e6809a1c7f460b17c9",
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+       "version_minor": 0
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+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/10\n",
+      "Val Loss: 0.03692007825399438\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.4473\n",
+      "layer1.0.conv2.weight: 0.6496\n",
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+      "layer3.2.conv2.weight: 0.8925\n",
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+      "layer4.0.conv1.weight: 0.7225\n",
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+      "layer4.0.conv3.weight: 0.7660\n",
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+      "layer4.2.conv2.weight: 0.9687\n",
+      "layer4.2.conv3.weight: 0.8709\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58e103e988ae452494e887e42d40e5ea",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/10\n",
+      "Training Loss: 0.036725726197747624\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "99694a447b274b2da56353ec12fd897e",
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+       "version_minor": 0
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+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/10\n",
+      "Val Loss: 0.046517357851068176\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.4712\n",
+      "layer1.0.conv2.weight: 0.6728\n",
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+      "layer3.3.conv1.weight: 0.8593\n",
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+      "layer4.0.conv1.weight: 0.7488\n",
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+      "layer4.2.conv3.weight: 0.8893\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d88362b1c1b043efafd998b3f606d60c",
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+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
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+     "metadata": {},
+     "output_type": "display_data"
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+     "name": "stdout",
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+     "text": [
+      "Training Epoch: 10/10\n",
+      "Training Loss: 0.032790856435894966\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 10/10\n"
+     ]
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "  0%|          | 0/3 [00:00<?, ?it/s]"
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/10\n",
+      "Val Loss: 0.0411220183596015\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "conv1.weight: 0.0000\n",
+      "layer1.0.conv1.weight: 0.4783\n",
+      "layer1.0.conv2.weight: 0.6819\n",
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+      "layer1.1.conv3.weight: 0.5606\n",
+      "layer1.2.conv1.weight: 0.5388\n",
+      "layer1.2.conv2.weight: 0.6055\n",
+      "layer1.2.conv3.weight: 0.6023\n",
+      "layer2.0.conv1.weight: 0.5475\n",
+      "layer2.0.conv2.weight: 0.7528\n",
+      "layer2.0.conv3.weight: 0.6869\n",
+      "layer2.0.downsample.0.weight: 0.8294\n",
+      "layer2.1.conv1.weight: 0.7904\n",
+      "layer2.1.conv2.weight: 0.8076\n",
+      "layer2.1.conv3.weight: 0.7448\n",
+      "layer2.2.conv1.weight: 0.7550\n",
+      "layer2.2.conv2.weight: 0.8084\n",
+      "layer2.2.conv3.weight: 0.7696\n",
+      "layer2.3.conv1.weight: 0.7220\n",
+      "layer2.3.conv2.weight: 0.7845\n",
+      "layer2.3.conv3.weight: 0.7395\n",
+      "layer3.0.conv1.weight: 0.6785\n",
+      "layer3.0.conv2.weight: 0.8685\n",
+      "layer3.0.conv3.weight: 0.7536\n",
+      "layer3.0.downsample.0.weight: 0.9200\n",
+      "layer3.1.conv1.weight: 0.8859\n",
+      "layer3.1.conv2.weight: 0.9169\n",
+      "layer3.1.conv3.weight: 0.8463\n",
+      "layer3.2.conv1.weight: 0.9046\n",
+      "layer3.2.conv2.weight: 0.9133\n",
+      "layer3.2.conv3.weight: 0.8662\n",
+      "layer3.3.conv1.weight: 0.8669\n",
+      "layer3.3.conv2.weight: 0.9201\n",
+      "layer3.3.conv3.weight: 0.8557\n",
+      "layer3.4.conv1.weight: 0.8697\n",
+      "layer3.4.conv2.weight: 0.9149\n",
+      "layer3.4.conv3.weight: 0.8586\n",
+      "layer3.5.conv1.weight: 0.8271\n",
+      "layer3.5.conv2.weight: 0.9181\n",
+      "layer3.5.conv3.weight: 0.8134\n",
+      "layer4.0.conv1.weight: 0.7590\n",
+      "layer4.0.conv2.weight: 0.9449\n",
+      "layer4.0.conv3.weight: 0.8032\n",
+      "layer4.0.downsample.0.weight: 0.9336\n",
+      "layer4.1.conv1.weight: 0.9031\n",
+      "layer4.1.conv2.weight: 0.9619\n",
+      "layer4.1.conv3.weight: 0.8624\n",
+      "layer4.2.conv1.weight: 0.9439\n",
+      "layer4.2.conv2.weight: 0.9772\n",
+      "layer4.2.conv3.weight: 0.8961\n",
+      "fc.weight: 0.0000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run transfer learning\n",
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n\\nChecking Sparsity Level:\")\n",
+    "    for (name, layer) in get_prunable_layers(model):\n",
+    "        print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")\n",
+    "    print(f\"\\n\")\n",
+    "    \n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "417ac105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_dir = \"experiment-0\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet-50-sparse-experiment-0.pth\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
new file mode 100644
index 00000000000..04c324b764e
--- /dev/null
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
@@ -0,0 +1,3210 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1ad80edf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torchvision import transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "from datasets import load_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f8eb3642",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration default\n",
+      "Reusing dataset beans (/home/ubuntu/.cache/huggingface/datasets/beans/default/0.0.0/d5abfbb94de45599d871182cff389bc870bf145b4829c4b0fe20f0cccd637cbd)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7700382ea347426fb3596590931e7f91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DatasetDict({\n",
+      "    train: Dataset({\n",
+      "        features: ['image_file_path', 'image', 'labels'],\n",
+      "        num_rows: 1034\n",
+      "    })\n",
+      "    validation: Dataset({\n",
+      "        features: ['image_file_path', 'image', 'labels'],\n",
+      "        num_rows: 133\n",
+      "    })\n",
+      "    test: Dataset({\n",
+      "        features: ['image_file_path', 'image', 'labels'],\n",
+      "        num_rows: 128\n",
+      "    })\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = load_dataset(\"beans\")\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b06fc4fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train/bean_rust/bean_rust_train.84.jpg\n",
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/bean_rust/bean_rust_val.36.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(dataset[\"train\"][0][\"image_file_path\"])\n",
+    "print(dataset[\"validation\"][0][\"image_file_path\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4ecdebad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\"\n",
+    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7d180bbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BATCH_SIZE = 32\n",
+    "NUM_LABELS = 3\n",
+    "\n",
+    "# transforms\n",
+    "imagenet_transform = transforms.Compose([\n",
+    "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
+    "   transforms.CenterCrop(size=(224, 224)),\n",
+    "   transforms.ToTensor(),\n",
+    "   transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
+    "])\n",
+    "\n",
+    "# datasets\n",
+    "train_dataset = torchvision.datasets.ImageFolder(\n",
+    "    root=train_path,\n",
+    "    transform=imagenet_transform\n",
+    ")\n",
+    "\n",
+    "val_dataset = torchvision.datasets.ImageFolder(\n",
+    "    root=val_path,\n",
+    "    transform=imagenet_transform\n",
+    ")\n",
+    "\n",
+    "# setup data loaders\n",
+    "train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16)\n",
+    "val_loader = DataLoader(val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c968c098",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "5f1b878a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    # loop through batches\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        # compute loss, run backpropogation\n",
+    "        outputs = model(inputs)  # model returns logits\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        # run evaluation\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    # return loss and evaluation metric\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3521e78",
+   "metadata": {},
+   "source": [
+    "## Train Dense Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4d554578",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MobileNetV2(\n",
+      "  (features): Sequential(\n",
+      "    (0): Conv2dNormActivation(\n",
+      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): ReLU6(inplace=True)\n",
+      "    )\n",
+      "    (1): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
+      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (2): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
+      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (3): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (4): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (5): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (6): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (7): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (8): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (9): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (10): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (11): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (12): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (13): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (14): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (15): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (16): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (17): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (18): Conv2dNormActivation(\n",
+      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): ReLU6(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (classifier): Sequential(\n",
+      "    (0): Dropout(p=0.2, inplace=False)\n",
+      "    (1): Linear(in_features=1280, out_features=3, bias=True)\n",
+      "  )\n",
+      ")\n",
+      "Using device: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = torchvision.models.mobilenet_v2(weights=torchvision.models.MobileNet_V2_Weights.DEFAULT)\n",
+    "model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)\n",
+    "model.to(device)\n",
+    "print(model)\n",
+    "print(f\"Using device: {device}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b0a623e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d4553beb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 10.0\r\n",
+      "init_lr: 0.0005\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "# Phase 1 Sparse Transfer Learning / Recovery\r\n",
+      "sparse_transfer_learning_modifiers:\r\n",
+      "  - !ConstantPruningModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    params: __ALL_PRUNABLE__\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat ./dense_model/dense-recipe.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1749e00d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d00d175b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2c6b000774e4eaca928b6141d7c3213",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/10\n",
+      "Training Loss: 0.49688769707625563\n",
+      "Top 1 Acc: 0.811411992263056\n",
+      "\n",
+      "Running Validation Epoch 1/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "66fe4e0c0851461db6d884a40a607444",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/10\n",
+      "Val Loss: 0.10483174873515963\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "Running Training Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f5b6964760f44e1f98094732aadae9fb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/10\n",
+      "Training Loss: 0.13233664106916299\n",
+      "Top 1 Acc: 0.9709864603481625\n",
+      "\n",
+      "Running Validation Epoch 2/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03589fa8706b4b12915f425382227d0e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/10\n",
+      "Val Loss: 0.10690611051395535\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "Running Training Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38fbbd378dcf47eba5ba2964d9515d90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/10\n",
+      "Training Loss: 0.061133282936432144\n",
+      "Top 1 Acc: 0.9893617021276596\n",
+      "\n",
+      "Running Validation Epoch 3/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ead52a74d6a84abfafcf7a8e095df868",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/10\n",
+      "Val Loss: 0.05752026969566941\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "Running Training Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6161054cadc746cc9f0972a15002e489",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/10\n",
+      "Training Loss: 0.03709277432327243\n",
+      "Top 1 Acc: 0.988394584139265\n",
+      "\n",
+      "Running Validation Epoch 4/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "393de368a783482dbf640966209195b8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/10\n",
+      "Val Loss: 0.09043196495622396\n",
+      "Top 1 Acc: 0.9699248120300752\n",
+      "\n",
+      "Running Training Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e07443d60a154ebc946e6e49c7c2e2bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/10\n",
+      "Training Loss: 0.011832633150995454\n",
+      "Top 1 Acc: 0.9990328820116054\n",
+      "\n",
+      "Running Validation Epoch 5/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8973eb27c13144489b86a1b4c8d7d0fd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/10\n",
+      "Val Loss: 0.05438814461231232\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "Running Training Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "91c3c9f4af124e609dc1690dffe6b98d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 6/10\n",
+      "Training Loss: 0.012920034544852873\n",
+      "Top 1 Acc: 0.9970986460348162\n",
+      "\n",
+      "Running Validation Epoch 6/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5abd456da44f4f11b1f6a495be13e49a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 6/10\n",
+      "Val Loss: 0.05623044967651367\n",
+      "Top 1 Acc: 0.9699248120300752\n",
+      "\n",
+      "Running Training Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3afa211b71a94cf8b40378aff72b4efa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 7/10\n",
+      "Training Loss: 0.008218912644114233\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 7/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33f81ef07ca64adb92d3bdf059152274",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 7/10\n",
+      "Val Loss: 0.05101724807173014\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "Running Training Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aeb0f166f778426f9af9739f97ab7ac1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 8/10\n",
+      "Training Loss: 0.006811827950348908\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 8/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4aad511d52e74ef981068b1cfffdf174",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 8/10\n",
+      "Val Loss: 0.0494396741501987\n",
+      "Top 1 Acc: 0.9548872180451128\n",
+      "\n",
+      "Running Training Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a7b8acace3c04d87b380c306b661159b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/10\n",
+      "Training Loss: 0.005579106432075302\n",
+      "Top 1 Acc: 0.9990328820116054\n",
+      "\n",
+      "Running Validation Epoch 9/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "889d3c2e6a2f4560a0692f82b09e28f9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 9/10\n",
+      "Val Loss: 0.050181417539715764\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "Running Training Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c662492b659f455499d40c9585a0918c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/10\n",
+      "Training Loss: 0.006127264015517678\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 10/10\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b4cf87af07d404b935b414ead0be10c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/10\n",
+      "Val Loss: 0.04676330639049411\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run transfer learning\n",
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1217c3e9",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features.0.0.weight: 0.0000\n",
+      "features.1.conv.0.0.weight: 0.0000\n",
+      "features.1.conv.1.weight: 0.0000\n",
+      "features.2.conv.0.0.weight: 0.0000\n",
+      "features.2.conv.1.0.weight: 0.0000\n",
+      "features.2.conv.2.weight: 0.0000\n",
+      "features.3.conv.0.0.weight: 0.0000\n",
+      "features.3.conv.1.0.weight: 0.0000\n",
+      "features.3.conv.2.weight: 0.0000\n",
+      "features.4.conv.0.0.weight: 0.0000\n",
+      "features.4.conv.1.0.weight: 0.0000\n",
+      "features.4.conv.2.weight: 0.0000\n",
+      "features.5.conv.0.0.weight: 0.0000\n",
+      "features.5.conv.1.0.weight: 0.0000\n",
+      "features.5.conv.2.weight: 0.0000\n",
+      "features.6.conv.0.0.weight: 0.0000\n",
+      "features.6.conv.1.0.weight: 0.0000\n",
+      "features.6.conv.2.weight: 0.0000\n",
+      "features.7.conv.0.0.weight: 0.0000\n",
+      "features.7.conv.1.0.weight: 0.0000\n",
+      "features.7.conv.2.weight: 0.0000\n",
+      "features.8.conv.0.0.weight: 0.0000\n",
+      "features.8.conv.1.0.weight: 0.0000\n",
+      "features.8.conv.2.weight: 0.0000\n",
+      "features.9.conv.0.0.weight: 0.0000\n",
+      "features.9.conv.1.0.weight: 0.0000\n",
+      "features.9.conv.2.weight: 0.0000\n",
+      "features.10.conv.0.0.weight: 0.0000\n",
+      "features.10.conv.1.0.weight: 0.0000\n",
+      "features.10.conv.2.weight: 0.0000\n",
+      "features.11.conv.0.0.weight: 0.0000\n",
+      "features.11.conv.1.0.weight: 0.0000\n",
+      "features.11.conv.2.weight: 0.0000\n",
+      "features.12.conv.0.0.weight: 0.0000\n",
+      "features.12.conv.1.0.weight: 0.0000\n",
+      "features.12.conv.2.weight: 0.0000\n",
+      "features.13.conv.0.0.weight: 0.0000\n",
+      "features.13.conv.1.0.weight: 0.0000\n",
+      "features.13.conv.2.weight: 0.0000\n",
+      "features.14.conv.0.0.weight: 0.0000\n",
+      "features.14.conv.1.0.weight: 0.0000\n",
+      "features.14.conv.2.weight: 0.0000\n",
+      "features.15.conv.0.0.weight: 0.0000\n",
+      "features.15.conv.1.0.weight: 0.0000\n",
+      "features.15.conv.2.weight: 0.0000\n",
+      "features.16.conv.0.0.weight: 0.0000\n",
+      "features.16.conv.1.0.weight: 0.0000\n",
+      "features.16.conv.2.weight: 0.0000\n",
+      "features.17.conv.0.0.weight: 0.0000\n",
+      "features.17.conv.1.0.weight: 0.0000\n",
+      "features.17.conv.2.weight: 0.0000\n",
+      "features.18.0.weight: 0.0000\n",
+      "classifier.1.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "11fa4cf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"dense_model\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"mobilenet-v2-dense-beans.pth\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273ce478",
+   "metadata": {},
+   "source": [
+    "## Prune The Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "8447d074",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "checkpoint = torch.load(\"./dense_model/training/mobilenet-v2-dense-beans.pth\")\n",
+    "model = torchvision.models.mobilenet_v2()\n",
+    "model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "model.to(device)\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9154d351",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MobileNetV2(\n",
+      "  (features): Sequential(\n",
+      "    (0): Conv2dNormActivation(\n",
+      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): ReLU6(inplace=True)\n",
+      "    )\n",
+      "    (1): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
+      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (2): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
+      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (3): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (4): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
+      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (5): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (6): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (7): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
+      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (8): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (9): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (10): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (11): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
+      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (12): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (13): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (14): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
+      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (15): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (16): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (17): InvertedResidual(\n",
+      "      (conv): Sequential(\n",
+      "        (0): Conv2dNormActivation(\n",
+      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (1): Conv2dNormActivation(\n",
+      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
+      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "          (2): ReLU6(inplace=True)\n",
+      "        )\n",
+      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (18): Conv2dNormActivation(\n",
+      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (2): ReLU6(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (classifier): Sequential(\n",
+      "    (0): Dropout(p=0.2, inplace=False)\n",
+      "    (1): Linear(in_features=1280, out_features=3, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "44578399",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 13.0\r\n",
+      "pruning_epochs: 10.0\r\n",
+      "init_lr: 0.0005\r\n",
+      "inter_func: cubic\r\n",
+      "mask_type: unstructured\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "# Pruning\r\n",
+      "pruning_modifiers:\r\n",
+      "  - !GlobalMagnitudePruningModifier\r\n",
+      "    init_sparsity: 0.05\r\n",
+      "    final_sparsity: 0.90\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(pruning_epochs)\r\n",
+      "    update_frequency: 1.0\r\n",
+      "    params: \r\n",
+      "        - 'features.0.0.weight'\r\n",
+      "        - 'features.18.0.weight'\r\n",
+      "        - 're:features.*.conv.*.weight'\r\n",
+      "        - 're:features.*.conv.*.*.weight'\r\n",
+      "    leave_enabled: True\r\n",
+      "    inter_func: eval(inter_func)\r\n",
+      "    mask_type: eval(mask_type)\r\n",
+      "\r\n",
+      "finetuning_modifiers:\r\n",
+      "  - !ConstantPruningModifier\r\n",
+      "    start_epoch: eval(pruning_epochs)\r\n",
+      "    params: __ALL_PRUNABLE__"
+     ]
+    }
+   ],
+   "source": [
+    "!cat recipe-0.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "68646bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "00d26fb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "features.0.0.weight: 0.0000\n",
+      "features.1.conv.0.0.weight: 0.0000\n",
+      "features.1.conv.1.weight: 0.0000\n",
+      "features.2.conv.0.0.weight: 0.0000\n",
+      "features.2.conv.1.0.weight: 0.0000\n",
+      "features.2.conv.2.weight: 0.0000\n",
+      "features.3.conv.0.0.weight: 0.0000\n",
+      "features.3.conv.1.0.weight: 0.0000\n",
+      "features.3.conv.2.weight: 0.0000\n",
+      "features.4.conv.0.0.weight: 0.0000\n",
+      "features.4.conv.1.0.weight: 0.0000\n",
+      "features.4.conv.2.weight: 0.0000\n",
+      "features.5.conv.0.0.weight: 0.0000\n",
+      "features.5.conv.1.0.weight: 0.0000\n",
+      "features.5.conv.2.weight: 0.0000\n",
+      "features.6.conv.0.0.weight: 0.0000\n",
+      "features.6.conv.1.0.weight: 0.0000\n",
+      "features.6.conv.2.weight: 0.0000\n",
+      "features.7.conv.0.0.weight: 0.0000\n",
+      "features.7.conv.1.0.weight: 0.0000\n",
+      "features.7.conv.2.weight: 0.0000\n",
+      "features.8.conv.0.0.weight: 0.0000\n",
+      "features.8.conv.1.0.weight: 0.0000\n",
+      "features.8.conv.2.weight: 0.0000\n",
+      "features.9.conv.0.0.weight: 0.0000\n",
+      "features.9.conv.1.0.weight: 0.0000\n",
+      "features.9.conv.2.weight: 0.0000\n",
+      "features.10.conv.0.0.weight: 0.0000\n",
+      "features.10.conv.1.0.weight: 0.0000\n",
+      "features.10.conv.2.weight: 0.0000\n",
+      "features.11.conv.0.0.weight: 0.0000\n",
+      "features.11.conv.1.0.weight: 0.0000\n",
+      "features.11.conv.2.weight: 0.0000\n",
+      "features.12.conv.0.0.weight: 0.0000\n",
+      "features.12.conv.1.0.weight: 0.0000\n",
+      "features.12.conv.2.weight: 0.0000\n",
+      "features.13.conv.0.0.weight: 0.0000\n",
+      "features.13.conv.1.0.weight: 0.0000\n",
+      "features.13.conv.2.weight: 0.0000\n",
+      "features.14.conv.0.0.weight: 0.0000\n",
+      "features.14.conv.1.0.weight: 0.0000\n",
+      "features.14.conv.2.weight: 0.0000\n",
+      "features.15.conv.0.0.weight: 0.0000\n",
+      "features.15.conv.1.0.weight: 0.0000\n",
+      "features.15.conv.2.weight: 0.0000\n",
+      "features.16.conv.0.0.weight: 0.0000\n",
+      "features.16.conv.1.0.weight: 0.0000\n",
+      "features.16.conv.2.weight: 0.0000\n",
+      "features.17.conv.0.0.weight: 0.0000\n",
+      "features.17.conv.1.0.weight: 0.0000\n",
+      "features.17.conv.2.weight: 0.0000\n",
+      "features.18.0.weight: 0.0000\n",
+      "classifier.1.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "601c8c21",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "45a3d93cbb884f808293e613b1af95df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/13\n",
+      "Training Loss: 0.05181791849088657\n",
+      "Top 1 Acc: 0.9835589941972921\n",
+      "\n",
+      "Running Validation Epoch 1/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3b1b5e3918a142809e09b1fcaaac71fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/13\n",
+      "Val Loss: 0.08574707275256514\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "features.0.0.weight: 0.0880\n",
+      "features.1.conv.0.0.weight: 0.0729\n",
+      "features.1.conv.1.weight: 0.0820\n",
+      "features.2.conv.0.0.weight: 0.0293\n",
+      "features.2.conv.1.0.weight: 0.0081\n",
+      "features.2.conv.2.weight: 0.0169\n",
+      "features.3.conv.0.0.weight: 0.0321\n",
+      "features.3.conv.1.0.weight: 0.0255\n",
+      "features.3.conv.2.weight: 0.0286\n",
+      "features.4.conv.0.0.weight: 0.0240\n",
+      "features.4.conv.1.0.weight: 0.0008\n",
+      "features.4.conv.2.weight: 0.0197\n",
+      "features.5.conv.0.0.weight: 0.0343\n",
+      "features.5.conv.1.0.weight: 0.0237\n",
+      "features.5.conv.2.weight: 0.0288\n",
+      "features.6.conv.0.0.weight: 0.0319\n",
+      "features.6.conv.1.0.weight: 0.0272\n",
+      "features.6.conv.2.weight: 0.0334\n",
+      "features.7.conv.0.0.weight: 0.0218\n",
+      "features.7.conv.1.0.weight: 0.0035\n",
+      "features.7.conv.2.weight: 0.0228\n",
+      "features.8.conv.0.0.weight: 0.0445\n",
+      "features.8.conv.1.0.weight: 0.0341\n",
+      "features.8.conv.2.weight: 0.0502\n",
+      "features.9.conv.0.0.weight: 0.0424\n",
+      "features.9.conv.1.0.weight: 0.0292\n",
+      "features.9.conv.2.weight: 0.0465\n",
+      "features.10.conv.0.0.weight: 0.0409\n",
+      "features.10.conv.1.0.weight: 0.0301\n",
+      "features.10.conv.2.weight: 0.0433\n",
+      "features.11.conv.0.0.weight: 0.0298\n",
+      "features.11.conv.1.0.weight: 0.0388\n",
+      "features.11.conv.2.weight: 0.0329\n",
+      "features.12.conv.0.0.weight: 0.0428\n",
+      "features.12.conv.1.0.weight: 0.0266\n",
+      "features.12.conv.2.weight: 0.0466\n",
+      "features.13.conv.0.0.weight: 0.0434\n",
+      "features.13.conv.1.0.weight: 0.0249\n",
+      "features.13.conv.2.weight: 0.0480\n",
+      "features.14.conv.0.0.weight: 0.0341\n",
+      "features.14.conv.1.0.weight: 0.0008\n",
+      "features.14.conv.2.weight: 0.0362\n",
+      "features.15.conv.0.0.weight: 0.0569\n",
+      "features.15.conv.1.0.weight: 0.0270\n",
+      "features.15.conv.2.weight: 0.0610\n",
+      "features.16.conv.0.0.weight: 0.0499\n",
+      "features.16.conv.1.0.weight: 0.0309\n",
+      "features.16.conv.2.weight: 0.0591\n",
+      "features.17.conv.0.0.weight: 0.0420\n",
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+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 2/13\n"
+     ]
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+       "  0%|          | 0/33 [00:00<?, ?it/s]"
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+      "Training Epoch: 2/13\n",
+      "Training Loss: 0.01928500584361962\n",
+      "Top 1 Acc: 0.9941972920696325\n",
+      "\n",
+      "Running Validation Epoch 2/13\n"
+     ]
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+      "Validation Epoch: 2/13\n",
+      "Val Loss: 0.04039517091587186\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
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+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 3/13\n"
+     ]
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+    {
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+       "  0%|          | 0/33 [00:00<?, ?it/s]"
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+      "Training Epoch: 3/13\n",
+      "Training Loss: 0.02513152473274562\n",
+      "Top 1 Acc: 0.9941972920696325\n",
+      "\n",
+      "Running Validation Epoch 3/13\n"
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+      "Val Loss: 0.04448752112220973\n",
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+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
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+      "\n",
+      "\n",
+      "Running Training Epoch 4/13\n"
+     ]
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+    {
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+      "Training Epoch: 4/13\n",
+      "Training Loss: 0.0277155696160414\n",
+      "Top 1 Acc: 0.9903288201160542\n",
+      "\n",
+      "Running Validation Epoch 4/13\n"
+     ]
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+      "Validation Epoch: 4/13\n",
+      "Val Loss: 0.05159115699352697\n",
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+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
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+      "\n",
+      "\n",
+      "Running Training Epoch 5/13\n"
+     ]
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+      "Training Loss: 0.05216440672732212\n",
+      "Top 1 Acc: 0.9874274661508704\n",
+      "\n",
+      "Running Validation Epoch 5/13\n"
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
+      "\n",
+      "Running Training Epoch 6/13\n"
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+      "Training Loss: 0.08560408513541475\n",
+      "Top 1 Acc: 0.9806576402321083\n",
+      "\n",
+      "Running Validation Epoch 6/13\n"
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+      "\n",
+      "\n",
+      "Running Training Epoch 7/13\n"
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+      "\n",
+      "Running Validation Epoch 7/13\n"
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+      "\n",
+      "\n",
+      "Running Training Epoch 8/13\n"
+     ]
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+      "\n",
+      "Running Validation Epoch 8/13\n"
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+      "\n",
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+      "\n",
+      "\n",
+      "Running Training Epoch 9/13\n"
+     ]
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+      "\n",
+      "Running Validation Epoch 9/13\n"
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+      "\n",
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+      "\n",
+      "\n",
+      "Running Training Epoch 10/13\n"
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+      "\n",
+      "Running Validation Epoch 10/13\n"
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+      "features.13.conv.0.0.weight: 0.8704\n",
+      "features.13.conv.1.0.weight: 0.8169\n",
+      "features.13.conv.2.weight: 0.9103\n",
+      "features.14.conv.0.0.weight: 0.7845\n",
+      "features.14.conv.1.0.weight: 0.6823\n",
+      "features.14.conv.2.weight: 0.8260\n",
+      "features.15.conv.0.0.weight: 0.9442\n",
+      "features.15.conv.1.0.weight: 0.7847\n",
+      "features.15.conv.2.weight: 0.9561\n",
+      "features.16.conv.0.0.weight: 0.9233\n",
+      "features.16.conv.1.0.weight: 0.7920\n",
+      "features.16.conv.2.weight: 0.9565\n",
+      "features.17.conv.0.0.weight: 0.8762\n",
+      "features.17.conv.1.0.weight: 0.9093\n",
+      "features.17.conv.2.weight: 0.9509\n",
+      "features.18.0.weight: 0.9779\n",
+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 11/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1b1fef1cd5644d10a5aa75ebb98829e2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 11/13\n",
+      "Training Loss: 0.08148531587512205\n",
+      "Top 1 Acc: 0.9787234042553191\n",
+      "\n",
+      "Running Validation Epoch 11/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "86a99646d91c48e9ac7524c8b3743110",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 11/13\n",
+      "Val Loss: 0.07646273151040077\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "features.0.0.weight: 0.4919\n",
+      "features.1.conv.0.0.weight: 0.4618\n",
+      "features.1.conv.1.weight: 0.5156\n",
+      "features.2.conv.0.0.weight: 0.5495\n",
+      "features.2.conv.1.0.weight: 0.2951\n",
+      "features.2.conv.2.weight: 0.4852\n",
+      "features.3.conv.0.0.weight: 0.6152\n",
+      "features.3.conv.1.0.weight: 0.4915\n",
+      "features.3.conv.2.weight: 0.6137\n",
+      "features.4.conv.0.0.weight: 0.5185\n",
+      "features.4.conv.1.0.weight: 0.2670\n",
+      "features.4.conv.2.weight: 0.5167\n",
+      "features.5.conv.0.0.weight: 0.6847\n",
+      "features.5.conv.1.0.weight: 0.5880\n",
+      "features.5.conv.2.weight: 0.7153\n",
+      "features.6.conv.0.0.weight: 0.6929\n",
+      "features.6.conv.1.0.weight: 0.6169\n",
+      "features.6.conv.2.weight: 0.7378\n",
+      "features.7.conv.0.0.weight: 0.5461\n",
+      "features.7.conv.1.0.weight: 0.3646\n",
+      "features.7.conv.2.weight: 0.6134\n",
+      "features.8.conv.0.0.weight: 0.8370\n",
+      "features.8.conv.1.0.weight: 0.7410\n",
+      "features.8.conv.2.weight: 0.8693\n",
+      "features.9.conv.0.0.weight: 0.8263\n",
+      "features.9.conv.1.0.weight: 0.7367\n",
+      "features.9.conv.2.weight: 0.8643\n",
+      "features.10.conv.0.0.weight: 0.8288\n",
+      "features.10.conv.1.0.weight: 0.7870\n",
+      "features.10.conv.2.weight: 0.8721\n",
+      "features.11.conv.0.0.weight: 0.7257\n",
+      "features.11.conv.1.0.weight: 0.7966\n",
+      "features.11.conv.2.weight: 0.7720\n",
+      "features.12.conv.0.0.weight: 0.8809\n",
+      "features.12.conv.1.0.weight: 0.7944\n",
+      "features.12.conv.2.weight: 0.9048\n",
+      "features.13.conv.0.0.weight: 0.8716\n",
+      "features.13.conv.1.0.weight: 0.8177\n",
+      "features.13.conv.2.weight: 0.9112\n",
+      "features.14.conv.0.0.weight: 0.7858\n",
+      "features.14.conv.1.0.weight: 0.6860\n",
+      "features.14.conv.2.weight: 0.8276\n",
+      "features.15.conv.0.0.weight: 0.9448\n",
+      "features.15.conv.1.0.weight: 0.7856\n",
+      "features.15.conv.2.weight: 0.9567\n",
+      "features.16.conv.0.0.weight: 0.9241\n",
+      "features.16.conv.1.0.weight: 0.7929\n",
+      "features.16.conv.2.weight: 0.9572\n",
+      "features.17.conv.0.0.weight: 0.8773\n",
+      "features.17.conv.1.0.weight: 0.9105\n",
+      "features.17.conv.2.weight: 0.9516\n",
+      "features.18.0.weight: 0.9785\n",
+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 12/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ca0fe6eeb4042439d0172b6899b6629",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 12/13\n",
+      "Training Loss: 0.0749947436605439\n",
+      "Top 1 Acc: 0.9816247582205029\n",
+      "\n",
+      "Running Validation Epoch 12/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f0a08db8228c4bc384cfb4bc60302d3c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 12/13\n",
+      "Val Loss: 0.07313197031617165\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "features.0.0.weight: 0.4919\n",
+      "features.1.conv.0.0.weight: 0.4618\n",
+      "features.1.conv.1.weight: 0.5156\n",
+      "features.2.conv.0.0.weight: 0.5495\n",
+      "features.2.conv.1.0.weight: 0.2951\n",
+      "features.2.conv.2.weight: 0.4852\n",
+      "features.3.conv.0.0.weight: 0.6152\n",
+      "features.3.conv.1.0.weight: 0.4915\n",
+      "features.3.conv.2.weight: 0.6137\n",
+      "features.4.conv.0.0.weight: 0.5185\n",
+      "features.4.conv.1.0.weight: 0.2670\n",
+      "features.4.conv.2.weight: 0.5167\n",
+      "features.5.conv.0.0.weight: 0.6847\n",
+      "features.5.conv.1.0.weight: 0.5880\n",
+      "features.5.conv.2.weight: 0.7153\n",
+      "features.6.conv.0.0.weight: 0.6929\n",
+      "features.6.conv.1.0.weight: 0.6169\n",
+      "features.6.conv.2.weight: 0.7378\n",
+      "features.7.conv.0.0.weight: 0.5461\n",
+      "features.7.conv.1.0.weight: 0.3646\n",
+      "features.7.conv.2.weight: 0.6134\n",
+      "features.8.conv.0.0.weight: 0.8370\n",
+      "features.8.conv.1.0.weight: 0.7410\n",
+      "features.8.conv.2.weight: 0.8693\n",
+      "features.9.conv.0.0.weight: 0.8263\n",
+      "features.9.conv.1.0.weight: 0.7367\n",
+      "features.9.conv.2.weight: 0.8643\n",
+      "features.10.conv.0.0.weight: 0.8288\n",
+      "features.10.conv.1.0.weight: 0.7870\n",
+      "features.10.conv.2.weight: 0.8721\n",
+      "features.11.conv.0.0.weight: 0.7257\n",
+      "features.11.conv.1.0.weight: 0.7966\n",
+      "features.11.conv.2.weight: 0.7720\n",
+      "features.12.conv.0.0.weight: 0.8809\n",
+      "features.12.conv.1.0.weight: 0.7944\n",
+      "features.12.conv.2.weight: 0.9048\n",
+      "features.13.conv.0.0.weight: 0.8716\n",
+      "features.13.conv.1.0.weight: 0.8177\n",
+      "features.13.conv.2.weight: 0.9112\n",
+      "features.14.conv.0.0.weight: 0.7858\n",
+      "features.14.conv.1.0.weight: 0.6860\n",
+      "features.14.conv.2.weight: 0.8276\n",
+      "features.15.conv.0.0.weight: 0.9448\n",
+      "features.15.conv.1.0.weight: 0.7856\n",
+      "features.15.conv.2.weight: 0.9567\n",
+      "features.16.conv.0.0.weight: 0.9241\n",
+      "features.16.conv.1.0.weight: 0.7929\n",
+      "features.16.conv.2.weight: 0.9572\n",
+      "features.17.conv.0.0.weight: 0.8773\n",
+      "features.17.conv.1.0.weight: 0.9105\n",
+      "features.17.conv.2.weight: 0.9516\n",
+      "features.18.0.weight: 0.9785\n",
+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n",
+      "Running Training Epoch 13/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae1760d53ad1445c87ba323ef6a9f2d2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 13/13\n",
+      "Training Loss: 0.08235379680991173\n",
+      "Top 1 Acc: 0.97678916827853\n",
+      "\n",
+      "Running Validation Epoch 13/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c65ce0fddfac48749bda269035f42c72",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 13/13\n",
+      "Val Loss: 0.07470075637102128\n",
+      "Top 1 Acc: 0.9774436090225563\n",
+      "\n",
+      "\n",
+      "\n",
+      "Checking Sparsity Level:\n",
+      "features.0.0.weight: 0.4919\n",
+      "features.1.conv.0.0.weight: 0.4618\n",
+      "features.1.conv.1.weight: 0.5156\n",
+      "features.2.conv.0.0.weight: 0.5495\n",
+      "features.2.conv.1.0.weight: 0.2951\n",
+      "features.2.conv.2.weight: 0.4852\n",
+      "features.3.conv.0.0.weight: 0.6152\n",
+      "features.3.conv.1.0.weight: 0.4915\n",
+      "features.3.conv.2.weight: 0.6137\n",
+      "features.4.conv.0.0.weight: 0.5185\n",
+      "features.4.conv.1.0.weight: 0.2670\n",
+      "features.4.conv.2.weight: 0.5167\n",
+      "features.5.conv.0.0.weight: 0.6847\n",
+      "features.5.conv.1.0.weight: 0.5880\n",
+      "features.5.conv.2.weight: 0.7153\n",
+      "features.6.conv.0.0.weight: 0.6929\n",
+      "features.6.conv.1.0.weight: 0.6169\n",
+      "features.6.conv.2.weight: 0.7378\n",
+      "features.7.conv.0.0.weight: 0.5461\n",
+      "features.7.conv.1.0.weight: 0.3646\n",
+      "features.7.conv.2.weight: 0.6134\n",
+      "features.8.conv.0.0.weight: 0.8370\n",
+      "features.8.conv.1.0.weight: 0.7410\n",
+      "features.8.conv.2.weight: 0.8693\n",
+      "features.9.conv.0.0.weight: 0.8263\n",
+      "features.9.conv.1.0.weight: 0.7367\n",
+      "features.9.conv.2.weight: 0.8643\n",
+      "features.10.conv.0.0.weight: 0.8288\n",
+      "features.10.conv.1.0.weight: 0.7870\n",
+      "features.10.conv.2.weight: 0.8721\n",
+      "features.11.conv.0.0.weight: 0.7257\n",
+      "features.11.conv.1.0.weight: 0.7966\n",
+      "features.11.conv.2.weight: 0.7720\n",
+      "features.12.conv.0.0.weight: 0.8809\n",
+      "features.12.conv.1.0.weight: 0.7944\n",
+      "features.12.conv.2.weight: 0.9048\n",
+      "features.13.conv.0.0.weight: 0.8716\n",
+      "features.13.conv.1.0.weight: 0.8177\n",
+      "features.13.conv.2.weight: 0.9112\n",
+      "features.14.conv.0.0.weight: 0.7858\n",
+      "features.14.conv.1.0.weight: 0.6860\n",
+      "features.14.conv.2.weight: 0.8276\n",
+      "features.15.conv.0.0.weight: 0.9448\n",
+      "features.15.conv.1.0.weight: 0.7856\n",
+      "features.15.conv.2.weight: 0.9567\n",
+      "features.16.conv.0.0.weight: 0.9241\n",
+      "features.16.conv.1.0.weight: 0.7929\n",
+      "features.16.conv.2.weight: 0.9572\n",
+      "features.17.conv.0.0.weight: 0.8773\n",
+      "features.17.conv.1.0.weight: 0.9105\n",
+      "features.17.conv.2.weight: 0.9516\n",
+      "features.18.0.weight: 0.9785\n",
+      "classifier.1.weight: 0.0000\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run transfer learning\n",
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "    \n",
+    "    print(f\"\\n\\nChecking Sparsity Level:\")\n",
+    "    for (name, layer) in get_prunable_layers(model):\n",
+    "        print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")\n",
+    "    print(f\"\\n\")\n",
+    "    \n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "417ac105",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"experiment-0\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"mobilenet_v2-sparse-experiment-0.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d7193990",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checkpoint2 = torch.load(\"./dense_model/training/mobilenet-v2-dense-beans.pth\")\n",
+    "model2 = torchvision.models.mobilenet_v2()\n",
+    "model2.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)\n",
+    "model2.load_state_dict(checkpoint['state_dict'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b4333b3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exporter = ModuleExporter(model2, output_dir=\"./test\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59702f10",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
new file mode 100644
index 00000000000..e5afb1d4499
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
@@ -0,0 +1,16 @@
+
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.0005
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
new file mode 100644
index 00000000000..ec8a3b33b1a
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
@@ -0,0 +1,56 @@
+# Epoch and Learning-Rate variables
+num_epochs: 13.0
+pruning_epochs: 10.0
+init_lr: 0.0003
+final_lr: 0.0001
+inter_func: cubic
+mask_type: unstructured
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: eval(final_lr)
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(pruning_epochs)
+    
+  - !LearningRateFunctionModifier
+    final_lr: eval(final_lr)
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: eval(pruning_epochs)
+    end_epoch: eval(num_epochs)
+
+# Pruning
+pruning_modifiers:
+  - !GlobalMagnitudePruningModifier
+    init_sparsity: 0.05
+    final_sparsity: 0.90
+    start_epoch: 0.0
+    end_epoch: eval(pruning_epochs)
+    update_frequency: 0.5
+    params: 
+        - 'features.conv0.weight'
+        - 're:features.denseblock1.*.conv1.weight'
+        - 're:features.denseblock1.*.conv2.weight'
+        - 're:features.transition1.conv.weight'
+        - 're:features.denseblock2.*.conv1.weight'
+        - 're:features.denseblock2.*.conv2.weight'
+        - 're:features.transition2.conv.weight'
+        - 're:features.denseblock3.*.conv1.weight'
+        - 're:features.denseblock3.*.conv2.weight'
+        - 're:features.transition3.conv.weight'
+        - 're:features.denseblock4.*.conv1.weight'
+        - 're:features.denseblock4.*.conv2.weight'
+    leave_enabled: True
+    inter_func: eval(inter_func)
+    mask_type: eval(mask_type)
+
+finetuning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: eval(pruning_epochs)
+    params: __ALL_PRUNABLE__
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
new file mode 100644
index 00000000000..7636af2000d
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
@@ -0,0 +1,22 @@
+
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.0005
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
new file mode 100644
index 00000000000..0d42bfc46a8
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
@@ -0,0 +1,40 @@
+# Epoch and Learning-Rate variables
+num_epochs: 13.0
+pruning_epochs: 10.0
+init_lr: 0.0005
+inter_func: cubic
+mask_type: unstructured
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Pruning
+pruning_modifiers:
+  - !GlobalMagnitudePruningModifier
+    init_sparsity: 0.05
+    final_sparsity: 0.90
+    start_epoch: 0.0
+    end_epoch: eval(pruning_epochs)
+    update_frequency: 1.0
+    params: 
+        - 'features.0.0.weight'
+        - 'features.18.0.weight'
+        - 're:features.*.conv.*.weight'
+        - 're:features.*.conv.*.*.weight'
+    leave_enabled: True
+    inter_func: eval(inter_func)
+    mask_type: eval(mask_type)
+
+finetuning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: eval(pruning_epochs)
+    params: __ALL_PRUNABLE__
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
new file mode 100644
index 00000000000..7636af2000d
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
@@ -0,0 +1,22 @@
+
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.0005
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
diff --git a/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
new file mode 100644
index 00000000000..549ceeaf098
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
@@ -0,0 +1,46 @@
+# Epoch and Learning-Rate variables
+num_epochs: 10.0
+init_lr: 0.00025
+inter_func: cubic
+mask_type: unstructured
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Pruning
+pruning_modifiers:
+   - !GlobalMagnitudePruningModifier
+     init_sparsity: 0.05
+     final_sparsity: 0.90
+     start_epoch: 0.0
+     end_epoch: eval(num_epochs)
+     update_frequency: 1.0
+     params: 
+        - 're:layer1.*.conv1.weight'
+        - 're:layer1.*.conv2.weight'
+        - 're:layer1.*.conv3.weight'
+        - 're:layer1.0.downsample.0.weight'
+        - 're:layer2.*.conv1.weight'
+        - 're:layer2.*.conv2.weight'
+        - 're:layer2.*.conv3.weight'
+        - 're:layer2.0.downsample.0.weight'
+        - 're:layer3.*.conv1.weight'
+        - 're:layer3.*.conv2.weight'
+        - 're:layer3.*.conv3.weight'
+        - 're:layer3.0.downsample.0.weight'
+        - 're:layer4.*.conv1.weight'
+        - 're:layer4.*.conv2.weight'
+        - 're:layer4.*.conv3.weight'
+        - 're:layer4.0.downsample.0.weight'
+     leave_enabled: True
+     inter_func: eval(inter_func)
+     mask_type: eval(mask_type)

From cccaacce60a1396def86ce6a891e22e7b7120b63 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 16:09:07 +0000
Subject: [PATCH 10/29] finished densenet example

---
 ...arsify-from-scratch-densenet-flowers.ipynb | 2035 +++++------------
 .../images/densenet-tensorboard-output.png    |  Bin 0 -> 477355 bytes
 .../densenet-flowers-pruning-recipe.yaml      |    4 +-
 3 files changed, 632 insertions(+), 1407 deletions(-)
 create mode 100644 integrations/torchvision/tutorials/images/densenet-tensorboard-output.png

diff --git a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
index f999fb24f57..eaa66ddb60c 100644
--- a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
+++ b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
@@ -1,25 +1,50 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "00e081b7",
+   "metadata": {},
+   "source": [
+    "# Sparsifying DenseNet121 from Scratch (Flower102)\n",
+    "\n",
+    "In this example, we will demonstrate how to sparsify an image classification model from scratch using SparseML's PyTorch integration. We train and prune [DenseNet121](https://pytorch.org/vision/main/models/generated/torchvision.models.densenet121.html) on the downstream [Oxford Flower 102 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Flowers102.html#:~:text=Oxford%20102%20Flower%20is%20an,scale%2C%20pose%20and%20light%20variations) using the Global Magnitude Pruning algorithm. \n",
+    "\n",
+    "## Agenda\n",
+    "\n",
+    "There are a few steps:\n",
+    "\n",
+    " 1. Setup the dataset\n",
+    " 2. Setup the PyTorch training loop\n",
+    " 3. Train a dense version of DenseNet121\n",
+    " 4. Run the GMP pruning algorithm on the dense model\n",
+    " \n",
+    "## Installation\n",
+    "\n",
+    "Install SparseML with `pip`:\n",
+    "\n",
+    "```\n",
+    "pip install sparseml[torchvision]\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "1ad80edf",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import torch\n",
     "import sparseml\n",
+    "import torchvision\n",
     "from sparseml.pytorch.optim import ScheduledModifierManager\n",
     "from sparseml.pytorch.utils import TensorBoardLogger, ModuleExporter, get_prunable_layers, tensor_sparsity\n",
-    "from sparseml.pytorch.utils.helpers import get_optim_learning_rate\n",
     "\n",
-    "import torch\n",
     "from torch.utils.data import DataLoader\n",
     "from torch.nn import CrossEntropyLoss\n",
     "from torch.optim import Adam\n",
     "\n",
-    "import torchvision\n",
     "from torchvision import transforms\n",
-    "\n",
     "from tqdm.auto import tqdm\n",
     "import math"
    ]
@@ -29,11 +54,11 @@
    "id": "3a128dcc",
    "metadata": {},
    "source": [
-    "## **Setup Dataset**\n",
+    "## **Step 1: Setup Dataset**\n",
     "\n",
-    "Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.\n",
+    "Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories.\n",
     "\n",
-    "The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories."
+    "We use the standard PyTorch `datasets` and `dataloaders` to manage the dataset."
    ]
   },
   {
@@ -79,7 +104,7 @@
    "id": "890cca4a",
    "metadata": {},
    "source": [
-    "## Setup Training Loop\n",
+    "## Step 2: Setup PyTorch Training Loop\n",
     "\n",
     "We will use this training loop below. This is standard PyTorch functionality."
    ]
@@ -145,552 +170,75 @@
    "id": "b385497a",
    "metadata": {},
    "source": [
-    "## **Part 1: Train DenseNet121 Model as Usual**\n",
+    "## **Part 3: Train DenseNet121 on Flowers102**\n",
     "\n",
-    "We download pretrained DenseNet121 from torchvision, setting it to use 102 classes."
+    "First, we will train a dense version of DenseNet121 on the Flowers dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "id": "4d554578",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DenseNet(\n",
-      "  (features): Sequential(\n",
-      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (relu0): ReLU(inplace=True)\n",
-      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-      "    (denseblock1): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition1): _Transition(\n",
-      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock2): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition2): _Transition(\n",
-      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock3): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer13): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer14): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer15): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer16): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer17): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer18): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer19): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer20): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer21): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer22): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer23): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer24): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition3): _Transition(\n",
-      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock4): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer13): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer14): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer15): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer16): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "  )\n",
-      "  (classifier): Linear(in_features=1024, out_features=102, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# download pre-trained model, setup classification head\n",
     "model = torchvision.models.densenet121(weights=torchvision.models.DenseNet121_Weights.DEFAULT)\n",
     "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
     "model.to(device)\n",
-    "print(model)"
+    "\n",
+    "# setup loss function and optimizer\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3) # lr will be override by sparseml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2161b6c",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "Next, we will use SparseML's recipes to set the hyperparameters of training loop. In this case, we will use the following recipe:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 10.0\n",
+    "init_lr: 0.0005\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "```\n",
+    "\n",
+    "As you can see, the recipe includes an `!EpochRangeModifier` and a `!LearningRateFunctionModifier`. These modifiers simply set the number of epochs to train for and the learning rate schedule. As a result, the final model will be dense."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "b0a623e1",
+   "execution_count": 5,
+   "id": "c1ad9112",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
+    "dense_recipe_path = \"./recipes/densenet-flowers-dense-recipe.yaml\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "d4553beb",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 6,
+   "id": "b4b0f03b",
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -716,28 +264,27 @@
     }
    ],
    "source": [
-    "!cat ./dense_model/dense-recipe.yaml"
+    "!cat ./recipes/densenet-flowers-dense-recipe.yaml"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5e132ebe",
+   "id": "11d180fe",
    "metadata": {},
    "source": [
-    "Update the Optimizer and Model with the logic from the recipe."
+    "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "id": "1749e00d",
    "metadata": {},
    "outputs": [],
    "source": [
     "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
-    "logger = TensorBoardLogger(log_path=\"./dense_model/training/tensorboard_outputs\")\n",
-    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+    "manager = ScheduledModifierManager.from_yaml(dense_recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
    ]
   },
   {
@@ -745,15 +292,15 @@
    "id": "ee67c917",
    "metadata": {},
    "source": [
-    "Kick off the transfer learning loop. We fine-tune onto the Flowers dataset, reaching 91.3% validation accuracy after 10 epochs."
+    "Kick off the transfer learning loop. Our run reached ~92.5% validation accuracy after 10 epochs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "id": "d00d175b",
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -766,7 +313,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c02f2af1dc1c465b870d978a25e456e9",
+       "model_id": "fb1f4f772bc540b0b3725dddfa1b0f49",
        "version_major": 2,
        "version_minor": 0
       },
@@ -782,8 +329,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 3.618388310074806\n",
-      "Top 1 Acc: 0.3\n",
+      "Training Loss: 3.6993999630212784\n",
+      "Top 1 Acc: 0.2823529411764706\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -791,7 +338,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b464cab80ed64170a8d853500e7c7a5c",
+       "model_id": "4865f146cabb4142b9eb8b7a6a7c59f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -807,8 +354,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 2.134434787556529\n",
-      "Top 1 Acc: 0.6274509803921569\n",
+      "Val Loss: 2.32899521663785\n",
+      "Top 1 Acc: 0.6235294117647059\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -816,7 +363,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "950e5fda34bf465a94f6ce90474eb17a",
+       "model_id": "d973db1bca35479c8e5482dc2a8b45e9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -832,8 +379,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 1.3103822488337755\n",
-      "Top 1 Acc: 0.9019607843137255\n",
+      "Training Loss: 1.3755246959626675\n",
+      "Top 1 Acc: 0.8823529411764706\n",
       "\n",
       "Running Validation Epoch 2/10\n"
      ]
@@ -841,7 +388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e97bc059e155478d8f9c815740be5d96",
+       "model_id": "2a896b5e2fe64e0f9353c2c0bb3ab2dc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -857,8 +404,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 1.1688878536224365\n",
-      "Top 1 Acc: 0.8598039215686275\n",
+      "Val Loss: 1.0808461774140596\n",
+      "Top 1 Acc: 0.85\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -866,7 +413,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "752e6dec2aa94f2984e8930f57cfc127",
+       "model_id": "bdd04f003b34476696b88bdcf3ffb8e8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -882,8 +429,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.3958540456369519\n",
-      "Top 1 Acc: 0.9931372549019608\n",
+      "Training Loss: 0.4065576898865402\n",
+      "Top 1 Acc: 0.9882352941176471\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -891,7 +438,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bde611c736264aa4b09225ce3ad1b1d3",
+       "model_id": "2dffd5826f924b0eb7bca6039f439d83",
        "version_major": 2,
        "version_minor": 0
       },
@@ -907,8 +454,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.7494302792474627\n",
-      "Top 1 Acc: 0.8813725490196078\n",
+      "Val Loss: 0.7663819249719381\n",
+      "Top 1 Acc: 0.8823529411764706\n",
       "\n",
       "Running Training Epoch 4/10\n"
      ]
@@ -916,7 +463,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41cda43bfcf649c99acaff23df2b8477",
+       "model_id": "1ec7462e075e446185eddbd2537748e3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -932,8 +479,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.12264440068975091\n",
-      "Top 1 Acc: 0.9990196078431373\n",
+      "Training Loss: 0.13117457507178187\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -941,7 +488,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "09dd43adbdfd433584e1ae345574a87a",
+       "model_id": "5ee0722460fb4916ae605a705703df8c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -957,8 +504,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.6009533200412989\n",
-      "Top 1 Acc: 0.9068627450980392\n",
+      "Val Loss: 0.5690533611923456\n",
+      "Top 1 Acc: 0.9127450980392157\n",
       "\n",
       "Running Training Epoch 5/10\n"
      ]
@@ -966,7 +513,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d70b0a06499844f0bd939aa8537dd8c1",
+       "model_id": "520064cc7f9a486391e6c8cf34dcc46f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -982,7 +529,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.06395778199657798\n",
+      "Training Loss: 0.05944129719864577\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 5/10\n"
@@ -991,7 +538,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dea7eca69ba24561b550f0f767eb6042",
+       "model_id": "56b6421ee84d48d7ab9411a7fea5cae0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1007,8 +554,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.5246470430865884\n",
-      "Top 1 Acc: 0.9166666666666666\n",
+      "Val Loss: 0.4922205451875925\n",
+      "Top 1 Acc: 0.9147058823529411\n",
       "\n",
       "Running Training Epoch 6/10\n"
      ]
@@ -1016,7 +563,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "065b397dfa644e2c8ea9a6443299b3df",
+       "model_id": "9b3c98c1b78b406e8e61ef9b3c27ad3f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1032,7 +579,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.041063663142267615\n",
+      "Training Loss: 0.04032939835451543\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 6/10\n"
@@ -1041,7 +588,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0f04580fc3d44b98caf400499b7698b",
+       "model_id": "0b7d5076d9fe4f91aaf6290e20ed73c0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1057,8 +604,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.5043302299454808\n",
-      "Top 1 Acc: 0.9156862745098039\n",
+      "Val Loss: 0.47672522626817226\n",
+      "Top 1 Acc: 0.9205882352941176\n",
       "\n",
       "Running Training Epoch 7/10\n"
      ]
@@ -1066,7 +613,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "140c927f45994de8b5c947c18b7ce4b8",
+       "model_id": "b43f39481dd94010b65d8a4bfa2c82bd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1082,7 +629,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.03354773804312572\n",
+      "Training Loss: 0.03349028015509248\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 7/10\n"
@@ -1091,7 +638,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "41fb0bf664aa4812a841a7c16a9d8a8e",
+       "model_id": "e31258f33c4b439b838a723a0b9e7787",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1107,8 +654,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.490260950056836\n",
-      "Top 1 Acc: 0.9166666666666666\n",
+      "Val Loss: 0.4664669381454587\n",
+      "Top 1 Acc: 0.9176470588235294\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -1116,7 +663,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e4c73a393ba74093aef7479554cbf926",
+       "model_id": "45670df899134e2688a5fae7778bb392",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1132,7 +679,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.03196021361509338\n",
+      "Training Loss: 0.03036679228534922\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 8/10\n"
@@ -1141,7 +688,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "357e2a67205743f89e0c8fb037ea3708",
+       "model_id": "7a3a457626624ce587eac6b63928b80a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1157,8 +704,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.4794748453423381\n",
-      "Top 1 Acc: 0.9156862745098039\n",
+      "Val Loss: 0.45540827978402376\n",
+      "Top 1 Acc: 0.9264705882352942\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -1166,7 +713,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b8b2118712a482f8828d5705d77e867",
+       "model_id": "8a396321f97e4010aa079b7a2954d1d3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1182,7 +729,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.027999908081255853\n",
+      "Training Loss: 0.028963472577743232\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
@@ -1191,7 +738,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e76c672c7cd84a1e8971a34b91dd8548",
+       "model_id": "51950b565767410f8ef9cb052fcb6fcb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1207,8 +754,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.4790585918817669\n",
-      "Top 1 Acc: 0.9137254901960784\n",
+      "Val Loss: 0.4513796488754451\n",
+      "Top 1 Acc: 0.9235294117647059\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -1216,7 +763,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cfc3d87fa604e80aea4483bf58da091",
+       "model_id": "1085b12cebf24a2a8e7f69e3d13c9991",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1232,7 +779,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.027712288312613964\n",
+      "Training Loss: 0.027131778770126402\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 10/10\n"
@@ -1241,7 +788,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c3894447ffec41518ccf903fce69e4b5",
+       "model_id": "aeb6b899572c49dab5cd0aed15ede593",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1257,8 +804,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.4804669988807291\n",
-      "Top 1 Acc: 0.9156862745098039\n",
+      "Val Loss: 0.45484112948179245\n",
+      "Top 1 Acc: 0.9245098039215687\n",
       "\n"
      ]
     }
@@ -1277,12 +824,8 @@
     "    print(f\"Running Validation Epoch {epoch_name}\")\n",
     "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
     "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
-    "    \n",
-    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
     "\n",
+    "# clean up\n",
     "manager.finalize(model)"
    ]
   },
@@ -1296,19 +839,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "11fa4cf2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "save_dir = \"dense_model\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
@@ -1317,32 +851,33 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "8a7d6309",
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4532442e",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Part 2: Prune The Model\n",
-    "\n",
-    "We load the model trained in the prior step as the starting point."
+    "torch.cuda.empty_cache()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4532442e",
+   "cell_type": "markdown",
+   "id": "8a7d6309",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "torch.cuda.empty_cache()"
+    "## Part 4: Prune The Model\n",
+    "\n",
+    "With a model trained on Flowers, we are now ready to apply the GMP algorithm to prune the model. The GMP algorithm is an interative pruning algorithm. At the end of each epoch, we identify the lowest magnitude weights (those closest to 0) and remove them from the network starting from an initial level of sparsity until a final level of sparsity. The remaining nonzero weights are then fine-tuned onto training dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 45,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# first, load the trained model from Part 3\n",
     "checkpoint = torch.load(\"./dense_model/training/densenet-121-dense-flowers.pth\")\n",
     "model = torchvision.models.densenet121()\n",
     "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
@@ -1354,620 +889,290 @@
     "optimizer = Adam(model.parameters(), lr=8e-3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7a05d884",
+   "metadata": {},
+   "source": [
+    "Next, we need to create a SparseML recipe which includes the GMP algorithm. The `!GlobalMagnitudePruningModifier` modifier instructs SparseML to apply the GMP algorithm at a global level (pruning the lowest magnitude weights across all layers).\n",
+    "\n",
+    "Firstly, we need to decide identify which parameters of the model to apply the GMP algorithm to. We can use the `get_prunable_layers` function to inspect:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 46,
    "id": "d367905a",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DenseNet(\n",
-      "  (features): Sequential(\n",
-      "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-      "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (relu0): ReLU(inplace=True)\n",
-      "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-      "    (denseblock1): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition1): _Transition(\n",
-      "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock2): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition2): _Transition(\n",
-      "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock3): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer13): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer14): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer15): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer16): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer17): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer18): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer19): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer20): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer21): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer22): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer23): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer24): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (transition3): _Transition(\n",
-      "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
-      "    )\n",
-      "    (denseblock4): _DenseBlock(\n",
-      "      (denselayer1): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer2): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer3): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer4): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer5): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer6): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer7): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer8): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer9): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer10): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer11): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer12): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer13): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer14): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer15): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "      (denselayer16): _DenseLayer(\n",
-      "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu1): ReLU(inplace=True)\n",
-      "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "        (relu2): ReLU(inplace=True)\n",
-      "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      )\n",
-      "    )\n",
-      "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "  )\n",
-      "  (classifier): Linear(in_features=1024, out_features=102, bias=True)\n",
-      ")\n"
+      "features.conv0\n",
+      "features.denseblock1.denselayer1.conv1\n",
+      "features.denseblock1.denselayer1.conv2\n",
+      "features.denseblock1.denselayer2.conv1\n",
+      "features.denseblock1.denselayer2.conv2\n",
+      "features.denseblock1.denselayer3.conv1\n",
+      "features.denseblock1.denselayer3.conv2\n",
+      "features.denseblock1.denselayer4.conv1\n",
+      "features.denseblock1.denselayer4.conv2\n",
+      "features.denseblock1.denselayer5.conv1\n",
+      "features.denseblock1.denselayer5.conv2\n",
+      "features.denseblock1.denselayer6.conv1\n",
+      "features.denseblock1.denselayer6.conv2\n",
+      "features.transition1.conv\n",
+      "features.denseblock2.denselayer1.conv1\n",
+      "features.denseblock2.denselayer1.conv2\n",
+      "features.denseblock2.denselayer2.conv1\n",
+      "features.denseblock2.denselayer2.conv2\n",
+      "features.denseblock2.denselayer3.conv1\n",
+      "features.denseblock2.denselayer3.conv2\n",
+      "features.denseblock2.denselayer4.conv1\n",
+      "features.denseblock2.denselayer4.conv2\n",
+      "features.denseblock2.denselayer5.conv1\n",
+      "features.denseblock2.denselayer5.conv2\n",
+      "features.denseblock2.denselayer6.conv1\n",
+      "features.denseblock2.denselayer6.conv2\n",
+      "features.denseblock2.denselayer7.conv1\n",
+      "features.denseblock2.denselayer7.conv2\n",
+      "features.denseblock2.denselayer8.conv1\n",
+      "features.denseblock2.denselayer8.conv2\n",
+      "features.denseblock2.denselayer9.conv1\n",
+      "features.denseblock2.denselayer9.conv2\n",
+      "features.denseblock2.denselayer10.conv1\n",
+      "features.denseblock2.denselayer10.conv2\n",
+      "features.denseblock2.denselayer11.conv1\n",
+      "features.denseblock2.denselayer11.conv2\n",
+      "features.denseblock2.denselayer12.conv1\n",
+      "features.denseblock2.denselayer12.conv2\n",
+      "features.transition2.conv\n",
+      "features.denseblock3.denselayer1.conv1\n",
+      "features.denseblock3.denselayer1.conv2\n",
+      "features.denseblock3.denselayer2.conv1\n",
+      "features.denseblock3.denselayer2.conv2\n",
+      "features.denseblock3.denselayer3.conv1\n",
+      "features.denseblock3.denselayer3.conv2\n",
+      "features.denseblock3.denselayer4.conv1\n",
+      "features.denseblock3.denselayer4.conv2\n",
+      "features.denseblock3.denselayer5.conv1\n",
+      "features.denseblock3.denselayer5.conv2\n",
+      "features.denseblock3.denselayer6.conv1\n",
+      "features.denseblock3.denselayer6.conv2\n",
+      "features.denseblock3.denselayer7.conv1\n",
+      "features.denseblock3.denselayer7.conv2\n",
+      "features.denseblock3.denselayer8.conv1\n",
+      "features.denseblock3.denselayer8.conv2\n",
+      "features.denseblock3.denselayer9.conv1\n",
+      "features.denseblock3.denselayer9.conv2\n",
+      "features.denseblock3.denselayer10.conv1\n",
+      "features.denseblock3.denselayer10.conv2\n",
+      "features.denseblock3.denselayer11.conv1\n",
+      "features.denseblock3.denselayer11.conv2\n",
+      "features.denseblock3.denselayer12.conv1\n",
+      "features.denseblock3.denselayer12.conv2\n",
+      "features.denseblock3.denselayer13.conv1\n",
+      "features.denseblock3.denselayer13.conv2\n",
+      "features.denseblock3.denselayer14.conv1\n",
+      "features.denseblock3.denselayer14.conv2\n",
+      "features.denseblock3.denselayer15.conv1\n",
+      "features.denseblock3.denselayer15.conv2\n",
+      "features.denseblock3.denselayer16.conv1\n",
+      "features.denseblock3.denselayer16.conv2\n",
+      "features.denseblock3.denselayer17.conv1\n",
+      "features.denseblock3.denselayer17.conv2\n",
+      "features.denseblock3.denselayer18.conv1\n",
+      "features.denseblock3.denselayer18.conv2\n",
+      "features.denseblock3.denselayer19.conv1\n",
+      "features.denseblock3.denselayer19.conv2\n",
+      "features.denseblock3.denselayer20.conv1\n",
+      "features.denseblock3.denselayer20.conv2\n",
+      "features.denseblock3.denselayer21.conv1\n",
+      "features.denseblock3.denselayer21.conv2\n",
+      "features.denseblock3.denselayer22.conv1\n",
+      "features.denseblock3.denselayer22.conv2\n",
+      "features.denseblock3.denselayer23.conv1\n",
+      "features.denseblock3.denselayer23.conv2\n",
+      "features.denseblock3.denselayer24.conv1\n",
+      "features.denseblock3.denselayer24.conv2\n",
+      "features.transition3.conv\n",
+      "features.denseblock4.denselayer1.conv1\n",
+      "features.denseblock4.denselayer1.conv2\n",
+      "features.denseblock4.denselayer2.conv1\n",
+      "features.denseblock4.denselayer2.conv2\n",
+      "features.denseblock4.denselayer3.conv1\n",
+      "features.denseblock4.denselayer3.conv2\n",
+      "features.denseblock4.denselayer4.conv1\n",
+      "features.denseblock4.denselayer4.conv2\n",
+      "features.denseblock4.denselayer5.conv1\n",
+      "features.denseblock4.denselayer5.conv2\n",
+      "features.denseblock4.denselayer6.conv1\n",
+      "features.denseblock4.denselayer6.conv2\n",
+      "features.denseblock4.denselayer7.conv1\n",
+      "features.denseblock4.denselayer7.conv2\n",
+      "features.denseblock4.denselayer8.conv1\n",
+      "features.denseblock4.denselayer8.conv2\n",
+      "features.denseblock4.denselayer9.conv1\n",
+      "features.denseblock4.denselayer9.conv2\n",
+      "features.denseblock4.denselayer10.conv1\n",
+      "features.denseblock4.denselayer10.conv2\n",
+      "features.denseblock4.denselayer11.conv1\n",
+      "features.denseblock4.denselayer11.conv2\n",
+      "features.denseblock4.denselayer12.conv1\n",
+      "features.denseblock4.denselayer12.conv2\n",
+      "features.denseblock4.denselayer13.conv1\n",
+      "features.denseblock4.denselayer13.conv2\n",
+      "features.denseblock4.denselayer14.conv1\n",
+      "features.denseblock4.denselayer14.conv2\n",
+      "features.denseblock4.denselayer15.conv1\n",
+      "features.denseblock4.denselayer15.conv2\n",
+      "features.denseblock4.denselayer16.conv1\n",
+      "features.denseblock4.denselayer16.conv2\n",
+      "classifier\n"
      ]
     }
    ],
    "source": [
-    "print(model)"
+    "# print parameters\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7a05d884",
+   "id": "e037a028",
    "metadata": {},
    "source": [
-    "We create a pruning recipe. We will start with the `GlobalMagnitudePruning` algorithm, pruning layers on average to 90%. We target all of the Conv layers (per the regexes in the `params` dictionary below) - which tie to the layer names above. We will run GMP for 10 epochs and then fine-tune for the final 3 epochs."
+    "We will apply pruning to each of the `convs` and exclude the `classifier` (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
+    "\n",
+    "    - 'features.conv0.weight'\n",
+    "    - 're:features.denseblock1.*.conv1.weight'\n",
+    "    - 're:features.denseblock1.*.conv2.weight'\n",
+    "    - 're:features.transition1.conv.weight'\n",
+    "    - 're:features.denseblock2.*.conv1.weight'\n",
+    "    - 're:features.denseblock2.*.conv2.weight'\n",
+    "    - 're:features.transition2.conv.weight'\n",
+    "    - 're:features.denseblock3.*.conv1.weight'\n",
+    "    - 're:features.denseblock3.*.conv2.weight'\n",
+    "    - 're:features.transition3.conv.weight'\n",
+    "    - 're:features.denseblock4.*.conv1.weight'\n",
+    "    - 're:features.denseblock4.*.conv2.weight'\n",
+    "\n",
+    "Here is what the recipe looks like:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 13.0\n",
+    "pruning_epochs: 10.0\n",
+    "init_lr: 0.00025\n",
+    "final_lr: 0.0001\n",
+    "inter_func: cubic\n",
+    "mask_type: unstructured\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: eval(final_lr)\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(pruning_epochs)\n",
+    "    \n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: eval(final_lr)\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: eval(pruning_epochs)\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "# Pruning\n",
+    "pruning_modifiers:\n",
+    "  - !GlobalMagnitudePruningModifier\n",
+    "    init_sparsity: 0.05\n",
+    "    final_sparsity: 0.85\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(pruning_epochs)\n",
+    "    update_frequency: 0.5\n",
+    "    params: \n",
+    "        - 'features.conv0.weight'\n",
+    "        - 're:features.denseblock1.*.conv1.weight'\n",
+    "        - 're:features.denseblock1.*.conv2.weight'\n",
+    "        - 're:features.transition1.conv.weight'\n",
+    "        - 're:features.denseblock2.*.conv1.weight'\n",
+    "        - 're:features.denseblock2.*.conv2.weight'\n",
+    "        - 're:features.transition2.conv.weight'\n",
+    "        - 're:features.denseblock3.*.conv1.weight'\n",
+    "        - 're:features.denseblock3.*.conv2.weight'\n",
+    "        - 're:features.transition3.conv.weight'\n",
+    "        - 're:features.denseblock4.*.conv1.weight'\n",
+    "        - 're:features.denseblock4.*.conv2.weight'\n",
+    "    leave_enabled: True\n",
+    "    inter_func: eval(inter_func)\n",
+    "    mask_type: eval(mask_type)\n",
+    "```\n",
+    "\n",
+    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an `init_sparsity` level of 5% and gradually increase sparsity to a `final_sparsity` level of 85% following a `cubic` curve. The pruning is applied in an `unstructured` manner, meaning that any weight can be pruned.\n",
+    "\n",
+    "Over the final 3 epochs, we will fine-tune the 85% pruned model further. Since we set `leave_enabled=True` the sparsity level will be maintained as the fine-tuning occurs.\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "9b17fc31",
+   "execution_count": 47,
+   "id": "b2add8fc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 13.0\r\n",
-      "pruning_epochs: 10.0\r\n",
-      "init_lr: 0.0003\r\n",
-      "final_lr: 0.0001\r\n",
-      "inter_func: cubic\r\n",
-      "mask_type: unstructured\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: eval(final_lr)\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(pruning_epochs)\r\n",
-      "    \r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: eval(final_lr)\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: eval(pruning_epochs)\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "# Pruning\r\n",
-      "pruning_modifiers:\r\n",
-      "  - !GlobalMagnitudePruningModifier\r\n",
-      "    init_sparsity: 0.05\r\n",
-      "    final_sparsity: 0.90\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(pruning_epochs)\r\n",
-      "    update_frequency: 0.5\r\n",
-      "    params: \r\n",
-      "        - 'features.conv0.weight'\r\n",
-      "        - 're:features.denseblock1.*.conv1.weight'\r\n",
-      "        - 're:features.denseblock1.*.conv2.weight'\r\n",
-      "        - 're:features.transition1.conv.weight'\r\n",
-      "        - 're:features.denseblock2.*.conv1.weight'\r\n",
-      "        - 're:features.denseblock2.*.conv2.weight'\r\n",
-      "        - 're:features.transition2.conv.weight'\r\n",
-      "        - 're:features.denseblock3.*.conv1.weight'\r\n",
-      "        - 're:features.denseblock3.*.conv2.weight'\r\n",
-      "        - 're:features.transition3.conv.weight'\r\n",
-      "        - 're:features.denseblock4.*.conv1.weight'\r\n",
-      "        - 're:features.denseblock4.*.conv2.weight'\r\n",
-      "    leave_enabled: True\r\n",
-      "    inter_func: eval(inter_func)\r\n",
-      "    mask_type: eval(mask_type)\r\n",
-      "\r\n",
-      "finetuning_modifiers:\r\n",
-      "  - !ConstantPruningModifier\r\n",
-      "    start_epoch: eval(pruning_epochs)\r\n",
-      "    params: __ALL_PRUNABLE__"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!cat recipe-0.yaml"
+    "pruning_recipe_path = \"./recipes/densenet-flowers-pruning-recipe.yaml\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "id": "9b17fc31",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "!cat ./recipes/densenet-flowers-pruning-recipe.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
    "id": "68646bc7",
    "metadata": {},
    "outputs": [],
    "source": [
     "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
+    "manager = ScheduledModifierManager.from_yaml(pruning_recipe_path)\n",
     "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs\")\n",
     "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7c4ba042",
+   "metadata": {},
+   "source": [
+    "Next, kick off the GMP training loop. \n",
+    "\n",
+    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to just the `optimizer` and `model` as usual, with all of the pruning-related logic specified by the declarative recipe interface.\n",
+    "\n",
+    "Our 85% sparsified model reaches ~91.5% validation accuracy (vs ~92.5% for the dense model)."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 50,
    "id": "601c8c21",
    "metadata": {
     "scrolled": false
@@ -1983,7 +1188,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e512276a1b94d328a06e1c5d0497ad9",
+       "model_id": "181013fb564f45828d9cce72589b3846",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1999,8 +1204,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/13\n",
-      "Training Loss: 0.05704788229195401\n",
-      "Top 1 Acc: 0.9970588235294118\n",
+      "Training Loss: 0.043860748992301524\n",
+      "Top 1 Acc: 0.9990196078431373\n",
       "\n",
       "Running Validation Epoch 1/13\n"
      ]
@@ -2008,7 +1213,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a6610db1025a4e408e01777550481193",
+       "model_id": "a8cdead2769d4377b6cf53fc91028e3f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2024,8 +1229,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/13\n",
-      "Val Loss: 0.5254904199391603\n",
-      "Top 1 Acc: 0.8970588235294118\n",
+      "Val Loss: 0.49546412169001997\n",
+      "Top 1 Acc: 0.8950980392156863\n",
       "\n",
       "Running Training Epoch 2/13\n"
      ]
@@ -2033,7 +1238,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddead9b1388b47e7856a34c7e38aad28",
+       "model_id": "a92e647b8b6e413284659d33dbb5bf9e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2049,8 +1254,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/13\n",
-      "Training Loss: 0.045509724208386615\n",
-      "Top 1 Acc: 0.996078431372549\n",
+      "Training Loss: 0.031713149510324\n",
+      "Top 1 Acc: 0.9980392156862745\n",
       "\n",
       "Running Validation Epoch 2/13\n"
      ]
@@ -2058,7 +1263,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0137f5c63a3f4a749078a913f0cabcee",
+       "model_id": "e8af1f0eb7514408a36fad527f4119c5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2074,8 +1279,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/13\n",
-      "Val Loss: 0.49433585489168763\n",
-      "Top 1 Acc: 0.888235294117647\n",
+      "Val Loss: 0.380035838810727\n",
+      "Top 1 Acc: 0.9117647058823529\n",
       "\n",
       "Running Training Epoch 3/13\n"
      ]
@@ -2083,7 +1288,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ca13cec88da1496e95c05201d51527b4",
+       "model_id": "cadd8b75f6a3468487921687fb7a1902",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2099,8 +1304,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/13\n",
-      "Training Loss: 0.05249260214623064\n",
-      "Top 1 Acc: 0.9941176470588236\n",
+      "Training Loss: 0.016048584191594273\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 3/13\n"
      ]
@@ -2108,7 +1313,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9be1acbb68544497b2292273cfaa14e1",
+       "model_id": "b23ad27123a84b9ab3e9d37bcfd94fc2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2124,8 +1329,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/13\n",
-      "Val Loss: 0.4971598202828318\n",
-      "Top 1 Acc: 0.8921568627450981\n",
+      "Val Loss: 0.3518236926756799\n",
+      "Top 1 Acc: 0.9156862745098039\n",
       "\n",
       "Running Training Epoch 4/13\n"
      ]
@@ -2133,7 +1338,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1bd3b6304db3419c95a294acd40659c2",
+       "model_id": "dd5bbfdb2df24c67b036c84f67258727",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2149,8 +1354,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/13\n",
-      "Training Loss: 0.04789798497222364\n",
-      "Top 1 Acc: 0.9970588235294118\n",
+      "Training Loss: 0.013376500370213762\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 4/13\n"
      ]
@@ -2158,7 +1363,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c961395abf764ec095859a79fce2bb8b",
+       "model_id": "c80d92282c594060bc65c655b0db0184",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2174,7 +1379,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/13\n",
-      "Val Loss: 0.405253738630563\n",
+      "Val Loss: 0.37410336383618414\n",
       "Top 1 Acc: 0.9098039215686274\n",
       "\n",
       "Running Training Epoch 5/13\n"
@@ -2183,7 +1388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "761637d2e00344fab4bda339bc4a0210",
+       "model_id": "4352b279fc0546458ce3e0717f41b051",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2199,7 +1404,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/13\n",
-      "Training Loss: 0.03558145748684183\n",
+      "Training Loss: 0.01488300270284526\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 5/13\n"
@@ -2208,7 +1413,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d7b4d1526c547e282f9a75f58ca9cf1",
+       "model_id": "d0e7239ec24f4a5cbaf9d38b942358b8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2224,8 +1429,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/13\n",
-      "Val Loss: 0.40998171758838\n",
-      "Top 1 Acc: 0.9098039215686274\n",
+      "Val Loss: 0.3517874537501484\n",
+      "Top 1 Acc: 0.9156862745098039\n",
       "\n",
       "Running Training Epoch 6/13\n"
      ]
@@ -2233,7 +1438,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "390a8c78d2d2414fa9d09025917048e4",
+       "model_id": "1448047bbf7f41fdbf84106aff303104",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2249,7 +1454,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/13\n",
-      "Training Loss: 0.043919957941398025\n",
+      "Training Loss: 0.020687898708274588\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 6/13\n"
@@ -2258,7 +1463,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0a209b2b76c845428e21b54bb3a6a507",
+       "model_id": "9efe9031bf4b4ebbae2d992dc3266230",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2274,8 +1479,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/13\n",
-      "Val Loss: 0.44851411995477974\n",
-      "Top 1 Acc: 0.9009803921568628\n",
+      "Val Loss: 0.38692406262271106\n",
+      "Top 1 Acc: 0.9137254901960784\n",
       "\n",
       "Running Training Epoch 7/13\n"
      ]
@@ -2283,7 +1488,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f661fa461e094e55bc9cffb2d7b343c5",
+       "model_id": "d27c0cbd9d6e4e02b44043050c58c2c1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2299,7 +1504,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/13\n",
-      "Training Loss: 0.06865048292092979\n",
+      "Training Loss: 0.029207701765699312\n",
       "Top 1 Acc: 0.9990196078431373\n",
       "\n",
       "Running Validation Epoch 7/13\n"
@@ -2308,7 +1513,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a81beb9cb8b1419da298f072304c8ae7",
+       "model_id": "f1752d47a0c348999b1ab92624ebca01",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2324,8 +1529,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/13\n",
-      "Val Loss: 0.500796954613179\n",
-      "Top 1 Acc: 0.8813725490196078\n",
+      "Val Loss: 0.4347131696995348\n",
+      "Top 1 Acc: 0.8990196078431373\n",
       "\n",
       "Running Training Epoch 8/13\n"
      ]
@@ -2333,7 +1538,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b00986f389b6483c972f952b56cbe06e",
+       "model_id": "e764a1922b0740488b5ceb97a636f5fa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2349,7 +1554,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/13\n",
-      "Training Loss: 0.08089102717349306\n",
+      "Training Loss: 0.05598480207845569\n",
       "Top 1 Acc: 0.9950980392156863\n",
       "\n",
       "Running Validation Epoch 8/13\n"
@@ -2358,7 +1563,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f8dcafdd8604d0faa7b64c5f5e79913",
+       "model_id": "e28f85a917a94204b50687e4397313ad",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2374,8 +1579,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/13\n",
-      "Val Loss: 0.4999986342154443\n",
-      "Top 1 Acc: 0.8911764705882353\n",
+      "Val Loss: 0.5028206340502948\n",
+      "Top 1 Acc: 0.8813725490196078\n",
       "\n",
       "Running Training Epoch 9/13\n"
      ]
@@ -2383,7 +1588,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "72813826e9b44537bc5a538c8bab0d0e",
+       "model_id": "5f340b8a413e417ca9bad5a3201dd55c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2399,7 +1604,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/13\n",
-      "Training Loss: 0.05980767833534628\n",
+      "Training Loss: 0.030482763570034876\n",
       "Top 1 Acc: 0.9990196078431373\n",
       "\n",
       "Running Validation Epoch 9/13\n"
@@ -2408,7 +1613,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3271f7a2f30246ccb4d0f7d52a5c88e5",
+       "model_id": "cd02f0a64301403bb37da14323dd4284",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2424,8 +1629,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/13\n",
-      "Val Loss: 0.4825127385556698\n",
-      "Top 1 Acc: 0.8960784313725491\n",
+      "Val Loss: 0.4592747155111283\n",
+      "Top 1 Acc: 0.8941176470588236\n",
       "\n",
       "Running Training Epoch 10/13\n"
      ]
@@ -2433,7 +1638,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "484ed067c6494cd2aaa6efebbc429ed1",
+       "model_id": "2ddef4c5bcba426784e011fc5a8659e5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2449,8 +1654,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/13\n",
-      "Training Loss: 0.029828229744452983\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.017036149831255898\n",
+      "Top 1 Acc: 0.9990196078431373\n",
       "\n",
       "Running Validation Epoch 10/13\n"
      ]
@@ -2458,7 +1663,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "494926a49a0d43ddb0388a178d648048",
+       "model_id": "4f8d775853324429b9f7061478101253",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2474,8 +1679,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/13\n",
-      "Val Loss: 0.45201362296938896\n",
-      "Top 1 Acc: 0.9009803921568628\n",
+      "Val Loss: 0.40747271745931357\n",
+      "Top 1 Acc: 0.9019607843137255\n",
       "\n",
       "Running Training Epoch 11/13\n"
      ]
@@ -2483,7 +1688,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc469c3661154167b2b6a2f8d217f577",
+       "model_id": "2ce09a16874e4e8c94deb40dea6bdb29",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2499,7 +1704,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 11/13\n",
-      "Training Loss: 0.017061863880371675\n",
+      "Training Loss: 0.008050348522374406\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 11/13\n"
@@ -2508,7 +1713,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d740893d1cef427491c9f1b94dfc97cb",
+       "model_id": "d8834eae2a924e6d8f6aa9c713f2cd09",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2524,8 +1729,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 11/13\n",
-      "Val Loss: 0.40311497752554715\n",
-      "Top 1 Acc: 0.9058823529411765\n",
+      "Val Loss: 0.36801655124872923\n",
+      "Top 1 Acc: 0.9068627450980392\n",
       "\n",
       "Running Training Epoch 12/13\n"
      ]
@@ -2533,7 +1738,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6efb89313174a7a8b9509755e7ac5f5",
+       "model_id": "1343cce0b7d9417ca5cf9b788e5bbcf3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2549,7 +1754,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 12/13\n",
-      "Training Loss: 0.01124472642550245\n",
+      "Training Loss: 0.005260431091301143\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 12/13\n"
@@ -2558,7 +1763,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b09c5f91fd804f89bd99e83ac865f23f",
+       "model_id": "5952900528a6494e9025afcce97fcef2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2574,8 +1779,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 12/13\n",
-      "Val Loss: 0.38544022431597114\n",
-      "Top 1 Acc: 0.907843137254902\n",
+      "Val Loss: 0.3571971161291003\n",
+      "Top 1 Acc: 0.9117647058823529\n",
       "\n",
       "Running Training Epoch 13/13\n"
      ]
@@ -2583,7 +1788,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8386e7eca94f4335a00b0f3ae5c21e37",
+       "model_id": "07cb82ea02634c619c57a823e1d2872e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2599,7 +1804,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 13/13\n",
-      "Training Loss: 0.009475693295826204\n",
+      "Training Loss: 0.004477048831176944\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 13/13\n"
@@ -2608,7 +1813,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80b3b1e8c68446bb94be4cf4d0ff37e3",
+       "model_id": "81030fbb34a04181ae3cb25acc470275",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2624,8 +1829,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 13/13\n",
-      "Val Loss: 0.38191458326764405\n",
-      "Top 1 Acc: 0.907843137254902\n",
+      "Val Loss: 0.3466116476338357\n",
+      "Top 1 Acc: 0.9137254901960784\n",
       "\n"
      ]
     }
@@ -2653,9 +1858,28 @@
     "manager.finalize(model)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "83dff592",
+   "metadata": {},
+   "source": [
+    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
+    "\n",
+    "![tensorboard output](./images/densenet-tensorboard-output.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9dea1bd",
+   "metadata": {},
+   "source": [
+    "We can print layer-by-layer sparsity as well."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 55,
    "id": "df5afa94",
    "metadata": {
     "scrolled": false
@@ -2665,157 +1889,154 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Checking Sparsity Level:\n",
-      "features.conv0.weight: 0.4670\n",
-      "features.denseblock1.denselayer1.conv1.weight: 0.7366\n",
-      "features.denseblock1.denselayer1.conv2.weight: 0.8294\n",
-      "features.denseblock1.denselayer2.conv1.weight: 0.7461\n",
-      "features.denseblock1.denselayer2.conv2.weight: 0.8658\n",
-      "features.denseblock1.denselayer3.conv1.weight: 0.7488\n",
-      "features.denseblock1.denselayer3.conv2.weight: 0.8242\n",
-      "features.denseblock1.denselayer4.conv1.weight: 0.8116\n",
-      "features.denseblock1.denselayer4.conv2.weight: 0.8263\n",
-      "features.denseblock1.denselayer5.conv1.weight: 0.8702\n",
-      "features.denseblock1.denselayer5.conv2.weight: 0.8846\n",
-      "features.denseblock1.denselayer6.conv1.weight: 0.8377\n",
-      "features.denseblock1.denselayer6.conv2.weight: 0.8414\n",
-      "features.transition1.conv.weight: 0.7090\n",
-      "features.denseblock2.denselayer1.conv1.weight: 0.9321\n",
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+      "features.denseblock4.denselayer12.conv1.weight: 0.8767\n",
+      "features.denseblock4.denselayer12.conv2.weight: 0.9562\n",
+      "features.denseblock4.denselayer13.conv1.weight: 0.8804\n",
+      "features.denseblock4.denselayer13.conv2.weight: 0.9558\n",
+      "features.denseblock4.denselayer14.conv1.weight: 0.8836\n",
+      "features.denseblock4.denselayer14.conv2.weight: 0.9550\n",
+      "features.denseblock4.denselayer15.conv1.weight: 0.8865\n",
+      "features.denseblock4.denselayer15.conv2.weight: 0.9575\n",
+      "features.denseblock4.denselayer16.conv1.weight: 0.8806\n",
+      "features.denseblock4.denselayer16.conv2.weight: 0.9506\n",
       "classifier.weight: 0.0000\n"
      ]
     }
    ],
    "source": [
-    "print(f\"Checking Sparsity Level:\")\n",
     "for (name, layer) in get_prunable_layers(model):\n",
     "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c1522d44",
+   "metadata": {},
+   "source": [
+    "Finally, export your model to ONNX."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 53,
    "id": "417ac105",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "save_dir = \"experiment-0\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"densenet-121-sparse-flowers-experiment-0.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-gmp-model.onnx\", convert_qat=True)"
+    "exporter.export_pytorch(name=\"densenet-121-sparse-flowers.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
    ]
   },
   {
@@ -2823,14 +2044,18 @@
    "id": "22c96232",
    "metadata": {},
    "source": [
-    "The resulting model is is 90% sparse and achieves validation accuracy of 90.3% (vs the unoptimized dense model at 91.3%) without much hyperparameter search,\n",
+    "## Wrap Up\n",
+    "\n",
+    "The resulting model is is 85% sparse and achieves validation accuracy of ~91.5% (vs the unoptimized dense model at ~92.5%) without much hyperparameter search.\n",
     "\n",
     "Key hyperparameter experiments you may want to run include:\n",
     "- Learning rate\n",
     "- Learning rate schedule\n",
     "- Sparsity level\n",
-    "- Skipping layers\n",
-    "- Number of pruning epochs"
+    "- Number of pruning epochs\n",
+    "\n",
+    "\n",
+    "DeepSparse supports speedup from pruning and quantization. To reach maximum performance, check out our examples of quantizing a model!"
    ]
   }
  ],
diff --git a/integrations/torchvision/tutorials/images/densenet-tensorboard-output.png b/integrations/torchvision/tutorials/images/densenet-tensorboard-output.png
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diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
index ec8a3b33b1a..f73c5455c47 100644
--- a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
@@ -1,7 +1,7 @@
 # Epoch and Learning-Rate variables
 num_epochs: 13.0
 pruning_epochs: 10.0
-init_lr: 0.0003
+init_lr: 0.00025
 final_lr: 0.0001
 inter_func: cubic
 mask_type: unstructured
@@ -29,7 +29,7 @@ training_modifiers:
 pruning_modifiers:
   - !GlobalMagnitudePruningModifier
     init_sparsity: 0.05
-    final_sparsity: 0.90
+    final_sparsity: 0.85
     start_epoch: 0.0
     end_epoch: eval(pruning_epochs)
     update_frequency: 0.5

From 4ab73544c4d5aed01b11a12800d4e82ef1590506 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 20:29:13 +0000
Subject: [PATCH 11/29] added all

---
 ...arsify-from-scratch-densenet-flowers.ipynb |   41 +-
 ...-sparsify-from-scatch-resnet50-beans.ipynb | 2233 ++++++----------
 ...rsify-from-scratch-mobilenetv2-beans.ipynb | 2364 ++++-------------
 ...> densenet-flowers-tensorboard-output.png} |  Bin
 .../mobilenetv2-beans-tensorboard-output.png  |  Bin 0 -> 161928 bytes
 .../resnet-beans-tensorboard-output.png       |  Bin 0 -> 102164 bytes
 .../mobilenetv2-beans-dense-recipe.yaml       |    8 +-
 .../mobilenetv2-beans-pruning-recipe.yaml     |    7 +-
 .../recipes/resnet50-beans-dense-recipe.yaml  |    8 +-
 .../resnet50-beans-pruning-recipe.yaml        |    8 +-
 10 files changed, 1360 insertions(+), 3309 deletions(-)
 rename integrations/torchvision/tutorials/images/{densenet-tensorboard-output.png => densenet-flowers-tensorboard-output.png} (100%)
 create mode 100644 integrations/torchvision/tutorials/images/mobilenetv2-beans-tensorboard-output.png
 create mode 100644 integrations/torchvision/tutorials/images/resnet-beans-tensorboard-output.png

diff --git a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
index eaa66ddb60c..171c56d35db 100644
--- a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
+++ b/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
@@ -71,7 +71,7 @@
     "NUM_LABELS = 102\n",
     "BATCH_SIZE = 32\n",
     "\n",
-    "# imagenet transformers\n",
+    "# imagenet transforms\n",
     "imagenet_transform = transforms.Compose([\n",
     "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
     "   transforms.CenterCrop(size=(224, 224)),\n",
@@ -170,7 +170,7 @@
    "id": "b385497a",
    "metadata": {},
    "source": [
-    "## **Part 3: Train DenseNet121 on Flowers102**\n",
+    "## **Step 3: Train DenseNet121 on Flowers102**\n",
     "\n",
     "First, we will train a dense version of DenseNet121 on the Flowers dataset."
    ]
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2161b6c",
+   "id": "eb400dc3",
    "metadata": {
     "scrolled": true
    },
@@ -227,7 +227,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "c1ad9112",
+   "id": "5f6a53f1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +237,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "b4b0f03b",
+   "id": "8653c9bc",
    "metadata": {},
    "outputs": [
     {
@@ -269,7 +269,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11d180fe",
+   "id": "c31aed1b",
    "metadata": {},
    "source": [
     "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
@@ -300,7 +300,7 @@
    "execution_count": 8,
    "id": "d00d175b",
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
@@ -865,7 +865,7 @@
    "id": "8a7d6309",
    "metadata": {},
    "source": [
-    "## Part 4: Prune The Model\n",
+    "## Step 4: Prune The Model\n",
     "\n",
     "With a model trained on Flowers, we are now ready to apply the GMP algorithm to prune the model. The GMP algorithm is an interative pruning algorithm. At the end of each epoch, we identify the lowest magnitude weights (those closest to 0) and remove them from the network starting from an initial level of sparsity until a final level of sparsity. The remaining nonzero weights are then fine-tuned onto training dataset."
    ]
@@ -904,7 +904,7 @@
    "execution_count": 46,
    "id": "d367905a",
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
@@ -1043,7 +1043,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e037a028",
+   "id": "322fa136",
    "metadata": {},
    "source": [
     "We will apply pruning to each of the `convs` and exclude the `classifier` (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
@@ -1126,7 +1126,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "b2add8fc",
+   "id": "b8518b39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1160,7 +1160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c4ba042",
+   "id": "da0a5909",
    "metadata": {},
    "source": [
     "Next, kick off the GMP training loop. \n",
@@ -1175,7 +1175,7 @@
    "execution_count": 50,
    "id": "601c8c21",
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
@@ -1836,6 +1836,7 @@
     }
    ],
    "source": [
+    "# run GMP algorithm\n",
     "epoch = 0\n",
     "for epoch in range(manager.max_epochs):\n",
     "    # run training loop\n",
@@ -1861,19 +1862,13 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "83dff592",
+   "id": "3be34fe5",
    "metadata": {},
    "source": [
     "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
     "\n",
-    "![tensorboard output](./images/densenet-tensorboard-output.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b9dea1bd",
-   "metadata": {},
-   "source": [
+    "![tensorboard output](./images/densenet-flowers-tensorboard-output.png)\n",
+    "\n",
     "We can print layer-by-layer sparsity as well."
    ]
   },
@@ -2020,7 +2015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1522d44",
+   "id": "18fed85b",
    "metadata": {},
    "source": [
     "Finally, export your model to ONNX."
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
index c57b5c7776e..f3726bb5a39 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
@@ -1,251 +1,83 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "00e081b7",
+   "metadata": {},
+   "source": [
+    "# Sparsifying ResNet-50 from Scratch (Beans)\n",
+    "\n",
+    "In this example, we will demonstrate how to sparsify an image classification model from scratch using SparseML's PyTorch integration. We train and prune [ResNet-50](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html) on the downstream [Beans dataset](https://huggingface.co/datasets/beans) using the Global Magnitude Pruning algorithm. \n",
+    "\n",
+    "## Agenda\n",
+    "\n",
+    "There are a few steps:\n",
+    "\n",
+    " 1. Setup the dataset\n",
+    " 2. Setup the PyTorch training loop\n",
+    " 3. Train a dense version of ResNet-50\n",
+    " 4. Run the GMP pruning algorithm on the dense model\n",
+    " \n",
+    "## Installation\n",
+    "\n",
+    "Install SparseML with `pip`:\n",
+    "\n",
+    "```\n",
+    "pip install sparseml[torchvision]\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "1ad80edf",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.12.1+cu116\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
     "import torch\n",
+    "import sparseml\n",
     "import torchvision\n",
-    "print(torch.__version__)"
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "from sparseml.pytorch.utils import TensorBoardLogger, ModuleExporter, get_prunable_layers, tensor_sparsity\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "from torchvision import transforms\n",
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import datasets"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4d554578",
+   "cell_type": "markdown",
+   "id": "c421a838",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ResNet(\n",
-      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "  (relu): ReLU(inplace=True)\n",
-      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-      "  (layer1): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer2): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (3): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer3): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (3): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (4): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (5): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer4): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-      "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
-      ")\n"
-     ]
-    }
-   ],
    "source": [
-    "NUM_LABELS = 3\n",
-    "model = torchvision.models.resnet50(weights=torchvision.models.ResNet50_Weights.DEFAULT)\n",
-    "model.fc = torch.nn.Linear(model.fc.in_features, NUM_LABELS)\n",
-    "print(model)"
+    "## Step 1: Setup Dataset\n",
+    "\n",
+    "Beans leaf dataset is a set of images of diseased and healthy leaves. Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.\n",
+    "\n",
+    "We will use the Hugging Face `datasets` library to download the data and the torchvision `ImageFolder` in the training loop.\n",
+    "\n",
+    "[Checkout the dataset card](https://huggingface.co/datasets/beans)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "3f49303e",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "beans_dataset = datasets.load_dataset(\"beans\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4115c9d8",
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using custom data configuration default\n",
-      "Reusing dataset beans (/home/ubuntu/.cache/huggingface/datasets/beans/default/0.0.0/d5abfbb94de45599d871182cff389bc870bf145b4829c4b0fe20f0cccd637cbd)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "631d7dbbd3b7461da9fdc14f1130f89e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -256,21 +88,32 @@
     }
    ],
    "source": [
-    "import datasets\n",
-    "beans_dataset = datasets.load_dataset(\"beans\")\n",
     "print(beans_dataset[\"train\"][0][\"image_file_path\"])\n",
     "print(beans_dataset[\"validation\"][0][\"image_file_path\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
+   "id": "00aaf463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\"\n",
+    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "7d180bbc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from torchvision import transforms\n",
+    "NUM_LABELS = 3\n",
+    "BATCH_SIZE = 64\n",
     "\n",
+    "# imagenet transforms\n",
     "imagenet_transform = transforms.Compose([\n",
     "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
     "   transforms.CenterCrop(size=(224, 224)),\n",
@@ -278,26 +121,50 @@
     "   transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
     "])\n",
     "\n",
+    "# datasets\n",
     "train_dataset = torchvision.datasets.ImageFolder(\n",
-    "    root=\"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\",\n",
+    "    root=train_path,\n",
     "    transform=imagenet_transform\n",
     ")\n",
     "\n",
     "val_dataset = torchvision.datasets.ImageFolder(\n",
-    "    root=\"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation\",\n",
+    "    root=val_path,\n",
     "    transform=imagenet_transform\n",
-    ")"
+    ")\n",
+    "\n",
+    "\n",
+    "# dataloaders\n",
+    "train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16)\n",
+    "val_loader = DataLoader(val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "132fc514",
+   "metadata": {},
+   "source": [
+    "## Step 2: Setup PyTorch Training Loop\n",
+    "\n",
+    "We will use this training loop below. This is standard PyTorch functionality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "5f1b878a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
    "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(device)\n",
     "\n",
     "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
     "    if train:\n",
@@ -337,83 +204,87 @@
     "    return loss, accuracy"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "21e250c6",
+   "metadata": {},
+   "source": [
+    "## **Step 3: Train ResNet-50 on Beans**\n",
+    "\n",
+    "First, we will train a dense version of ResNet-50 on the Beans dataset."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "b0a623e1",
+   "execution_count": 7,
+   "id": "4d554578",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using device: cuda\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import Adam\n",
+    "# download pre-trained model, setup classification head\n",
+    "model = torchvision.models.resnet50(weights=torchvision.models.ResNet50_Weights.DEFAULT)\n",
+    "model.fc = torch.nn.Linear(model.fc.in_features, NUM_LABELS)\n",
+    "model.to(device)\n",
     "\n",
-    "# hyperparameters\n",
-    "BATCH_SIZE = 64\n",
+    "# setup loss function and optimizer\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3) # lr will be override by sparseml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a82a98",
+   "metadata": {},
+   "source": [
+    "Next, we will use SparseML's recipes to set the hyperparameters of training loop. In this case, we will use the following recipe:\n",
     "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(f\"Using device: {device}\")\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 10.0\n",
+    "init_lr: 0.0005\n",
     "\n",
-    "# setup data loaders\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16\n",
-    ")\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
     "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "```\n",
+    "\n",
+    "As you can see, the recipe includes an `!EpochRangeModifier` and a `!LearningRateFunctionModifier`. These modifiers simply set the number of epochs to train for and the learning rate schedule. As a result, the final model will be dense."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "d4553beb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 10.0\r\n",
-      "init_lr: 0.0005\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: 0.0\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "# Phase 1 Sparse Transfer Learning / Recovery\r\n",
-      "sparse_transfer_learning_modifiers:\r\n",
-      "  - !ConstantPruningModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    params: __ALL_PRUNABLE__\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "!cat ./recipes/resnet50-beans-dense-recipe.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1b19a8d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dense_recipe_path = \"./recipes/resnet50-beans-dense-recipe.yaml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e00a7a9",
+   "metadata": {},
    "source": [
-    "!cat ./dense_model/dense-recipe.yaml"
+    "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
    ]
   },
   {
@@ -423,91 +294,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
-    "\n",
     "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
+    "manager = ScheduledModifierManager.from_yaml(dense_recipe_path)\n",
     "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "1217c3e9",
+   "cell_type": "markdown",
+   "id": "f3b56352",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.0000\n",
-      "layer1.0.conv2.weight: 0.0000\n",
-      "layer1.0.conv3.weight: 0.0000\n",
-      "layer1.0.downsample.0.weight: 0.0000\n",
-      "layer1.1.conv1.weight: 0.0000\n",
-      "layer1.1.conv2.weight: 0.0000\n",
-      "layer1.1.conv3.weight: 0.0000\n",
-      "layer1.2.conv1.weight: 0.0000\n",
-      "layer1.2.conv2.weight: 0.0000\n",
-      "layer1.2.conv3.weight: 0.0000\n",
-      "layer2.0.conv1.weight: 0.0000\n",
-      "layer2.0.conv2.weight: 0.0000\n",
-      "layer2.0.conv3.weight: 0.0000\n",
-      "layer2.0.downsample.0.weight: 0.0000\n",
-      "layer2.1.conv1.weight: 0.0000\n",
-      "layer2.1.conv2.weight: 0.0000\n",
-      "layer2.1.conv3.weight: 0.0000\n",
-      "layer2.2.conv1.weight: 0.0000\n",
-      "layer2.2.conv2.weight: 0.0000\n",
-      "layer2.2.conv3.weight: 0.0000\n",
-      "layer2.3.conv1.weight: 0.0000\n",
-      "layer2.3.conv2.weight: 0.0000\n",
-      "layer2.3.conv3.weight: 0.0000\n",
-      "layer3.0.conv1.weight: 0.0000\n",
-      "layer3.0.conv2.weight: 0.0000\n",
-      "layer3.0.conv3.weight: 0.0000\n",
-      "layer3.0.downsample.0.weight: 0.0000\n",
-      "layer3.1.conv1.weight: 0.0000\n",
-      "layer3.1.conv2.weight: 0.0000\n",
-      "layer3.1.conv3.weight: 0.0000\n",
-      "layer3.2.conv1.weight: 0.0000\n",
-      "layer3.2.conv2.weight: 0.0000\n",
-      "layer3.2.conv3.weight: 0.0000\n",
-      "layer3.3.conv1.weight: 0.0000\n",
-      "layer3.3.conv2.weight: 0.0000\n",
-      "layer3.3.conv3.weight: 0.0000\n",
-      "layer3.4.conv1.weight: 0.0000\n",
-      "layer3.4.conv2.weight: 0.0000\n",
-      "layer3.4.conv3.weight: 0.0000\n",
-      "layer3.5.conv1.weight: 0.0000\n",
-      "layer3.5.conv2.weight: 0.0000\n",
-      "layer3.5.conv3.weight: 0.0000\n",
-      "layer4.0.conv1.weight: 0.0000\n",
-      "layer4.0.conv2.weight: 0.0000\n",
-      "layer4.0.conv3.weight: 0.0000\n",
-      "layer4.0.downsample.0.weight: 0.0000\n",
-      "layer4.1.conv1.weight: 0.0000\n",
-      "layer4.1.conv2.weight: 0.0000\n",
-      "layer4.1.conv3.weight: 0.0000\n",
-      "layer4.2.conv1.weight: 0.0000\n",
-      "layer4.2.conv2.weight: 0.0000\n",
-      "layer4.2.conv3.weight: 0.0000\n",
-      "fc.weight: 0.0000\n"
-     ]
-    }
-   ],
    "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+    "Kick off the transfer learning loop. Our run reached ~99% validation accuracy after 10 epochs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "d00d175b",
    "metadata": {
     "scrolled": false
@@ -523,7 +325,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddc6372a986f463ebfd4eb9a87f3a394",
+       "model_id": "975dfb9440f748c296156a532b0e4cb2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -539,8 +341,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 0.4339083959074581\n",
-      "Top 1 Acc: 0.8404255319148937\n",
+      "Training Loss: 0.4227903384496184\n",
+      "Top 1 Acc: 0.8355899419729207\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -548,7 +350,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50459accdf814889a17105987bb67241",
+       "model_id": "1ac44c8fd5ab41e4945a053976c1ce4f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -564,8 +366,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 0.248091588417689\n",
-      "Top 1 Acc: 0.9548872180451128\n",
+      "Val Loss: 0.166859846872588\n",
+      "Top 1 Acc: 0.9398496240601504\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -573,7 +375,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "83df4e5627e44235b934280a8401808a",
+       "model_id": "5ac4ab50f4b74673a929620a215c5265",
        "version_major": 2,
        "version_minor": 0
       },
@@ -589,7 +391,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 0.06769604531719405\n",
+      "Training Loss: 0.06358965676661361\n",
       "Top 1 Acc: 0.9816247582205029\n",
       "\n",
       "Running Validation Epoch 2/10\n"
@@ -598,7 +400,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4861ebb90907415c96ca05b18eb6fd1f",
+       "model_id": "fac7b5df3ae64f94bb6ed1b19a795c19",
        "version_major": 2,
        "version_minor": 0
       },
@@ -614,8 +416,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 0.16374425676379664\n",
-      "Top 1 Acc: 0.924812030075188\n",
+      "Val Loss: 0.6292845177813433\n",
+      "Top 1 Acc: 0.9548872180451128\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -623,7 +425,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b68fbd6e4e04a21ba278144368a48f4",
+       "model_id": "673576da2a604abdacb88963abddb880",
        "version_major": 2,
        "version_minor": 0
       },
@@ -639,8 +441,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.02384376264971626\n",
-      "Top 1 Acc: 0.9932301740812379\n",
+      "Training Loss: 0.032505514428896064\n",
+      "Top 1 Acc: 0.9893617021276596\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -648,7 +450,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c12285e040534bd0b2a1e22649ddc1f9",
+       "model_id": "8d988e7a3c164f0a9c8ef194b7100d51",
        "version_major": 2,
        "version_minor": 0
       },
@@ -664,7 +466,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.049745342917352296\n",
+      "Val Loss: 0.047614920418709517\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 4/10\n"
@@ -673,7 +475,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce8896643f1345828a2058e04a497b0f",
+       "model_id": "475850d6ad78458184acaa2a21f5b9db",
        "version_major": 2,
        "version_minor": 0
       },
@@ -689,8 +491,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.09208150484653957\n",
-      "Top 1 Acc: 0.9922630560928434\n",
+      "Training Loss: 0.03107739576850744\n",
+      "Top 1 Acc: 0.9903288201160542\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -698,7 +500,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1443e8ceaee4563884b43951fadb32d",
+       "model_id": "ec810e59cf784d58956449df7a77779d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -714,7 +516,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.0414201499883499\n",
+      "Val Loss: 0.038283314109624676\n",
       "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 5/10\n"
@@ -723,7 +525,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e158ef40dbb24e1e82dba48a5175094f",
+       "model_id": "60680c7c2e9b4ed281ed4b444ce475a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -739,8 +541,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.05627283230697846\n",
-      "Top 1 Acc: 0.9835589941972921\n",
+      "Training Loss: 0.11940328791217111\n",
+      "Top 1 Acc: 0.9941972920696325\n",
       "\n",
       "Running Validation Epoch 5/10\n"
      ]
@@ -748,7 +550,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "35196861a6e04149b0da473fd551894f",
+       "model_id": "b4444fa067714efda9fc5f6eb7354599",
        "version_major": 2,
        "version_minor": 0
       },
@@ -764,8 +566,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.018425415522263695\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Val Loss: 0.04354451159209324\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 6/10\n"
      ]
@@ -773,7 +575,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f376300a2992438e987705b61ed3622c",
+       "model_id": "c49c726e3ce141ea8a4236cc1d1e3eb2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -789,8 +591,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.01825398319017361\n",
-      "Top 1 Acc: 0.995164410058027\n",
+      "Training Loss: 0.05448708479183123\n",
+      "Top 1 Acc: 0.9893617021276596\n",
       "\n",
       "Running Validation Epoch 6/10\n"
      ]
@@ -798,7 +600,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e6989019b3844c5a8babb06109624adf",
+       "model_id": "f84aaa08b92b4486ada7d57d2e96338e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -814,7 +616,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.03305983874209536\n",
+      "Val Loss: 0.05759213243921598\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 7/10\n"
@@ -823,7 +625,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b87fa5de0374b5e835cc9506c001459",
+       "model_id": "49a0611edd8740aea4ebe3861df90e64",
        "version_major": 2,
        "version_minor": 0
       },
@@ -839,8 +641,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.019919913084081867\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Loss: 0.01337281458408517\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 7/10\n"
      ]
@@ -848,7 +650,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "985407c44f8f43c9826e187ce6ee211b",
+       "model_id": "7b13f9fccf7e4bac95d533bff9d40c21",
        "version_major": 2,
        "version_minor": 0
       },
@@ -864,8 +666,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.009156467043794692\n",
-      "Top 1 Acc: 1.0\n",
+      "Val Loss: 0.026064214219028752\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -873,7 +675,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a02f509c95664f7f97826611c5bfe256",
+       "model_id": "5516a804e8714ed2a0a326593304fc31",
        "version_major": 2,
        "version_minor": 0
       },
@@ -889,8 +691,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.027912832608254737\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Loss: 0.0028814816066776126\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 8/10\n"
      ]
@@ -898,7 +700,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1fe9ca9b315447d7bd4726d5ad51c3bb",
+       "model_id": "36497f568ff9499d823b742bfb627d07",
        "version_major": 2,
        "version_minor": 0
       },
@@ -914,8 +716,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.008448541047982872\n",
-      "Top 1 Acc: 1.0\n",
+      "Val Loss: 0.026710195777316887\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -923,7 +725,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0733093fce85461ba8206421e11783c4",
+       "model_id": "faaf8be44fc44587b5cb96fa338c8dba",
        "version_major": 2,
        "version_minor": 0
       },
@@ -939,7 +741,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.006125200964400873\n",
+      "Training Loss: 0.0022678900106043063\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
@@ -948,7 +750,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fe544acaa799482389d56fc964a92a6e",
+       "model_id": "6e4f22651d6e41cb9d1e98e8cc536314",
        "version_major": 2,
        "version_minor": 0
       },
@@ -964,8 +766,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.006565663294168189\n",
-      "Top 1 Acc: 1.0\n",
+      "Val Loss: 0.024938953380721312\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -973,7 +775,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d77f15fd932b4bea875da981f61428cf",
+       "model_id": "f0844eedab5d436e890e55eb5401fe96",
        "version_major": 2,
        "version_minor": 0
       },
@@ -989,7 +791,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.003272765227045645\n",
+      "Training Loss: 0.0028374234782805774\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 10/10\n"
@@ -998,7 +800,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45026f95773c4f2799d18ed949360aab",
+       "model_id": "d244e2fc3059410a8f6a01cda2e77417",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1014,8 +816,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.007307609868197081\n",
-      "Top 1 Acc: 1.0\n",
+      "Val Loss: 0.021825105145884056\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n"
      ]
     }
@@ -1027,26 +829,20 @@
     "    # run training loop\n",
     "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
     "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
     "\n",
     "    # run validation loop\n",
     "    print(f\"Running Validation Epoch {epoch_name}\")\n",
     "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
     "\n",
     "manager.finalize(model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "11fa4cf2",
    "metadata": {},
    "outputs": [],
@@ -1055,75 +851,33 @@
     "\n",
     "save_dir = \"dense_model\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet-50-dense-beans.pth\")"
+    "exporter.export_pytorch(name=\"resnet-50-dense-beans.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "9b17fc31",
+   "execution_count": 15,
+   "id": "1617f476",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "848572dd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 10.0\r\n",
-      "init_lr: 0.00025\r\n",
-      "inter_func: cubic\r\n",
-      "mask_type: unstructured\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: 0.0\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "# Pruning\r\n",
-      "pruning_modifiers:\r\n",
-      "   - !GlobalMagnitudePruningModifier\r\n",
-      "     init_sparsity: 0.05\r\n",
-      "     final_sparsity: 0.90\r\n",
-      "     start_epoch: 0.0\r\n",
-      "     end_epoch: eval(num_epochs)\r\n",
-      "     update_frequency: 1.0\r\n",
-      "     params: \r\n",
-      "        - 're:layer1.*.conv1.weight'\r\n",
-      "        - 're:layer1.*.conv2.weight'\r\n",
-      "        - 're:layer1.*.conv3.weight'\r\n",
-      "        - 're:layer1.0.downsample.0.weight'\r\n",
-      "        - 're:layer2.*.conv1.weight'\r\n",
-      "        - 're:layer2.*.conv2.weight'\r\n",
-      "        - 're:layer2.*.conv3.weight'\r\n",
-      "        - 're:layer2.0.downsample.0.weight'\r\n",
-      "        - 're:layer3.*.conv1.weight'\r\n",
-      "        - 're:layer3.*.conv2.weight'\r\n",
-      "        - 're:layer3.*.conv3.weight'\r\n",
-      "        - 're:layer3.0.downsample.0.weight'\r\n",
-      "        - 're:layer4.*.conv1.weight'\r\n",
-      "        - 're:layer4.*.conv2.weight'\r\n",
-      "        - 're:layer4.*.conv3.weight'\r\n",
-      "        - 're:layer4.0.downsample.0.weight'\r\n",
-      "     leave_enabled: True\r\n",
-      "     inter_func: eval(inter_func)\r\n",
-      "     mask_type: eval(mask_type)\r\n"
-     ]
-    }
-   ],
    "source": [
-    "%cat recipe-0.yaml"
+    "## Step 4: Prune The Model\n",
+    "\n",
+    "With a model trained on Beans, we are now ready to apply the GMP algorithm to prune the model. The GMP algorithm is an interative pruning algorithm. At the end of each epoch, we identify the lowest magnitude weights (those closest to 0) and remove them from the network starting from an initial level of sparsity until a final level of sparsity. The remaining nonzero weights are then fine-tuned onto training dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 16,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
@@ -1139,10 +893,22 @@
     "optimizer = Adam(model.parameters(), lr=8e-3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "edb62dc9",
+   "metadata": {
+    "scrolled": false
+   },
+   "source": [
+    "Next, we need to create a SparseML recipe which includes the GMP algorithm. The `!GlobalMagnitudePruningModifier` modifier instructs SparseML to apply the GMP algorithm at a global level (pruning the lowest magnitude weights across all layers).\n",
+    "\n",
+    "Firstly, we need to decide identify which parameters of the model to apply the GMP algorithm to. We can use the `get_prunable_layers` function to inspect:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "9154d351",
+   "execution_count": 17,
+   "id": "8950185f",
    "metadata": {
     "scrolled": false
    },
@@ -1151,277 +917,201 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ResNet(\n",
-      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
-      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "  (relu): ReLU(inplace=True)\n",
-      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
-      "  (layer1): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer2): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (3): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer3): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (3): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (4): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (5): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (layer4): Sequential(\n",
-      "    (0): Bottleneck(\n",
-      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "      (downsample): Sequential(\n",
-      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
-      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (1): Bottleneck(\n",
-      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "    (2): Bottleneck(\n",
-      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
-      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (relu): ReLU(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
-      "  (fc): Linear(in_features=2048, out_features=3, bias=True)\n",
-      ")\n"
+      "conv1\n",
+      "layer1.0.conv1\n",
+      "layer1.0.conv2\n",
+      "layer1.0.conv3\n",
+      "layer1.0.downsample.0\n",
+      "layer1.1.conv1\n",
+      "layer1.1.conv2\n",
+      "layer1.1.conv3\n",
+      "layer1.2.conv1\n",
+      "layer1.2.conv2\n",
+      "layer1.2.conv3\n",
+      "layer2.0.conv1\n",
+      "layer2.0.conv2\n",
+      "layer2.0.conv3\n",
+      "layer2.0.downsample.0\n",
+      "layer2.1.conv1\n",
+      "layer2.1.conv2\n",
+      "layer2.1.conv3\n",
+      "layer2.2.conv1\n",
+      "layer2.2.conv2\n",
+      "layer2.2.conv3\n",
+      "layer2.3.conv1\n",
+      "layer2.3.conv2\n",
+      "layer2.3.conv3\n",
+      "layer3.0.conv1\n",
+      "layer3.0.conv2\n",
+      "layer3.0.conv3\n",
+      "layer3.0.downsample.0\n",
+      "layer3.1.conv1\n",
+      "layer3.1.conv2\n",
+      "layer3.1.conv3\n",
+      "layer3.2.conv1\n",
+      "layer3.2.conv2\n",
+      "layer3.2.conv3\n",
+      "layer3.3.conv1\n",
+      "layer3.3.conv2\n",
+      "layer3.3.conv3\n",
+      "layer3.4.conv1\n",
+      "layer3.4.conv2\n",
+      "layer3.4.conv3\n",
+      "layer3.5.conv1\n",
+      "layer3.5.conv2\n",
+      "layer3.5.conv3\n",
+      "layer4.0.conv1\n",
+      "layer4.0.conv2\n",
+      "layer4.0.conv3\n",
+      "layer4.0.downsample.0\n",
+      "layer4.1.conv1\n",
+      "layer4.1.conv2\n",
+      "layer4.1.conv3\n",
+      "layer4.2.conv1\n",
+      "layer4.2.conv2\n",
+      "layer4.2.conv3\n",
+      "fc\n"
      ]
     }
    ],
    "source": [
-    "print(model)"
+    "# print parameters\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "68646bc7",
+   "cell_type": "markdown",
+   "id": "f5ef3bdf",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+    "We will apply pruning to each of the convs and exclude the fc layer (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
+    "    \n",
+    "    - 'conv1.weight'\n",
+    "    - 're:layer1.*.conv1.weight'\n",
+    "    - 're:layer1.*.conv2.weight'\n",
+    "    - 're:layer1.*.conv3.weight'\n",
+    "    - 're:layer1.0.downsample.0.weight'\n",
+    "    - 're:layer2.*.conv1.weight'\n",
+    "    - 're:layer2.*.conv2.weight'\n",
+    "    - 're:layer2.*.conv3.weight'\n",
+    "    - 're:layer2.0.downsample.0.weight'\n",
+    "    - 're:layer3.*.conv1.weight'\n",
+    "    - 're:layer3.*.conv2.weight'\n",
+    "    - 're:layer3.*.conv3.weight'\n",
+    "    - 're:layer3.0.downsample.0.weight'\n",
+    "    - 're:layer4.*.conv1.weight'\n",
+    "    - 're:layer4.*.conv2.weight'\n",
+    "    - 're:layer4.*.conv3.weight'\n",
+    "    - 're:layer4.0.downsample.0.weight'\n",
+    "\n",
+    "Here is what the recipe looks like:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 13.0\n",
+    "pruning_epochs: 10.0\n",
+    "init_lr: 0.00025\n",
+    "inter_func: cubic\n",
+    "mask_type: unstructured\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "# Pruning\n",
+    "pruning_modifiers:\n",
+    "   - !GlobalMagnitudePruningModifier\n",
+    "     init_sparsity: 0.05\n",
+    "     final_sparsity: 0.90\n",
+    "     start_epoch: 0.0\n",
+    "     end_epoch: eval(pruning_epochs)\n",
+    "     update_frequency: 1.0\n",
+    "     params: \n",
+    "        - 'conv1.weight'\n",
+    "        - 're:layer1.*.conv1.weight'\n",
+    "        - 're:layer1.*.conv2.weight'\n",
+    "        - 're:layer1.*.conv3.weight'\n",
+    "        - 're:layer1.0.downsample.0.weight'\n",
+    "        - 're:layer2.*.conv1.weight'\n",
+    "        - 're:layer2.*.conv2.weight'\n",
+    "        - 're:layer2.*.conv3.weight'\n",
+    "        - 're:layer2.0.downsample.0.weight'\n",
+    "        - 're:layer3.*.conv1.weight'\n",
+    "        - 're:layer3.*.conv2.weight'\n",
+    "        - 're:layer3.*.conv3.weight'\n",
+    "        - 're:layer3.0.downsample.0.weight'\n",
+    "        - 're:layer4.*.conv1.weight'\n",
+    "        - 're:layer4.*.conv2.weight'\n",
+    "        - 're:layer4.*.conv3.weight'\n",
+    "        - 're:layer4.0.downsample.0.weight'\n",
+    "     leave_enabled: True\n",
+    "     inter_func: eval(inter_func)\n",
+    "     mask_type: eval(mask_type)\n",
+    "```\n",
+    "\n",
+    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
+    "\n",
+    "Over the final 3 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "00d26fb4",
+   "execution_count": null,
+   "id": "0c492e33",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.0000\n",
-      "layer1.0.conv2.weight: 0.0000\n",
-      "layer1.0.conv3.weight: 0.0000\n",
-      "layer1.0.downsample.0.weight: 0.0000\n",
-      "layer1.1.conv1.weight: 0.0000\n",
-      "layer1.1.conv2.weight: 0.0000\n",
-      "layer1.1.conv3.weight: 0.0000\n",
-      "layer1.2.conv1.weight: 0.0000\n",
-      "layer1.2.conv2.weight: 0.0000\n",
-      "layer1.2.conv3.weight: 0.0000\n",
-      "layer2.0.conv1.weight: 0.0000\n",
-      "layer2.0.conv2.weight: 0.0000\n",
-      "layer2.0.conv3.weight: 0.0000\n",
-      "layer2.0.downsample.0.weight: 0.0000\n",
-      "layer2.1.conv1.weight: 0.0000\n",
-      "layer2.1.conv2.weight: 0.0000\n",
-      "layer2.1.conv3.weight: 0.0000\n",
-      "layer2.2.conv1.weight: 0.0000\n",
-      "layer2.2.conv2.weight: 0.0000\n",
-      "layer2.2.conv3.weight: 0.0000\n",
-      "layer2.3.conv1.weight: 0.0000\n",
-      "layer2.3.conv2.weight: 0.0000\n",
-      "layer2.3.conv3.weight: 0.0000\n",
-      "layer3.0.conv1.weight: 0.0000\n",
-      "layer3.0.conv2.weight: 0.0000\n",
-      "layer3.0.conv3.weight: 0.0000\n",
-      "layer3.0.downsample.0.weight: 0.0000\n",
-      "layer3.1.conv1.weight: 0.0000\n",
-      "layer3.1.conv2.weight: 0.0000\n",
-      "layer3.1.conv3.weight: 0.0000\n",
-      "layer3.2.conv1.weight: 0.0000\n",
-      "layer3.2.conv2.weight: 0.0000\n",
-      "layer3.2.conv3.weight: 0.0000\n",
-      "layer3.3.conv1.weight: 0.0000\n",
-      "layer3.3.conv2.weight: 0.0000\n",
-      "layer3.3.conv3.weight: 0.0000\n",
-      "layer3.4.conv1.weight: 0.0000\n",
-      "layer3.4.conv2.weight: 0.0000\n",
-      "layer3.4.conv3.weight: 0.0000\n",
-      "layer3.5.conv1.weight: 0.0000\n",
-      "layer3.5.conv2.weight: 0.0000\n",
-      "layer3.5.conv3.weight: 0.0000\n",
-      "layer4.0.conv1.weight: 0.0000\n",
-      "layer4.0.conv2.weight: 0.0000\n",
-      "layer4.0.conv3.weight: 0.0000\n",
-      "layer4.0.downsample.0.weight: 0.0000\n",
-      "layer4.1.conv1.weight: 0.0000\n",
-      "layer4.1.conv2.weight: 0.0000\n",
-      "layer4.1.conv3.weight: 0.0000\n",
-      "layer4.2.conv1.weight: 0.0000\n",
-      "layer4.2.conv2.weight: 0.0000\n",
-      "layer4.2.conv3.weight: 0.0000\n",
-      "fc.weight: 0.0000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+    "!cat recipes/resnet50-beans-pruning-recipe.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "601c8c21",
+   "execution_count": 19,
+   "id": "9cc0e8ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pruning_recipe_path = \"./recipes/resnet50-beans-pruning-recipe.yaml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "68646bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(pruning_recipe_path)\n",
+    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs\")\n",
+    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ab5b75b",
+   "metadata": {},
+   "source": [
+    "Next, kick off the GMP training loop. \n",
+    "\n",
+    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to just the `optimizer` and `model` as usual, with all of the pruning-related logic specified by the declarative recipe interface.\n",
+    "\n",
+    "Our 90% pruned model reaches ~99% validation accuracy (vs ~99% for the dense model)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "601c8c21",
    "metadata": {
     "scrolled": false
    },
@@ -1430,13 +1120,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running Training Epoch 1/10\n"
+      "Running Training Epoch 1/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "64ff653882a846d382ff70d2c9a9a261",
+       "model_id": "a687385d2d3244c189ffac1e58c63f4b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1451,17 +1141,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 1/10\n",
-      "Training Loss: 0.009745531282319194\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Epoch: 1/13\n",
+      "Training Loss: 0.016460627817329678\n",
+      "Top 1 Acc: 0.995164410058027\n",
       "\n",
-      "Running Validation Epoch 1/10\n"
+      "Running Validation Epoch 1/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ade404c92c814dc987f0db7b70b101c7",
+       "model_id": "f75f7df5e4dd444c990312a33b5e55dd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1476,76 +1166,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 1/10\n",
-      "Val Loss: 0.03642222502579292\n",
+      "Validation Epoch: 1/13\n",
+      "Val Loss: 0.025849940720945597\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.0156\n",
-      "layer1.0.conv2.weight: 0.0299\n",
-      "layer1.0.conv3.weight: 0.0468\n",
-      "layer1.0.downsample.0.weight: 0.0452\n",
-      "layer1.1.conv1.weight: 0.0623\n",
-      "layer1.1.conv2.weight: 0.0414\n",
-      "layer1.1.conv3.weight: 0.0328\n",
-      "layer1.2.conv1.weight: 0.0204\n",
-      "layer1.2.conv2.weight: 0.0228\n",
-      "layer1.2.conv3.weight: 0.0385\n",
-      "layer2.0.conv1.weight: 0.0201\n",
-      "layer2.0.conv2.weight: 0.0337\n",
-      "layer2.0.conv3.weight: 0.0354\n",
-      "layer2.0.downsample.0.weight: 0.0452\n",
-      "layer2.1.conv1.weight: 0.0430\n",
-      "layer2.1.conv2.weight: 0.0410\n",
-      "layer2.1.conv3.weight: 0.0340\n",
-      "layer2.2.conv1.weight: 0.0350\n",
-      "layer2.2.conv2.weight: 0.0398\n",
-      "layer2.2.conv3.weight: 0.0384\n",
-      "layer2.3.conv1.weight: 0.0297\n",
-      "layer2.3.conv2.weight: 0.0348\n",
-      "layer2.3.conv3.weight: 0.0418\n",
-      "layer3.0.conv1.weight: 0.0280\n",
-      "layer3.0.conv2.weight: 0.0441\n",
-      "layer3.0.conv3.weight: 0.0345\n",
-      "layer3.0.downsample.0.weight: 0.0551\n",
-      "layer3.1.conv1.weight: 0.0492\n",
-      "layer3.1.conv2.weight: 0.0541\n",
-      "layer3.1.conv3.weight: 0.0430\n",
-      "layer3.2.conv1.weight: 0.0506\n",
-      "layer3.2.conv2.weight: 0.0511\n",
-      "layer3.2.conv3.weight: 0.0486\n",
-      "layer3.3.conv1.weight: 0.0428\n",
-      "layer3.3.conv2.weight: 0.0527\n",
-      "layer3.3.conv3.weight: 0.0412\n",
-      "layer3.4.conv1.weight: 0.0438\n",
-      "layer3.4.conv2.weight: 0.0504\n",
-      "layer3.4.conv3.weight: 0.0482\n",
-      "layer3.5.conv1.weight: 0.0380\n",
-      "layer3.5.conv2.weight: 0.0488\n",
-      "layer3.5.conv3.weight: 0.0383\n",
-      "layer4.0.conv1.weight: 0.0313\n",
-      "layer4.0.conv2.weight: 0.0505\n",
-      "layer4.0.conv3.weight: 0.0333\n",
-      "layer4.0.downsample.0.weight: 0.0511\n",
-      "layer4.1.conv1.weight: 0.0444\n",
-      "layer4.1.conv2.weight: 0.0595\n",
-      "layer4.1.conv3.weight: 0.0412\n",
-      "layer4.2.conv1.weight: 0.0575\n",
-      "layer4.2.conv2.weight: 0.0707\n",
-      "layer4.2.conv3.weight: 0.0466\n",
-      "fc.weight: 0.0000\n",
-      "\n",
-      "\n",
-      "Running Training Epoch 2/10\n"
+      "Running Training Epoch 2/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "38d0e8566f17400d90237dcc75d62a33",
+       "model_id": "4b9800318d914258896ddf34e4a34c54",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1560,17 +1191,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 2/10\n",
-      "Training Loss: 0.015113459074414363\n",
-      "Top 1 Acc: 0.9961315280464217\n",
+      "Training Epoch: 2/13\n",
+      "Training Loss: 0.009291439395918347\n",
+      "Top 1 Acc: 0.9970986460348162\n",
       "\n",
-      "Running Validation Epoch 2/10\n"
+      "Running Validation Epoch 2/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "249d4ceffa7e406cab363e7088925422",
+       "model_id": "895ac43d40ba4fd0a4f52caaa6e97dcb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1585,76 +1216,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 2/10\n",
-      "Val Loss: 0.09295554442602831\n",
-      "Top 1 Acc: 0.9774436090225563\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.1057\n",
-      "layer1.0.conv2.weight: 0.1675\n",
-      "layer1.0.conv3.weight: 0.1412\n",
-      "layer1.0.downsample.0.weight: 0.1352\n",
-      "layer1.1.conv1.weight: 0.1551\n",
-      "layer1.1.conv2.weight: 0.1612\n",
-      "layer1.1.conv3.weight: 0.1602\n",
-      "layer1.2.conv1.weight: 0.1138\n",
-      "layer1.2.conv2.weight: 0.1321\n",
-      "layer1.2.conv3.weight: 0.1959\n",
-      "layer2.0.conv1.weight: 0.1172\n",
-      "layer2.0.conv2.weight: 0.1901\n",
-      "layer2.0.conv3.weight: 0.1868\n",
-      "layer2.0.downsample.0.weight: 0.2431\n",
-      "layer2.1.conv1.weight: 0.2298\n",
-      "layer2.1.conv2.weight: 0.2323\n",
-      "layer2.1.conv3.weight: 0.1968\n",
-      "layer2.2.conv1.weight: 0.1970\n",
-      "layer2.2.conv2.weight: 0.2298\n",
-      "layer2.2.conv3.weight: 0.2151\n",
-      "layer2.3.conv1.weight: 0.1743\n",
-      "layer2.3.conv2.weight: 0.1960\n",
-      "layer2.3.conv3.weight: 0.2179\n",
-      "layer3.0.conv1.weight: 0.1623\n",
-      "layer3.0.conv2.weight: 0.2515\n",
-      "layer3.0.conv3.weight: 0.1914\n",
-      "layer3.0.downsample.0.weight: 0.3051\n",
-      "layer3.1.conv1.weight: 0.2738\n",
-      "layer3.1.conv2.weight: 0.3052\n",
-      "layer3.1.conv3.weight: 0.2408\n",
-      "layer3.2.conv1.weight: 0.2821\n",
-      "layer3.2.conv2.weight: 0.2875\n",
-      "layer3.2.conv3.weight: 0.2696\n",
-      "layer3.3.conv1.weight: 0.2445\n",
-      "layer3.3.conv2.weight: 0.2954\n",
-      "layer3.3.conv3.weight: 0.2352\n",
-      "layer3.4.conv1.weight: 0.2484\n",
-      "layer3.4.conv2.weight: 0.2833\n",
-      "layer3.4.conv3.weight: 0.2671\n",
-      "layer3.5.conv1.weight: 0.2132\n",
-      "layer3.5.conv2.weight: 0.2781\n",
-      "layer3.5.conv3.weight: 0.2168\n",
-      "layer4.0.conv1.weight: 0.1792\n",
-      "layer4.0.conv2.weight: 0.2859\n",
-      "layer4.0.conv3.weight: 0.1912\n",
-      "layer4.0.downsample.0.weight: 0.2878\n",
-      "layer4.1.conv1.weight: 0.2527\n",
-      "layer4.1.conv2.weight: 0.3319\n",
-      "layer4.1.conv3.weight: 0.2348\n",
-      "layer4.2.conv1.weight: 0.3199\n",
-      "layer4.2.conv2.weight: 0.3903\n",
-      "layer4.2.conv3.weight: 0.2633\n",
-      "fc.weight: 0.0000\n",
-      "\n",
+      "Validation Epoch: 2/13\n",
+      "Val Loss: 0.02041603586015602\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "Running Training Epoch 3/10\n"
+      "Running Training Epoch 3/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8cbddc0bfc5148f38735d36f84fb9b90",
+       "model_id": "1ac0de20101d4e94888bc5c25dc36203",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1669,17 +1241,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 3/10\n",
-      "Training Loss: 0.02089866736616172\n",
+      "Training Epoch: 3/13\n",
+      "Training Loss: 0.012328761325026041\n",
       "Top 1 Acc: 0.9941972920696325\n",
       "\n",
-      "Running Validation Epoch 3/10\n"
+      "Running Validation Epoch 3/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cbd250fdf3f8465c93e35cf7cfe92e4c",
+       "model_id": "28fd64b3fe6343beb2d66230be57f937",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1694,76 +1266,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 3/10\n",
-      "Val Loss: 0.08353681061453244\n",
-      "Top 1 Acc: 0.9624060150375939\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.1858\n",
-      "layer1.0.conv2.weight: 0.2866\n",
-      "layer1.0.conv3.weight: 0.2258\n",
-      "layer1.0.downsample.0.weight: 0.2141\n",
-      "layer1.1.conv1.weight: 0.2385\n",
-      "layer1.1.conv2.weight: 0.2649\n",
-      "layer1.1.conv3.weight: 0.2523\n",
-      "layer1.2.conv1.weight: 0.1957\n",
-      "layer1.2.conv2.weight: 0.2279\n",
-      "layer1.2.conv3.weight: 0.2909\n",
-      "layer2.0.conv1.weight: 0.2060\n",
-      "layer2.0.conv2.weight: 0.3258\n",
-      "layer2.0.conv3.weight: 0.3037\n",
-      "layer2.0.downsample.0.weight: 0.4047\n",
-      "layer2.1.conv1.weight: 0.3805\n",
-      "layer2.1.conv2.weight: 0.3882\n",
-      "layer2.1.conv3.weight: 0.3278\n",
-      "layer2.2.conv1.weight: 0.3349\n",
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-      "layer4.2.conv3.weight: 0.4406\n",
-      "fc.weight: 0.0000\n",
-      "\n",
+      "Validation Epoch: 3/13\n",
+      "Val Loss: 0.010778552386909723\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 4/10\n"
+      "Running Training Epoch 4/13\n"
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@@ -1778,17 +1291,17 @@
      "name": "stdout",
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      "text": [
-      "Training Epoch: 4/10\n",
-      "Training Loss: 0.015480193025295567\n",
-      "Top 1 Acc: 0.9961315280464217\n",
+      "Training Epoch: 4/13\n",
+      "Training Loss: 0.006804226791513536\n",
+      "Top 1 Acc: 0.9980657640232108\n",
       "\n",
-      "Running Validation Epoch 4/10\n"
+      "Running Validation Epoch 4/13\n"
      ]
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@@ -1803,76 +1316,17 @@
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      "output_type": "stream",
      "text": [
-      "Validation Epoch: 4/10\n",
-      "Val Loss: 0.014964782571648053\n",
-      "Top 1 Acc: 0.9924812030075187\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.2566\n",
-      "layer1.0.conv2.weight: 0.3874\n",
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-      "layer4.2.conv3.weight: 0.5834\n",
-      "fc.weight: 0.0000\n",
-      "\n",
+      "Validation Epoch: 4/13\n",
+      "Val Loss: 0.006039845136304696\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 5/10\n"
+      "Running Training Epoch 5/13\n"
      ]
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     {
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@@ -1887,17 +1341,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 5/10\n",
-      "Training Loss: 0.006244629046277088\n",
+      "Training Epoch: 5/13\n",
+      "Training Loss: 0.012682446326567409\n",
       "Top 1 Acc: 0.9980657640232108\n",
       "\n",
-      "Running Validation Epoch 5/10\n"
+      "Running Validation Epoch 5/13\n"
      ]
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     {
      "data": {
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@@ -1912,76 +1366,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 5/10\n",
-      "Val Loss: 0.007931285625090823\n",
+      "Validation Epoch: 5/13\n",
+      "Val Loss: 0.00994978565722704\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.3132\n",
-      "layer1.0.conv2.weight: 0.4789\n",
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-      "layer4.2.conv3.weight: 0.6948\n",
-      "fc.weight: 0.0000\n",
-      "\n",
-      "\n",
-      "Running Training Epoch 6/10\n"
+      "Running Training Epoch 6/13\n"
      ]
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     {
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@@ -1996,17 +1391,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 6/10\n",
-      "Training Loss: 0.010191820685149115\n",
-      "Top 1 Acc: 0.9970986460348162\n",
+      "Training Epoch: 6/13\n",
+      "Training Loss: 0.0117055864265079\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 6/10\n"
+      "Running Validation Epoch 6/13\n"
      ]
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@@ -2021,76 +1416,67 @@
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      "output_type": "stream",
      "text": [
-      "Validation Epoch: 6/10\n",
-      "Val Loss: 0.013040244715133062\n",
+      "Validation Epoch: 6/13\n",
+      "Val Loss: 0.01689507276751101\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
+      "Running Training Epoch 7/13\n"
+     ]
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+    {
+     "data": {
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+    {
+     "name": "stdout",
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+     "text": [
+      "Training Epoch: 7/13\n",
+      "Training Loss: 0.012449477751301053\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
+      "Running Validation Epoch 7/13\n"
+     ]
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+    {
+     "data": {
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+      "Validation Epoch: 7/13\n",
+      "Val Loss: 0.02214170355970661\n",
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       "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.3665\n",
-      "layer1.0.conv2.weight: 0.5532\n",
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-      "layer4.2.conv3.weight: 0.7776\n",
-      "fc.weight: 0.0000\n",
-      "\n",
-      "\n",
-      "Running Training Epoch 7/10\n"
+      "Running Training Epoch 8/13\n"
      ]
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     {
      "data": {
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@@ -2105,17 +1491,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 7/10\n",
-      "Training Loss: 0.028277927598751643\n",
-      "Top 1 Acc: 0.9970986460348162\n",
+      "Training Epoch: 8/13\n",
+      "Training Loss: 0.01196294802460162\n",
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       "\n",
-      "Running Validation Epoch 7/10\n"
+      "Running Validation Epoch 8/13\n"
      ]
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     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
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        "version_minor": 0
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@@ -2130,76 +1516,117 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 7/10\n",
-      "Val Loss: 0.027645598476131756\n",
+      "Validation Epoch: 8/13\n",
+      "Val Loss: 0.02145360441257556\n",
       "Top 1 Acc: 1.0\n",
       "\n",
+      "Running Training Epoch 9/13\n"
+     ]
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 9/13\n",
+      "Training Loss: 0.01053273505257333\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
+      "Running Validation Epoch 9/13\n"
+     ]
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+     "output_type": "display_data"
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+     "text": [
+      "Validation Epoch: 9/13\n",
+      "Val Loss: 0.02385075483471155\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.4146\n",
-      "layer1.0.conv2.weight: 0.6099\n",
-      "layer1.0.conv3.weight: 0.4810\n",
-      "layer1.0.downsample.0.weight: 0.4547\n",
-      "layer1.1.conv1.weight: 0.4962\n",
-      "layer1.1.conv2.weight: 0.5668\n",
-      "layer1.1.conv3.weight: 0.4924\n",
-      "layer1.2.conv1.weight: 0.4672\n",
-      "layer1.2.conv2.weight: 0.5302\n",
-      "layer1.2.conv3.weight: 0.5394\n",
-      "layer2.0.conv1.weight: 0.4760\n",
-      "layer2.0.conv2.weight: 0.6813\n",
-      "layer2.0.conv3.weight: 0.6140\n",
-      "layer2.0.downsample.0.weight: 0.7674\n",
-      "layer2.1.conv1.weight: 0.7262\n",
-      "layer2.1.conv2.weight: 0.7451\n",
-      "layer2.1.conv3.weight: 0.6732\n",
-      "layer2.2.conv1.weight: 0.6854\n",
-      "layer2.2.conv2.weight: 0.7446\n",
-      "layer2.2.conv3.weight: 0.7042\n",
-      "layer2.3.conv1.weight: 0.6469\n",
-      "layer2.3.conv2.weight: 0.7082\n",
-      "layer2.3.conv3.weight: 0.6685\n",
-      "layer3.0.conv1.weight: 0.6048\n",
-      "layer3.0.conv2.weight: 0.8069\n",
-      "layer3.0.conv3.weight: 0.6779\n",
-      "layer3.0.downsample.0.weight: 0.8744\n",
-      "layer3.1.conv1.weight: 0.8290\n",
-      "layer3.1.conv2.weight: 0.8717\n",
-      "layer3.1.conv3.weight: 0.7826\n",
-      "layer3.2.conv1.weight: 0.8528\n",
-      "layer3.2.conv2.weight: 0.8624\n",
-      "layer3.2.conv3.weight: 0.8075\n",
-      "layer3.3.conv1.weight: 0.8027\n",
-      "layer3.3.conv2.weight: 0.8722\n",
-      "layer3.3.conv3.weight: 0.7889\n",
-      "layer3.4.conv1.weight: 0.8073\n",
-      "layer3.4.conv2.weight: 0.8622\n",
-      "layer3.4.conv3.weight: 0.7983\n",
-      "layer3.5.conv1.weight: 0.7526\n",
-      "layer3.5.conv2.weight: 0.8639\n",
-      "layer3.5.conv3.weight: 0.7386\n",
-      "layer4.0.conv1.weight: 0.6762\n",
-      "layer4.0.conv2.weight: 0.8933\n",
-      "layer4.0.conv3.weight: 0.7177\n",
-      "layer4.0.downsample.0.weight: 0.8815\n",
-      "layer4.1.conv1.weight: 0.8387\n",
-      "layer4.1.conv2.weight: 0.9241\n",
-      "layer4.1.conv3.weight: 0.7919\n",
-      "layer4.2.conv1.weight: 0.9043\n",
-      "layer4.2.conv2.weight: 0.9547\n",
-      "layer4.2.conv3.weight: 0.8350\n",
-      "fc.weight: 0.0000\n",
+      "Running Training Epoch 10/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bd969b4487d34464944cd720f734fc9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 10/13\n",
+      "Training Loss: 0.004325077581383726\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
+      "Running Validation Epoch 10/13\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c8549b27bfb54e208303eddf59acf0b1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 10/13\n",
+      "Val Loss: 0.017839766883601744\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "Running Training Epoch 8/10\n"
+      "Running Training Epoch 11/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "040dac607a08475691f7697cb6d6ab12",
+       "model_id": "33b3406e7b4f44ffab8765971a5eba20",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2214,17 +1641,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 8/10\n",
-      "Training Loss: 0.0318604385589852\n",
+      "Training Epoch: 11/13\n",
+      "Training Loss: 0.003297735289807486\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 8/10\n"
+      "Running Validation Epoch 11/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e39e2a04a13447e6809a1c7f460b17c9",
+       "model_id": "93e5a79272034e3da9fe9f7b2ce79d5a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2239,76 +1666,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 8/10\n",
-      "Val Loss: 0.03692007825399438\n",
+      "Validation Epoch: 11/13\n",
+      "Val Loss: 0.012569367730369171\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.4473\n",
-      "layer1.0.conv2.weight: 0.6496\n",
-      "layer1.0.conv3.weight: 0.5182\n",
-      "layer1.0.downsample.0.weight: 0.4882\n",
-      "layer1.1.conv1.weight: 0.5353\n",
-      "layer1.1.conv2.weight: 0.6064\n",
-      "layer1.1.conv3.weight: 0.5291\n",
-      "layer1.2.conv1.weight: 0.5075\n",
-      "layer1.2.conv2.weight: 0.5724\n",
-      "layer1.2.conv3.weight: 0.5753\n",
-      "layer2.0.conv1.weight: 0.5150\n",
-      "layer2.0.conv2.weight: 0.7218\n",
-      "layer2.0.conv3.weight: 0.6547\n",
-      "layer2.0.downsample.0.weight: 0.8039\n",
-      "layer2.1.conv1.weight: 0.7629\n",
-      "layer2.1.conv2.weight: 0.7809\n",
-      "layer2.1.conv3.weight: 0.7136\n",
-      "layer2.2.conv1.weight: 0.7243\n",
-      "layer2.2.conv2.weight: 0.7808\n",
-      "layer2.2.conv3.weight: 0.7411\n",
-      "layer2.3.conv1.weight: 0.6886\n",
-      "layer2.3.conv2.weight: 0.7515\n",
-      "layer2.3.conv3.weight: 0.7091\n",
-      "layer3.0.conv1.weight: 0.6459\n",
-      "layer3.0.conv2.weight: 0.8426\n",
-      "layer3.0.conv3.weight: 0.7206\n",
-      "layer3.0.downsample.0.weight: 0.9014\n",
-      "layer3.1.conv1.weight: 0.8622\n",
-      "layer3.1.conv2.weight: 0.8985\n",
-      "layer3.1.conv3.weight: 0.8194\n",
-      "layer3.2.conv1.weight: 0.8831\n",
-      "layer3.2.conv2.weight: 0.8925\n",
-      "layer3.2.conv3.weight: 0.8410\n",
-      "layer3.3.conv1.weight: 0.8398\n",
-      "layer3.3.conv2.weight: 0.9006\n",
-      "layer3.3.conv3.weight: 0.8277\n",
-      "layer3.4.conv1.weight: 0.8433\n",
-      "layer3.4.conv2.weight: 0.8935\n",
-      "layer3.4.conv3.weight: 0.8327\n",
-      "layer3.5.conv1.weight: 0.7953\n",
-      "layer3.5.conv2.weight: 0.8958\n",
-      "layer3.5.conv3.weight: 0.7811\n",
-      "layer4.0.conv1.weight: 0.7225\n",
-      "layer4.0.conv2.weight: 0.9244\n",
-      "layer4.0.conv3.weight: 0.7660\n",
-      "layer4.0.downsample.0.weight: 0.9126\n",
-      "layer4.1.conv1.weight: 0.8765\n",
-      "layer4.1.conv2.weight: 0.9474\n",
-      "layer4.1.conv3.weight: 0.8325\n",
-      "layer4.2.conv1.weight: 0.9283\n",
-      "layer4.2.conv2.weight: 0.9687\n",
-      "layer4.2.conv3.weight: 0.8709\n",
-      "fc.weight: 0.0000\n",
-      "\n",
-      "\n",
-      "Running Training Epoch 9/10\n"
+      "Running Training Epoch 12/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "58e103e988ae452494e887e42d40e5ea",
+       "model_id": "5d1a07c0564a4be8a55c882e95c58de0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2323,17 +1691,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 9/10\n",
-      "Training Loss: 0.036725726197747624\n",
+      "Training Epoch: 12/13\n",
+      "Training Loss: 0.004005543901311124\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 9/10\n"
+      "Running Validation Epoch 12/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99694a447b274b2da56353ec12fd897e",
+       "model_id": "5003c9fb24924629ae11f10bda705979",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2348,76 +1716,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 9/10\n",
-      "Val Loss: 0.046517357851068176\n",
+      "Validation Epoch: 12/13\n",
+      "Val Loss: 0.013252861564978957\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.4712\n",
-      "layer1.0.conv2.weight: 0.6728\n",
-      "layer1.0.conv3.weight: 0.5400\n",
-      "layer1.0.downsample.0.weight: 0.5073\n",
-      "layer1.1.conv1.weight: 0.5588\n",
-      "layer1.1.conv2.weight: 0.6290\n",
-      "layer1.1.conv3.weight: 0.5516\n",
-      "layer1.2.conv1.weight: 0.5294\n",
-      "layer1.2.conv2.weight: 0.5957\n",
-      "layer1.2.conv3.weight: 0.5948\n",
-      "layer2.0.conv1.weight: 0.5384\n",
-      "layer2.0.conv2.weight: 0.7441\n",
-      "layer2.0.conv3.weight: 0.6780\n",
-      "layer2.0.downsample.0.weight: 0.8222\n",
-      "layer2.1.conv1.weight: 0.7831\n",
-      "layer2.1.conv2.weight: 0.8004\n",
-      "layer2.1.conv3.weight: 0.7358\n",
-      "layer2.2.conv1.weight: 0.7461\n",
-      "layer2.2.conv2.weight: 0.8004\n",
-      "layer2.2.conv3.weight: 0.7619\n",
-      "layer2.3.conv1.weight: 0.7125\n",
-      "layer2.3.conv2.weight: 0.7751\n",
-      "layer2.3.conv3.weight: 0.7315\n",
-      "layer3.0.conv1.weight: 0.6695\n",
-      "layer3.0.conv2.weight: 0.8614\n",
-      "layer3.0.conv3.weight: 0.7442\n",
-      "layer3.0.downsample.0.weight: 0.9151\n",
-      "layer3.1.conv1.weight: 0.8794\n",
-      "layer3.1.conv2.weight: 0.9120\n",
-      "layer3.1.conv3.weight: 0.8389\n",
-      "layer3.2.conv1.weight: 0.8988\n",
-      "layer3.2.conv2.weight: 0.9078\n",
-      "layer3.2.conv3.weight: 0.8592\n",
-      "layer3.3.conv1.weight: 0.8593\n",
-      "layer3.3.conv2.weight: 0.9149\n",
-      "layer3.3.conv3.weight: 0.8482\n",
-      "layer3.4.conv1.weight: 0.8624\n",
-      "layer3.4.conv2.weight: 0.9092\n",
-      "layer3.4.conv3.weight: 0.8514\n",
-      "layer3.5.conv1.weight: 0.8185\n",
-      "layer3.5.conv2.weight: 0.9122\n",
-      "layer3.5.conv3.weight: 0.8044\n",
-      "layer4.0.conv1.weight: 0.7488\n",
-      "layer4.0.conv2.weight: 0.9396\n",
-      "layer4.0.conv3.weight: 0.7929\n",
-      "layer4.0.downsample.0.weight: 0.9281\n",
-      "layer4.1.conv1.weight: 0.8959\n",
-      "layer4.1.conv2.weight: 0.9582\n",
-      "layer4.1.conv3.weight: 0.8543\n",
-      "layer4.2.conv1.weight: 0.9398\n",
-      "layer4.2.conv2.weight: 0.9750\n",
-      "layer4.2.conv3.weight: 0.8893\n",
-      "fc.weight: 0.0000\n",
-      "\n",
-      "\n",
-      "Running Training Epoch 10/10\n"
+      "Running Training Epoch 13/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d88362b1c1b043efafd998b3f606d60c",
+       "model_id": "ce3f08d614364ae0bc2a09f80dee38a3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2432,17 +1741,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 10/10\n",
-      "Training Loss: 0.032790856435894966\n",
+      "Training Epoch: 13/13\n",
+      "Training Loss: 0.005194885152912534\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 10/10\n"
+      "Running Validation Epoch 13/13\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c120c79e4424fbdbf80afed481e3b94",
+       "model_id": "9362c25ec45a43f1992d5a501bfd3df8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2457,111 +1766,157 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 10/10\n",
-      "Val Loss: 0.0411220183596015\n",
+      "Validation Epoch: 13/13\n",
+      "Val Loss: 0.012128440197557211\n",
       "Top 1 Acc: 1.0\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "conv1.weight: 0.0000\n",
-      "layer1.0.conv1.weight: 0.4783\n",
-      "layer1.0.conv2.weight: 0.6819\n",
-      "layer1.0.conv3.weight: 0.5481\n",
-      "layer1.0.downsample.0.weight: 0.5146\n",
-      "layer1.1.conv1.weight: 0.5688\n",
-      "layer1.1.conv2.weight: 0.6389\n",
-      "layer1.1.conv3.weight: 0.5606\n",
-      "layer1.2.conv1.weight: 0.5388\n",
-      "layer1.2.conv2.weight: 0.6055\n",
-      "layer1.2.conv3.weight: 0.6023\n",
-      "layer2.0.conv1.weight: 0.5475\n",
-      "layer2.0.conv2.weight: 0.7528\n",
-      "layer2.0.conv3.weight: 0.6869\n",
-      "layer2.0.downsample.0.weight: 0.8294\n",
-      "layer2.1.conv1.weight: 0.7904\n",
-      "layer2.1.conv2.weight: 0.8076\n",
-      "layer2.1.conv3.weight: 0.7448\n",
-      "layer2.2.conv1.weight: 0.7550\n",
-      "layer2.2.conv2.weight: 0.8084\n",
-      "layer2.2.conv3.weight: 0.7696\n",
-      "layer2.3.conv1.weight: 0.7220\n",
-      "layer2.3.conv2.weight: 0.7845\n",
-      "layer2.3.conv3.weight: 0.7395\n",
-      "layer3.0.conv1.weight: 0.6785\n",
-      "layer3.0.conv2.weight: 0.8685\n",
-      "layer3.0.conv3.weight: 0.7536\n",
-      "layer3.0.downsample.0.weight: 0.9200\n",
-      "layer3.1.conv1.weight: 0.8859\n",
-      "layer3.1.conv2.weight: 0.9169\n",
-      "layer3.1.conv3.weight: 0.8463\n",
-      "layer3.2.conv1.weight: 0.9046\n",
-      "layer3.2.conv2.weight: 0.9133\n",
-      "layer3.2.conv3.weight: 0.8662\n",
-      "layer3.3.conv1.weight: 0.8669\n",
-      "layer3.3.conv2.weight: 0.9201\n",
-      "layer3.3.conv3.weight: 0.8557\n",
-      "layer3.4.conv1.weight: 0.8697\n",
-      "layer3.4.conv2.weight: 0.9149\n",
-      "layer3.4.conv3.weight: 0.8586\n",
-      "layer3.5.conv1.weight: 0.8271\n",
-      "layer3.5.conv2.weight: 0.9181\n",
-      "layer3.5.conv3.weight: 0.8134\n",
-      "layer4.0.conv1.weight: 0.7590\n",
-      "layer4.0.conv2.weight: 0.9449\n",
-      "layer4.0.conv3.weight: 0.8032\n",
-      "layer4.0.downsample.0.weight: 0.9336\n",
-      "layer4.1.conv1.weight: 0.9031\n",
-      "layer4.1.conv2.weight: 0.9619\n",
-      "layer4.1.conv3.weight: 0.8624\n",
-      "layer4.2.conv1.weight: 0.9439\n",
-      "layer4.2.conv2.weight: 0.9772\n",
-      "layer4.2.conv3.weight: 0.8961\n",
-      "fc.weight: 0.0000\n",
-      "\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "# run transfer learning\n",
+    "# run GMP algorithm\n",
     "epoch = 0\n",
     "for epoch in range(manager.max_epochs):\n",
     "    # run training loop\n",
     "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
     "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
     "\n",
     "    # run validation loop\n",
     "    print(f\"Running Validation Epoch {epoch_name}\")\n",
     "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
     "    \n",
-    "    print(f\"\\n\\nChecking Sparsity Level:\")\n",
-    "    for (name, layer) in get_prunable_layers(model):\n",
-    "        print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")\n",
-    "    print(f\"\\n\")\n",
+    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
     "    \n",
     "manager.finalize(model)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7aa91abe",
+   "metadata": {},
+   "source": [
+    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
+    "\n",
+    "![tensorboard output](./images/resnet-beans-tensorboard-output.png)\n",
+    "\n",
+    "We can print layer-by-layer sparsity as well."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 22,
+   "id": "65fb7b12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1.weight: 0.4039\n",
+      "layer1.0.conv1.weight: 0.4758\n",
+      "layer1.0.conv2.weight: 0.6846\n",
+      "layer1.0.conv3.weight: 0.5506\n",
+      "layer1.0.downsample.0.weight: 0.5157\n",
+      "layer1.1.conv1.weight: 0.5690\n",
+      "layer1.1.conv2.weight: 0.6410\n",
+      "layer1.1.conv3.weight: 0.5604\n",
+      "layer1.2.conv1.weight: 0.5430\n",
+      "layer1.2.conv2.weight: 0.6075\n",
+      "layer1.2.conv3.weight: 0.6064\n",
+      "layer2.0.conv1.weight: 0.5498\n",
+      "layer2.0.conv2.weight: 0.7556\n",
+      "layer2.0.conv3.weight: 0.6881\n",
+      "layer2.0.downsample.0.weight: 0.8303\n",
+      "layer2.1.conv1.weight: 0.7918\n",
+      "layer2.1.conv2.weight: 0.8089\n",
+      "layer2.1.conv3.weight: 0.7450\n",
+      "layer2.2.conv1.weight: 0.7564\n",
+      "layer2.2.conv2.weight: 0.8100\n",
+      "layer2.2.conv3.weight: 0.7706\n",
+      "layer2.3.conv1.weight: 0.7239\n",
+      "layer2.3.conv2.weight: 0.7862\n",
+      "layer2.3.conv3.weight: 0.7415\n",
+      "layer3.0.conv1.weight: 0.6814\n",
+      "layer3.0.conv2.weight: 0.8697\n",
+      "layer3.0.conv3.weight: 0.7558\n",
+      "layer3.0.downsample.0.weight: 0.9209\n",
+      "layer3.1.conv1.weight: 0.8875\n",
+      "layer3.1.conv2.weight: 0.9177\n",
+      "layer3.1.conv3.weight: 0.8482\n",
+      "layer3.2.conv1.weight: 0.9054\n",
+      "layer3.2.conv2.weight: 0.9143\n",
+      "layer3.2.conv3.weight: 0.8672\n",
+      "layer3.3.conv1.weight: 0.8686\n",
+      "layer3.3.conv2.weight: 0.9209\n",
+      "layer3.3.conv3.weight: 0.8573\n",
+      "layer3.4.conv1.weight: 0.8710\n",
+      "layer3.4.conv2.weight: 0.9158\n",
+      "layer3.4.conv3.weight: 0.8599\n",
+      "layer3.5.conv1.weight: 0.8293\n",
+      "layer3.5.conv2.weight: 0.9191\n",
+      "layer3.5.conv3.weight: 0.8144\n",
+      "layer4.0.conv1.weight: 0.7612\n",
+      "layer4.0.conv2.weight: 0.9458\n",
+      "layer4.0.conv3.weight: 0.8048\n",
+      "layer4.0.downsample.0.weight: 0.9347\n",
+      "layer4.1.conv1.weight: 0.9039\n",
+      "layer4.1.conv2.weight: 0.9626\n",
+      "layer4.1.conv3.weight: 0.8643\n",
+      "layer4.2.conv1.weight: 0.9441\n",
+      "layer4.2.conv2.weight: 0.9775\n",
+      "layer4.2.conv3.weight: 0.8976\n",
+      "fc.weight: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00805b02",
+   "metadata": {},
+   "source": [
+    "Finally, export your model to ONNX."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "id": "417ac105",
    "metadata": {},
    "outputs": [],
    "source": [
     "save_dir = \"experiment-0\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet-50-sparse-experiment-0.pth\")"
+    "exporter.export_pytorch(name=\"resnet-50-sparse-beans.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7960612c",
+   "metadata": {},
+   "source": [
+    "## Wrap Up\n",
+    "\n",
+    "The resulting model is is 90% sparse and achieves validation accuracy of ~100% (vs the unoptimized dense model at ~99%) without much hyperparameter search.\n",
+    "\n",
+    "Key hyperparameter experiments you may want to run include:\n",
+    "- Learning rate\n",
+    "- Learning rate schedule\n",
+    "- Sparsity level\n",
+    "- Number of pruning epochs\n",
+    "\n",
+    "DeepSparse supports speedup from pruning and quantization. To reach maximum performance, check out our examples of quantizing a model!"
    ]
   }
  ],
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
index 04c324b764e..b5e63054248 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
@@ -1,83 +1,80 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "00e081b7",
+   "metadata": {},
+   "source": [
+    "# Sparsifying MobileNetv2 from Scratch (Beans)\n",
+    "\n",
+    "In this example, we will demonstrate how to sparsify an image classification model from scratch using SparseML's PyTorch integration. We train and prune [MobileNetv2](https://pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v2.html) on the downstream [Beans dataset](https://huggingface.co/datasets/beans) using the Global Magnitude Pruning algorithm. \n",
+    "\n",
+    "## Agenda\n",
+    "\n",
+    "There are a few steps:\n",
+    "\n",
+    " 1. Setup the dataset\n",
+    " 2. Setup the PyTorch training loop\n",
+    " 3. Train a dense version of MobileNetv2\n",
+    " 4. Run the GMP pruning algorithm on the dense model\n",
+    " \n",
+    "## Installation\n",
+    "\n",
+    "Install SparseML with `pip`:\n",
+    "\n",
+    "```\n",
+    "pip install sparseml[torchvision]\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "1ad80edf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sparseml\n",
-    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
     "import torch\n",
+    "import sparseml\n",
     "import torchvision\n",
-    "from torchvision import transforms\n",
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "from sparseml.pytorch.utils import TensorBoardLogger, ModuleExporter, get_prunable_layers, tensor_sparsity\n",
     "from torch.utils.data import DataLoader\n",
     "from torch.nn import CrossEntropyLoss\n",
     "from torch.optim import Adam\n",
+    "from torchvision import transforms\n",
     "from tqdm.auto import tqdm\n",
     "import math\n",
-    "from datasets import load_dataset"
+    "import datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79303b28",
+   "metadata": {},
+   "source": [
+    "## Step 1: Setup Dataset\n",
+    "\n",
+    "Beans leaf dataset is a set of images of diseased and healthy leaves. Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.\n",
+    "\n",
+    "We will use the Hugging Face `datasets` library to download the data and the torchvision `ImageFolder` in the training loop.\n",
+    "\n",
+    "[Checkout the dataset card](https://huggingface.co/datasets/beans)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f8eb3642",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using custom data configuration default\n",
-      "Reusing dataset beans (/home/ubuntu/.cache/huggingface/datasets/beans/default/0.0.0/d5abfbb94de45599d871182cff389bc870bf145b4829c4b0fe20f0cccd637cbd)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7700382ea347426fb3596590931e7f91",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DatasetDict({\n",
-      "    train: Dataset({\n",
-      "        features: ['image_file_path', 'image', 'labels'],\n",
-      "        num_rows: 1034\n",
-      "    })\n",
-      "    validation: Dataset({\n",
-      "        features: ['image_file_path', 'image', 'labels'],\n",
-      "        num_rows: 133\n",
-      "    })\n",
-      "    test: Dataset({\n",
-      "        features: ['image_file_path', 'image', 'labels'],\n",
-      "        num_rows: 128\n",
-      "    })\n",
-      "})\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dataset = load_dataset(\"beans\")\n",
-    "print(dataset)"
+    "beans_dataset = datasets.load_dataset(\"beans\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "b06fc4fd",
    "metadata": {},
    "outputs": [
@@ -91,32 +88,32 @@
     }
    ],
    "source": [
-    "print(dataset[\"train\"][0][\"image_file_path\"])\n",
-    "print(dataset[\"validation\"][0][\"image_file_path\"])"
+    "print(beans_dataset[\"train\"][0][\"image_file_path\"])\n",
+    "print(beans_dataset[\"validation\"][0][\"image_file_path\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "4ecdebad",
    "metadata": {},
    "outputs": [],
    "source": [
     "train_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\"\n",
-    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/\" "
+    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation\" "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "7d180bbc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "BATCH_SIZE = 32\n",
     "NUM_LABELS = 3\n",
+    "BATCH_SIZE = 32\n",
     "\n",
-    "# transforms\n",
+    "# imagenet transforms\n",
     "imagenet_transform = transforms.Compose([\n",
     "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
     "   transforms.CenterCrop(size=(224, 224)),\n",
@@ -135,28 +132,39 @@
     "    transform=imagenet_transform\n",
     ")\n",
     "\n",
-    "# setup data loaders\n",
+    "# dataloaders\n",
     "train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16)\n",
     "val_loader = DataLoader(val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c968c098",
+   "cell_type": "markdown",
+   "id": "132fc514",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
+    "## Step 2: Setup PyTorch Training Loop\n",
+    "\n",
+    "We will use this training loop below. This is standard PyTorch functionality."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "5f1b878a",
+   "execution_count": 6,
+   "id": "c968c098",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuda\n"
+     ]
+    }
+   ],
    "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(device)\n",
+    "\n",
     "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
     "    if train:\n",
     "        model.train()\n",
@@ -200,334 +208,62 @@
    "id": "a3521e78",
    "metadata": {},
    "source": [
-    "## Train Dense Model"
+    "## **Step 3: Train MobileNet-v2 on Beans**\n",
+    "\n",
+    "First, we will train a dense version of MobileNetv2 on the Beans dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "4d554578",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MobileNetV2(\n",
-      "  (features): Sequential(\n",
-      "    (0): Conv2dNormActivation(\n",
-      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (2): ReLU6(inplace=True)\n",
-      "    )\n",
-      "    (1): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
-      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (2): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
-      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (3): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (4): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (5): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (6): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (7): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (8): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (9): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (10): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (11): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (12): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (13): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (14): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (15): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (16): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (17): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (18): Conv2dNormActivation(\n",
-      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (2): ReLU6(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (classifier): Sequential(\n",
-      "    (0): Dropout(p=0.2, inplace=False)\n",
-      "    (1): Linear(in_features=1280, out_features=3, bias=True)\n",
-      "  )\n",
-      ")\n",
-      "Using device: cuda\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# download pre-trained model, setup classification head\n",
     "model = torchvision.models.mobilenet_v2(weights=torchvision.models.MobileNet_V2_Weights.DEFAULT)\n",
     "model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)\n",
     "model.to(device)\n",
-    "print(model)\n",
-    "print(f\"Using device: {device}\")"
+    "\n",
+    "# setup loss function and optimizer\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3) # lr will be override by sparseml"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "b0a623e1",
+   "cell_type": "markdown",
+   "id": "08337acc",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
+    "Next, we will use SparseML's recipes to set the hyperparameters of training loop. In this case, we will use the following recipe:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 10.0\n",
+    "init_lr: 0.0005\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "```\n",
+    "\n",
+    "As you can see, the recipe includes an `!EpochRangeModifier` and a `!LearningRateFunctionModifier`. These modifiers simply set the number of epochs to train for and the learning rate schedule. As a result, the final model will be dense."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "d4553beb",
+   "execution_count": 17,
+   "id": "ceb62a05",
    "metadata": {},
    "outputs": [
     {
@@ -549,35 +285,55 @@
       "    init_lr: eval(init_lr)\r\n",
       "    lr_func: cosine\r\n",
       "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "# Phase 1 Sparse Transfer Learning / Recovery\r\n",
-      "sparse_transfer_learning_modifiers:\r\n",
-      "  - !ConstantPruningModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    params: __ALL_PRUNABLE__\r\n"
+      "    end_epoch: eval(num_epochs)"
      ]
     }
    ],
    "source": [
-    "!cat ./dense_model/dense-recipe.yaml"
+    "!cat ./recipes/mobilenetv2-beans-dense-recipe.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
+   "id": "0ff717b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dense_recipe_path = \"./recipes/mobilenetv2-beans-dense-recipe.yaml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09ee05d1",
+   "metadata": {},
+   "source": [
+    "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "1749e00d",
    "metadata": {},
    "outputs": [],
    "source": [
     "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./dense_model/dense-recipe.yaml\")\n",
+    "manager = ScheduledModifierManager.from_yaml(dense_recipe_path)\n",
     "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "db9ed163",
+   "metadata": {},
+   "source": [
+    "Kick off the transfer learning loop. Our run reached ~99% validation accuracy after 10 epochs."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "d00d175b",
    "metadata": {
     "scrolled": false
@@ -593,7 +349,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2c6b000774e4eaca928b6141d7c3213",
+       "model_id": "aaa6edfc1be94d68a8eee93e1691e3aa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -609,8 +365,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 0.49688769707625563\n",
-      "Top 1 Acc: 0.811411992263056\n",
+      "Training Loss: 0.47380749520027277\n",
+      "Top 1 Acc: 0.8152804642166345\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -618,7 +374,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "66fe4e0c0851461db6d884a40a607444",
+       "model_id": "b703e41c6e804bbeacf25aace9f67748",
        "version_major": 2,
        "version_minor": 0
       },
@@ -634,8 +390,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 0.10483174873515963\n",
-      "Top 1 Acc: 0.9624060150375939\n",
+      "Val Loss: 0.12894474416971208\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -643,7 +399,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f5b6964760f44e1f98094732aadae9fb",
+       "model_id": "e03a4c8f3706490ab3642e81b149c890",
        "version_major": 2,
        "version_minor": 0
       },
@@ -659,8 +415,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 0.13233664106916299\n",
-      "Top 1 Acc: 0.9709864603481625\n",
+      "Training Loss: 0.09328031384696563\n",
+      "Top 1 Acc: 0.9690522243713733\n",
       "\n",
       "Running Validation Epoch 2/10\n"
      ]
@@ -668,7 +424,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03589fa8706b4b12915f425382227d0e",
+       "model_id": "4a00338db9b54e1ebf5be2154d026bd4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -684,8 +440,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 0.10690611051395535\n",
-      "Top 1 Acc: 0.9624060150375939\n",
+      "Val Loss: 0.1064663665369153\n",
+      "Top 1 Acc: 0.9548872180451128\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -693,7 +449,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "38fbbd378dcf47eba5ba2964d9515d90",
+       "model_id": "87566c12df134f38ad2d9f831998f01c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -709,8 +465,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.061133282936432144\n",
-      "Top 1 Acc: 0.9893617021276596\n",
+      "Training Loss: 0.04011029944839803\n",
+      "Top 1 Acc: 0.9903288201160542\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -718,7 +474,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ead52a74d6a84abfafcf7a8e095df868",
+       "model_id": "f66ea6ca87944d63ab5d8a2a3fb07969",
        "version_major": 2,
        "version_minor": 0
       },
@@ -734,8 +490,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.05752026969566941\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.06654092976823449\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
       "Running Training Epoch 4/10\n"
      ]
@@ -743,7 +499,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6161054cadc746cc9f0972a15002e489",
+       "model_id": "9c0ab06fdf344eb4a98aa54b02f6e4b0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -759,8 +515,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.03709277432327243\n",
-      "Top 1 Acc: 0.988394584139265\n",
+      "Training Loss: 0.013448385923931544\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -768,7 +524,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "393de368a783482dbf640966209195b8",
+       "model_id": "f2d999410c2f42fe8ef5c695c51c908d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -784,7 +540,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.09043196495622396\n",
+      "Val Loss: 0.04685080051422119\n",
       "Top 1 Acc: 0.9699248120300752\n",
       "\n",
       "Running Training Epoch 5/10\n"
@@ -793,7 +549,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e07443d60a154ebc946e6e49c7c2e2bd",
+       "model_id": "178d61945ca94dffa214bdb40d7d6bfd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -809,8 +565,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.011832633150995454\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Loss: 0.011129878745343762\n",
+      "Top 1 Acc: 0.9970986460348162\n",
       "\n",
       "Running Validation Epoch 5/10\n"
      ]
@@ -818,7 +574,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8973eb27c13144489b86a1b4c8d7d0fd",
+       "model_id": "37456b087b96435dadb09b871e32694b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -834,7 +590,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.05438814461231232\n",
+      "Val Loss: 0.04289984308416024\n",
       "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 6/10\n"
@@ -843,7 +599,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "91c3c9f4af124e609dc1690dffe6b98d",
+       "model_id": "4b8d82516d1d40659e6b2ae7aabf59fb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -859,8 +615,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.012920034544852873\n",
-      "Top 1 Acc: 0.9970986460348162\n",
+      "Training Loss: 0.007474540554650241\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 6/10\n"
      ]
@@ -868,7 +624,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5abd456da44f4f11b1f6a495be13e49a",
+       "model_id": "b5bc30bbadd84ddebcc25510617ae2ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -884,8 +640,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.05623044967651367\n",
-      "Top 1 Acc: 0.9699248120300752\n",
+      "Val Loss: 0.038813505135476586\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 7/10\n"
      ]
@@ -893,7 +649,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3afa211b71a94cf8b40378aff72b4efa",
+       "model_id": "3df44744907d4833b6e925f8ed7ef4be",
        "version_major": 2,
        "version_minor": 0
       },
@@ -909,8 +665,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.008218912644114233\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Loss: 0.022992545157621586\n",
+      "Top 1 Acc: 0.9961315280464217\n",
       "\n",
       "Running Validation Epoch 7/10\n"
      ]
@@ -918,7 +674,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33f81ef07ca64adb92d3bdf059152274",
+       "model_id": "9b42a80e927a41559038ed25a5ba85d9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -934,8 +690,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.05101724807173014\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.038337701372802256\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -943,7 +699,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aeb0f166f778426f9af9739f97ab7ac1",
+       "model_id": "cfb91384c3ee4dc0aa18605ca65295b3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -959,8 +715,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.006811827950348908\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.01563219685693074\n",
+      "Top 1 Acc: 0.9941972920696325\n",
       "\n",
       "Running Validation Epoch 8/10\n"
      ]
@@ -968,7 +724,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4aad511d52e74ef981068b1cfffdf174",
+       "model_id": "ce3a2318d0d14f12b79e6d011afecaaa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -984,8 +740,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.0494396741501987\n",
-      "Top 1 Acc: 0.9548872180451128\n",
+      "Val Loss: 0.052491285931319\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -993,7 +749,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a7b8acace3c04d87b380c306b661159b",
+       "model_id": "15077760bb514451a4fc7fbc45762a6d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1009,8 +765,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.005579106432075302\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Loss: 0.004495378677154694\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
      ]
@@ -1018,7 +774,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "889d3c2e6a2f4560a0692f82b09e28f9",
+       "model_id": "770b9912502a4f5eaf820fda80fb0b5c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1034,8 +790,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.050181417539715764\n",
-      "Top 1 Acc: 0.9624060150375939\n",
+      "Val Loss: 0.03483513812534511\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -1043,7 +799,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c662492b659f455499d40c9585a0918c",
+       "model_id": "ec9f06d2c1b44fcb8273d49bd106a542",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1059,8 +815,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.006127264015517678\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Loss: 0.003678643000911865\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 10/10\n"
      ]
@@ -1068,7 +824,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b4cf87af07d404b935b414ead0be10c",
+       "model_id": "57a78c45e414444593c0dbcc0e08761e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1084,8 +840,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.04676330639049411\n",
-      "Top 1 Acc: 0.9624060150375939\n",
+      "Val Loss: 0.04160818513482809\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n"
      ]
     }
@@ -1097,100 +853,20 @@
     "    # run training loop\n",
     "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
     "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
     "\n",
     "    # run validation loop\n",
     "    print(f\"Running Validation Epoch {epoch_name}\")\n",
     "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
     "\n",
     "manager.finalize(model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "1217c3e9",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features.0.0.weight: 0.0000\n",
-      "features.1.conv.0.0.weight: 0.0000\n",
-      "features.1.conv.1.weight: 0.0000\n",
-      "features.2.conv.0.0.weight: 0.0000\n",
-      "features.2.conv.1.0.weight: 0.0000\n",
-      "features.2.conv.2.weight: 0.0000\n",
-      "features.3.conv.0.0.weight: 0.0000\n",
-      "features.3.conv.1.0.weight: 0.0000\n",
-      "features.3.conv.2.weight: 0.0000\n",
-      "features.4.conv.0.0.weight: 0.0000\n",
-      "features.4.conv.1.0.weight: 0.0000\n",
-      "features.4.conv.2.weight: 0.0000\n",
-      "features.5.conv.0.0.weight: 0.0000\n",
-      "features.5.conv.1.0.weight: 0.0000\n",
-      "features.5.conv.2.weight: 0.0000\n",
-      "features.6.conv.0.0.weight: 0.0000\n",
-      "features.6.conv.1.0.weight: 0.0000\n",
-      "features.6.conv.2.weight: 0.0000\n",
-      "features.7.conv.0.0.weight: 0.0000\n",
-      "features.7.conv.1.0.weight: 0.0000\n",
-      "features.7.conv.2.weight: 0.0000\n",
-      "features.8.conv.0.0.weight: 0.0000\n",
-      "features.8.conv.1.0.weight: 0.0000\n",
-      "features.8.conv.2.weight: 0.0000\n",
-      "features.9.conv.0.0.weight: 0.0000\n",
-      "features.9.conv.1.0.weight: 0.0000\n",
-      "features.9.conv.2.weight: 0.0000\n",
-      "features.10.conv.0.0.weight: 0.0000\n",
-      "features.10.conv.1.0.weight: 0.0000\n",
-      "features.10.conv.2.weight: 0.0000\n",
-      "features.11.conv.0.0.weight: 0.0000\n",
-      "features.11.conv.1.0.weight: 0.0000\n",
-      "features.11.conv.2.weight: 0.0000\n",
-      "features.12.conv.0.0.weight: 0.0000\n",
-      "features.12.conv.1.0.weight: 0.0000\n",
-      "features.12.conv.2.weight: 0.0000\n",
-      "features.13.conv.0.0.weight: 0.0000\n",
-      "features.13.conv.1.0.weight: 0.0000\n",
-      "features.13.conv.2.weight: 0.0000\n",
-      "features.14.conv.0.0.weight: 0.0000\n",
-      "features.14.conv.1.0.weight: 0.0000\n",
-      "features.14.conv.2.weight: 0.0000\n",
-      "features.15.conv.0.0.weight: 0.0000\n",
-      "features.15.conv.1.0.weight: 0.0000\n",
-      "features.15.conv.2.weight: 0.0000\n",
-      "features.16.conv.0.0.weight: 0.0000\n",
-      "features.16.conv.1.0.weight: 0.0000\n",
-      "features.16.conv.2.weight: 0.0000\n",
-      "features.17.conv.0.0.weight: 0.0000\n",
-      "features.17.conv.1.0.weight: 0.0000\n",
-      "features.17.conv.2.weight: 0.0000\n",
-      "features.18.0.weight: 0.0000\n",
-      "classifier.1.weight: 0.0000\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "11fa4cf2",
    "metadata": {},
    "outputs": [],
@@ -1199,7 +875,8 @@
     "\n",
     "save_dir = \"dense_model\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"mobilenet-v2-dense-beans.pth\")"
+    "exporter.export_pytorch(name=\"mobilenet-v2-dense-beans.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
    ]
   },
   {
@@ -1207,12 +884,14 @@
    "id": "273ce478",
    "metadata": {},
    "source": [
-    "## Prune The Model"
+    "## Step 4: Prune The Model\n",
+    "\n",
+    "With a model trained on Beans, we are now ready to apply the GMP algorithm to prune the model. The GMP algorithm is an interative pruning algorithm. At the end of each epoch, we identify the lowest magnitude weights (those closest to 0) and remove them from the network starting from an initial level of sparsity until a final level of sparsity. The remaining nonzero weights are then fine-tuned onto training dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 22,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
@@ -1229,310 +908,18 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "9154d351",
+   "cell_type": "markdown",
+   "id": "7f6f45e1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MobileNetV2(\n",
-      "  (features): Sequential(\n",
-      "    (0): Conv2dNormActivation(\n",
-      "      (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "      (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (2): ReLU6(inplace=True)\n",
-      "    )\n",
-      "    (1): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n",
-      "          (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (2): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n",
-      "          (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (3): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (4): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)\n",
-      "          (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (5): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (6): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (7): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)\n",
-      "          (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (8): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (9): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (10): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (11): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)\n",
-      "          (1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (12): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (13): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (14): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)\n",
-      "          (1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (15): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (16): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (17): InvertedResidual(\n",
-      "      (conv): Sequential(\n",
-      "        (0): Conv2dNormActivation(\n",
-      "          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (1): Conv2dNormActivation(\n",
-      "          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)\n",
-      "          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "          (2): ReLU6(inplace=True)\n",
-      "        )\n",
-      "        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      )\n",
-      "    )\n",
-      "    (18): Conv2dNormActivation(\n",
-      "      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
-      "      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "      (2): ReLU6(inplace=True)\n",
-      "    )\n",
-      "  )\n",
-      "  (classifier): Sequential(\n",
-      "    (0): Dropout(p=0.2, inplace=False)\n",
-      "    (1): Linear(in_features=1280, out_features=3, bias=True)\n",
-      "  )\n",
-      ")\n"
-     ]
-    }
-   ],
    "source": [
-    "print(model)"
+    "Next, we need to create a SparseML recipe which includes the GMP algorithm. The `!GlobalMagnitudePruningModifier` modifier instructs SparseML to apply the GMP algorithm at a global level (pruning the lowest magnitude weights across all layers).\n",
+    "\n",
+    "Firstly, we need to decide identify which parameters of the model to apply the GMP algorithm to. We can use the `get_prunable_layers` function to inspect:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 23,
    "id": "44578399",
    "metadata": {},
    "outputs": [
@@ -1540,139 +927,173 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 13.0\r\n",
-      "pruning_epochs: 10.0\r\n",
-      "init_lr: 0.0005\r\n",
-      "inter_func: cubic\r\n",
-      "mask_type: unstructured\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: 0.0\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "# Pruning\r\n",
-      "pruning_modifiers:\r\n",
-      "  - !GlobalMagnitudePruningModifier\r\n",
-      "    init_sparsity: 0.05\r\n",
-      "    final_sparsity: 0.90\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(pruning_epochs)\r\n",
-      "    update_frequency: 1.0\r\n",
-      "    params: \r\n",
-      "        - 'features.0.0.weight'\r\n",
-      "        - 'features.18.0.weight'\r\n",
-      "        - 're:features.*.conv.*.weight'\r\n",
-      "        - 're:features.*.conv.*.*.weight'\r\n",
-      "    leave_enabled: True\r\n",
-      "    inter_func: eval(inter_func)\r\n",
-      "    mask_type: eval(mask_type)\r\n",
-      "\r\n",
-      "finetuning_modifiers:\r\n",
-      "  - !ConstantPruningModifier\r\n",
-      "    start_epoch: eval(pruning_epochs)\r\n",
-      "    params: __ALL_PRUNABLE__"
+      "features.0.0\n",
+      "features.1.conv.0.0\n",
+      "features.1.conv.1\n",
+      "features.2.conv.0.0\n",
+      "features.2.conv.1.0\n",
+      "features.2.conv.2\n",
+      "features.3.conv.0.0\n",
+      "features.3.conv.1.0\n",
+      "features.3.conv.2\n",
+      "features.4.conv.0.0\n",
+      "features.4.conv.1.0\n",
+      "features.4.conv.2\n",
+      "features.5.conv.0.0\n",
+      "features.5.conv.1.0\n",
+      "features.5.conv.2\n",
+      "features.6.conv.0.0\n",
+      "features.6.conv.1.0\n",
+      "features.6.conv.2\n",
+      "features.7.conv.0.0\n",
+      "features.7.conv.1.0\n",
+      "features.7.conv.2\n",
+      "features.8.conv.0.0\n",
+      "features.8.conv.1.0\n",
+      "features.8.conv.2\n",
+      "features.9.conv.0.0\n",
+      "features.9.conv.1.0\n",
+      "features.9.conv.2\n",
+      "features.10.conv.0.0\n",
+      "features.10.conv.1.0\n",
+      "features.10.conv.2\n",
+      "features.11.conv.0.0\n",
+      "features.11.conv.1.0\n",
+      "features.11.conv.2\n",
+      "features.12.conv.0.0\n",
+      "features.12.conv.1.0\n",
+      "features.12.conv.2\n",
+      "features.13.conv.0.0\n",
+      "features.13.conv.1.0\n",
+      "features.13.conv.2\n",
+      "features.14.conv.0.0\n",
+      "features.14.conv.1.0\n",
+      "features.14.conv.2\n",
+      "features.15.conv.0.0\n",
+      "features.15.conv.1.0\n",
+      "features.15.conv.2\n",
+      "features.16.conv.0.0\n",
+      "features.16.conv.1.0\n",
+      "features.16.conv.2\n",
+      "features.17.conv.0.0\n",
+      "features.17.conv.1.0\n",
+      "features.17.conv.2\n",
+      "features.18.0\n",
+      "classifier.1\n"
      ]
     }
    ],
    "source": [
-    "!cat recipe-0.yaml"
+    "# print parameters\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5c95228",
+   "metadata": {},
+   "source": [
+    "We will apply pruning to each of the convs and exclude the classifier layer (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
+    "    \n",
+    "    - 'features.0.0.weight'\n",
+    "    - 'features.18.0.weight'\n",
+    "    - 're:features.*.conv.*.weight'\n",
+    "    - 're:features.*.conv.*.*.weight'\n",
+    "\n",
+    "Here is what the recipe looks like:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 13.0\n",
+    "pruning_epochs: 10.0\n",
+    "init_lr: 0.0005\n",
+    "inter_func: cubic\n",
+    "mask_type: unstructured\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "# Pruning\n",
+    "pruning_modifiers:\n",
+    "  - !GlobalMagnitudePruningModifier\n",
+    "    init_sparsity: 0.05\n",
+    "    final_sparsity: 0.90\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(pruning_epochs)\n",
+    "    update_frequency: 1.0\n",
+    "    params: \n",
+    "        - 'features.0.0.weight'\n",
+    "        - 'features.18.0.weight'\n",
+    "        - 're:features.*.conv.*.weight'\n",
+    "        - 're:features.*.conv.*.*.weight'\n",
+    "    leave_enabled: True\n",
+    "    inter_func: eval(inter_func)\n",
+    "    mask_type: eval(mask_type)\n",
+    "```\n",
+    "\n",
+    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
+    "\n",
+    "Over the final 3 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "id": "68646bc7",
+   "execution_count": null,
+   "id": "c2e5e7c2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(\"./recipe-0.yaml\")\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
+    "!cat ./recipes/mobilenetv2-beans-pruning-recipe.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "id": "00d26fb4",
+   "execution_count": 28,
+   "id": "238f3a83",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features.0.0.weight: 0.0000\n",
-      "features.1.conv.0.0.weight: 0.0000\n",
-      "features.1.conv.1.weight: 0.0000\n",
-      "features.2.conv.0.0.weight: 0.0000\n",
-      "features.2.conv.1.0.weight: 0.0000\n",
-      "features.2.conv.2.weight: 0.0000\n",
-      "features.3.conv.0.0.weight: 0.0000\n",
-      "features.3.conv.1.0.weight: 0.0000\n",
-      "features.3.conv.2.weight: 0.0000\n",
-      "features.4.conv.0.0.weight: 0.0000\n",
-      "features.4.conv.1.0.weight: 0.0000\n",
-      "features.4.conv.2.weight: 0.0000\n",
-      "features.5.conv.0.0.weight: 0.0000\n",
-      "features.5.conv.1.0.weight: 0.0000\n",
-      "features.5.conv.2.weight: 0.0000\n",
-      "features.6.conv.0.0.weight: 0.0000\n",
-      "features.6.conv.1.0.weight: 0.0000\n",
-      "features.6.conv.2.weight: 0.0000\n",
-      "features.7.conv.0.0.weight: 0.0000\n",
-      "features.7.conv.1.0.weight: 0.0000\n",
-      "features.7.conv.2.weight: 0.0000\n",
-      "features.8.conv.0.0.weight: 0.0000\n",
-      "features.8.conv.1.0.weight: 0.0000\n",
-      "features.8.conv.2.weight: 0.0000\n",
-      "features.9.conv.0.0.weight: 0.0000\n",
-      "features.9.conv.1.0.weight: 0.0000\n",
-      "features.9.conv.2.weight: 0.0000\n",
-      "features.10.conv.0.0.weight: 0.0000\n",
-      "features.10.conv.1.0.weight: 0.0000\n",
-      "features.10.conv.2.weight: 0.0000\n",
-      "features.11.conv.0.0.weight: 0.0000\n",
-      "features.11.conv.1.0.weight: 0.0000\n",
-      "features.11.conv.2.weight: 0.0000\n",
-      "features.12.conv.0.0.weight: 0.0000\n",
-      "features.12.conv.1.0.weight: 0.0000\n",
-      "features.12.conv.2.weight: 0.0000\n",
-      "features.13.conv.0.0.weight: 0.0000\n",
-      "features.13.conv.1.0.weight: 0.0000\n",
-      "features.13.conv.2.weight: 0.0000\n",
-      "features.14.conv.0.0.weight: 0.0000\n",
-      "features.14.conv.1.0.weight: 0.0000\n",
-      "features.14.conv.2.weight: 0.0000\n",
-      "features.15.conv.0.0.weight: 0.0000\n",
-      "features.15.conv.1.0.weight: 0.0000\n",
-      "features.15.conv.2.weight: 0.0000\n",
-      "features.16.conv.0.0.weight: 0.0000\n",
-      "features.16.conv.1.0.weight: 0.0000\n",
-      "features.16.conv.2.weight: 0.0000\n",
-      "features.17.conv.0.0.weight: 0.0000\n",
-      "features.17.conv.1.0.weight: 0.0000\n",
-      "features.17.conv.2.weight: 0.0000\n",
-      "features.18.0.weight: 0.0000\n",
-      "classifier.1.weight: 0.0000\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+    "pruning_recipe_path = \"./recipes/mobilenetv2-beans-pruning-recipe.yaml\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 32,
+   "id": "68646bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(pruning_recipe_path)\n",
+    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs\")\n",
+    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71213f1b",
+   "metadata": {},
+   "source": [
+    "Next, kick off the GMP training loop. \n",
+    "\n",
+    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to just the `optimizer` and `model` as usual, with all of the pruning-related logic specified by the declarative recipe interface.\n",
+    "\n",
+    "Our 90% pruned model reaches ~99% validation accuracy (vs ~99% for the dense model)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "id": "601c8c21",
    "metadata": {
     "scrolled": false
@@ -1688,7 +1109,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45a3d93cbb884f808293e613b1af95df",
+       "model_id": "1532a057991c4f92ae19103ed357b47d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1704,8 +1125,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/13\n",
-      "Training Loss: 0.05181791849088657\n",
-      "Top 1 Acc: 0.9835589941972921\n",
+      "Training Loss: 0.030314430911940606\n",
+      "Top 1 Acc: 0.9893617021276596\n",
       "\n",
       "Running Validation Epoch 1/13\n"
      ]
@@ -1713,7 +1134,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b1b5e3918a142809e09b1fcaaac71fe",
+       "model_id": "f8229930b27d4f22a4b9071e2047f0cd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1729,66 +1150,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/13\n",
-      "Val Loss: 0.08574707275256514\n",
-      "Top 1 Acc: 0.9624060150375939\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.0880\n",
-      "features.1.conv.0.0.weight: 0.0729\n",
-      "features.1.conv.1.weight: 0.0820\n",
-      "features.2.conv.0.0.weight: 0.0293\n",
-      "features.2.conv.1.0.weight: 0.0081\n",
-      "features.2.conv.2.weight: 0.0169\n",
-      "features.3.conv.0.0.weight: 0.0321\n",
-      "features.3.conv.1.0.weight: 0.0255\n",
-      "features.3.conv.2.weight: 0.0286\n",
-      "features.4.conv.0.0.weight: 0.0240\n",
-      "features.4.conv.1.0.weight: 0.0008\n",
-      "features.4.conv.2.weight: 0.0197\n",
-      "features.5.conv.0.0.weight: 0.0343\n",
-      "features.5.conv.1.0.weight: 0.0237\n",
-      "features.5.conv.2.weight: 0.0288\n",
-      "features.6.conv.0.0.weight: 0.0319\n",
-      "features.6.conv.1.0.weight: 0.0272\n",
-      "features.6.conv.2.weight: 0.0334\n",
-      "features.7.conv.0.0.weight: 0.0218\n",
-      "features.7.conv.1.0.weight: 0.0035\n",
-      "features.7.conv.2.weight: 0.0228\n",
-      "features.8.conv.0.0.weight: 0.0445\n",
-      "features.8.conv.1.0.weight: 0.0341\n",
-      "features.8.conv.2.weight: 0.0502\n",
-      "features.9.conv.0.0.weight: 0.0424\n",
-      "features.9.conv.1.0.weight: 0.0292\n",
-      "features.9.conv.2.weight: 0.0465\n",
-      "features.10.conv.0.0.weight: 0.0409\n",
-      "features.10.conv.1.0.weight: 0.0301\n",
-      "features.10.conv.2.weight: 0.0433\n",
-      "features.11.conv.0.0.weight: 0.0298\n",
-      "features.11.conv.1.0.weight: 0.0388\n",
-      "features.11.conv.2.weight: 0.0329\n",
-      "features.12.conv.0.0.weight: 0.0428\n",
-      "features.12.conv.1.0.weight: 0.0266\n",
-      "features.12.conv.2.weight: 0.0466\n",
-      "features.13.conv.0.0.weight: 0.0434\n",
-      "features.13.conv.1.0.weight: 0.0249\n",
-      "features.13.conv.2.weight: 0.0480\n",
-      "features.14.conv.0.0.weight: 0.0341\n",
-      "features.14.conv.1.0.weight: 0.0008\n",
-      "features.14.conv.2.weight: 0.0362\n",
-      "features.15.conv.0.0.weight: 0.0569\n",
-      "features.15.conv.1.0.weight: 0.0270\n",
-      "features.15.conv.2.weight: 0.0610\n",
-      "features.16.conv.0.0.weight: 0.0499\n",
-      "features.16.conv.1.0.weight: 0.0309\n",
-      "features.16.conv.2.weight: 0.0591\n",
-      "features.17.conv.0.0.weight: 0.0420\n",
-      "features.17.conv.1.0.weight: 0.0058\n",
-      "features.17.conv.2.weight: 0.0554\n",
-      "features.18.0.weight: 0.0610\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.10016061989590526\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
       "Running Training Epoch 2/13\n"
      ]
@@ -1796,7 +1159,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f16282ed566640b0a5932df06e6cbf44",
+       "model_id": "b044a0e5d8414ea68ee41547c4a27210",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1812,8 +1175,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/13\n",
-      "Training Loss: 0.01928500584361962\n",
-      "Top 1 Acc: 0.9941972920696325\n",
+      "Training Loss: 0.03320332106721418\n",
+      "Top 1 Acc: 0.9903288201160542\n",
       "\n",
       "Running Validation Epoch 2/13\n"
      ]
@@ -1821,7 +1184,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8c088aadcbc842a188c6f148e5af0f21",
+       "model_id": "e5dd99cf59ab499db75f0cebe474a798",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1837,66 +1200,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/13\n",
-      "Val Loss: 0.04039517091587186\n",
-      "Top 1 Acc: 0.9849624060150376\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.1910\n",
-      "features.1.conv.0.0.weight: 0.1319\n",
-      "features.1.conv.1.weight: 0.1875\n",
-      "features.2.conv.0.0.weight: 0.1680\n",
-      "features.2.conv.1.0.weight: 0.0532\n",
-      "features.2.conv.2.weight: 0.1115\n",
-      "features.3.conv.0.0.weight: 0.1450\n",
-      "features.3.conv.1.0.weight: 0.1142\n",
-      "features.3.conv.2.weight: 0.1534\n",
-      "features.4.conv.0.0.weight: 0.1279\n",
-      "features.4.conv.1.0.weight: 0.0085\n",
-      "features.4.conv.2.weight: 0.1152\n",
-      "features.5.conv.0.0.weight: 0.1758\n",
-      "features.5.conv.1.0.weight: 0.1337\n",
-      "features.5.conv.2.weight: 0.1759\n",
-      "features.6.conv.0.0.weight: 0.1637\n",
-      "features.6.conv.1.0.weight: 0.1337\n",
-      "features.6.conv.2.weight: 0.1898\n",
-      "features.7.conv.0.0.weight: 0.1243\n",
-      "features.7.conv.1.0.weight: 0.0069\n",
-      "features.7.conv.2.weight: 0.1381\n",
-      "features.8.conv.0.0.weight: 0.2351\n",
-      "features.8.conv.1.0.weight: 0.1765\n",
-      "features.8.conv.2.weight: 0.2569\n",
-      "features.9.conv.0.0.weight: 0.2288\n",
-      "features.9.conv.1.0.weight: 0.1837\n",
-      "features.9.conv.2.weight: 0.2562\n",
-      "features.10.conv.0.0.weight: 0.2262\n",
-      "features.10.conv.1.0.weight: 0.1927\n",
-      "features.10.conv.2.weight: 0.2558\n",
-      "features.11.conv.0.0.weight: 0.1801\n",
-      "features.11.conv.1.0.weight: 0.2274\n",
-      "features.11.conv.2.weight: 0.1899\n",
-      "features.12.conv.0.0.weight: 0.2417\n",
-      "features.12.conv.1.0.weight: 0.1530\n",
-      "features.12.conv.2.weight: 0.2620\n",
-      "features.13.conv.0.0.weight: 0.2393\n",
-      "features.13.conv.1.0.weight: 0.1458\n",
-      "features.13.conv.2.weight: 0.2699\n",
-      "features.14.conv.0.0.weight: 0.1983\n",
-      "features.14.conv.1.0.weight: 0.0023\n",
-      "features.14.conv.2.weight: 0.2086\n",
-      "features.15.conv.0.0.weight: 0.3115\n",
-      "features.15.conv.1.0.weight: 0.1448\n",
-      "features.15.conv.2.weight: 0.3267\n",
-      "features.16.conv.0.0.weight: 0.2854\n",
-      "features.16.conv.1.0.weight: 0.1593\n",
-      "features.16.conv.2.weight: 0.3279\n",
-      "features.17.conv.0.0.weight: 0.2446\n",
-      "features.17.conv.1.0.weight: 0.0365\n",
-      "features.17.conv.2.weight: 0.3121\n",
-      "features.18.0.weight: 0.3458\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.05205255227629095\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 3/13\n"
      ]
@@ -1904,7 +1209,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab8d6edec3194b9582d4116e9314ca1d",
+       "model_id": "ff9d00403a5c4a4a951d7c4c7a280da0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1920,8 +1225,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/13\n",
-      "Training Loss: 0.02513152473274562\n",
-      "Top 1 Acc: 0.9941972920696325\n",
+      "Training Loss: 0.02863485404558367\n",
+      "Top 1 Acc: 0.9922630560928434\n",
       "\n",
       "Running Validation Epoch 3/13\n"
      ]
@@ -1929,7 +1234,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3e2265f99bca47e8b7c668a736d1b153",
+       "model_id": "e239549148104b41a2c845856c105288",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1945,66 +1250,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/13\n",
-      "Val Loss: 0.04448752112220973\n",
-      "Top 1 Acc: 0.9924812030075187\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.2558\n",
-      "features.1.conv.0.0.weight: 0.1944\n",
-      "features.1.conv.1.weight: 0.2910\n",
-      "features.2.conv.0.0.weight: 0.2728\n",
-      "features.2.conv.1.0.weight: 0.0891\n",
-      "features.2.conv.2.weight: 0.1910\n",
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-      "features.3.conv.1.0.weight: 0.1975\n",
-      "features.3.conv.2.weight: 0.2564\n",
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-      "features.4.conv.2.weight: 0.2018\n",
-      "features.5.conv.0.0.weight: 0.2970\n",
-      "features.5.conv.1.0.weight: 0.2193\n",
-      "features.5.conv.2.weight: 0.2944\n",
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-      "features.7.conv.0.0.weight: 0.2137\n",
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-      "features.8.conv.0.0.weight: 0.3953\n",
-      "features.8.conv.1.0.weight: 0.2914\n",
-      "features.8.conv.2.weight: 0.4281\n",
-      "features.9.conv.0.0.weight: 0.3828\n",
-      "features.9.conv.1.0.weight: 0.3137\n",
-      "features.9.conv.2.weight: 0.4207\n",
-      "features.10.conv.0.0.weight: 0.3823\n",
-      "features.10.conv.1.0.weight: 0.3484\n",
-      "features.10.conv.2.weight: 0.4315\n",
-      "features.11.conv.0.0.weight: 0.3088\n",
-      "features.11.conv.1.0.weight: 0.3733\n",
-      "features.11.conv.2.weight: 0.3270\n",
-      "features.12.conv.0.0.weight: 0.4098\n",
-      "features.12.conv.1.0.weight: 0.2824\n",
-      "features.12.conv.2.weight: 0.4380\n",
-      "features.13.conv.0.0.weight: 0.4031\n",
-      "features.13.conv.1.0.weight: 0.2764\n",
-      "features.13.conv.2.weight: 0.4497\n",
-      "features.14.conv.0.0.weight: 0.3376\n",
-      "features.14.conv.1.0.weight: 0.0069\n",
-      "features.14.conv.2.weight: 0.3574\n",
-      "features.15.conv.0.0.weight: 0.5099\n",
-      "features.15.conv.1.0.weight: 0.2616\n",
-      "features.15.conv.2.weight: 0.5345\n",
-      "features.16.conv.0.0.weight: 0.4732\n",
-      "features.16.conv.1.0.weight: 0.2870\n",
-      "features.16.conv.2.weight: 0.5376\n",
-      "features.17.conv.0.0.weight: 0.4112\n",
-      "features.17.conv.1.0.weight: 0.1214\n",
-      "features.17.conv.2.weight: 0.5147\n",
-      "features.18.0.weight: 0.5670\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.04446154618635774\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 4/13\n"
      ]
@@ -2012,7 +1259,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5d30d22947442c3aa9a796ab1f3a0f0",
+       "model_id": "0394c6e5c0d749daae91ac40c14d9577",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2028,8 +1275,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/13\n",
-      "Training Loss: 0.0277155696160414\n",
-      "Top 1 Acc: 0.9903288201160542\n",
+      "Training Loss: 0.028403193393552847\n",
+      "Top 1 Acc: 0.9932301740812379\n",
       "\n",
       "Running Validation Epoch 4/13\n"
      ]
@@ -2037,7 +1284,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e438aeaf9d274f31aa15e0880e59276f",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -2053,66 +1300,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/13\n",
-      "Val Loss: 0.05159115699352697\n",
-      "Top 1 Acc: 0.9849624060150376\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.3241\n",
-      "features.1.conv.0.0.weight: 0.2535\n",
-      "features.1.conv.1.weight: 0.3340\n",
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-      "features.2.conv.2.weight: 0.2582\n",
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-      "features.6.conv.0.0.weight: 0.3833\n",
-      "features.6.conv.1.0.weight: 0.3409\n",
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-      "features.8.conv.1.0.weight: 0.3961\n",
-      "features.8.conv.2.weight: 0.5641\n",
-      "features.9.conv.0.0.weight: 0.5110\n",
-      "features.9.conv.1.0.weight: 0.4265\n",
-      "features.9.conv.2.weight: 0.5567\n",
-      "features.10.conv.0.0.weight: 0.5105\n",
-      "features.10.conv.1.0.weight: 0.4737\n",
-      "features.10.conv.2.weight: 0.5682\n",
-      "features.11.conv.0.0.weight: 0.4186\n",
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-      "features.11.conv.2.weight: 0.4411\n",
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-      "features.12.conv.1.0.weight: 0.4186\n",
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-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.021256184950470925\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 5/13\n"
      ]
@@ -2120,7 +1309,7 @@
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      "text": [
       "Training Epoch: 5/13\n",
-      "Training Loss: 0.05216440672732212\n",
-      "Top 1 Acc: 0.9874274661508704\n",
+      "Training Loss: 0.10820412065720919\n",
+      "Top 1 Acc: 0.9671179883945842\n",
       "\n",
       "Running Validation Epoch 5/13\n"
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@@ -2161,74 +1350,16 @@
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      "text": [
       "Validation Epoch: 5/13\n",
-      "Val Loss: 0.033026803645771\n",
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       "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.3889\n",
-      "features.1.conv.0.0.weight: 0.3368\n",
-      "features.1.conv.1.weight: 0.3809\n",
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-      "features.12.conv.1.0.weight: 0.5409\n",
-      "features.12.conv.2.weight: 0.6970\n",
-      "features.13.conv.0.0.weight: 0.6503\n",
-      "features.13.conv.1.0.weight: 0.5656\n",
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-      "features.15.conv.0.0.weight: 0.7713\n",
-      "features.15.conv.1.0.weight: 0.5274\n",
-      "features.15.conv.2.weight: 0.7982\n",
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-      "features.18.0.weight: 0.8380\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
-      "\n",
       "Running Training Epoch 6/13\n"
      ]
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     {
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      "output_type": "stream",
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       "Training Epoch: 6/13\n",
-      "Training Loss: 0.08560408513541475\n",
-      "Top 1 Acc: 0.9806576402321083\n",
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       "\n",
       "Running Validation Epoch 6/13\n"
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      "text": [
       "Validation Epoch: 6/13\n",
-      "Val Loss: 0.047504376620054245\n",
-      "Top 1 Acc: 0.9924812030075187\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4282\n",
-      "features.1.conv.0.0.weight: 0.3854\n",
-      "features.1.conv.1.weight: 0.4277\n",
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-      "features.13.conv.0.0.weight: 0.7355\n",
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-      "features.16.conv.2.weight: 0.8720\n",
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-      "features.18.0.weight: 0.9067\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.07981351003982126\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 7/13\n"
      ]
@@ -2336,7 +1409,7 @@
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      "data": {
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      "output_type": "stream",
      "text": [
       "Training Epoch: 7/13\n",
-      "Training Loss: 0.13826644358535609\n",
-      "Top 1 Acc: 0.9555125725338491\n",
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+      "Top 1 Acc: 0.9332688588007737\n",
       "\n",
       "Running Validation Epoch 7/13\n"
      ]
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     {
      "data": {
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@@ -2377,74 +1450,16 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/13\n",
-      "Val Loss: 0.06480909623205662\n",
+      "Val Loss: 0.07885787561535836\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4583\n",
-      "features.1.conv.0.0.weight: 0.4201\n",
-      "features.1.conv.1.weight: 0.4590\n",
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-      "features.12.conv.2.weight: 0.8414\n",
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-      "features.15.conv.1.0.weight: 0.7072\n",
-      "features.15.conv.2.weight: 0.9140\n",
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-      "features.18.0.weight: 0.9459\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
-      "\n",
       "Running Training Epoch 8/13\n"
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     {
      "data": {
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      "output_type": "stream",
      "text": [
       "Training Epoch: 8/13\n",
-      "Training Loss: 0.24723159487951885\n",
-      "Top 1 Acc: 0.9168278529980658\n",
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       "\n",
       "Running Validation Epoch 8/13\n"
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     {
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@@ -2485,66 +1500,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/13\n",
-      "Val Loss: 0.12623528838157655\n",
-      "Top 1 Acc: 0.9699248120300752\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4769\n",
-      "features.1.conv.0.0.weight: 0.4410\n",
-      "features.1.conv.1.weight: 0.4902\n",
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-      "\n",
+      "Val Loss: 0.08072680607438087\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 9/13\n"
      ]
@@ -2552,7 +1509,7 @@
     {
      "data": {
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      "output_type": "stream",
      "text": [
       "Training Epoch: 9/13\n",
-      "Training Loss: 0.1767726969538313\n",
-      "Top 1 Acc: 0.9487427466150871\n",
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       "\n",
       "Running Validation Epoch 9/13\n"
      ]
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     {
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@@ -2593,74 +1550,16 @@
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       "Validation Epoch: 9/13\n",
-      "Val Loss: 0.14729779064655305\n",
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       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4873\n",
-      "features.1.conv.0.0.weight: 0.4549\n",
-      "features.1.conv.1.weight: 0.5000\n",
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-      "features.12.conv.1.0.weight: 0.7863\n",
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-      "\n",
-      "\n",
       "Running Training Epoch 10/13\n"
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      "output_type": "stream",
      "text": [
       "Training Epoch: 10/13\n",
-      "Training Loss: 0.12266924277399525\n",
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       "\n",
       "Running Validation Epoch 10/13\n"
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       "Validation Epoch: 10/13\n",
-      "Val Loss: 0.104463392496109\n",
-      "Top 1 Acc: 0.9849624060150376\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4919\n",
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-      "\n",
+      "Val Loss: 0.06663795448839664\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 11/13\n"
      ]
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      "output_type": "stream",
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       "Training Epoch: 11/13\n",
-      "Training Loss: 0.08148531587512205\n",
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       "\n",
       "Running Validation Epoch 11/13\n"
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       "Validation Epoch: 11/13\n",
-      "Val Loss: 0.07646273151040077\n",
-      "Top 1 Acc: 0.9774436090225563\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4919\n",
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-      "features.1.conv.1.weight: 0.5156\n",
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-      "\n",
+      "Val Loss: 0.0655035775154829\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 12/13\n"
      ]
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      "output_type": "stream",
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       "Training Epoch: 12/13\n",
-      "Training Loss: 0.0749947436605439\n",
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       "\n",
       "Running Validation Epoch 12/13\n"
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     {
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      "text": [
       "Validation Epoch: 12/13\n",
-      "Val Loss: 0.07313197031617165\n",
-      "Top 1 Acc: 0.9774436090225563\n",
-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
-      "features.0.0.weight: 0.4919\n",
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-      "\n",
+      "Val Loss: 0.061416388303041455\n",
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       "\n",
       "Running Training Epoch 13/13\n"
      ]
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      "output_type": "stream",
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       "Training Epoch: 13/13\n",
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       "\n",
       "Running Validation Epoch 13/13\n"
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       "Validation Epoch: 13/13\n",
-      "Val Loss: 0.07470075637102128\n",
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-      "\n",
-      "\n",
-      "\n",
-      "Checking Sparsity Level:\n",
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-      "features.11.conv.2.weight: 0.7720\n",
-      "features.12.conv.0.0.weight: 0.8809\n",
-      "features.12.conv.1.0.weight: 0.7944\n",
-      "features.12.conv.2.weight: 0.9048\n",
-      "features.13.conv.0.0.weight: 0.8716\n",
-      "features.13.conv.1.0.weight: 0.8177\n",
-      "features.13.conv.2.weight: 0.9112\n",
-      "features.14.conv.0.0.weight: 0.7858\n",
-      "features.14.conv.1.0.weight: 0.6860\n",
-      "features.14.conv.2.weight: 0.8276\n",
-      "features.15.conv.0.0.weight: 0.9448\n",
-      "features.15.conv.1.0.weight: 0.7856\n",
-      "features.15.conv.2.weight: 0.9567\n",
-      "features.16.conv.0.0.weight: 0.9241\n",
-      "features.16.conv.1.0.weight: 0.7929\n",
-      "features.16.conv.2.weight: 0.9572\n",
-      "features.17.conv.0.0.weight: 0.8773\n",
-      "features.17.conv.1.0.weight: 0.9105\n",
-      "features.17.conv.2.weight: 0.9516\n",
-      "features.18.0.weight: 0.9785\n",
-      "classifier.1.weight: 0.0000\n",
-      "\n",
+      "Val Loss: 0.06589164286851883\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n"
      ]
     }
    ],
    "source": [
-    "# run transfer learning\n",
+    "# run GMP algorithm\n",
     "epoch = 0\n",
     "for epoch in range(manager.max_epochs):\n",
     "    # run training loop\n",
     "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
     "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
     "\n",
     "    # run validation loop\n",
     "    print(f\"Running Validation Epoch {epoch_name}\")\n",
     "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
     "    \n",
-    "    print(f\"\\n\\nChecking Sparsity Level:\")\n",
-    "    for (name, layer) in get_prunable_layers(model):\n",
-    "        print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")\n",
-    "    print(f\"\\n\")\n",
+    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
     "    \n",
     "manager.finalize(model)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "a59b80c2",
+   "metadata": {},
+   "source": [
+    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
+    "\n",
+    "![tensorboard output](./images/mobilenetv2-beans-tensorboard-output.png)\n",
+    "\n",
+    "We can print layer-by-layer sparsity as well."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "417ac105",
+   "execution_count": 35,
+   "id": "cae6584a",
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/ubuntu/rshaw/python-venvs/sparseml-env/lib/python3.8/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
-      "  warnings.warn(\n"
+      "features.0.0.weight: 0.4861\n",
+      "features.1.conv.0.0.weight: 0.4688\n",
+      "features.1.conv.1.weight: 0.5156\n",
+      "features.2.conv.0.0.weight: 0.5547\n",
+      "features.2.conv.1.0.weight: 0.2905\n",
+      "features.2.conv.2.weight: 0.4865\n",
+      "features.3.conv.0.0.weight: 0.6102\n",
+      "features.3.conv.1.0.weight: 0.4961\n",
+      "features.3.conv.2.weight: 0.6111\n",
+      "features.4.conv.0.0.weight: 0.5229\n",
+      "features.4.conv.1.0.weight: 0.2731\n",
+      "features.4.conv.2.weight: 0.5187\n",
+      "features.5.conv.0.0.weight: 0.6901\n",
+      "features.5.conv.1.0.weight: 0.5880\n",
+      "features.5.conv.2.weight: 0.7126\n",
+      "features.6.conv.0.0.weight: 0.6934\n",
+      "features.6.conv.1.0.weight: 0.6111\n",
+      "features.6.conv.2.weight: 0.7402\n",
+      "features.7.conv.0.0.weight: 0.5495\n",
+      "features.7.conv.1.0.weight: 0.3634\n",
+      "features.7.conv.2.weight: 0.6127\n",
+      "features.8.conv.0.0.weight: 0.8370\n",
+      "features.8.conv.1.0.weight: 0.7405\n",
+      "features.8.conv.2.weight: 0.8673\n",
+      "features.9.conv.0.0.weight: 0.8272\n",
+      "features.9.conv.1.0.weight: 0.7384\n",
+      "features.9.conv.2.weight: 0.8634\n",
+      "features.10.conv.0.0.weight: 0.8271\n",
+      "features.10.conv.1.0.weight: 0.7870\n",
+      "features.10.conv.2.weight: 0.8725\n",
+      "features.11.conv.0.0.weight: 0.7251\n",
+      "features.11.conv.1.0.weight: 0.7980\n",
+      "features.11.conv.2.weight: 0.7725\n",
+      "features.12.conv.0.0.weight: 0.8820\n",
+      "features.12.conv.1.0.weight: 0.7932\n",
+      "features.12.conv.2.weight: 0.9047\n",
+      "features.13.conv.0.0.weight: 0.8708\n",
+      "features.13.conv.1.0.weight: 0.8169\n",
+      "features.13.conv.2.weight: 0.9118\n",
+      "features.14.conv.0.0.weight: 0.7866\n",
+      "features.14.conv.1.0.weight: 0.6883\n",
+      "features.14.conv.2.weight: 0.8266\n",
+      "features.15.conv.0.0.weight: 0.9452\n",
+      "features.15.conv.1.0.weight: 0.7866\n",
+      "features.15.conv.2.weight: 0.9565\n",
+      "features.16.conv.0.0.weight: 0.9238\n",
+      "features.16.conv.1.0.weight: 0.7926\n",
+      "features.16.conv.2.weight: 0.9577\n",
+      "features.17.conv.0.0.weight: 0.8770\n",
+      "features.17.conv.1.0.weight: 0.9112\n",
+      "features.17.conv.2.weight: 0.9516\n",
+      "features.18.0.weight: 0.9784\n",
+      "classifier.1.weight: 0.0000\n"
      ]
     }
    ],
    "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"experiment-0\"\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"mobilenet_v2-sparse-experiment-0.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "d7193990",
+   "cell_type": "markdown",
+   "id": "00805b02",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "checkpoint2 = torch.load(\"./dense_model/training/mobilenet-v2-dense-beans.pth\")\n",
-    "model2 = torchvision.models.mobilenet_v2()\n",
-    "model2.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, NUM_LABELS)\n",
-    "model2.load_state_dict(checkpoint['state_dict'])"
+    "Finally, export your model to ONNX."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "b4333b3a",
+   "execution_count": 37,
+   "id": "4a44061c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "exporter = ModuleExporter(model2, output_dir=\"./test\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
+    "save_dir = \"experiment-0\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"mobilenet_v2-sparse-beans.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59702f10",
+   "cell_type": "markdown",
+   "id": "7960612c",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Wrap Up\n",
+    "\n",
+    "The resulting model is is 90% sparse and achieves validation accuracy of ~99% (vs the unoptimized dense model at ~99%) without much hyperparameter search.\n",
+    "\n",
+    "Key hyperparameter experiments you may want to run include:\n",
+    "- Learning rate\n",
+    "- Learning rate schedule\n",
+    "- Sparsity level\n",
+    "- Number of pruning epochs\n",
+    "\n",
+    "DeepSparse supports speedup from pruning and quantization. To reach maximum performance, check out our examples of quantizing a model!"
+   ]
   }
  ],
  "metadata": {
diff --git a/integrations/torchvision/tutorials/images/densenet-tensorboard-output.png b/integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png
similarity index 100%
rename from integrations/torchvision/tutorials/images/densenet-tensorboard-output.png
rename to integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png
diff --git a/integrations/torchvision/tutorials/images/mobilenetv2-beans-tensorboard-output.png b/integrations/torchvision/tutorials/images/mobilenetv2-beans-tensorboard-output.png
new file mode 100644
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HcmV?d00001

diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
index 7636af2000d..cd66d90908f 100644
--- a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-dense-recipe.yaml
@@ -13,10 +13,4 @@ training_modifiers:
     init_lr: eval(init_lr)
     lr_func: cosine
     start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
+    end_epoch: eval(num_epochs)
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
index 0d42bfc46a8..f995f3194af 100644
--- a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
@@ -32,9 +32,4 @@ pruning_modifiers:
         - 're:features.*.conv.*.*.weight'
     leave_enabled: True
     inter_func: eval(inter_func)
-    mask_type: eval(mask_type)
-
-finetuning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: eval(pruning_epochs)
-    params: __ALL_PRUNABLE__
\ No newline at end of file
+    mask_type: eval(mask_type)
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
index 7636af2000d..cd66d90908f 100644
--- a/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/resnet50-beans-dense-recipe.yaml
@@ -13,10 +13,4 @@ training_modifiers:
     init_lr: eval(init_lr)
     lr_func: cosine
     start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
+    end_epoch: eval(num_epochs)
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
index 549ceeaf098..1bc94036805 100644
--- a/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
@@ -1,5 +1,6 @@
 # Epoch and Learning-Rate variables
-num_epochs: 10.0
+num_epochs: 13.0
+pruning_epochs: 10.0
 init_lr: 0.00025
 inter_func: cubic
 mask_type: unstructured
@@ -22,9 +23,10 @@ pruning_modifiers:
      init_sparsity: 0.05
      final_sparsity: 0.90
      start_epoch: 0.0
-     end_epoch: eval(num_epochs)
+     end_epoch: eval(pruning_epochs)
      update_frequency: 1.0
      params: 
+        - 'conv1.weight'
         - 're:layer1.*.conv1.weight'
         - 're:layer1.*.conv2.weight'
         - 're:layer1.*.conv3.weight'
@@ -43,4 +45,4 @@ pruning_modifiers:
         - 're:layer4.0.downsample.0.weight'
      leave_enabled: True
      inter_func: eval(inter_func)
-     mask_type: eval(mask_type)
+     mask_type: eval(mask_type)
\ No newline at end of file

From 22834e7f6e3af6290ed716f75a9ad0aa46fb5405 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 23:35:27 +0000
Subject: [PATCH 12/29] updated densenet example to include quantization

---
 integrations/torchvision/README.md            |    4 +-
 ...rchvision-python-transfer-imagenette.ipynb |   39 +-
 ...ratch-densenet-flowers-pruned-quant.ipynb} | 1127 +++++++++++------
 ...rsify-from-scratch-mobilenetv2-beans.ipynb |   59 +-
 ...parsify-from-scratch-resnet50-beans.ipynb} |    0
 .../densenet-flowers-tensorboard-output.png   |  Bin 477355 -> 700598 bytes
 .../densenet-flowers-pruning-recipe.yaml      |    4 +-
 .../recipes/quantization-recipe.yaml          |   29 +
 8 files changed, 809 insertions(+), 453 deletions(-)
 rename integrations/torchvision/tutorials/{docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb => docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb} (61%)
 rename integrations/torchvision/tutorials/{docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb => docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb} (100%)
 create mode 100644 integrations/torchvision/tutorials/recipes/quantization-recipe.yaml

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index bc58efbf18d..bc895aec666 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -39,7 +39,9 @@ pip install sparseml[torchvision]
 - [Sparse Transfer Learning with the CLI](tutorials/sparse-transfer-learning.md)
 - [Sparse Transfer Learning with the Python API](tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
 - Sparsification from Scratch with the CLI (coming soon!)
-- Sparsification from Scratch with the Python API (coming soon!)
+- [Sparsification from Scratch with the Python API - ResNet-50](tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb)
+- [Sparsification from Scratch with the Python API - MobileNetv2](tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb)
+- [Sparsification from Scratch with the Python API - DenseNet-121](tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb )
 
 ## Quick Tour
 
diff --git a/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb b/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
index bdf0a4d5685..69cc1f0138d 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb
@@ -276,7 +276,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "KCMRFLFN8024"
@@ -358,7 +357,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "id": "nrgF_ur_B66y"
@@ -764,15 +762,7 @@
     "id": "eJv4S9fkGjdO",
     "outputId": "df458b06-3ff4-4a0d-c1c2-3689799abaf5"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "img_path = \"/root/.cache/nm_datasets/imagenette/imagenette-320/val/n03417042/ILSVRC2012_val_00001144.JPEG\" \n",
     "images = [img_path]\n",
@@ -800,30 +790,7 @@
     "id": "79Xj9eqvJXaD",
     "outputId": "2bdfbe4d-ab3c-4cc4-c54f-a6be6cbb2b2b"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fd544213940>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 431.1x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from PIL import Image\n",
     "import numpy as np\n",
@@ -857,7 +824,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.10"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
diff --git a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
similarity index 61%
rename from integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
rename to integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
index 171c56d35db..725441a825c 100644
--- a/integrations/torchvision/tutorials/docs-docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "1ad80edf",
    "metadata": {},
    "outputs": [],
@@ -69,7 +69,7 @@
    "outputs": [],
    "source": [
     "NUM_LABELS = 102\n",
-    "BATCH_SIZE = 32\n",
+    "BATCH_SIZE = 16\n",
     "\n",
     "# imagenet transforms\n",
     "imagenet_transform = transforms.Compose([\n",
@@ -236,33 +236,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "8653c9bc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 10.0\r\n",
-      "init_lr: 0.0005\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: 0.0\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!cat ./recipes/densenet-flowers-dense-recipe.yaml"
    ]
@@ -277,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "1749e00d",
    "metadata": {},
    "outputs": [],
@@ -292,12 +269,12 @@
    "id": "ee67c917",
    "metadata": {},
    "source": [
-    "Kick off the transfer learning loop. Our run reached ~92.5% validation accuracy after 10 epochs."
+    "Kick off the transfer learning loop. Our run reached ~91.8% validation accuracy after 10 epochs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "d00d175b",
    "metadata": {
     "scrolled": true
@@ -313,12 +290,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb1f4f772bc540b0b3725dddfa1b0f49",
+       "model_id": "e3bff056f89743c3a030bf38186113dd",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -329,8 +306,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 3.6993999630212784\n",
-      "Top 1 Acc: 0.2823529411764706\n",
+      "Training Loss: 3.705597158521414\n",
+      "Top 1 Acc: 0.25392156862745097\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -338,12 +315,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4865f146cabb4142b9eb8b7a6a7c59f9",
+       "model_id": "b6979449794948349a2ccc8599f9958d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -354,8 +331,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 2.32899521663785\n",
-      "Top 1 Acc: 0.6235294117647059\n",
+      "Val Loss: 2.1234971778467298\n",
+      "Top 1 Acc: 0.5637254901960784\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -363,12 +340,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d973db1bca35479c8e5482dc2a8b45e9",
+       "model_id": "6a116d25fa6a45d193d8203737d4ecfb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/32 [00:00<?, ?it/s]"
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -379,8 +356,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 1.3755246959626675\n",
-      "Top 1 Acc: 0.8823529411764706\n",
+      "Training Loss: 1.32985198777169\n",
+      "Top 1 Acc: 0.8382352941176471\n",
       "\n",
       "Running Validation Epoch 2/10\n"
      ]
@@ -388,12 +365,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2a896b5e2fe64e0f9353c2c0bb3ab2dc",
+       "model_id": "1181f510948745b69dbd75ff476135c8",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -404,8 +381,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 1.0808461774140596\n",
-      "Top 1 Acc: 0.85\n",
+      "Val Loss: 0.956481144297868\n",
+      "Top 1 Acc: 0.8303921568627451\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -413,12 +390,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bdd04f003b34476696b88bdcf3ffb8e8",
+       "model_id": "c130a002f8944a3ea6a3a4c1544c3c7a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -429,8 +406,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.4065576898865402\n",
-      "Top 1 Acc: 0.9882352941176471\n",
+      "Training Loss: 0.4278648835606873\n",
+      "Top 1 Acc: 0.9803921568627451\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -438,12 +415,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2dffd5826f924b0eb7bca6039f439d83",
+       "model_id": "a5cc3447dac84178b3800c1a30325e23",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -454,8 +431,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.7663819249719381\n",
-      "Top 1 Acc: 0.8823529411764706\n",
+      "Val Loss: 0.6528619016171433\n",
+      "Top 1 Acc: 0.8578431372549019\n",
       "\n",
       "Running Training Epoch 4/10\n"
      ]
@@ -463,12 +440,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ec7462e075e446185eddbd2537748e3",
+       "model_id": "d68ce2162a92413d9a74ae4b8c200a95",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/32 [00:00<?, ?it/s]"
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -479,8 +456,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.13117457507178187\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.15628680447116494\n",
+      "Top 1 Acc: 0.9970588235294118\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -488,12 +465,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ee0722460fb4916ae605a705703df8c",
+       "model_id": "f527bc8cfa6c4222b5703eeeaa2c07e9",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -504,8 +481,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.5690533611923456\n",
-      "Top 1 Acc: 0.9127450980392157\n",
+      "Val Loss: 0.5102186386066023\n",
+      "Top 1 Acc: 0.9049019607843137\n",
       "\n",
       "Running Training Epoch 5/10\n"
      ]
@@ -513,12 +490,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "520064cc7f9a486391e6c8cf34dcc46f",
+       "model_id": "d60459fa3c46401baa940ff07489ee04",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -529,7 +506,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.05944129719864577\n",
+      "Training Loss: 0.060788551229052246\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 5/10\n"
@@ -538,12 +515,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "56b6421ee84d48d7ab9411a7fea5cae0",
+       "model_id": "f2b5ab2844704170b08ffe5857d3bf0d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -554,8 +531,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.4922205451875925\n",
-      "Top 1 Acc: 0.9147058823529411\n",
+      "Val Loss: 0.42239774897461757\n",
+      "Top 1 Acc: 0.9107843137254902\n",
       "\n",
       "Running Training Epoch 6/10\n"
      ]
@@ -563,12 +540,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9b3c98c1b78b406e8e61ef9b3c27ad3f",
+       "model_id": "65c3b9cd9e844ee096d6152cc07146ae",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -579,8 +556,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.04032939835451543\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.0473741386376787\n",
+      "Top 1 Acc: 0.9990196078431373\n",
       "\n",
       "Running Validation Epoch 6/10\n"
      ]
@@ -588,12 +565,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b7d5076d9fe4f91aaf6290e20ed73c0",
+       "model_id": "9ee91c900ebb419c8da0edb371d73dda",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -604,8 +581,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.47672522626817226\n",
-      "Top 1 Acc: 0.9205882352941176\n",
+      "Val Loss: 0.40489213640103117\n",
+      "Top 1 Acc: 0.9098039215686274\n",
       "\n",
       "Running Training Epoch 7/10\n"
      ]
@@ -613,12 +590,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b43f39481dd94010b65d8a4bfa2c82bd",
+       "model_id": "c29b7b84bbaa42629bd39eb7b233f111",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -629,7 +606,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.03349028015509248\n",
+      "Training Loss: 0.03090776341559831\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 7/10\n"
@@ -638,12 +615,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e31258f33c4b439b838a723a0b9e7787",
+       "model_id": "a8f37fdd27b647f0a8b00a987906e355",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -654,8 +631,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.4664669381454587\n",
-      "Top 1 Acc: 0.9176470588235294\n",
+      "Val Loss: 0.39284458926704247\n",
+      "Top 1 Acc: 0.9166666666666666\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -663,12 +640,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45670df899134e2688a5fae7778bb392",
+       "model_id": "f09644d377b843d6969b52284d3b80f4",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -679,7 +656,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.03036679228534922\n",
+      "Training Loss: 0.027710435475455597\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 8/10\n"
@@ -688,12 +665,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7a3a457626624ce587eac6b63928b80a",
+       "model_id": "1f0d5025929b4cc2837a4e4b27e969ad",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -704,8 +681,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.45540827978402376\n",
-      "Top 1 Acc: 0.9264705882352942\n",
+      "Val Loss: 0.3832304854586255\n",
+      "Top 1 Acc: 0.9176470588235294\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -713,12 +690,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a396321f97e4010aa079b7a2954d1d3",
+       "model_id": "81aee6700e444b8cb6cb99d31c69980a",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -729,7 +706,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.028963472577743232\n",
+      "Training Loss: 0.026527045309194364\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
@@ -738,12 +715,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51950b565767410f8ef9cb052fcb6fcb",
+       "model_id": "c0e66e476ac049f487204671bb3c41ce",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -754,8 +731,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.4513796488754451\n",
-      "Top 1 Acc: 0.9235294117647059\n",
+      "Val Loss: 0.36842548519780394\n",
+      "Top 1 Acc: 0.9186274509803921\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -763,12 +740,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1085b12cebf24a2a8e7f69e3d13c9991",
+       "model_id": "756a007a3b02480b8872b65fb88277ab",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -779,7 +756,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.027131778770126402\n",
+      "Training Loss: 0.025542080067680217\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 10/10\n"
@@ -788,12 +765,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aeb6b899572c49dab5cd0aed15ede593",
+       "model_id": "affa32f0824e4f77a830bf1392c8b3cb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -804,8 +781,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.45484112948179245\n",
-      "Top 1 Acc: 0.9245098039215687\n",
+      "Val Loss: 0.3675568748876685\n",
+      "Top 1 Acc: 0.9186274509803921\n",
       "\n"
      ]
     }
@@ -844,15 +821,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_dir = \"dense_model\"\n",
+    "save_dir = \"densenet-models\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"densenet-121-dense-flowers.pth\")\n",
+    "exporter.export_pytorch(name=\"dense-model.pth\")\n",
     "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "id": "4532442e",
    "metadata": {},
    "outputs": [],
@@ -872,13 +849,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 17,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
    "source": [
     "# first, load the trained model from Part 3\n",
-    "checkpoint = torch.load(\"./dense_model/training/densenet-121-dense-flowers.pth\")\n",
+    "checkpoint = torch.load(\"./densenet-models/training/dense-model.pth\")\n",
     "model = torchvision.models.densenet121()\n",
     "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
     "model.load_state_dict(checkpoint['state_dict'])\n",
@@ -901,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 18,
    "id": "d367905a",
    "metadata": {
     "scrolled": true
@@ -1046,20 +1023,7 @@
    "id": "322fa136",
    "metadata": {},
    "source": [
-    "We will apply pruning to each of the `convs` and exclude the `classifier` (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
-    "\n",
-    "    - 'features.conv0.weight'\n",
-    "    - 're:features.denseblock1.*.conv1.weight'\n",
-    "    - 're:features.denseblock1.*.conv2.weight'\n",
-    "    - 're:features.transition1.conv.weight'\n",
-    "    - 're:features.denseblock2.*.conv1.weight'\n",
-    "    - 're:features.denseblock2.*.conv2.weight'\n",
-    "    - 're:features.transition2.conv.weight'\n",
-    "    - 're:features.denseblock3.*.conv1.weight'\n",
-    "    - 're:features.denseblock3.*.conv2.weight'\n",
-    "    - 're:features.transition3.conv.weight'\n",
-    "    - 're:features.denseblock4.*.conv1.weight'\n",
-    "    - 're:features.denseblock4.*.conv2.weight'\n",
+    "We will apply pruning to each of the `convs` and exclude the `classifier` (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network.\n",
     "\n",
     "Here is what the recipe looks like:\n",
     "\n",
@@ -1067,7 +1031,7 @@
     "# Epoch and Learning-Rate variables\n",
     "num_epochs: 13.0\n",
     "pruning_epochs: 10.0\n",
-    "init_lr: 0.00025\n",
+    "init_lr: 0.0005\n",
     "final_lr: 0.0001\n",
     "inter_func: cubic\n",
     "mask_type: unstructured\n",
@@ -1095,7 +1059,7 @@
     "pruning_modifiers:\n",
     "  - !GlobalMagnitudePruningModifier\n",
     "    init_sparsity: 0.05\n",
-    "    final_sparsity: 0.85\n",
+    "    final_sparsity: 0.9\n",
     "    start_epoch: 0.0\n",
     "    end_epoch: eval(pruning_epochs)\n",
     "    update_frequency: 0.5\n",
@@ -1117,15 +1081,14 @@
     "    mask_type: eval(mask_type)\n",
     "```\n",
     "\n",
-    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an `init_sparsity` level of 5% and gradually increase sparsity to a `final_sparsity` level of 85% following a `cubic` curve. The pruning is applied in an `unstructured` manner, meaning that any weight can be pruned.\n",
+    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an `init_sparsity` level of 5% and gradually increase sparsity to a `final_sparsity` level of 90% following a `cubic` curve across each of the `convs` in the network. The pruning is applied in an `unstructured` manner, meaning that any weight can be pruned in any pattern.\n",
     "\n",
-    "Over the final 3 epochs, we will fine-tune the 85% pruned model further. Since we set `leave_enabled=True` the sparsity level will be maintained as the fine-tuning occurs.\n",
-    "\n"
+    "Over the final 3 epochs, we fine-tune the 90% pruned model further. Since we set `leave_enabled=True` the sparsity level will be maintained as the fine-tuning occurs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 19,
    "id": "b8518b39",
    "metadata": {},
    "outputs": [],
@@ -1147,14 +1110,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 22,
    "id": "68646bc7",
    "metadata": {},
    "outputs": [],
    "source": [
     "# create ScheduledModifierManager and Optimizer wrapper\n",
     "manager = ScheduledModifierManager.from_yaml(pruning_recipe_path)\n",
-    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs\")\n",
+    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs/densenet/pruning-run\")\n",
     "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
    ]
   },
@@ -1165,14 +1128,14 @@
    "source": [
     "Next, kick off the GMP training loop. \n",
     "\n",
-    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to just the `optimizer` and `model` as usual, with all of the pruning-related logic specified by the declarative recipe interface.\n",
+    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to use the `optimizer` and `model` as usual, with all of the pruning-related logic handled by SparseML.\n",
     "\n",
-    "Our 85% sparsified model reaches ~91.5% validation accuracy (vs ~92.5% for the dense model)."
+    "Our 90% pruned model reaches ~91.8% validation accuracy (vs ~91.9% for the dense model)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 23,
    "id": "601c8c21",
    "metadata": {
     "scrolled": true
@@ -1188,12 +1151,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "181013fb564f45828d9cce72589b3846",
+       "model_id": "5790f1d7b4ae4329af773cc7a7537fe2",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1204,8 +1167,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/13\n",
-      "Training Loss: 0.043860748992301524\n",
-      "Top 1 Acc: 0.9990196078431373\n",
+      "Training Loss: 0.22910855180816725\n",
+      "Top 1 Acc: 0.9735294117647059\n",
       "\n",
       "Running Validation Epoch 1/13\n"
      ]
@@ -1213,12 +1176,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8cdead2769d4377b6cf53fc91028e3f",
+       "model_id": "bb1be77737da4057a7553cc3546c5092",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1229,8 +1192,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/13\n",
-      "Val Loss: 0.49546412169001997\n",
-      "Top 1 Acc: 0.8950980392156863\n",
+      "Val Loss: 0.9752587381517515\n",
+      "Top 1 Acc: 0.7333333333333333\n",
       "\n",
       "Running Training Epoch 2/13\n"
      ]
@@ -1238,12 +1201,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a92e647b8b6e413284659d33dbb5bf9e",
+       "model_id": "b7c7a74efde14715a4a7148cbc748a67",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1254,8 +1217,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/13\n",
-      "Training Loss: 0.031713149510324\n",
-      "Top 1 Acc: 0.9980392156862745\n",
+      "Training Loss: 0.1926842465181835\n",
+      "Top 1 Acc: 0.9676470588235294\n",
       "\n",
       "Running Validation Epoch 2/13\n"
      ]
@@ -1263,12 +1226,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e8af1f0eb7514408a36fad527f4119c5",
+       "model_id": "6b841d8f660e415781a589f4c2537c6c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/32 [00:00<?, ?it/s]"
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1279,8 +1242,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/13\n",
-      "Val Loss: 0.380035838810727\n",
-      "Top 1 Acc: 0.9117647058823529\n",
+      "Val Loss: 0.5537524847895838\n",
+      "Top 1 Acc: 0.85\n",
       "\n",
       "Running Training Epoch 3/13\n"
      ]
@@ -1288,12 +1251,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cadd8b75f6a3468487921687fb7a1902",
+       "model_id": "0b04f316fb1b480bb9e53ba6745aca99",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1304,8 +1267,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/13\n",
-      "Training Loss: 0.016048584191594273\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.0884120583650656\n",
+      "Top 1 Acc: 0.9882352941176471\n",
       "\n",
       "Running Validation Epoch 3/13\n"
      ]
@@ -1313,12 +1276,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b23ad27123a84b9ab3e9d37bcfd94fc2",
+       "model_id": "4db527bfb864464a818217a04dc6b9c3",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1329,8 +1292,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/13\n",
-      "Val Loss: 0.3518236926756799\n",
-      "Top 1 Acc: 0.9156862745098039\n",
+      "Val Loss: 0.458682878066611\n",
+      "Top 1 Acc: 0.8764705882352941\n",
       "\n",
       "Running Training Epoch 4/13\n"
      ]
@@ -1338,12 +1301,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dd5bbfdb2df24c67b036c84f67258727",
+       "model_id": "9715e9c734da4f598febbe6f6115f3f4",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1354,8 +1317,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/13\n",
-      "Training Loss: 0.013376500370213762\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.07008300906454679\n",
+      "Top 1 Acc: 0.9941176470588236\n",
       "\n",
       "Running Validation Epoch 4/13\n"
      ]
@@ -1363,12 +1326,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c80d92282c594060bc65c655b0db0184",
+       "model_id": "10fca0f38b4d486eb37fbe85151eae00",
        "version_major": 2,
        "version_minor": 0
       },
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+       "  0%|          | 0/64 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1379,8 +1342,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/13\n",
-      "Val Loss: 0.37410336383618414\n",
-      "Top 1 Acc: 0.9098039215686274\n",
+      "Val Loss: 0.4294259559828788\n",
+      "Top 1 Acc: 0.8872549019607843\n",
       "\n",
       "Running Training Epoch 5/13\n"
      ]
@@ -1388,12 +1351,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4352b279fc0546458ce3e0717f41b051",
+       "model_id": "50143e51efae42bdb8e27f2a103f187c",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1404,8 +1367,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/13\n",
-      "Training Loss: 0.01488300270284526\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.058840728845098056\n",
+      "Top 1 Acc: 0.9941176470588236\n",
       "\n",
       "Running Validation Epoch 5/13\n"
      ]
@@ -1413,12 +1376,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0e7239ec24f4a5cbaf9d38b942358b8",
+       "model_id": "612376311bfd4f47805fd0a943d5da8e",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1429,8 +1392,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/13\n",
-      "Val Loss: 0.3517874537501484\n",
-      "Top 1 Acc: 0.9156862745098039\n",
+      "Val Loss: 0.4169776111084502\n",
+      "Top 1 Acc: 0.8970588235294118\n",
       "\n",
       "Running Training Epoch 6/13\n"
      ]
@@ -1438,12 +1401,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1448047bbf7f41fdbf84106aff303104",
+       "model_id": "c2b54036b759447a9f11b1d38b85be1d",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1454,8 +1417,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/13\n",
-      "Training Loss: 0.020687898708274588\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.06827072701707948\n",
+      "Top 1 Acc: 0.9931372549019608\n",
       "\n",
       "Running Validation Epoch 6/13\n"
      ]
@@ -1463,12 +1426,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9efe9031bf4b4ebbae2d992dc3266230",
+       "model_id": "1b944f8a61c241df9c9b81cd97fda4e3",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1479,8 +1442,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/13\n",
-      "Val Loss: 0.38692406262271106\n",
-      "Top 1 Acc: 0.9137254901960784\n",
+      "Val Loss: 0.4382545033295173\n",
+      "Top 1 Acc: 0.8921568627450981\n",
       "\n",
       "Running Training Epoch 7/13\n"
      ]
@@ -1488,12 +1451,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d27c0cbd9d6e4e02b44043050c58c2c1",
+       "model_id": "637f8d0ce14943cabdab66efe3755b21",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1504,8 +1467,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/13\n",
-      "Training Loss: 0.029207701765699312\n",
-      "Top 1 Acc: 0.9990196078431373\n",
+      "Training Loss: 0.07268102490343153\n",
+      "Top 1 Acc: 0.9921568627450981\n",
       "\n",
       "Running Validation Epoch 7/13\n"
      ]
@@ -1513,12 +1476,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1752d47a0c348999b1ab92624ebca01",
+       "model_id": "e80a8bd36ef14a20983353949775e5a4",
        "version_major": 2,
        "version_minor": 0
       },
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       ]
      },
      "metadata": {},
@@ -1529,8 +1492,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/13\n",
-      "Val Loss: 0.4347131696995348\n",
-      "Top 1 Acc: 0.8990196078431373\n",
+      "Val Loss: 0.4186342091416009\n",
+      "Top 1 Acc: 0.8941176470588236\n",
       "\n",
       "Running Training Epoch 8/13\n"
      ]
@@ -1538,12 +1501,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e764a1922b0740488b5ceb97a636f5fa",
+       "model_id": "ef52b8ed50b746409cfe942bf1043cdd",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1554,7 +1517,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/13\n",
-      "Training Loss: 0.05598480207845569\n",
+      "Training Loss: 0.062260200167656876\n",
       "Top 1 Acc: 0.9950980392156863\n",
       "\n",
       "Running Validation Epoch 8/13\n"
@@ -1563,12 +1526,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e28f85a917a94204b50687e4397313ad",
+       "model_id": "3998fd77d6ae4769b9e829f6b7303a75",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1579,8 +1542,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/13\n",
-      "Val Loss: 0.5028206340502948\n",
-      "Top 1 Acc: 0.8813725490196078\n",
+      "Val Loss: 0.4408616948639974\n",
+      "Top 1 Acc: 0.8872549019607843\n",
       "\n",
       "Running Training Epoch 9/13\n"
      ]
@@ -1588,12 +1551,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f340b8a413e417ca9bad5a3201dd55c",
+       "model_id": "ec575690c5074b558e9ca9cc9b00b91c",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1604,8 +1567,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/13\n",
-      "Training Loss: 0.030482763570034876\n",
-      "Top 1 Acc: 0.9990196078431373\n",
+      "Training Loss: 0.0411255351791624\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/13\n"
      ]
@@ -1613,12 +1576,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd02f0a64301403bb37da14323dd4284",
+       "model_id": "91c6e3618a1c4d3fa6c19768dd6d2bdb",
        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1629,8 +1592,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/13\n",
-      "Val Loss: 0.4592747155111283\n",
-      "Top 1 Acc: 0.8941176470588236\n",
+      "Val Loss: 0.4180755140114343\n",
+      "Top 1 Acc: 0.9019607843137255\n",
       "\n",
       "Running Training Epoch 10/13\n"
      ]
@@ -1638,12 +1601,12 @@
     {
      "data": {
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        "version_major": 2,
        "version_minor": 0
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       ]
      },
      "metadata": {},
@@ -1654,8 +1617,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/13\n",
-      "Training Loss: 0.017036149831255898\n",
-      "Top 1 Acc: 0.9990196078431373\n",
+      "Training Loss: 0.027908188028959557\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 10/13\n"
      ]
@@ -1663,12 +1626,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f8d775853324429b9f7061478101253",
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        "version_major": 2,
        "version_minor": 0
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      },
      "metadata": {},
@@ -1679,8 +1642,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/13\n",
-      "Val Loss: 0.40747271745931357\n",
-      "Top 1 Acc: 0.9019607843137255\n",
+      "Val Loss: 0.40239793025830295\n",
+      "Top 1 Acc: 0.8980392156862745\n",
       "\n",
       "Running Training Epoch 11/13\n"
      ]
@@ -1688,12 +1651,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ce09a16874e4e8c94deb40dea6bdb29",
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        "version_major": 2,
        "version_minor": 0
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      "metadata": {},
@@ -1704,7 +1667,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 11/13\n",
-      "Training Loss: 0.008050348522374406\n",
+      "Training Loss: 0.02035851358959917\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 11/13\n"
@@ -1713,12 +1676,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d8834eae2a924e6d8f6aa9c713f2cd09",
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        "version_major": 2,
        "version_minor": 0
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      },
      "metadata": {},
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      "output_type": "stream",
      "text": [
       "Validation Epoch: 11/13\n",
-      "Val Loss: 0.36801655124872923\n",
-      "Top 1 Acc: 0.9068627450980392\n",
+      "Val Loss: 0.3589820931883878\n",
+      "Top 1 Acc: 0.9088235294117647\n",
       "\n",
       "Running Training Epoch 12/13\n"
      ]
@@ -1738,12 +1701,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1343cce0b7d9417ca5cf9b788e5bbcf3",
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        "version_major": 2,
        "version_minor": 0
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      "metadata": {},
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      "output_type": "stream",
      "text": [
       "Training Epoch: 12/13\n",
-      "Training Loss: 0.005260431091301143\n",
+      "Training Loss: 0.012050284800352529\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 12/13\n"
@@ -1763,12 +1726,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5952900528a6494e9025afcce97fcef2",
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        "version_major": 2,
        "version_minor": 0
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      "metadata": {},
@@ -1779,8 +1742,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 12/13\n",
-      "Val Loss: 0.3571971161291003\n",
-      "Top 1 Acc: 0.9117647058823529\n",
+      "Val Loss: 0.35020815586904064\n",
+      "Top 1 Acc: 0.9127450980392157\n",
       "\n",
       "Running Training Epoch 13/13\n"
      ]
@@ -1788,12 +1751,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "07cb82ea02634c619c57a823e1d2872e",
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        "version_major": 2,
        "version_minor": 0
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      },
      "metadata": {},
@@ -1804,7 +1767,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 13/13\n",
-      "Training Loss: 0.004477048831176944\n",
+      "Training Loss: 0.007597094325319631\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 13/13\n"
@@ -1813,12 +1776,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "81030fbb34a04181ae3cb25acc470275",
+       "model_id": "80fa6f95af8a43b8bdbeffaa502061e1",
        "version_major": 2,
        "version_minor": 0
       },
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      },
      "metadata": {},
@@ -1829,8 +1792,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 13/13\n",
-      "Val Loss: 0.3466116476338357\n",
-      "Top 1 Acc: 0.9137254901960784\n",
+      "Val Loss: 0.3375227072065172\n",
+      "Top 1 Acc: 0.9186274509803921\n",
       "\n"
      ]
     }
@@ -1860,155 +1823,160 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
-   "id": "3be34fe5",
+   "id": "22c96232",
    "metadata": {},
    "source": [
     "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
     "\n",
     "![tensorboard output](./images/densenet-flowers-tensorboard-output.png)\n",
     "\n",
-    "We can print layer-by-layer sparsity as well."
+    "The resulting model is is 90% sparse and achieves validation accuracy of ~91.8% (vs the unoptimized dense model at ~92.5%) without much hyperparameter search. Key hyperparameter experiments you may want to run include:\n",
+    "- Learning rate\n",
+    "- Learning rate schedule\n",
+    "- Sparsity level\n",
+    "- Number of pruning epochs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 26,
    "id": "df5afa94",
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
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+      "features.denseblock3.denselayer12.conv1.weight: 0.9233\n",
+      "features.denseblock3.denselayer12.conv2.weight: 0.8895\n",
+      "features.denseblock3.denselayer13.conv1.weight: 0.9379\n",
+      "features.denseblock3.denselayer13.conv2.weight: 0.8606\n",
+      "features.denseblock3.denselayer14.conv1.weight: 0.9445\n",
+      "features.denseblock3.denselayer14.conv2.weight: 0.8798\n",
+      "features.denseblock3.denselayer15.conv1.weight: 0.9243\n",
+      "features.denseblock3.denselayer15.conv2.weight: 0.8870\n",
+      "features.denseblock3.denselayer16.conv1.weight: 0.9368\n",
+      "features.denseblock3.denselayer16.conv2.weight: 0.9131\n",
+      "features.denseblock3.denselayer17.conv1.weight: 0.9187\n",
+      "features.denseblock3.denselayer17.conv2.weight: 0.9029\n",
+      "features.denseblock3.denselayer18.conv1.weight: 0.9293\n",
+      "features.denseblock3.denselayer18.conv2.weight: 0.8975\n",
+      "features.denseblock3.denselayer19.conv1.weight: 0.9232\n",
+      "features.denseblock3.denselayer19.conv2.weight: 0.8956\n",
+      "features.denseblock3.denselayer20.conv1.weight: 0.9250\n",
+      "features.denseblock3.denselayer20.conv2.weight: 0.9041\n",
+      "features.denseblock3.denselayer21.conv1.weight: 0.9216\n",
+      "features.denseblock3.denselayer21.conv2.weight: 0.8955\n",
+      "features.denseblock3.denselayer22.conv1.weight: 0.9245\n",
+      "features.denseblock3.denselayer22.conv2.weight: 0.8802\n",
+      "features.denseblock3.denselayer23.conv1.weight: 0.9230\n",
+      "features.denseblock3.denselayer23.conv2.weight: 0.9111\n",
+      "features.denseblock3.denselayer24.conv1.weight: 0.9264\n",
+      "features.denseblock3.denselayer24.conv2.weight: 0.9092\n",
+      "features.transition3.conv.weight: 0.8496\n",
+      "features.denseblock4.denselayer1.conv1.weight: 0.8750\n",
+      "features.denseblock4.denselayer1.conv2.weight: 0.9133\n",
+      "features.denseblock4.denselayer2.conv1.weight: 0.8727\n",
+      "features.denseblock4.denselayer2.conv2.weight: 0.9468\n",
+      "features.denseblock4.denselayer3.conv1.weight: 0.8816\n",
+      "features.denseblock4.denselayer3.conv2.weight: 0.9228\n",
+      "features.denseblock4.denselayer4.conv1.weight: 0.9007\n",
+      "features.denseblock4.denselayer4.conv2.weight: 0.9406\n",
+      "features.denseblock4.denselayer5.conv1.weight: 0.9005\n",
+      "features.denseblock4.denselayer5.conv2.weight: 0.9582\n",
+      "features.denseblock4.denselayer6.conv1.weight: 0.8966\n",
+      "features.denseblock4.denselayer6.conv2.weight: 0.9577\n",
+      "features.denseblock4.denselayer7.conv1.weight: 0.9111\n",
+      "features.denseblock4.denselayer7.conv2.weight: 0.9601\n",
+      "features.denseblock4.denselayer8.conv1.weight: 0.9141\n",
+      "features.denseblock4.denselayer8.conv2.weight: 0.9624\n",
+      "features.denseblock4.denselayer9.conv1.weight: 0.9165\n",
+      "features.denseblock4.denselayer9.conv2.weight: 0.9656\n",
+      "features.denseblock4.denselayer10.conv1.weight: 0.9182\n",
+      "features.denseblock4.denselayer10.conv2.weight: 0.9647\n",
+      "features.denseblock4.denselayer11.conv1.weight: 0.9191\n",
+      "features.denseblock4.denselayer11.conv2.weight: 0.9645\n",
+      "features.denseblock4.denselayer12.conv1.weight: 0.9226\n",
+      "features.denseblock4.denselayer12.conv2.weight: 0.9655\n",
+      "features.denseblock4.denselayer13.conv1.weight: 0.9245\n",
+      "features.denseblock4.denselayer13.conv2.weight: 0.9638\n",
+      "features.denseblock4.denselayer14.conv1.weight: 0.9280\n",
+      "features.denseblock4.denselayer14.conv2.weight: 0.9642\n",
+      "features.denseblock4.denselayer15.conv1.weight: 0.9290\n",
+      "features.denseblock4.denselayer15.conv2.weight: 0.9638\n",
+      "features.denseblock4.denselayer16.conv1.weight: 0.9251\n",
+      "features.denseblock4.denselayer16.conv2.weight: 0.9610\n",
       "classifier.weight: 0.0000\n"
      ]
     }
    ],
    "source": [
+    "print(\"Sparsity By Layer:\")\n",
     "for (name, layer) in get_prunable_layers(model):\n",
     "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
    ]
@@ -2023,34 +1991,445 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "id": "417ac105",
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_dir = \"experiment-0\"\n",
+    "save_dir = \"densenet-models\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"densenet-121-sparse-flowers.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"sparse-model.onnx\", convert_qat=True)"
+    "exporter.export_pytorch(name=\"densenet-pruned-fp32.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"pruned-fp32-model.onnx\", convert_qat=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "22c96232",
+   "id": "c379d623",
    "metadata": {},
    "source": [
-    "## Wrap Up\n",
+    "## Step 5: Quantize the Model\n",
     "\n",
-    "The resulting model is is 85% sparse and achieves validation accuracy of ~91.5% (vs the unoptimized dense model at ~92.5%) without much hyperparameter search.\n",
+    "With a sparse model trained on Flowers, we are now ready to apply the QAT algorithm to quantize the model. Generally, we can run QAT for 5-6 epochs. Quantizing the model together with pruning allows us to gain a speedup when running with DeepSparse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "11f97312",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "fda0599c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first, load the trained model from Step 4\n",
+    "checkpoint = torch.load(\"./densenet-models/training/densenet-pruned-fp32.pth\")\n",
+    "model = torchvision.models.densenet121()\n",
+    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
+    "model.load_state_dict(checkpoint['state_dict'])\n",
+    "model.to(device)\n",
     "\n",
-    "Key hyperparameter experiments you may want to run include:\n",
-    "- Learning rate\n",
-    "- Learning rate schedule\n",
-    "- Sparsity level\n",
-    "- Number of pruning epochs\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0473fcbd",
+   "metadata": {},
+   "source": [
+    "Next, we will create a Quantization recipe. We will us the `QuantizationModifier` (which instructs SparseML to run QAT) along with the `ConstantPruningModifier` (which instructs SparseML to maintain sparsity).\n",
+    "\n",
+    "Here is what the recipe looks like:\n",
+    "\n",
+    "```yaml\n",
+    "# Epoch and Learning-Rate variables\n",
+    "num_epochs: 6.0\n",
+    "init_lr: 0.0004\n",
     "\n",
+    "# quantization variables\n",
+    "quantization_epochs: 6.0\n",
+    "\n",
+    "training_modifiers:\n",
+    "  - !EpochRangeModifier\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
     "\n",
-    "DeepSparse supports speedup from pruning and quantization. To reach maximum performance, check out our examples of quantizing a model!"
+    "  - !LearningRateFunctionModifier\n",
+    "    final_lr: 0.0\n",
+    "    init_lr: eval(init_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: 0.0\n",
+    "    end_epoch: eval(num_epochs)\n",
+    "\n",
+    "# Phase 1 Sparse Transfer Learning / Recovery\n",
+    "sparse_transfer_learning_modifiers:\n",
+    "  - !ConstantPruningModifier\n",
+    "    start_epoch: 0.0\n",
+    "    params: __ALL_PRUNABLE__\n",
+    "\n",
+    "# Phase 2 Apply quantization\n",
+    "sparse_quantized_transfer_learning_modifiers:\n",
+    "  - !QuantizationModifier\n",
+    "    start_epoch: eval(num_epochs - quantization_epochs)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6d2c1be3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantization_recipe_path = \"./recipes/quantization-recipe.yaml\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "6b8a4324",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(quantization_recipe_path)\n",
+    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs/densenet/quantization-run\")\n",
+    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "208fc85c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Training Epoch 1/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ed2d6e3d1ac42369f8a9ea020909d50",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 1/5\n",
+      "Training Loss: 0.020167092072369996\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 1/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8426880f7f1c4c2aa5aa022b0909288d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 1/5\n",
+      "Val Loss: 0.3898960210317455\n",
+      "Top 1 Acc: 0.8980392156862745\n",
+      "\n",
+      "Running Training Epoch 2/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c368eb0488549a0b50aca44c594bbf5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 2/5\n",
+      "Training Loss: 0.011033405537091312\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 2/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "03148f10352a4213a5b49f7db26e2b4f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 2/5\n",
+      "Val Loss: 0.381910876960319\n",
+      "Top 1 Acc: 0.8970588235294118\n",
+      "\n",
+      "Running Training Epoch 3/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73988d5735404cc08795116a2d27e19e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 3/5\n",
+      "Training Loss: 0.006457825023971964\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 3/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61e8db2842564befb05c6c18582093ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 3/5\n",
+      "Val Loss: 0.33566539320781885\n",
+      "Top 1 Acc: 0.9137254901960784\n",
+      "\n",
+      "Running Training Epoch 4/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d548a06770834517ad9db8fdb99a1ae3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/5\n",
+      "Training Loss: 0.003771477568989212\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 4/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13afa51fa11b44aaa237067823ae9137",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/5\n",
+      "Val Loss: 0.34420355190604823\n",
+      "Top 1 Acc: 0.907843137254902\n",
+      "\n",
+      "Running Training Epoch 5/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "857e1593d5ca457ba73918723f04c008",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 5/5\n",
+      "Training Loss: 0.002937028719316004\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 5/5\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3be1fd3d62ed487fb2d6bacecebc4160",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/64 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 5/5\n",
+      "Val Loss: 0.3459806305090751\n",
+      "Top 1 Acc: 0.9088235294117647\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run QAT algorithm\n",
+    "epoch = 0\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    \n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
+    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
+    "    \n",
+    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
+    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52ac51e0",
+   "metadata": {},
+   "source": [
+    "The resulting model is is 90% sparse and INT8 quantized and achieves validation accuracy of ~90.9% (vs the unoptimized dense model at ~91.9%) without much hyperparameter search. Key hyperparameter experiments you may want to run for QAT are:\n",
+    "- Learning rate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eec331b7",
+   "metadata": {},
+   "source": [
+    "Finally, export to ONNX:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "87f21bc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-03-14 23:27:05 sparseml.pytorch.sparsification.quantization.quantize_qat_export INFO     Converted 59 quantizable Conv ops with weight and bias to ConvInteger and Add\n",
+      "2023-03-14 23:27:05 sparseml.pytorch.sparsification.quantization.quantize_qat_export INFO     Converted 1 quantizable Gemm ops with weight and bias to MatMulInteger and Add\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_dir = \"densenet-models\"\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"densenet-pruned-int8.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"pruned-int8-model.onnx\", convert_qat=True)"
    ]
   }
  ],
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
index b5e63054248..8e1fec22d4b 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79303b28",
+   "id": "0d30f71c",
    "metadata": {},
    "source": [
     "## Step 1: Setup Dataset\n",
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "08337acc",
+   "id": "31064d5c",
    "metadata": {},
    "source": [
     "Next, we will use SparseML's recipes to set the hyperparameters of training loop. In this case, we will use the following recipe:\n",
@@ -262,33 +262,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "ceb62a05",
+   "execution_count": null,
+   "id": "2723a40f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\r\n",
-      "# Epoch and Learning-Rate variables\r\n",
-      "num_epochs: 10.0\r\n",
-      "init_lr: 0.0005\r\n",
-      "\r\n",
-      "training_modifiers:\r\n",
-      "  - !EpochRangeModifier\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)\r\n",
-      "\r\n",
-      "  - !LearningRateFunctionModifier\r\n",
-      "    final_lr: 0.0\r\n",
-      "    init_lr: eval(init_lr)\r\n",
-      "    lr_func: cosine\r\n",
-      "    start_epoch: 0.0\r\n",
-      "    end_epoch: eval(num_epochs)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!cat ./recipes/mobilenetv2-beans-dense-recipe.yaml"
    ]
@@ -296,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "0ff717b8",
+   "id": "cec41d57",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +282,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09ee05d1",
+   "id": "f7cba342",
    "metadata": {},
    "source": [
     "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
@@ -325,7 +302,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db9ed163",
+   "id": "a00c4ceb",
    "metadata": {},
    "source": [
     "Kick off the transfer learning loop. Our run reached ~99% validation accuracy after 10 epochs."
@@ -909,7 +886,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f6f45e1",
+   "id": "1b188d60",
    "metadata": {},
    "source": [
     "Next, we need to create a SparseML recipe which includes the GMP algorithm. The `!GlobalMagnitudePruningModifier` modifier instructs SparseML to apply the GMP algorithm at a global level (pruning the lowest magnitude weights across all layers).\n",
@@ -921,7 +898,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "id": "44578399",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -991,7 +970,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5c95228",
+   "id": "7249f54d",
    "metadata": {},
    "source": [
     "We will apply pruning to each of the convs and exclude the classifier layer (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network, so we can use the following list to identify the relevant layers for pruning:\n",
@@ -1049,7 +1028,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c2e5e7c2",
+   "id": "ca5c28b2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1059,7 +1038,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "238f3a83",
+   "id": "b55a7c74",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1081,7 +1060,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71213f1b",
+   "id": "37d4b6a4",
    "metadata": {},
    "source": [
     "Next, kick off the GMP training loop. \n",
@@ -1781,7 +1760,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a59b80c2",
+   "id": "64aabe37",
    "metadata": {},
    "source": [
     "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
@@ -1794,7 +1773,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "cae6584a",
+   "id": "2bf48ed2",
    "metadata": {},
    "outputs": [
     {
@@ -1873,7 +1852,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "4a44061c",
+   "id": "48fb7c0e",
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb
similarity index 100%
rename from integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb
rename to integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb
diff --git a/integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png b/integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png
index a59171cda0a58029f63958691bb2c052fa8224bb..d73aaca887d1e6ee91f99817c5ac643b28113f06 100644
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z<5FH)`!gmEh-)B&u-1Prm(F%a_3O)6r|wUZ@B}iqXVYZ6zHbR5_jv*)1}yhuLP1zz
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F|6h`&(+~gv

diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
index f73c5455c47..b23986e4d24 100644
--- a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
@@ -1,7 +1,7 @@
 # Epoch and Learning-Rate variables
 num_epochs: 13.0
 pruning_epochs: 10.0
-init_lr: 0.00025
+init_lr: 0.0005
 final_lr: 0.0001
 inter_func: cubic
 mask_type: unstructured
@@ -29,7 +29,7 @@ training_modifiers:
 pruning_modifiers:
   - !GlobalMagnitudePruningModifier
     init_sparsity: 0.05
-    final_sparsity: 0.85
+    final_sparsity: 0.9
     start_epoch: 0.0
     end_epoch: eval(pruning_epochs)
     update_frequency: 0.5
diff --git a/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml b/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml
new file mode 100644
index 00000000000..6df4f5ff921
--- /dev/null
+++ b/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml
@@ -0,0 +1,29 @@
+# Epoch and Learning-Rate variables
+num_epochs: 5.0
+init_lr: 0.0004
+
+# quantization variables
+quantization_epochs: 6.0
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !LearningRateFunctionModifier
+    final_lr: 0.0
+    init_lr: eval(init_lr)
+    lr_func: cosine
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+# Phase 2 Apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(num_epochs - quantization_epochs)
\ No newline at end of file

From 894dbaa4b9645efecab61a4f7b6703c649530bf2 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 20:27:58 -0400
Subject: [PATCH 13/29] Update README.md

---
 integrations/torchvision/README.md | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index bc895aec666..3fd2ad04d11 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -21,7 +21,6 @@ This directory explains how to use SparseML's `torchvision` integration to train
 There are two main workflows enabled by SparseML:
 
 - **Sparse Transfer Learning** - fine-tune a pre-sparsified checkpoint on your own dataset **[RECOMMENDED]**
-
 - **Sparsification from Scratch** - apply pruning and quantization to sparsify `torchvision` models from scratch
 
 Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse.
@@ -36,12 +35,11 @@ pip install sparseml[torchvision]
 
 ## Tutorials
 
-- [Sparse Transfer Learning with the CLI](tutorials/sparse-transfer-learning.md)
-- [Sparse Transfer Learning with the Python API](tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
-- Sparsification from Scratch with the CLI (coming soon!)
-- [Sparsification from Scratch with the Python API - ResNet-50](tutorials/docs-torchvision-sparsify-from-scatch-resnet50-beans.ipynb)
+- [Sparse Transfer Learning with the CLI - ResNet-50](tutorials/sparse-transfer-learning.md)
+- [Sparse Transfer Learning with the Python API - ResNet-50](tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
+- [Sparsification from Scratch with the Python API - ResNet-50](tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb)
 - [Sparsification from Scratch with the Python API - MobileNetv2](tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb)
-- [Sparsification from Scratch with the Python API - DenseNet-121](tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers.ipynb )
+- [Sparsification from Scratch with the Python API - DenseNet-121](tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb)
 
 ## Quick Tour
 

From 1526838e9a7a0bee7d386d6e1edbeeba463f9b44 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 20:28:24 -0400
Subject: [PATCH 14/29] Update README.md

---
 integrations/torchvision/README.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index 3fd2ad04d11..ee9ab8e90bc 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -83,8 +83,6 @@ manager.finalize(model)
 
 Note that the `model`, `optimizer`, and `dataset` are all standard PyTorch objects. We simply pass a recipe to the SparseML `SchedulerModifierManager` and SparseML handles the rest!
 
-See the tutorials for full working examples of the Python API.
-
 ### SparseML CLI
 
 In addition to the code-level API, SparseML offers pre-made training pipelines for image classification via the CLI interface.

From 3e55ef1f21f9afb75f377785d63eb15a02063896 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 20:48:54 -0400
Subject: [PATCH 15/29] Update README.md

---
 README.md | 86 ++++++++++++++++---------------------------------------
 1 file changed, 24 insertions(+), 62 deletions(-)

diff --git a/README.md b/README.md
index 4b79a65e3b0..ff642c8d338 100644
--- a/README.md
+++ b/README.md
@@ -53,92 +53,54 @@ limitations under the License.
 
 ## Overview
 
-SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art [sparsification](https://docs.neuralmagic.com/user-guides/sparsification) algorithms such as pruning and quantization to any neural network. 
+SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. Once sparsified, the models can be deployed with [DeepSparse](https://github.com/neuralmagic/deepsparse) for GPU-class performance on CPUs.
+
+
 General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.
 
-The [GitHub repository](https://github.com/neuralmagic/sparseml) contains integrations within the PyTorch, Keras, and TensorFlow V1 ecosystems, allowing for seamless model sparsification.
+The [GitHub repository](https://github.com/neuralmagic/sparseml) contains integrations within the PyTorch, Hugging Face, and Ultralytics ecosystems, allowing for seamless model sparsification.
 
 <img alt="SparseML Flow" src="https://docs.neuralmagic.com/docs/source/infographics/sparseml.png" width="960px" />
 
-## Highlights
-
-### Integrations
+## Integrations
 
 <p>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/pytorch">
+    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/torchvision">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/pytorch-torchvision.png" width="136px" />
     </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3">
-        <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/ultralytics-yolov3.png" width="136px" />
-    </a>
     <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov5">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/ultralytics-yolov5.png" width="136px" />
     </a>
     <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/huggingface-transformers.png" width="136px" />
     </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/rwightman-timm">
-        <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/rwightman-timm.png" width="136px" />
-    </a>
 </p>
 
-### Creating Sparse Models
-
-<p>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/pytorch/notebooks/classification.ipynb">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/classification_resnet-50.png" width="136px" />
-    </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/detection_yolov3.png" width="136px" />
-    </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov5/tutorials/sparsifying_yolov5_using_recipes.md">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/detection_yolov5.png" width="136px" />
-    </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers/tutorials/sparsifying_bert_using_recipes.md">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/nlp_bert.png" width="136px" />
-    </a>
-</p>
-
-### Transfer Learning from Sparse Models
-
-<p>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/classification_resnet-50.png" width="136px" />
-    </a>
-    <a href="https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/detection_yolov3.png" width="136px" />
-    </a>
-    <a href="https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md">
-        <img src="https://docs.neuralmagic.com/docs/source/tutorials/detection_yolov5.png" width="136px" />
-    </a>
-</p>
-
-
 ## Tutorials
 
-### 🖼️ Computer Vision
+### PyTorch
+- [Sparse Transfer Learning with the CLI](integrations/torchvision/tutorials/sparse-transfer-learning.md)
+- [Sparse Transfer Learning with the Python API](integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
+- [Sparsify From Scatch with the CLI](integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb)
 
-- [Sparsifying PyTorch Models Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md)
-- [Sparsifying YOLOv3 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md)
-- [Sparsifying YOLOv5 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/sparsifying_yolov5_using_recipes.md)
-- [Sparsifying YOLACT Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md)
-- [Sparse Transfer Learning for Image Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md)
-- [Sparse Transfer Learning With YOLOv3](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md)
-- [Sparse Transfer Learning With YOLOv5](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md)
+### Hugging Face Transformers
+- [Sparse Transfer Learning Overview with the Python API](integrations/huggingface-transformers/tutorials/sparse-transfer-learning-bert-python.md)
+- [Sparse Transfer Learning Overview with the CLI](integrations/huggingface-transformers/tutorials/sparse-transfer-learning-bert.md)
+- [Sparse Transfer Learning for Sentiment Analysis](integrations/huggingface-transformers/tutorials/sentiment-analysis/sentiment-analysis-cli.md)
+- [Sparse Transfer Learning for Text Classification](integrations/huggingface-transformers/tutorials/text-classification/text-classification-cli.md)
+- [Sparse Transfer Learning for Token Classification](integrations/huggingface-transformers/tutorials/token-classification/token-classification-cli.md)
+- [Sparse Transfer Learning for Question Answering](integrations/huggingface-transformers/tutorials/question-answering/question-answering-cli.md)
 
-&emsp; **Notebooks**
+### Ultralytics YOLOv5
 
-- [Keras Image Classification Model Pruning Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/keras/notebooks/classification.ipynb)
-- [PyTorch Image Classification Model Pruning Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/notebooks/classification.ipynb)
-- [PyTorch Image Detection Model Pruning Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/notebooks/detection.ipynb)
-- [Sparse-Quantized Transfer Learning in PyTorch Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb)
-- [Torchvision Classification Model Pruning Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/pytorch/notebooks/torchvision.ipynb)
-- [TensorFlow v1 Classification Model Pruning Using SparseML](https://github.com/neuralmagic/sparseml/blob/main/integrations/tensorflow_v1/notebooks/classification.ipynb)
+- [Sparse Transfer Learning with the CLI](integrations/ultralytics-yolov5/tutorials/sparse-transfer-learning.md)
+- [Sparsify From Scatch with the CLI](integrations/ultralytics-yolov5/tutorials/sparsify-from-scratch.md)
 
-### 📰 NLP
-- [Sparsifying BERT Models Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/tutorials/sparsifying_bert_using_recipes.md)
-- [Sparse Transfer Learning With BERT](https://github.com/neuralmagic/sparseml/blob/main/integrations/huggingface-transformers/tutorials/bert_sparse_transfer_learning.md)
+### Examples
 
+- [PyTorch](integrations/torchvision#tutorials)
+- [Hugging Face](integrations/huggingface-transformers#tutorials)
+- [Ultralytics YOLOv5](integrations/ultralytics-yolov5#tutorials)
 
 ## Installation
 

From ce399ef0dd3f1aeaec4cdd90ff3bb6ad4bc0e00b Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 20:51:14 -0400
Subject: [PATCH 16/29] added images

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diff --git a/integrations/examples/README.md b/integrations/examples/README.md
deleted file mode 100644
index 25f69459dcf..00000000000
--- a/integrations/examples/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Examples
-
-This directory contains old examples of SparseML integrated with other libraries. Note that these pathways
-have not been tested recently, but we have left there here as examples of how to integrate SparseML into a new repository.
\ No newline at end of file
diff --git a/integrations/examples/dbolya-yolact/README.md b/integrations/examples/dbolya-yolact/README.md
deleted file mode 100644
index 8d0be24b308..00000000000
--- a/integrations/examples/dbolya-yolact/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML YOLACT Integration
-
-This directory combines the [SparseML](../../) recipe-driven approach with the 
-[dbolya/yolact](https://github.com/dbolya/yolact) repository.
-By integrating the training flows in the `yolact` repository with the SparseML 
-code base,
-we enable model sparsification techniques on the popular 
-[YOLACT architecture](https://arxiv.org/abs/1804.02767)
-creating smaller and faster deployable versions.
-The techniques include, but are not limited to:
-
-- Pruning
-- Quantization
-- Pruning and Quantization
-- Sparse Transfer Learning
-
-## Installation
-
-We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
-A virtual environment can be created using the following commands:
-
-```bash
-python3 -m venv venv # create a venv virtual environment
-source venv/bin/activate # activate venv
-pip install --upgrade pip # upgrade pip
-```
-To begin, install `sparseml[torchvision]>=1.1`
-
-```bash
-pip install "sparseml[torchvision]>=1.1"
-```
-
-Note: This integration requires `python>=3.7,<3.10`
-
-
-## Quick Tour
-
-### Downloading COCO
-
-The `sparseml.yolact.download` utility provides an easy to use interface to download
-the `COCO` dataset.
-
-Simply invoke:
-```bash
-sparseml.yolact.download
-```
-
-To download  a test `COCO` dataset, run `sparseml.yolact.download --test`. For more information
-on this utility append the `--help` option. (Note: by default, the dataset is downloaded to the code execution directory. )
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-`sparseml.yolact.train` adds a `--recipe` argument to the 
-[`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-Example `train` command;
-be sure to provide the correct dataset and recipe path to the utility based on your local setup:
-
-```bash
-sparseml.yolact.train --resume \
-zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---recipe ./recipes/yolact.pruned.md \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
-
-### SparseZoo
-
-| Sparsification Type | Description                                                                       | Zoo Stub                                                                     | COCO mAP@all | Size on Disk | DeepSparse Performance** |
-|---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|--------------|--------------------------|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none           | 0.288        | 170 MB       | -- img/sec               |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none       | 0.286        | 30.1 MB      | -- img/sec               |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned82_quant-none | 0.282        | 9.7 MB       | -- img/sec               |
-
-These models can also be viewed on the [SparseZoo Website](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=segmentation&page=1).
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name                                | Description                                                                                                           |
-|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [recipes](./recipes)                            | Typical recipes for sparsifying YOLACT models along with any downloaded recipes from the SparseZoo.                   |
-| [yolact](./yolact)                              | Integration repository folder used to train and sparsify YOLACT models (`setup_integration.sh` must run first).       |
-| [README.md](./README.md)                        | Readme file.                                                                                                          |
-| [tutorials](./tutorials)                        | Easy to follow sparsification tutorials for YOLACT  models.                                                            |
-
-### Exporting for Inference
-
-After sparsifying a model, the 
-`sparseml.yolact.export_onnx` utility
-converts the model into deployment formats such as [ONNX](https://onnx.ai/).
-The export process is modified such that the quantized and pruned models are 
-corrected and folded properly.
-
-For example, the following command can be run to export a trained/sparsified YOLACT
-model's checkpoint:
-
-```bash
-sparseml.yolact.export_onnx --checkpoint ./quantized-yolact/model.pth \
-    --name quantized-yolact.onnx
-```
-
-
-### DeepSparse
-
-The [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) accepts ONNX 
-formats and is engineered to significantly speed up inference on CPUs for 
-the sparsified models from this integration. [Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact) scripts can be found in the DeepSparse repository.
-
-## Citation
-```bibtex
-@inproceedings{yolact-iccv2019,
-  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  title     = {YOLACT: {Real-time} Instance Segmentation},
-  booktitle = {ICCV},
-  year      = {2019},
-}
-```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md b/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md
deleted file mode 100644
index b3435b049d2..00000000000
--- a/integrations/examples/dbolya-yolact/recipes/yolact.pruned.md
+++ /dev/null
@@ -1,174 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Hyperparams
-num_epochs: 60
-
-# Pruning Hyperparams
-init_sparsity: 0.35
-pruning_start_target: 0
-pruning_end_target: 0.5
-pruning_update_frequency: 0.2
-mask_type: unstructured
-prune_none_target_sparsity: 0.75
-prune_low_target_sparsity: 0.83
-prune_mid_target_sparsity: 0.87
-prune_high_target_sparsity: 0.92
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0
-    end_epoch: eval(num_epochs)
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-    - backbone.layers.0.1.conv1.0.weight
-    - backbone.layers.0.1.conv2.0.weight
-    - backbone.layers.1.0.0.weight
-    - backbone.layers.1.1.conv1.0.weight
-    - backbone.layers.1.1.conv2.0.weight
-    - backbone.layers.1.2.conv1.0.weight
-    - backbone.layers.1.2.conv2.0.weight
-    - backbone._preconv.0.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_none_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.2.0.0.weight
-      - backbone.layers.2.1.conv1.0.weight
-      - backbone.layers.2.1.conv2.0.weight
-      - backbone.layers.2.2.conv1.0.weight
-      - backbone.layers.2.2.conv2.0.weight
-      - backbone.layers.2.3.conv1.0.weight
-      - backbone.layers.2.3.conv2.0.weight
-      - backbone.layers.2.4.conv1.0.weight
-      - backbone.layers.2.4.conv2.0.weight
-      - backbone.layers.2.5.conv1.0.weight
-      - backbone.layers.2.5.conv2.0.weight
-      - backbone.layers.2.6.conv1.0.weight
-      - backbone.layers.2.6.conv2.0.weight
-      - backbone.layers.2.7.conv1.0.weight
-      - backbone.layers.2.7.conv2.0.weight
-      - backbone.layers.2.8.conv1.0.weight
-      - backbone.layers.2.8.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_low_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - proto_net.0.weight
-      - proto_net.2.weight
-      - proto_net.4.weight
-      - proto_net.8.weight
-      - proto_net.10.weight
-      - fpn.lat_layers.0.weight
-      - fpn.lat_layers.1.weight
-      - fpn.lat_layers.2.weight
-      - fpn.pred_layers.0.weight
-      - fpn.pred_layers.1.weight
-      - fpn.pred_layers.2.weight
-      - fpn.downsample_layers.0.weight
-      - fpn.downsample_layers.1.weight
-      - prediction_layers.0.upfeature.0.weight
-      - prediction_layers.0.bbox_layer.weight
-      - prediction_layers.0.conf_layer.weight
-      - prediction_layers.0.mask_layer.weight
-      - semantic_seg_conv.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_mid_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.3.0.0.weight
-      - backbone.layers.3.1.conv1.0.weight
-      - backbone.layers.3.1.conv2.0.weight
-      - backbone.layers.3.2.conv1.0.weight
-      - backbone.layers.3.2.conv2.0.weight
-      - backbone.layers.3.3.conv1.0.weight
-      - backbone.layers.3.3.conv2.0.weight
-      - backbone.layers.3.4.conv1.0.weight
-      - backbone.layers.3.4.conv2.0.weight
-      - backbone.layers.3.5.conv1.0.weight
-      - backbone.layers.3.5.conv2.0.weight
-      - backbone.layers.3.6.conv1.0.weight
-      - backbone.layers.3.6.conv2.0.weight
-      - backbone.layers.3.7.conv1.0.weight
-      - backbone.layers.3.7.conv2.0.weight
-      - backbone.layers.3.8.conv1.0.weight
-      - backbone.layers.3.8.conv2.0.weight
-      - backbone.layers.4.0.0.weight
-      - backbone.layers.4.1.conv1.0.weight
-      - backbone.layers.4.1.conv2.0.weight
-      - backbone.layers.4.2.conv1.0.weight
-      - backbone.layers.4.2.conv2.0.weight
-      - backbone.layers.4.3.conv1.0.weight
-      - backbone.layers.4.3.conv2.0.weight
-      - backbone.layers.4.4.conv1.0.weight
-      - backbone.layers.4.4.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_high_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_epochs)
-    end_epoch: eval(pruning_end_target * num_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
----
-
-# YOLACT Pruned
-
-
-This recipe creates a sparse, [YOLACT](https://github.com/dbolya/yolact) model 
-that recovers to 99.37% of its baseline accuracy on the COCO dataset 
-(50.16, 46.57 mAP@0.5 baseline Vs 49.36, 46.37 mAP@0.5 for this recipe for bounding box, mask).
-Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup. 
-
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md b/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md
deleted file mode 100644
index 5b2c365710c..00000000000
--- a/integrations/examples/dbolya-yolact/recipes/yolact.pruned_quant.md
+++ /dev/null
@@ -1,204 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Hyperparams
-num_epochs: 62
-init_lr: 0.001
-num_quantization_epochs: 2
-num_pruning_epochs: eval(num_epochs - num_quantization_epochs)
-quantization_observer_end_target: 0.5
-
-# Pruning Hyperparams
-init_sparsity: 0.2
-pruning_start_target: 0
-pruning_end_target: 0.5
-pruning_update_frequency: 0.2
-mask_type: [1, 4]
-prune_none_target_sparsity: 0.6
-prune_low_target_sparsity: 0.7
-prune_mid_target_sparsity: 0.8
-prune_high_target_sparsity: 0.85
-
-# quantization hyperparams
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0
-    end_epoch: eval(num_epochs)
-
-  # pruning
-  - !SetWeightDecayModifier
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    weight_decay: 0.0005
-
-  - !LearningRateFunctionModifier
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    lr_func: linear
-    init_lr: eval(init_lr)
-    final_lr: eval(0.01 * init_lr)
-  # quantization
-  - !SetWeightDecayModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    weight_decay: 0.0
-
-  - !LearningRateFunctionModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    end_epoch: eval(num_epochs)
-    lr_func: linear
-    init_lr: eval(0.01 * init_lr)
-    final_lr: eval(0.001 * init_lr)
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-    - backbone.layers.0.1.conv1.0.weight
-    - backbone.layers.0.1.conv2.0.weight
-    - backbone.layers.1.0.0.weight
-    - backbone.layers.1.1.conv1.0.weight
-    - backbone.layers.1.1.conv2.0.weight
-    - backbone.layers.1.2.conv1.0.weight
-    - backbone.layers.1.2.conv2.0.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_none_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.2.0.0.weight
-      - backbone.layers.2.1.conv1.0.weight
-      - backbone.layers.2.1.conv2.0.weight
-      - backbone.layers.2.2.conv1.0.weight
-      - backbone.layers.2.2.conv2.0.weight
-      - backbone.layers.2.3.conv1.0.weight
-      - backbone.layers.2.3.conv2.0.weight
-      - backbone.layers.2.4.conv1.0.weight
-      - backbone.layers.2.4.conv2.0.weight
-      - backbone.layers.2.5.conv1.0.weight
-      - backbone.layers.2.5.conv2.0.weight
-      - backbone.layers.2.6.conv1.0.weight
-      - backbone.layers.2.6.conv2.0.weight
-      - backbone.layers.2.7.conv1.0.weight
-      - backbone.layers.2.7.conv2.0.weight
-      - backbone.layers.2.8.conv1.0.weight
-      - backbone.layers.2.8.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_low_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - proto_net.0.weight
-      - proto_net.2.weight
-      - proto_net.4.weight
-      - proto_net.8.weight
-      - proto_net.10.weight
-      - fpn.lat_layers.0.weight
-      - fpn.lat_layers.1.weight
-      - fpn.lat_layers.2.weight
-      - fpn.pred_layers.0.weight
-      - fpn.pred_layers.1.weight
-      - fpn.pred_layers.2.weight
-      - fpn.downsample_layers.0.weight
-      - fpn.downsample_layers.1.weight
-      - prediction_layers.0.upfeature.0.weight
-      - prediction_layers.0.bbox_layer.weight
-      - prediction_layers.0.conf_layer.weight
-      - prediction_layers.0.mask_layer.weight
-      - semantic_seg_conv.weight
-
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_mid_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-  - !GMPruningModifier
-    params:
-      - backbone.layers.3.0.0.weight
-      - backbone.layers.3.1.conv1.0.weight
-      - backbone.layers.3.1.conv2.0.weight
-      - backbone.layers.3.2.conv1.0.weight
-      - backbone.layers.3.2.conv2.0.weight
-      - backbone.layers.3.3.conv1.0.weight
-      - backbone.layers.3.3.conv2.0.weight
-      - backbone.layers.3.4.conv1.0.weight
-      - backbone.layers.3.4.conv2.0.weight
-      - backbone.layers.3.5.conv1.0.weight
-      - backbone.layers.3.5.conv2.0.weight
-      - backbone.layers.3.6.conv1.0.weight
-      - backbone.layers.3.6.conv2.0.weight
-      - backbone.layers.3.7.conv1.0.weight
-      - backbone.layers.3.7.conv2.0.weight
-      - backbone.layers.3.8.conv1.0.weight
-      - backbone.layers.3.8.conv2.0.weight
-      - backbone.layers.4.0.0.weight
-      - backbone.layers.4.1.conv1.0.weight
-      - backbone.layers.4.1.conv2.0.weight
-      - backbone.layers.4.2.conv1.0.weight
-      - backbone.layers.4.2.conv2.0.weight
-      - backbone.layers.4.3.conv1.0.weight
-      - backbone.layers.4.3.conv2.0.weight
-      - backbone.layers.4.4.conv1.0.weight
-      - backbone.layers.4.4.conv2.0.weight
-    init_sparsity: eval(init_sparsity)
-    final_sparsity: eval(prune_high_target_sparsity)
-    start_epoch: eval(pruning_start_target * num_pruning_epochs)
-    end_epoch: eval(pruning_end_target * num_pruning_epochs)
-    update_frequency: eval(pruning_update_frequency)
-    mask_type: eval(mask_type)
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: eval(num_epochs - num_quantization_epochs)
-    disable_quantization_observer_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
-    freeze_bn_stats_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
----
-
-# YOLACT Pruned Quantized
-
-This recipe creates a sparse quantized, [YOLACT](https://github.com/dbolya/yolact) model that achieves about 
-98% recovery on the COCO dataset when compared against baseline ( 30.61, 28.80 mAP@all baseline Vs 29.82, 28.26 mAP@all for this recipe for bounding box, mask).
-Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup.
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned_quant.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
diff --git a/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md b/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md
deleted file mode 100644
index f992d0420c1..00000000000
--- a/integrations/examples/dbolya-yolact/recipes/yolact.quantized.md
+++ /dev/null
@@ -1,74 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-num_epochs: 2
-quantization_start_target: 0
-quantization_end_target: 1
-quantization_observer_end_target: 0.5
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-  - !SetWeightDecayModifier
-    start_epoch: eval(quantization_start_target * num_epochs)
-    weight_decay: 0.0
-
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: eval(quantization_end_target * num_epochs)
-    lr_func: linear
-    init_lr: 0.00001
-    final_lr: 0.000001
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: eval(quantization_start_target * num_epochs)
-    disable_quantization_observer_epoch: eval(quantization_observer_end_target * num_epochs)
-    freeze_bn_stats_epoch: eval(quantization_observer_end_target * num_epochs)
----
-
-# YOLACT Quantized
-
-This recipe quantizes a [YOLACT](https://github.com/dbolya/yolact) model.
-Training was done using 4 GPUs with a total batch size of 64 using the 
-[SparseML integration with dbolya/yolact](../).
-When running, adjust hyper-parameters based on the training environment and dataset.
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](../README.md).
-The following command can be used to launch this recipe. 
-Adjust the script command for your GPU device setup.
-YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
-
-*script command:*
-
-```
-sparseml.yolact.train \
---recipe=../recipes/yolact.quantized.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
\ No newline at end of file
diff --git a/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md b/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
deleted file mode 100644
index daef9205b9c..00000000000
--- a/integrations/examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
+++ /dev/null
@@ -1,226 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying YOLACT Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters 
-and instructions needed to create highly accurate pruned and pruned-quantized [YOLACT](https://arxiv.org/abs/1904.02689) segmentation models.
-
-## Overview
-
-Neural Magic's ML team creates recipes that allow anyone to plug in their data and leverage SparseML's recipe-driven 
-approach on top of [YOLACT](https://github.com/dbolya/yolact) training pipelines.
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and 
-quantization, among others. This sparsification process results in many benefits for deployment environments, 
-including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the 
-complicated process and number of hyper-parameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-
-* Creating a pre-trained model to establish a baseline. You will set up your data and then train the model.
-* Applying a recipe to select the trade-off between the amount of recovery to the baseline training performance with 
-the amount of sparsification for inference performance.
-* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) 
-through a compression algorithm to reduce its deployment size and run it in an inference engine such as 
-[DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-The examples listed in this tutorial are all performed on the COCO dataset.
-
-Before diving in, be sure to go through the setup as listed out in the [README](../README.md) for this integration.
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Creating a Pre-trained Model
-
-Before applying one of the recipes, you must first create the pre-trained model to sparsify further. 
-The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. 
-Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
- 
-Creating a pre-trained model involves three steps: 
-1) Setting up the data.
-2) Fetching a model backbone. 
-3) Training the model.
-
-If training with COCO, skip steps 2 and 3 and use the following baseline SparseZoo stub,
-`zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none` with the `--resume` argument
-in the [`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py)
-
-**Note**: If using your custom data, the [YOLACT](https://github.com/dbolya/yolact) repo mentions a post for [training custom data](https://github.com/dbolya/yolact/issues/70#issuecomment-504283008). 
-Otherwise, setup scripts for [COCO](https://cocodataset.org/#home) can be found under the [yolact/data/scripts path](https://github.com/neuralmagic/yolact/tree/master/data/scripts).
-
-### Setting Up the Data
-
-1. For this tutorial, run the COCO setup script with the following command from the root of the `yolact` repository:
-    ```bash
-    sparseml.yolact.download
-    ```
-   
-Based on your internet connection, downloading and validation of the COCO dataset will take around 10 minutes to complete.
-    The script downloads the COCO dataset into a `coco` folder under the data directory.
-Once completed, the data is ready for training with the folder structure in the following state (only directories are shown for brevity):
-```
-    └─ data
-       └─ coco
-          ├── annotations
-          └── images
- ```
-   
- You are ready to train the model.
-
-### Downloading Model Backbone
-
-1. Training YOLACT from scratch requires a pretrained-backbone model, 
-   currently SparseML supports training with `DarkNet-53` backbone. 
-   - Download ImageNet-pre-trained [`Darknet-53` backbone](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing)
-     and put it in `./weights` directory. The directory structure should look like the following:
-````
-    └── Project_directory
-        ├── data
-        │   └─ coco
-        │      ├── annotations
-        │      └── images
-        │   
-        └── weights
-            └── darknet53.pth
-````
-
-2) Run the following command to kickstart training
-```bash
-sparseml.yolact.train --resume \
-./weights/darknet53.pth \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images \
---backbone
-```
-The weights are stored in the `./weights` directory by default and use the `<config>_<epoch>_<iter>.pth` naming 
-convention
-
-3) Validate that the training command was completed successfully by checking under the `./weights` directory for the trained weights.
-   Upon success, the resulting directory structure should look like the following (a few directories are missing content for brevity):
-```
-└── Project_Directory
-    ├── data
-    │    └── coco
-    │        ├── annotations
-    │        └── images
-    │   
-    └── weights
-        ├── darknet53.pth
-        └── yolact_darknet53_54_800000.pth
- ```
-
-You are ready to use the weights at `./weights/yolact_darknet53_54_800000.pth` with the Neural Magic recipes to create a sparsified model.
-You can also download this baseline, pre-trained checkpoint directly from the [SparseZoo UI](https://sparsezoo.neuralmagic.com/models/cv%2Fsegmentation%2Fyolact-darknet53%2Fpytorch%2Fdbolya%2Fcoco%2Fbase-none),
-or pass its model stub directly to the `--resume` argument while invoking the training script.
-## Applying a Recipe
-
-In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-The [`recipes` folder](../recipes) contains multiple files, each offering certain advantages over others. 
-The table below compares these tradeoffs and shows how to run them on the COCO dataset.
-1. Review this table, which lists recipes, commands, and results.
-
-| Sparsification Type |                                    Description                                    | COCO mAP@all | Size on Disk | DeepSparse Performance** |                                        Commands                                        |
-|:-------------------:|:---------------------------------------------------------------------------------:|:------------:|:------------:|:------------------------:|:--------------------------------------------------------------------------------------:|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.288        | 170 MB       | 29.7 img/sec               | `python train.py`                                                                      |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.286        | 30.1 MB      | 61.6 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned.md`       |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.282        | 9.7 MB       | 144.4 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned_quant.md` |
-
-   \*\* DeepSparse Performance measured on an AWS c5.12xlarge instance with 24 cores, batch size 64, and 550x550 input with version 0.12.0 of the DeepSparse Engine i.e. `deepsparse.benchmark --batch_size 64 --scenario sync [model_path]`
-   
-2. Select a recipe to use on top of the pre-trained model you created.
-
-    - Check your CPU hardware support for quantized networks (VNNI instruction set) using the DeepSparse API:
-      ```bash
-      deepsparse.check_hardware
-      ```
-    - If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using the  `pruned` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
-    - If your hardware does support quantized networks, we recommend using the `pruned quantized` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
-    - When running quantized models, the memory footprint for training will significantly increase (roughly 3x). It is recommended to train at a high batch size at first. This will fail with an out-of-memory exception once quantization starts. Once this happens, use the weights from that run to resume training with lower batch size.
-
-3. To begin applying one of the recipes, use the `--recipe` argument within the YOLACT [train script](https://github.com/neuralmagic/yolact/blob/master/train.py).
-   The recipe argument is combined with our previous training command and COCO pre-trained weights to run the recipes over the model. For example, a command for pruning YOLACT would look like this:
-```bash
-sparseml.yolact.train \
---recipe=../recipes/yolact.pruned.md \
---resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
---save_folder=./pruned \
---train_info ./data/coco/annotations/instances_train2017.json \
---validation_info ./data/coco/annotations/instances_val2017.json \
---train_images ./data/coco/images \
---validation_images ./data/coco/images
-```
-After applying a recipe, you are ready to export for inference.
-
-## Exporting for Inference
-
-This step loads a checkpoint file along with the recipe used if any and converts it into the more common inference formats. 
-Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-When you applied a recipe in the previous step, the sparsification run created a new `./pruned` directory under the `yolact` directory:
-
-```
-└── Project_directory
-    ├── data
-    │   ├── coco
-    │   │   ├── annotations
-    │   │   └── images
-    │   └── scripts
-    ├── pruned
-    │    └── yolact_darknet53_3_29316.pth 
-    └── weights
-        └── yolact_darknet53_54_800000.pth
-```
-
-These weights under `./pruned` can be loaded into the `sparseml.yolact.train` and `sparseml.yolact.export_onnx` utilities now. 
-However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-[`sparseml.yolact.export_onnx`](https://github.com/neuralmagic/yolact/blob/master/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-
-1. Enter the following command to load the PyTorch graph, convert to ONNX, and correct any misformatted pieces of the graph for the pruned and quantized models.
-
-    ```bash
-    sparseml.yolact.export_onnx --checkpoint PATH_TO_SPARSIFIED_WEIGHTS
-    ```
-    
-    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-
-2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-   The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
-   An example for benchmarking and deploying YOLACT models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLACT models for image segmentation tasks. 
-In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported it to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/blob/main/src/deepsparse/yolact/README.md) for an example for benchmarking and deploying YOLACT models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Citation
-
-```bibtex
-@inproceedings{yolact-iccv2019,
-  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
-  title     = {YOLACT: {Real-time} Instance Segmentation},
-  booktitle = {ICCV},
-  year      = {2019},
-}
-```
diff --git a/integrations/examples/keras/README.md b/integrations/examples/keras/README.md
deleted file mode 100644
index c3ffdc4d9b3..00000000000
--- a/integrations/examples/keras/README.md
+++ /dev/null
@@ -1,94 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML Keras Integration
-
-This directory combines the SparseML recipe-driven approach with standard Keras training flows for models within
-[Keras Applications](https://keras.io/api/applications/) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom Keras training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Coming soon!
-
-## Tutorials
-
-- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/keras/notebooks/classification.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[tf_keras]`
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=keras) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=keras) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| notebooks            | Jupyter notebooks to walk through sparsifiation within standard Keras training flows                                  |
-| recipes              | Typical recipes for sparsifying Keras models along with any downloaded recipes from the SparseZoo.                    |
-| tutorials            | Tutorial walkthroughs for how to sparsify Keras models using recipes.                                                 |
-| classification.py    | Utility training script for sparsifying classification models in Keras (loads SparseML models).                       |
-| prune_resnet20.py    | Simple example walking through pruning a ResNet-20 model on the Cifar dataset.                                        |
-| README.md            | Readme file.                                                                                                          |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/keras/classification.py b/integrations/examples/keras/classification.py
deleted file mode 100644
index 35644f01e36..00000000000
--- a/integrations/examples/keras/classification.py
+++ /dev/null
@@ -1,898 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Perform tasks on image classification models in Keras including:
-* Model training/pruning
-* Model evaluation
-* ONNX export
-
-
-##########
-train command help:
-usage: classification.py train [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                               [--recipe-path RECIPE_PATH]
-                               [--sparse-transfer-learn] [--eval-mode]
-                               --train-batch-size TRAIN_BATCH_SIZE
-                               --test-batch-size TEST_BATCH_SIZE
-                               [--logs-dir LOGS_DIR]
-                               [--save-best-after SAVE_BEST_AFTER]
-                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
-                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
-Train and/or prune an image classification model
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --model-kwargs MODEL_KWARGS
-                        key word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        Key word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --transfer-class-type TRANSFER_CLASS_TYPE
-                        Type of the target class for transfer ("single" for softmax
-                        function or "multi" for sigmoid function); takes effects only
-                        if the specified dataset is different from the pretrained
-                        dataset, implying a transfer learning usecase
-  --transfer-top-layer TRANSFER_TOP_LAYER
-                        Name of the layer which a custom dense layer will be put on;
-                        None by default to imply the layer right under the top of the
-                        original model will be used (in other words, layer[-2])
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --train-dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for train dataset loading
-  --train-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for train dataset loading
-  --train-prefetch-buffer-size PREFETCH_BUFFER_SIZE
-                        Prefetch buffer size for train dataset loading
-  --val-dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for val dataset loading
-  --val-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for val dataset loading
-  --val-prefetch-buffer-size PREFETCH_BUFFER_SIZE
-                        Prefetch buffer size for val dataset loading
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. If set to
-                        'transfer_learning', then will create a schedule to
-                        enable sparse transfer learning. Recipe from SparseZoo
-                        could be specified using model stub starting with "zoo:"
-  --sparse-transfer-learn
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode           Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
-                        epochs to save checkpoints at
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-################
-Evaluate command help:
-usage: classification.py evaluate [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               --test-batch-size TEST_BATCH_SIZE
-Evaluate an image classification model
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while evaluating
-################
-Export command help:
-usage: classification.py export [-h] --arch-key ARCH_KEY
-                                [--pretrained PRETRAINED]
-                                [--pretrained-dataset PRETRAINED_DATASET]
-                                [--checkpoint-path CHECKPOINT_PATH]
-                                --dataset-path DATASET_PATH
-                                [--dataset-kwargs DATASET_KWARGS]
-                                [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                                [--num-samples NUM_SAMPLES]
-                                [--onnx-opset OPSET]
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. Specify "zoo" to load a model from SparseZoo
-                        based on other information such as arch key and pretrained
-                        dataset.
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.keras.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        key word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --num-samples NUM_SAMPLES
-                        Number of samples to generate, default to 100
-  --onnx-opset OPSET
-                        The ONNX opset to export to, default to 11
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --export-debug-mode EXPORT_DEBUG
-                        The debug mode for ONNX export (passed into keras2onnx).
-                        Default to False.
-
-
-############
-EXAMPLES
-############
-
-#############
-Example command for fine-tuning a ResNet50 model from Keras Application, pretrained on
-Imagenet, for the Imagenette dataset. The recipe could be used to define learning rate
-schedule. Note that there're no pruning performed from this experiment.
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --pretrained-dataset imagenet \
-     --dataset imagenette \
-     --dataset-path <DATASET_DIR> \
-     --train-batch-size 64 \
-     --recipe-path <RECIPE_PATH>
-############
-Example command for pruning a model with a recipe. Assuming that a ResNet50 model
-from Keras Application was fine-tuned for the Imagenette dataset. The following
-command specifies the model checkpoint after fine-tuning, and a recipe that could
-be used next to prune its weights:
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --train-batch-size 64 \
-     --recipe-path <RECIPE_PATH>
-
-In the above command, specifying "zoo" for --checkpoint-path forces loading the
-base model from SparseZoo. The following command prunes a ResNet50 from Keras
-Applications fine-tuned for Imagenette dataset, using a recipe from SparseZoo:
-
-python scripts/keras_classification.py train \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path zoo \
-     --pretrained-dataset imagenette \
-     --dataset imagenette \
-     --dataset-path <DATASET_DIR> \
-     --train-batch-size 64 \
-     --recipe-path zoo:cv/classification/resnet_v1-50/keras/\
-        keras.applications/imagenette/pruned-moderate?recipe_type=original
-############
-Example command for loading a ResNet50 model from a checkpoint, then
-evaluate it on the validation of the Imagenette dataset.
-python scripts/keras_classification.py evaluate \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --test-batch-size 64
-############
-Example command for loading a ResNet50 model from a checkpoint, then export it
-into ONNX format. It also generates a number of inputs, outputs and labels.
-python scripts/keras_classification.py export \
-     --arch-key keras_applications.ResNet50 \
-     --checkpoint-path <MODEL_CHECKPOINT> \
-     --dataset imagenette \
-     --dataset-path /hdd/datasets/imagenette \
-     --num-samples <NUM_SAMPLES>
-     --save-dir <EXPORT_DIR>
-"""
-
-import argparse
-import inspect
-import json
-import math
-import os
-from typing import Optional, Tuple
-
-import numpy
-import tensorflow
-
-from sparseml import get_main_logger
-from sparseml.keras.datasets import Dataset, DatasetRegistry
-from sparseml.keras.models import ModelRegistry
-from sparseml.keras.optim import ScheduledModifierManager, remove_pruning_masks
-from sparseml.keras.utils import (
-    LossesAndMetricsLoggingCallback,
-    ModelExporter,
-    TensorBoardLogger,
-    keras,
-)
-from sparseml.utils import create_dirs
-
-
-LOGGER = get_main_logger()
-TRAIN_COMMAND = "train"
-EVAL_COMMAND = "evaluate"
-EXPORT_COMMAND = "export"
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Run tasks on classification models and datasets "
-        "using the sparseml API"
-    )
-
-    subparsers = parser.add_subparsers(dest="command")
-    train_parser = subparsers.add_parser(
-        TRAIN_COMMAND,
-        description="Train and/or prune an image classification model",
-    )
-    eval_parser = subparsers.add_parser(
-        EVAL_COMMAND,
-        description="Evaluate an image classification model on a dataset",
-    )
-    export_parser = subparsers.add_parser(
-        EXPORT_COMMAND,
-        description="Export a model to onnx as well as "
-        "store sample inputs, outputs, and labels",
-    )
-
-    parsers = [
-        train_parser,
-        eval_parser,
-        export_parser,
-    ]
-    for par in parsers:
-        # general arguments
-        # model args
-        par.add_argument(
-            "--arch-key",
-            type=str,
-            required=True,
-            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception with the list)",
-        )
-        par.add_argument(
-            "--pretrained",
-            type=str,
-            default=True,
-            help="The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]",
-        )
-        par.add_argument(
-            "--pretrained-dataset",
-            type=str,
-            default=None,
-            help="The dataset to load pretrained weights for if pretrained is set. "
-            "Default is None which will load the default dataset for the architecture."
-            " Ex can be set to imagenet, cifar10, etc",
-        )
-        par.add_argument(
-            "--checkpoint-path",
-            type=str,
-            default=None,
-            help="A path to a previous checkpoint to load the state from and "
-            "resume the state for. If provided, pretrained will be ignored",
-        )
-        par.add_argument(
-            "--model-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to model constructor, should be "
-            " given as a json object",
-        )
-
-        # dataset args
-        par.add_argument(
-            "--dataset",
-            type=str,
-            required=True,
-            help="The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or loadable by a "
-            "dataset in sparseml.keras.datasets",
-        )
-        par.add_argument(
-            "--dataset-path",
-            type=str,
-            required=True,
-            help="The root path to where the dataset is stored",
-        )
-        par.add_argument(
-            "--dataset-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to dataset constructor, should be "
-            " given as a json object",
-        )
-
-        # logging and saving
-        par.add_argument(
-            "--model-tag",
-            type=str,
-            default=None,
-            help="A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        )
-        par.add_argument(
-            "--save-dir",
-            type=str,
-            default="keras_classification",
-            help="The path to the directory for saving results",
-        )
-
-    # task specific arguments: training
-    train_parser.add_argument(
-        "--dataset-parallel-calls",
-        type=int,
-        default=4,
-        help="the number of parallel workers for dataset loading",
-    )
-    train_parser.add_argument(
-        "--train-shuffle-buffer-size",
-        type=int,
-        default=None,
-        help="Shuffle buffer size for dataset loading",
-    )
-    train_parser.add_argument(
-        "--train-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for train dataset loading",
-    )
-    train_parser.add_argument(
-        "--test-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for test dataset loading",
-    )
-    train_parser.add_argument(
-        "--recipe-path",
-        type=str,
-        default=None,
-        help="The path to the yaml file containing the modifiers and "
-        "schedule to apply them with. If set to 'transfer_learning', "
-        "then will create a schedule to enable sparse transfer learning",
-    )
-    train_parser.add_argument(
-        "--transfer-class-type",
-        type=str,
-        default="single",
-        help="Type of target function: 'single' for softmax, 'multi' for sigmoid "
-        "or 'linear' for linear function. This option takes effects only when the "
-        "specified dataset is different from the pretrained dataset, implying a "
-        "transfer learning usecase.",
-    )
-    train_parser.add_argument(
-        "--transfer-top-layer",
-        type=str,
-        default=None,
-        help="Name of the layer which a custom dense layer will be put on. Default "
-        "to None to imply the layer right under the top of the original model "
-        "will be used (in other words, layer[-2])",
-    )
-    train_parser.add_argument(
-        "--train-batch-size",
-        type=int,
-        required=True,
-        help="The batch size to use while training",
-    )
-    train_parser.add_argument(
-        "--test-batch-size",
-        type=int,
-        required=False,
-        help="The batch size to use while testing; default to the train " "batch size",
-    )
-    train_parser.add_argument(
-        "--log-dir",
-        type=str,
-        default=os.path.join("keras_classification_train", "tensorboard-logs"),
-        help="The path to the directory for saving logs",
-    )
-    train_parser.add_argument(
-        "--log-epoch",
-        type=bool,
-        default=True,
-        help="Whether logging should be performed at the end of each epoch",
-    )
-    train_parser.add_argument(
-        "--log-batch",
-        type=bool,
-        default=False,
-        help="Whether logging should be performed at the end of each training " "batch",
-    )
-    train_parser.add_argument(
-        "--log-steps",
-        type=int,
-        default=-1,
-        help="Whether logging should be performed after every specified number "
-        "of steps",
-    )
-    train_parser.add_argument(
-        "--save-best-only",
-        type=bool,
-        default=True,
-        help="Save model only with better monitored metric",
-    )
-    train_parser.add_argument(
-        "--optim",
-        type=str,
-        default="SGD",
-        help="The optimizer type to use, e.g., 'Adam', 'SGD' etc",
-    )
-    train_parser.add_argument(
-        "--optim-args",
-        type=json.loads,
-        default={"momentum": 0.9, "nesterov": True},
-        # default={},
-        help="Additional args to be passed to the optimizer passed in"
-        " as a json object",
-    )
-    train_parser.add_argument(
-        "--run-eagerly",
-        type=bool,
-        default=True,
-        help="Run training in eager execution mode",
-    )
-
-    # task specific arguments: evaluation
-    eval_parser.add_argument(
-        "--test-batch-size",
-        type=int,
-        required=False,
-        help="The batch size to use while testing; default to the train " "batch size",
-    )
-    eval_parser.add_argument(
-        "--dataset-parallel-calls",
-        type=int,
-        default=4,
-        help="the number of parallel workers for dataset loading",
-    )
-    eval_parser.add_argument(
-        "--test-prefetch-buffer-size",
-        type=int,
-        default=None,
-        help="Prefetch buffer size for test dataset loading",
-    )
-
-    # task specific arguments: export
-    export_parser.add_argument(
-        "--num-samples",
-        type=int,
-        default=100,
-        help="The number of samples to export along with the model onnx "
-        "and pth files (sample inputs and labels as well as the outputs "
-        "from model execution)",
-    )
-    export_parser.add_argument(
-        "--onnx-opset",
-        type=int,
-        default=11,
-        help="The onnx opset to use for export. Default is 11",
-    )
-    export_parser.add_argument(
-        "--export-debug-mode",
-        type=bool,
-        default=False,
-        help="The debug mode for ONNX export (passed into keras2onnx). "
-        "Default to False.",
-    )
-
-    return parser.parse_args()
-
-
-def setup_save_and_log_dirs(args) -> Tuple[str, Optional[str]]:
-    # Saving dir setup
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-    if not args.model_tag:
-        model_tag = "{}@{}".format(args.arch_key, args.dataset)
-        model_id = model_tag
-        model_inc = 0
-
-        while os.path.exists(os.path.join(save_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-    LOGGER.info("Model directory is set to {}".format(save_dir))
-
-    # log dir setup
-    log_dir = (
-        os.path.abspath(os.path.expanduser(args.log_dir))
-        if args.command == TRAIN_COMMAND
-        else None
-    )
-    if args.command == TRAIN_COMMAND:
-        log_dir = os.path.join(log_dir, model_id)
-        create_dirs(log_dir)
-        LOGGER.info("Logging directory is set to {}".format(log_dir))
-    else:
-        log_dir = None
-    return save_dir, log_dir
-
-
-def create_dataset(
-    args, train: bool, image_size: Tuple[int, int]
-) -> Tuple[Dataset, int]:
-    kwargs = args.dataset_kwargs
-    dataset = DatasetRegistry.create(
-        args.dataset,
-        root=args.dataset_path,
-        train=train,
-        image_size=image_size,
-        **kwargs,
-    )
-    LOGGER.info(
-        "created {} dataset: {}, images to resize to {}".format(
-            "train" if train else "val", dataset, image_size
-        )
-    )
-
-    # get num_classes
-    if args.dataset == "imagefolder":
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-
-    return dataset, num_classes
-
-
-def build_dataset(
-    args, dataset: Dataset, train: bool = True
-) -> tensorflow.data.Dataset:
-    test_batch_size = (
-        args.test_batch_size if args.test_batch_size else args.train_batch_size
-    )
-    batch_size = args.train_batch_size if train else test_batch_size
-    if train:
-        shuffle_buffer_size = (
-            args.train_shuffle_buffer_size
-            if args.train_shuffle_buffer_size
-            else dataset.num_images
-        )
-    else:
-        shuffle_buffer_size = None
-
-    if train:
-        prefetch_buffer_size = (
-            args.train_prefetch_buffer_size
-            if args.train_prefetch_buffer_size
-            else batch_size * 8
-        )
-    else:
-        prefetch_buffer_size = (
-            args.test_prefetch_buffer_size
-            if args.test_prefetch_buffer_size
-            else batch_size * 8
-        )
-    built_dataset = dataset.build(
-        batch_size,
-        repeat_count=1,
-        shuffle_buffer_size=shuffle_buffer_size,
-        prefetch_buffer_size=prefetch_buffer_size,
-        num_parallel_calls=args.dataset_parallel_calls,
-    )
-    return built_dataset
-
-
-def create_model(args, input_shape, num_classes):
-    kwargs = args.model_kwargs
-
-    if args.checkpoint_path == "zoo":
-        zoo_model = ModelRegistry.create_zoo_model(
-            args.arch_key, args.pretrained, args.pretrained_dataset
-        )
-        model_file_paths = zoo_model.download_framework_files(extensions=[".h5"])
-        if not model_file_paths:
-            model_file_paths = zoo_model.download_framework_files(extensions=[".tf"])
-        if not model_file_paths:
-            raise RuntimeError("Error downloading model from SparseZoo")
-        model_file_path = model_file_paths[0]
-        model = keras.models.load_model(model_file_path)
-    else:
-        model = ModelRegistry.create(
-            args.arch_key,
-            args.pretrained,
-            args.checkpoint_path,
-            args.pretrained_dataset,
-            input_shape=input_shape,
-            **kwargs,
-        )
-
-    if (
-        args.pretrained_dataset is not None
-        and args.dataset is not None
-        and args.pretrained_dataset != args.dataset
-    ):
-        # Set up the model for fine-tuning
-        # The trainablility of model's parameters should be controlled
-        # through recipe; we therefore do not set "model.trainable" to False here.
-        if args.transfer_class_type == "single":
-            activation = "softmax"
-        elif args.transfer_class_type == "multi":
-            activation = "sigmoid"
-        elif args.transfer_class_type == "linear":
-            activation = None
-        else:
-            raise ValueError(
-                "Unknown transfer_class_type; expected: 'single', 'softmax' or 'linear'"
-            )
-        if args.transfer_top_layer is not None:
-            transfer_top_layer = None
-            for i in list(range(len(model.layers)))[::-1]:
-                if model.layers[i].name == args.transfer_top_layer:
-                    transfer_top_layer = args.transfer_top_layer
-                    break
-            if transfer_top_layer is None:
-                raise ValueError(
-                    "Top layer for transfer learning {} not found".format(
-                        args.transfer_top_layer
-                    )
-                )
-        else:
-            transfer_top_layer = model.layers[-2]
-        new_output = keras.layers.Dense(num_classes, activation=activation)(
-            transfer_top_layer.output
-        )
-        model = keras.Model(model.input, new_output)
-
-    return model
-
-
-def create_optimizer(args):
-    optim_const = {}
-    for opt_name in dir(keras.optimizers):
-        opt = getattr(keras.optimizers, opt_name)
-        if inspect.isclass(opt) and opt_name != "Optimizer":
-            optim_const[opt_name] = opt
-    if args.optim not in optim_const:
-        raise ValueError(
-            "Unknown optimizer {}. Supported: {}".format(
-                args.optim, list(optim_const.keys())
-            )
-        )
-    optimizer = optim_const[args.optim](**args.optim_args)
-    return optimizer
-
-
-def get_logging_update_freq(args):
-    if args.log_steps > 0:
-        update_freq = args.log_steps
-    elif args.log_batch:
-        update_freq = "batch"
-    elif args.log_epoch:
-        update_freq = "epoch"
-    else:
-        update_freq = None
-    return update_freq
-
-
-def handle_train_command(args):
-    save_dir, log_dir = setup_save_and_log_dirs(args)
-
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    # Load datasets
-    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
-    num_train_images = train_dataset.num_images
-    train_dataset = build_dataset(args, train_dataset, train=True)
-
-    val_dataset, _ = create_dataset(args, train=False, image_size=image_size)
-    val_dataset = build_dataset(args, val_dataset, train=False)
-
-    # Create model
-    model = create_model(args, input_shape, num_classes=num_classes)
-
-    # Create optimizer
-    optimizer = create_optimizer(args)
-
-    # Logging
-    if log_dir:
-        update_freq = get_logging_update_freq(args)
-        if update_freq is None:
-            raise ValueError(
-                "Logging requires update frequency to take effect; use either "
-                "'log-epoch', 'log-batch' or 'log-steps' option."
-            )
-        loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
-    else:
-        loggers = []
-
-    # Model saving
-    checkpoint_filepath = os.path.join(
-        save_dir, "model.{epoch:02d}-{val_accuracy:.2f}.tf"
-    )
-    checkpoint_callback = keras.callbacks.ModelCheckpoint(
-        filepath=checkpoint_filepath,
-        save_weights_only=False,
-        monitor="val_accuracy",
-        mode="max",
-        save_best_only=True,
-        verbose=1,
-    )
-
-    # Manager
-    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
-
-    # Enhance model
-    steps_per_epoch = math.ceil(num_train_images / args.train_batch_size)
-    model, optimizer, callbacks = manager.modify(
-        model, optimizer, steps_per_epoch, loggers=loggers
-    )
-    if loggers:
-        callbacks.append(LossesAndMetricsLoggingCallback(loggers))
-    callbacks.append(checkpoint_callback)
-
-    model.compile(
-        loss=keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-        run_eagerly=args.run_eagerly,
-    )
-    model.fit(
-        train_dataset,
-        epochs=manager.max_epochs,
-        callbacks=callbacks,
-        validation_data=val_dataset,
-    )
-
-
-def handle_eval_command(args):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    # Load dataset
-    val_dataset, num_classes = create_dataset(args, train=False, image_size=image_size)
-    val_dataset = build_dataset(args, val_dataset, train=False)
-
-    # Create model
-    model = create_model(args, input_shape, num_classes=num_classes)
-
-    # Evaluate
-    result = model.evaluate(val_dataset)
-    print(dict(zip(model.metrics_names, result)))
-
-
-def handle_export_command(args):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    image_size = (input_shape[0], input_shape[1])
-
-    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
-    train_dataset = train_dataset.build(args.num_samples)
-
-    model = create_model(args, input_shape, num_classes=num_classes)
-    model = remove_pruning_masks(model)
-    exporter = ModelExporter(model, args.save_dir)
-    exporter.export_onnx(opset=args.onnx_opset, debug_mode=args.export_debug_mode)
-
-    for img_batch, label_batch in train_dataset.take(1):
-        output_batch = model(img_batch)
-        numpy.save(os.path.join(args.save_dir, "inputs.npy"), img_batch)
-        numpy.save(os.path.join(args.save_dir, "outputs.npy"), output_batch)
-        numpy.save(os.path.join(args.save_dir, "labels.npy"), label_batch)
-
-
-def main(args):
-    if args.command == TRAIN_COMMAND:
-        handle_train_command(args)
-    elif args.command == EVAL_COMMAND:
-        handle_eval_command(args)
-    elif args.command == EXPORT_COMMAND:
-        handle_export_command(args)
-    else:
-        raise ValueError("Unknown command passed in")
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-    main(args_)
diff --git a/integrations/examples/keras/notebooks/classification.ipynb b/integrations/examples/keras/notebooks/classification.ipynb
deleted file mode 100644
index f08a5b0ab50..00000000000
--- a/integrations/examples/keras/notebooks/classification.ipynb
+++ /dev/null
@@ -1,433 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021 Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Keras Classification Model Pruning Using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Inference Engine. You will:\n",
-    "- Set up the model and dataset\n",
-    "- Integrate the Keras training flow with SparseML\n",
-    "- Prune the model using the Keras+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your Keras training flow.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo;\n",
-    "* Tensorflow >=2.1, which includes Keras and TensorBoard;\n",
-    "* keras2onnx.\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "In this notebook, you will prune a simple convolution neural network model trained on the MNIST dataset. The pretrained model's architecture and weights are downloaded from the SparseZoo model repo. The dataset is downloaded directly from  Keras datasets library."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Up the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell defines a procedure to download a model from the SparseZoo; additionally, for convenience it also returns the path to an optimization recipe. You construct a Keras model instance from the pretrained to prune in a later step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from sparseml.keras.utils import keras\n",
-    "from sparsezoo import Model\n",
-    "\n",
-    "# Root directory for the notebook artifacts\n",
-    "root_dir = \"./notebooks/keras\"\n",
-    "\n",
-    "def download_model_and_recipe(root_dir: str):\n",
-    "    \"\"\"\n",
-    "    Download pretrained model and a pruning recipe\n",
-    "    \"\"\"\n",
-    "    model_dir = os.path.join(root_dir, \"mnist\")\n",
-    "    zoo_model = Model(model_dir)\n",
-    "\n",
-    "\n",
-    "    model_file_path = zoo_model.training.default.get_file(\"model.h5\").path\n",
-    "    if not os.path.exists(model_file_path) or not model_file_path.endswith(\".h5\"):\n",
-    "        raise RuntimeError(\"Model file not found: {}\".format(model_file_path))\n",
-    "    recipe_file_path = zoo_model.recipes.default.path\n",
-    "    if not os.path.exists(recipe_file_path):\n",
-    "        raise RuntimeError(\"Recipe file not found: {}\".format(recipe_file_path))\n",
-    "    return model_file_path, recipe_file_path\n",
-    "\n",
-    "model_file_path, recipe_file_path = download_model_and_recipe(root_dir)\n",
-    "\n",
-    "print(\"Loading model {}\".format(model_file_path))\n",
-    "model = keras.models.load_model(model_file_path)\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set Up the Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will download the MNIST dataset from Keras datasets library as follows. You will also normalize the data before using it for training and evaluation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "\n",
-    "# Number of classes\n",
-    "num_classes = 10\n",
-    "\n",
-    "# Load MNIST dataset\n",
-    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
-    "\n",
-    "# Normalize data\n",
-    "x_train = x_train.astype('float32') / 255\n",
-    "x_test = x_test.astype('float32') / 255\n",
-    "\n",
-    "# Add batch dimension (for older TF versions)\n",
-    "x_train = numpy.expand_dims(x_train, -1)\n",
-    "x_test = numpy.expand_dims(x_test, -1)\n",
-    "\n",
-    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
-    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
-    "\n",
-    "print(\"Dataset loaded and normalized\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Before pruning the model, you could run the cell below to verify the accuracy of the model on the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = model.evaluate(x_test, y_test)\n",
-    "print(\"Test loss, accuracy: \", res)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Pruning the Pretrained Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this section, you will prune the above pretrained Keras model using the SparseML model optimization library. Recall that a common training workflow in Keras is first to compile the model with the appropriate losses, metrics and an optimizer, then to train the model using the `fit()` method of the `Model` class. The SparseML library makes it easy to extend this training workflow to perform gradual pruning based on weight magnitudes.\n",
-    "\n",
-    "Given a pretrained model, the pruning workflow can be summarized as follows:\n",
-    "1. Create a recipe for pruning, which could be done effectively using the Sparsify toolkit\n",
-    "2. Instantiate a Keras optimizer instance (such as SGD or Adam)\n",
-    "3. Instantiate a `ScheduledModifierManager` object from the recipe\n",
-    "4. Enhance the model and optimizer with pruning data structures by calling the manager's `modify` method. At this step, you have options to define the loggers used during the pruning process. The results of this step are a model to be pruned, an optimizer that should be used and a list of callbacks\n",
-    "5. [Optional] Add to the callback list any additional callbacks such as model checkpoint and the SparseML built-in LossesAndMetricsLogging callback\n",
-    "6. Compile and fit the modified model using Keras built-in APIs, using the optimizer and callback list\n",
-    "7. Erase the pruning information in the enhanced model, and get back the original model with pruned weights\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, you will set up a directory path for logging and the frequency for the logging update."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "# Logging directory\n",
-    "log_dir = \"./tensorboard/mnist:\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
-    "print(\"Logging directory: {}\".format(log_dir))\n",
-    "\n",
-    "# Number of steps before the next logging should take place\n",
-    "# Use \"epoch\" or \"batch\" to log at every training epoch or batch (respectively)\n",
-    "update_freq = 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following cell contains the few steps required for pruning using the SparseML library, ultimately resulting in a modified model, optimizer and a list of callbacks incoporating the optimization logics. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "import math\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "\n",
-    "from sparseml.keras.optim import ScheduledModifierManager\n",
-    "from sparseml.keras.utils import LossesAndMetricsLoggingCallback, TensorBoardLogger\n",
-    "\n",
-    "# Training batch size\n",
-    "batch_size = 32\n",
-    "\n",
-    "# Number of steps per epoch\n",
-    "steps_per_epoch = math.ceil(len(x_train) / batch_size)\n",
-    "\n",
-    "# Create a manager from the recipe\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_file_path)\n",
-    "\n",
-    "# Create an optimizer\n",
-    "optimizer = Adam()\n",
-    "\n",
-    "# Optional: Create a TensorBoardLogger instance\n",
-    "loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)\n",
-    "\n",
-    "# Modify the model and optimizer\n",
-    "model_for_pruning, optimizer, callbacks = manager.modify(model, optimizer, steps_per_epoch, loggers=loggers)\n",
-    "\n",
-    "# Include your own callbacks. Here you will use the built-in LossesAndMetricsLoggingCallback\n",
-    "callbacks.append(LossesAndMetricsLoggingCallback(loggers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now you are ready to compile the modified model using the losses and metrics of your choice, and the optimizer enhanced by the mananger. The last step is to train the model using its `fit()` method, passing in (among other information) the list of callbacks constructed above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Compile the modified model\n",
-    "model_for_pruning.compile(\n",
-    "    loss=keras.losses.categorical_crossentropy,\n",
-    "    optimizer=optimizer,\n",
-    "    metrics=['accuracy'],\n",
-    "    run_eagerly=True\n",
-    ")\n",
-    "\n",
-    "# Prune the model\n",
-    "model_for_pruning.fit(x_train, y_train, batch_size=batch_size, epochs=manager.max_epochs,\n",
-    "                      validation_data=(x_test, y_test), shuffle=True, callbacks=callbacks)\n",
-    "\n",
-    "print(\"Pruning finished\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is time to verify the accuracy of the pruned model. Later on you can also check the sparsity level of the ONNX version of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Verify the pruned model's accuracy\n",
-    "res = model_for_pruning.evaluate(x_test, y_test)\n",
-    "print(\"Validation loss, accuracy: \", res)\n",
-    "\n",
-    "# Erase the enhanced information used for pruning \n",
-    "pruned_model = manager.finalize(model_for_pruning)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Given that you used a TensorBoardLogger, you can now observe the logging information in TensorBoard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext tensorboard\n",
-    "%tensorboard --logdir ./tensorboard"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Examine the Pruned Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can observe the layers of the pruned Keras model using its `layers` property and `get_weights()` method. First, print out the list of layers of this model, and recall that in this optimization recipe we pruned all the Conv2D layers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pruned_model.layers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can observe the weights of any Conv2D that you pruned, and notice that the majority of them are zeros."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change the layer index to examine the layers. Choose one among 1, 4, 7, 10 as the layer\n",
-    "# indices of the pruned layers.\n",
-    "layer_index = 10\n",
-    "pruned_model.layers[layer_index].get_weights()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To observe the overall sparsity of the model, as well as the sparsity level of each layer, run the following cell. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.keras.utils import sparsity\n",
-    "from pprint import pprint\n",
-    "\n",
-    "model_sparsity, layer_sparsity_dict = sparsity(pruned_model)\n",
-    "print(\"Model sparsity: {}\".format(model_sparsity))\n",
-    "print(\"Layer sparsities:\")\n",
-    "pprint(layer_sparsity_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For Keras, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to.\n",
-    "\n",
-    "#### Note:\n",
-    "The `keras2onnx` is known to cause issues during the conversion; in particular, if you installed Tensorflow 2.4, you might encounter the `AttributeError: 'KerasTensor' object has no attribute 'graph'`. You might need to downgrade to Tensorflow 2.2 or 2.3 for the ONNX export to work properly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.keras.utils import ModelExporter\n",
-    "\n",
-    "save_dir = \"keras_classification\"\n",
-    "onnx_file_name = \"pruned_mnist.onnx\"\n",
-    "\n",
-    "exporter = ModelExporter(pruned_model, output_dir=save_dir)\n",
-    "exporter.export_onnx(name=onnx_file_name)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the DeepSparse Engine"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/keras/prune_resnet20.py b/integrations/examples/keras/prune_resnet20.py
deleted file mode 100644
index f4429da738b..00000000000
--- a/integrations/examples/keras/prune_resnet20.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Example of pruning a ResNet20-v1 model pretrained on the Cifar-10 dataset.
-The pretrained model and this pruning script were adapted from:
-https://keras.io/zh/integrations/cifar10_resnet/
-
-Run the following command from the top repo directory:
-
-   python3 integrations/keras/prune_resnet20.py
-
-"""
-
-import math
-import os
-from datetime import datetime
-
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.callbacks import ModelCheckpoint
-from tensorflow.keras.datasets import cifar10
-from tensorflow.keras.preprocessing.image import ImageDataGenerator
-
-from sparseml.keras.optim import ScheduledModifierManager
-from sparseml.keras.utils.callbacks import LossesAndMetricsLoggingCallback
-from sparseml.keras.utils.exporter import ModelExporter
-from sparseml.keras.utils.logger import TensorBoardLogger
-from sparsezoo import Model
-
-
-# Root directory
-root_dir = "./integrations/keras"
-
-# Logging setup
-log_dir = os.path.join(root_dir, "tensorboard", "resnet20_v1")
-log_dir += ":" + datetime.now().strftime("%Y%m%d-%H%M%S")
-update_freq = 100  # Logging update every this many training steps
-
-# Train/dataset setting
-num_classes = 10
-batch_size = 32
-subtract_pixel_mean = True
-data_augmentation = True
-
-# Pruned model directory
-pruned_model_dir = os.path.join(root_dir, "pruned")
-if not os.path.isdir(pruned_model_dir):
-    os.makedirs(pruned_model_dir)
-
-
-def download_model_and_recipe(root_dir: str):
-    """
-    Download pretrained model and a pruning recipe
-    """
-
-    # Use the recipe stub
-    recipe_file_path = (
-        "zoo:cv/classification/resnet_v1-20/keras/sparseml/cifar_10/pruned-conservative"
-    )
-
-    # Load base model to prune
-    base_zoo_model = Model(recipe_file_path)
-    base_zoo_model.path = os.path.join(root_dir, "resnet20_v1")
-    checkpoint = base_zoo_model.training.default
-    model_file_path = checkpoint.get_file("model.h5").path
-    recipe_file_path = base_zoo_model.recipes.default.path
-    if not os.path.exists(model_file_path) or not model_file_path.endswith(".h5"):
-        raise RuntimeError("Model file not found: {}".format(model_file_path))
-
-    return model_file_path, recipe_file_path
-
-
-def load_and_normalize_cifar10(subtract_pixel_mean: bool = True):
-    """
-    Load and normalize the Cifar-10 dataset
-    """
-    # Load the CIFAR10 data.
-    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
-
-    # Normalize data.
-    X_train = X_train.astype("float32") / 255
-    X_test = X_test.astype("float32") / 255
-
-    # If subtract pixel mean is enabled
-    if subtract_pixel_mean:
-        X_train_mean = np.mean(X_train, axis=0)
-        X_train -= X_train_mean
-        X_test -= X_train_mean
-
-    # Convert class vectors to binary class matrices.
-    y_train = tf.keras.utils.to_categorical(y_train, num_classes)
-    y_test = tf.keras.utils.to_categorical(y_test, num_classes)
-
-    return (X_train, y_train), (X_test, y_test)
-
-
-def model_checkpoint_callback():
-    """
-    Create model checkpoint callback
-    """
-    pruned_model_name = "resnet20_v1.pruned.{epoch:03d}.h5"
-    pruned_filepath = os.path.join(pruned_model_dir, pruned_model_name)
-
-    # Prepare a callback for model saving
-    checkpoint = ModelCheckpoint(
-        filepath=pruned_filepath, monitor="val_accuracy", verbose=1, save_best_only=True
-    )
-    return checkpoint
-
-
-def main():
-    print("Load and normalize Cifar-10 dataset")
-    (X_train, y_train), (X_test, y_test) = load_and_normalize_cifar10()
-
-    model_file_path, recipe_file_path = download_model_and_recipe(root_dir)
-
-    print("Load pretrained model")
-    base_model = tf.keras.models.load_model(model_file_path)
-    base_model.summary()
-
-    scores = base_model.evaluate(X_test, y_test, verbose=1)
-    print("Test loss:", scores[0])
-    print("Test accuracy:", scores[1])
-
-    optimizer = tf.keras.optimizers.Adam()
-
-    steps_per_epoch = math.ceil(len(X_train) / batch_size)
-
-    # Enhance the model and optimizer for pruning using the manager
-    loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
-    manager = ScheduledModifierManager.from_yaml(recipe_file_path)
-    model_for_pruning, optimizer, callbacks = manager.modify(
-        base_model, optimizer, steps_per_epoch, loggers=loggers
-    )
-    callbacks.append(LossesAndMetricsLoggingCallback(loggers))
-    callbacks.append(model_checkpoint_callback())
-
-    # Compile the enhanced model
-    model_for_pruning.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-        run_eagerly=True,
-    )
-
-    # Run training and pruning, with or without data augmentation.
-    if not data_augmentation:
-        print("Not using data augmentation.")
-        model_for_pruning.fit(
-            X_train,
-            y_train,
-            batch_size=batch_size,
-            validation_data=(X_test, y_test),
-            shuffle=True,
-            callbacks=callbacks,
-            epochs=manager.max_epochs,
-        )
-    else:
-        print("Using real-time data augmentation.")
-        # This will do preprocessing and realtime data augmentation:
-        datagen = ImageDataGenerator(
-            # set input mean to 0 over the dataset
-            featurewise_center=False,
-            # set each sample mean to 0
-            samplewise_center=False,
-            # divide inputs by std of dataset
-            featurewise_std_normalization=False,
-            # divide each input by its std
-            samplewise_std_normalization=False,
-            # apply ZCA whitening
-            zca_whitening=False,
-            # epsilon for ZCA whitening
-            zca_epsilon=1e-06,
-            # randomly rotate images in the range (deg 0 to 180)
-            rotation_range=0,
-            # randomly shift images horizontally
-            width_shift_range=0.1,
-            # randomly shift images vertically
-            height_shift_range=0.1,
-            # set range for random shear
-            shear_range=0.0,
-            # set range for random zoom
-            zoom_range=0.0,
-            # set range for random channel shifts
-            channel_shift_range=0.0,
-            # set mode for filling points outside the input boundaries
-            fill_mode="nearest",
-            # value used for fill_mode = "constant"
-            cval=0.0,
-            # randomly flip images
-            horizontal_flip=True,
-            # randomly flip images
-            vertical_flip=False,
-            # set rescaling factor (applied before any other transformation)
-            rescale=None,
-            # set function that will be applied on each input
-            preprocessing_function=None,
-            # image data format, either "channels_first" or "channels_last"
-            data_format=None,
-            # fraction of images reserved for validation (strictly between 0 and 1)
-            validation_split=0.0,
-        )
-
-        # Compute quantities required for featurewise normalization
-        # (std, mean, and principal components if ZCA whitening is applied).
-        datagen.fit(X_train)
-
-        # Fit the model on the batches generated by datagen.flow().
-        model_for_pruning.fit_generator(
-            datagen.flow(X_train, y_train, batch_size=batch_size),
-            validation_data=(X_test, y_test),
-            epochs=manager.max_epochs,
-            verbose=1,
-            workers=4,
-            callbacks=callbacks,
-        )
-
-    # Erase pruning masks and export to ONNX model
-    pruned_model = manager.finalize(model_for_pruning)
-    exporter = ModelExporter(pruned_model, output_dir=pruned_model_dir)
-    onnx_model_name = "pruned_resnet20_v1.onnx"
-    exporter.export_onnx(name=onnx_model_name)
-    print(
-        "Model exported to {}".format(os.path.join(pruned_model_dir, onnx_model_name))
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/examples/keras/recipes/.gitkeep b/integrations/examples/keras/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/keras/tutorials/.gitkeep b/integrations/examples/keras/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/pytorch/README.md b/integrations/examples/pytorch/README.md
deleted file mode 100644
index ca0a38fbb0f..00000000000
--- a/integrations/examples/pytorch/README.md
+++ /dev/null
@@ -1,99 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML PyTorch Integration
-
-This directory combines the SparseML recipe-driven approach with standard PyTorch training flows for models within
-[torchvision](https://pytorch.org/vision/stable/index.html) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom PyTorch training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Blog: [ResNet-50 on CPUs: Sparsifying for Better Performance on CPUs](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse/)
-
-## Tutorials
-
-- [Classification](./notebooks/classification.ipynb)
-- [Detection](./notebooks/detection.ipynb)
-- [Sparse Quantized Transfer Learning](./notebooks/sparse_quantized_transfer_learning.ipynb)
-- [Torchvision](./notebooks/torchvision.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[torchvision]`
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=pytorch) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=pytorch) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name                                             | Description                                                                                                           |
-|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [notebooks](./notebooks)                                     | Jupyter notebooks to walk through sparsifiation within standard PyTorch training flows                                |
-| [recipes](./recipes)                                         | Typical recipes for sparsifying PyTorch models along with any downloaded recipes from the SparseZoo.                  |
-| [tutorials](./tutorials)                                     | Tutorial walkthroughs for how to sparsify PyTorch models using recipes.                                               |
-| [README.md](./README.md)                                     | Readme file.                                                                                                          |
-| [torchvision_sparsification.py](./torchvision_sparsification.py) | Example training script for sparsifying PyTorch torchvision classification models.                                    |
-| `sparseml.image_classification.train`                        | Utility training script for sparsifying classification and detection models in PyTorch (loads SparseML models).       |
-| `sparseml.image_classification.export`                       | Utility script for exporting pytorch model to ONNX file along with sample inputs, outputs and labels.                 |
-| `sparseml.image_classification.lr_analysis`                  |Utility script to Run learning rate sensitivity analysis for a desired image classification architecture |`sparseml.image_classification.pr_sensitivity`                |Utility script to Run a kernel sparsity (pruning) analysis for a desired image classification architecture |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/pytorch/argparser_/nm_argparser_.py b/integrations/examples/pytorch/argparser_/nm_argparser_.py
deleted file mode 100644
index f460a012054..00000000000
--- a/integrations/examples/pytorch/argparser_/nm_argparser_.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import dataclasses
-import json
-import re
-import sys
-from argparse import ArgumentParser, ArgumentTypeError
-from copy import copy
-from enum import Enum
-from pathlib import Path
-from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
-
-
-DataClass = NewType("DataClass", Any)
-DataClassType = NewType("DataClassType", Any)
-
-
-# From https://stackoverflow.com/questions/15008758
-# /parsing-boolean-values-with-argparse
-def string_to_bool(v):
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise ArgumentTypeError(
-            f"Truthy value expected: got {v} but expected one of yes/no,"
-            f"true/false, t/f, y/n, 1/0 (case insensitive)."
-        )
-
-
-# Inspired from https://huggingface.co/transformers/_modules
-# /transformers/hf_argparser.html
-class NmArgumentParser(ArgumentParser):
-    """
-    This subclass of `argparse.ArgumentParser` uses type hints on dataclasses
-    to generate arguments.
-
-    The class is designed to play well with the native argparse. In particular,
-    you can add more (non-dataclass backed) arguments to the parser after
-    initialization and you'll get the output back after parsing as an additional
-    namespace. Optional: To create sub argument groups use the
-    `_argument_group_name` attribute in the dataclass.
-
-    Note: __post_init__(...) for specific dataclasses passed is executed only
-    when parse_args_into_dataclasses(...) function is called because it needs
-    actual instantiation of the dataclass.
-    """
-
-    dataclass_types: Iterable[DataClassType]
-
-    def __init__(
-        self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs
-    ):
-        """
-        :param dataclass_types: Dataclass type, or list of dataclass types
-            for which we will "fill" instances with the parsed args.
-        :param kwargs: (Optional) Passed to `argparse.ArgumentParser()` in
-            the regular way.
-        """
-        super().__init__(**kwargs)
-        if dataclasses.is_dataclass(dataclass_types):
-            dataclass_types = [dataclass_types]
-        self.dataclass_types = dataclass_types
-        for dataclass_ in self.dataclass_types:
-            self._add_dataclass_arguments(dataclass_)
-
-    def _add_dataclass_arguments(self, dataclass_: DataClassType):
-        if hasattr(dataclass_, "_argument_group_name"):
-            parser = self.add_argument_group(dataclass_._argument_group_name)
-        else:
-            parser = self
-        for field in dataclasses.fields(dataclass_):
-            if not field.init:
-                continue
-
-            name, kwargs = field.name, field.metadata.copy()
-
-            keep_underscores_key = "keep_underscores"
-            keep_underscores = kwargs.get(keep_underscores_key)
-            _field_name = name if keep_underscores else name.replace("_", "-")
-
-            # cleanup
-            if keep_underscores_key in kwargs:
-                del kwargs[keep_underscores_key]
-
-            # field.metadata is not used at all by Data Classes,
-            # it is provided as a third-party extension mechanism.
-            if isinstance(field.type, str):
-                raise ImportError(
-                    "This implementation is not compatible with Postponed "
-                    "Evaluation of Annotations (PEP 563),"
-                    "which can be opted in from Python 3.7 with "
-                    "`from __future__ import annotations`."
-                )
-            typestring = str(field.type)
-            for prim_type in (int, float, str):
-                for collection in (List,):
-                    if (
-                        typestring == f"typing.Union["
-                        f"{collection[prim_type]}, NoneType]"
-                        or typestring == f"typing.Optional" f"[{collection[prim_type]}]"
-                    ):
-                        field.type = collection[prim_type]
-                if (
-                    typestring == f"typing.Union[" f"{prim_type.__name__}, NoneType]"
-                    or typestring == f"typing.Optional[" f"{prim_type.__name__}]"
-                ):
-                    field.type = prim_type
-
-            if isinstance(field.type, type) and issubclass(field.type, Enum):
-                kwargs["choices"] = [x.value for x in field.type]
-                kwargs["type"] = type(kwargs["choices"][0])
-                if field.default is not dataclasses.MISSING:
-                    kwargs["default"] = field.default
-                else:
-                    kwargs["required"] = True
-            elif field.type is bool or field.type == Optional[bool]:
-                if field.default is True:
-                    kwargs_copy = copy(kwargs)
-                    if "help" in kwargs_copy:
-                        kwargs_copy["help"] = f"Do not {kwargs_copy['help'].lower()}"
-                    parser.add_argument(
-                        f"--no-{_field_name}",
-                        action="store_false",
-                        dest=_field_name,
-                        **kwargs_copy,
-                    )
-
-                # Hack because type=bool in argparse does not behave as we want.
-                kwargs["type"] = string_to_bool
-                if field.type is bool or (
-                    field.default is not None
-                    and field.default is not dataclasses.MISSING
-                ):
-                    # Default value is False if we have no default
-                    # when of type bool.
-                    if field.default is dataclasses.MISSING:
-                        default = False
-                    else:
-                        default = field.default
-                    # This is the value that will get picked if
-                    # we don't include --field_name in any way
-                    kwargs["default"] = default
-
-                    # This tells argparse we accept 0 or 1
-                    # value after --field_name
-                    kwargs["nargs"] = "?"
-                    # This is the value that will get picked
-                    # if we do --field_name (without value)
-                    kwargs["const"] = True
-            elif (
-                hasattr(field.type, "__origin__")
-                and re.search(r"^typing\.List\[(.*)\]$", str(field.type)) is not None
-            ):
-                kwargs["nargs"] = "+"
-                kwargs["type"] = field.type.__args__[0]
-                assert all(
-                    x == kwargs["type"] for x in field.type.__args__
-                ), f"{field.name} cannot be a List of mixed types"
-                if field.default_factory is not dataclasses.MISSING:
-                    kwargs["default"] = field.default_factory()
-                elif field.default is dataclasses.MISSING:
-                    kwargs["required"] = True
-            else:
-                kwargs["type"] = field.type
-                if field.default is not dataclasses.MISSING:
-                    kwargs["default"] = field.default
-                elif field.default_factory is not dataclasses.MISSING:
-                    kwargs["default"] = field.default_factory()
-                else:
-                    kwargs["required"] = True
-            parser.add_argument(f"--{_field_name}", **kwargs)
-
-    def parse_args_into_dataclasses(
-        self,
-        args=None,
-        return_remaining_strings=False,
-        look_for_args_file=True,
-        args_filename=None,
-    ) -> Tuple[DataClass, ...]:
-        """
-        Parse command-line args into instances of the specified dataclass types.
-
-        This relies on argparse's `ArgumentParser.parse_known_args`.
-        See the doc at:
-        docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.
-        parse_args
-
-
-        :param args: List of strings to parse. The default is taken from
-            sys.argv. (same as argparse.ArgumentParser)
-        :param return_remaining_strings: If true, also return a list of
-            remaining argument strings.
-        :param look_for_args_file: If true, will look for a ".args" file with
-            the same base name as the entry point script for this process,
-            and will append its potential content to the command line args.
-        :param args_filename: If not None, will uses this file instead of the
-            ".args" file specified in the previous argument.
-        :returns: Tuple consisting of:
-                    - the dataclass instances in the same order as they were
-                    passed to the initializer.abspath
-                    - if applicable, an additional namespace for more
-                    (non-dataclass backed) arguments added to the parser
-                    after initialization.
-                    - The potential list of remaining argument strings.
-                    (same as argparse.ArgumentParser.parse_known_args)
-        """
-        if args_filename or (look_for_args_file and len(sys.argv)):
-            if args_filename:
-                args_file = Path(args_filename)
-            else:
-                args_file = Path(sys.argv[0]).with_suffix(".args")
-
-            if args_file.exists():
-                fargs = args_file.read_text().split()
-                args = fargs + args if args is not None else fargs + sys.argv[1:]
-                # in case of duplicate arguments the first one has precedence
-                # so we append rather than prepend.
-        namespace, remaining_args = self.parse_known_args(args=args)
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in vars(namespace).items() if k in keys}
-            for k in keys:
-                delattr(namespace, k)
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        if len(namespace.__dict__) > 0:
-            # additional namespace.
-            outputs.append(namespace)
-        if return_remaining_strings:
-            return (*outputs, remaining_args)
-        else:
-            if remaining_args:
-                raise ValueError(
-                    f"Some specified arguments are not used by the "
-                    f"NmArgumentParser: {remaining_args}"
-                )
-
-            return (*outputs,)
-
-    def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
-        """
-        Alternative helper method that does not use `argparse` at all,
-        instead loading a json file and populating the dataclass types.
-        """
-        data = json.loads(Path(json_file).read_text())
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in data.items() if k in keys}
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        return (*outputs,)
-
-    def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
-        """
-        Alternative helper method that does not use `argparse` at all,
-        instead uses a dict and populating the dataclass types.
-        """
-        outputs = []
-        for dtype in self.dataclass_types:
-            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
-            inputs = {k: v for k, v in args.items() if k in keys}
-            obj = dtype(**inputs)
-            outputs.append(obj)
-        return (*outputs,)
diff --git a/integrations/examples/pytorch/argparser_/test_nmargparser.py b/integrations/examples/pytorch/argparser_/test_nmargparser.py
deleted file mode 100644
index a7d18a66b79..00000000000
--- a/integrations/examples/pytorch/argparser_/test_nmargparser.py
+++ /dev/null
@@ -1,283 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import unittest
-from argparse import Namespace
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import List, Optional
-
-from nm_argparser_ import NmArgumentParser, string_to_bool
-
-
-def list_field(default=None, metadata=None):
-    return field(default_factory=lambda: default, metadata=metadata)
-
-
-@dataclass
-class BasicExample:
-    foo: int
-    bar: float
-    baz: str
-    flag: bool
-
-
-@dataclass
-class WithDefaultExample:
-    foo: int = 42
-    baz: str = field(default="toto", metadata={"help": "help message"})
-
-
-@dataclass
-class WithDefaultBoolExample:
-    foo: bool = False
-    baz: bool = True
-    opt: Optional[bool] = None
-
-
-class BasicEnum(Enum):
-    titi = "titi"
-    toto = "toto"
-
-
-@dataclass
-class EnumExample:
-    foo: BasicEnum = "toto"
-
-    def __post_init__(self):
-        self.foo = BasicEnum(self.foo)
-
-
-@dataclass
-class OptionalExample:
-    foo: Optional[int] = None
-    bar: Optional[float] = field(default=None, metadata={"help": "help message"})
-    baz: Optional[str] = None
-    ces: Optional[List[str]] = list_field(default=[])
-    des: Optional[List[int]] = list_field(default=[])
-
-
-@dataclass
-class ListExample:
-    foo_int: List[int] = list_field(default=[])
-    bar_int: List[int] = list_field(default=[1, 2, 3])
-    foo_str: List[str] = list_field(default=["Hallo", "Bonjour", "Hello"])
-    foo_float: List[float] = list_field(default=[0.1, 0.2, 0.3])
-
-
-@dataclass
-class RequiredExample:
-    required_list: List[int] = field()
-    required_str: str = field()
-    required_enum: BasicEnum = field()
-
-    def __post_init__(self):
-        self.required_enum = BasicEnum(self.required_enum)
-
-
-@dataclass
-class NonHyphenatedExample:
-    non_hyphenated: int = field(
-        metadata={
-            "keep_underscores": True,
-        }
-    )
-
-
-class NmArgumentParserTest(unittest.TestCase):
-    def argparsersEqual(
-        self, parser: argparse.ArgumentParser, expected_parser: argparse.ArgumentParser
-    ) -> bool:
-        """
-        Small helper to check pseudo-equality of parsed arguments on
-        `ArgumentParser` instances.
-        """
-        self.assertEqual(len(parser._actions), len(expected_parser._actions))
-        for x, y in zip(parser._actions, expected_parser._actions):
-            xx = {k: v for k, v in vars(x).items() if k != "container"}
-            yy = {k: v for k, v in vars(y).items() if k != "container"}
-            self.assertEqual(xx, yy)
-
-    def test_basic(self):
-        parser = NmArgumentParser(BasicExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", type=int, required=True)
-        expected.add_argument("--bar", type=float, required=True)
-        expected.add_argument("--baz", type=str, required=True)
-        expected.add_argument(
-            "--flag", type=string_to_bool, default=False, const=True, nargs="?"
-        )
-        self.argparsersEqual(parser, expected)
-
-        args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
-        (example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False)
-        self.assertFalse(example.flag)
-
-    def test_with_default(self):
-        parser = NmArgumentParser(WithDefaultExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", default=42, type=int)
-        expected.add_argument("--baz", default="toto", type=str, help="help message")
-        self.argparsersEqual(parser, expected)
-
-    def test_with_default_bool(self):
-        parser = NmArgumentParser(WithDefaultBoolExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument(
-            "--foo", type=string_to_bool, default=False, const=True, nargs="?"
-        )
-        expected.add_argument("--no-baz", action="store_false", dest="baz")
-        expected.add_argument(
-            "--baz", type=string_to_bool, default=True, const=True, nargs="?"
-        )
-        expected.add_argument("--opt", type=string_to_bool, default=None)
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args, Namespace(foo=False, baz=True, opt=None))
-
-        args = parser.parse_args(["--foo", "--no-baz"])
-        self.assertEqual(args, Namespace(foo=True, baz=False, opt=None))
-
-        args = parser.parse_args(["--foo", "--baz"])
-        self.assertEqual(args, Namespace(foo=True, baz=True, opt=None))
-
-        args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"])
-        self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
-
-        args = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"])
-        self.assertEqual(args, Namespace(foo=False, baz=False, opt=False))
-
-    def test_with_enum(self):
-        parser = NmArgumentParser(EnumExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument(
-            "--foo", default="toto", choices=["titi", "toto"], type=str
-        )
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args.foo, "toto")
-        enum_ex = parser.parse_args_into_dataclasses([])[0]
-        self.assertEqual(enum_ex.foo, BasicEnum.toto)
-
-        args = parser.parse_args(["--foo", "titi"])
-        self.assertEqual(args.foo, "titi")
-        enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0]
-        self.assertEqual(enum_ex.foo, BasicEnum.titi)
-
-    def test_with_list(self):
-        parser = NmArgumentParser(ListExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo-int", nargs="+", default=[], type=int)
-        expected.add_argument("--bar-int", nargs="+", default=[1, 2, 3], type=int)
-        expected.add_argument(
-            "--foo-str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str
-        )
-        expected.add_argument(
-            "--foo-float", nargs="+", default=[0.1, 0.2, 0.3], type=float
-        )
-
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(
-            args,
-            Namespace(
-                foo_int=[],
-                bar_int=[1, 2, 3],
-                foo_str=["Hallo", "Bonjour", "Hello"],
-                foo_float=[0.1, 0.2, 0.3],
-            ),
-        )
-
-        args = parser.parse_args(
-            "--foo-int 1 --bar-int 2 3 --foo-str parser expected_parser "
-            "c --foo-float 0.1 0.7".split()
-        )
-        self.assertEqual(
-            args,
-            Namespace(
-                foo_int=[1],
-                bar_int=[2, 3],
-                foo_str=["parser", "expected_parser", "c"],
-                foo_float=[0.1, 0.7],
-            ),
-        )
-
-    def test_with_optional(self):
-        parser = NmArgumentParser(OptionalExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--foo", default=None, type=int)
-        expected.add_argument("--bar", default=None, type=float, help="help message")
-        expected.add_argument("--baz", default=None, type=str)
-        expected.add_argument("--ces", nargs="+", default=[], type=str)
-        expected.add_argument("--des", nargs="+", default=[], type=int)
-        self.argparsersEqual(parser, expected)
-
-        args = parser.parse_args([])
-        self.assertEqual(args, Namespace(foo=None, bar=None, baz=None, ces=[], des=[]))
-
-        args = parser.parse_args(
-            "--foo 12 --bar 3.14 --baz 42 --ces parser expected_parser "
-            "c --des 1 2 3".split()
-        )
-        self.assertEqual(
-            args,
-            Namespace(
-                foo=12,
-                bar=3.14,
-                baz="42",
-                ces=["parser", "expected_parser", "c"],
-                des=[1, 2, 3],
-            ),
-        )
-
-    def test_with_required(self):
-        parser = NmArgumentParser(RequiredExample)
-
-        expected = argparse.ArgumentParser()
-        expected.add_argument("--required-list", nargs="+", type=int, required=True)
-        expected.add_argument("--required-str", type=str, required=True)
-        expected.add_argument(
-            "--required-enum", type=str, choices=["titi", "toto"], required=True
-        )
-        self.argparsersEqual(parser, expected)
-
-    def test_parse_dict(self):
-        parser = NmArgumentParser(BasicExample)
-
-        args_dict = {
-            "foo": 12,
-            "bar": 3.14,
-            "baz": "42",
-            "flag": True,
-        }
-
-        parsed_args = parser.parse_dict(args_dict)[0]
-        args = BasicExample(**args_dict)
-        self.assertEqual(parsed_args, args)
-
-    def test_non_hyphenated(self):
-        parser = NmArgumentParser(dataclass_types=NonHyphenatedExample)
-        args = parser.parse_args("--non_hyphenated 1".split())
-
-        self.assertEqual(args, Namespace(non_hyphenated=1))
diff --git a/integrations/examples/pytorch/export.py b/integrations/examples/pytorch/export.py
deleted file mode 100644
index 230304b6fc4..00000000000
--- a/integrations/examples/pytorch/export.py
+++ /dev/null
@@ -1,358 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-usage: export.py [-h] --arch-key ARCH_KEY --dataset DATASET
-                 --dataset-path DATASET_PATH
-                 [--checkpoint-path CHECKPOINT_PATH]
-                 [--num-samples NUM_SAMPLES] [--onnx-opset ONNX_OPSET]
-                 [--use-zipfile-serialization-if-available
-                 USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE]
-                 [--pretrained PRETRAINED]
-                 [--pretrained-dataset PRETRAINED_DATASET]
-                 [--model-kwargs MODEL_KWARGS]
-                 [--dataset-kwargs DATASET_KWARGS]
-                 [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-
-Utility script to export a model to onnx and also store sample
-inputs/outputs
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list
-                        (will raise an exception with the list)
-  --dataset DATASET     The dataset to use for exporting, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder
-                        for a generic dataset setup with an image
-                        folder structure setup like imagenet or
-                        loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the
-                        state from and resume the state for. If
-                        provided, pretrained will be ignored . If using
-                        a SparseZoo recipe, can also provide 'zoo' to
-                        load the base weights associated with that
-                        recipe
-  --num-samples NUM_SAMPLES
-                        The number of samples to export along with the
-                        model onnx and pth files (sample inputs and
-                        labels as well as the outputs from model
-                        execution)
-  --onnx-opset ONNX_OPSET
-                        The onnx opset to use for export.
-  --use-zipfile-serialization-if-available
-                        USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE
-                        for torch >= 1.6.0 only exports the Module's
-                        state dict using the new zipfile serialization.
-                        Default is True, has no affect on lower torch
-                        versions
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default
-                        is true to load the default pretrained weights
-                        for the model. Otherwise should be set to the
-                        desired weights type: [base, optim, optim-
-                        perf]. To not load any weights set to one of
-                        [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will
-                        load the default dataset for the architecture.
-                        Ex can be set to imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model
-                        constructor, should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results
-                        under save-dir, defaults to the model arch and
-                        dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-##########
-Example command for exporting ResNet50:
-python integrations/pytorch/export.py \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
-"""
-import json
-from dataclasses import dataclass, field
-from typing import Any, Optional, Tuple
-
-from torch.nn import Module
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
-from sparseml.pytorch.utils import ModuleExporter
-from sparseml.utils import convert_to_bool
-
-
-CURRENT_TASK = utils.Tasks.EXPORT
-LOGGER = get_main_logger()
-
-
-@dataclass
-class ExportArgs:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    exporting tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for analysis, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param num_samples: The number of samples to export along with the model
-        onnx and pth files (sample inputs and labels as well as the outputs
-        from model execution). Default is 100.
-    :param onnx_opset: The onnx opset to use for export. Default is 11.
-    :param use_zipfile_serialization_if_available: for torch >= 1.6.0 only
-        exports the Module's state dict using the new zipfile serialization.
-        Default is True, has no affect on lower torch versions.
-    :param pretrained: The type of pretrained weights to use,
-        default is true to load the default pretrained weights for the model.
-        Otherwise should be set to the desired weights type: [base, optim,
-        optim-perf]. To not load any weights set to one of [none, false].
-    :param pretrained_dataset: The dataset to load pretrained weights for if
-        pretrained is set. Default is None which will load the default
-        dataset for the architecture. Ex can be set to imagenet, cifar10, etc.
-    :param model_kwargs: Keyword arguments to be passed to model constructor,
-        should be given as a json object.
-    :param dataset_kwargs: Keyword arguments to be passed to dataset
-        constructor, should be given as a json object.
-    :param model_tag: A tag to use for the model for saving results under
-        save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results.
-    """
-
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception "
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for exporting, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored . If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    num_samples: int = field(
-        default=100,
-        metadata={
-            "help": "The number of samples to export along with the model onnx "
-            "and pth files (sample inputs and labels as well as the "
-            "outputs from model execution)"
-        },
-    )
-
-    onnx_opset: int = field(
-        default=TORCH_DEFAULT_ONNX_OPSET,
-        metadata={
-            "help": "The onnx opset to use for export. "
-            f"Default is {TORCH_DEFAULT_ONNX_OPSET}"
-        },
-    )
-
-    use_zipfile_serialization_if_available: convert_to_bool = field(
-        default=True,
-        metadata={
-            "help": "for torch >= 1.6.0 only exports the Module's state dict "
-            "using the new zipfile serialization. Default is True, "
-            "has no affect on lower torch versions"
-        },
-    )
-
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for "
-            "the model. Otherwise should be set to the desired weights "
-            "type: [base, optim, optim-perf]. To not load any weights "
-            "set to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained "
-            "is set. Default is None which will load the default "
-            "dataset for the architecture. Ex can be set to imagenet, "
-            "cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be  given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.local_rank: int = -1
-        self.is_main_process: bool = True
-
-
-def export(
-    args: ExportArgs, model: Module, val_loader: DataLoader, save_dir: str
-) -> None:
-    """
-    Utility method to export the model and data
-
-    :param args : An ExportArgs object containing config for export task.
-    :param model: loaded model architecture to export
-    :param val_loader: A DataLoader for validation data
-    :param save_dir: Directory to store checkpoints at during exporting process
-    """
-    exporter = ModuleExporter(model, save_dir)
-
-    # export PyTorch state dict
-    LOGGER.info(f"exporting pytorch in {save_dir}")
-
-    exporter.export_pytorch(
-        use_zipfile_serialization_if_available=(
-            args.use_zipfile_serialization_if_available
-        )
-    )
-    onnx_exported = False
-
-    for batch, data in tqdm(
-        enumerate(val_loader),
-        desc="Exporting samples",
-        total=args.num_samples if args.num_samples > 1 else 1,
-    ):
-        if not onnx_exported:
-            # export onnx file using first sample for graph freezing
-            LOGGER.info(f"exporting onnx in {save_dir}")
-            exporter.export_onnx(data[0], opset=args.onnx_opset, convert_qat=True)
-            onnx_exported = True
-
-        if args.num_samples > 0:
-            exporter.export_samples(
-                sample_batches=[data[0]], sample_labels=[data[1]], exp_counter=batch
-            )
-
-
-def export_setup(args_: ExportArgs) -> Tuple[Module, Optional[str], Any]:
-    """
-    Pre-export setup
-
-    :param args_ : An ExportArgs object containing config for export task.
-    """
-    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
-
-    # model creation
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-    model = utils.create_model(args_, num_classes)
-    return model, save_dir, val_loader
-
-
-def main():
-    """
-    Driver function
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=ExportArgs,
-        description="Utility script to export a model to onnx "
-        "and also store sample inputs/outputs",
-    )
-    (args_,) = _parser.parse_args_into_dataclasses()
-    model, save_dir, val_loader = export_setup(args_)
-    export(args_, model, val_loader, save_dir)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/examples/pytorch/lr_analysis.py b/integrations/examples/pytorch/lr_analysis.py
deleted file mode 100644
index a9dc0e94058..00000000000
--- a/integrations/examples/pytorch/lr_analysis.py
+++ /dev/null
@@ -1,408 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-usage: lr_analysis.py [-h] --batch-size BATCH_SIZE --arch-key ARCH_KEY
-                        --dataset DATASET --dataset-path DATASET_PATH [
-                        --pretrained PRETRAINED]
-                      [--pretrained-dataset PRETRAINED_DATASET] [
-                      --model-kwargs MODEL_KWARGS]
-                      [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
-                      [--save-dir SAVE_DIR] [--device DEVICE]
-                      [--loader-num-workers LOADER_NUM_WORKERS] [
-                      --no-loader-pin-memory]
-                      [--loader-pin-memory [LOADER_PIN_MEMORY]]
-                      [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
-                      [--optim-args OPTIM_ARGS] [--final-lr FINAL_LR]
-                      [--steps-per-measurement STEPS_PER_MEASUREMENT]
-
-Utility script to Run a learning rate sensitivity analysis for a desired
-image classification architecture
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --batch-size BATCH_SIZE
-                        The batch size to use for analysis
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16, mobilenet
-                        put as help to see the full list (will raise an
-                        exception with the list)
-  --dataset DATASET     The dataset to use for analysis, ex: imagenet,
-                        imagenette, cifar10,
-                        etc. Set to imagefolder for a generic dataset setup
-                        with an imagefolder structure setup like imagenet or
-                        loadable by a dataset in sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is
-                        true to load the default pretrained weights for the
-                        model. Otherwise should be set to the desired weights
-                        type: [base, optim, optim-perf]. To not load any
-                        weights set to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if pretrained
-                        is set. Default is None which will load the default
-                        dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for
-                        data parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained
-                        will be ignored. If using a SparseZoo recipe,
-                        can also provide 'zoo' to load the base weights
-                        associated with that recipe
-  --init-lr INIT_LR     The initial learning rate to use for the sensitivity
-                        analysis
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed in
-                        as a json object
-  --final-lr FINAL_LR   The final learning rate to use for the sensitivity
-                        analysis
-  --steps-per-measurement STEPS_PER_MEASUREMENT The number of steps (batches)
-                        to run for each measurement
-#########
-EXAMPLES
-#########
-
-##########
-Example command for running LR sensitivity analysis on mobilenet:
-python integrations/pytorch/lr_analysis.py \
-    --arch-key mobilenet --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012 --batch-size 2
-
-Example command for running LR sensitivity analysis on mobilenet using
-imagenette:
-python integrations/pytorch/lr_analysis.py \
-    --arch-key mobilenet --dataset imagenette \
-    --dataset-path ~/datasets/ --batch-size 2
-"""
-
-import json
-import os
-from dataclasses import dataclass, field
-
-from torch.nn import Module
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import default_exponential_check_lrs, lr_loss_sensitivity
-from sparseml.pytorch.utils import PythonLogger, default_device, model_to_device
-
-
-CURRENT_TASK = utils.Tasks.LR_ANALYSIS
-LOGGER = get_main_logger()
-
-
-@dataclass
-class LRAnalysisArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    learning rate analysis.
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`
-    arguments that can be specified on the command line.
-
-    :param batch_size: The batch size to use for analysis.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for analysis, ex imagenet,
-        imagenette, etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param pretrained: The type of pretrained weights to use default is true to
-        load the default pretrained weights for the model. Otherwise should be
-        set to the desired weights type: [base, optim, optim-perf];
-        To not load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load pretrained
-        weights for; if pretrained is set; Default is None which will load the
-        default dataset for the architecture; Ex can be set to imagenet,
-        cifar10, etc.
-    :param model_kwargs: json object containing keyword arguments to be passed
-        to the model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be passed
-        to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results under
-        save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on (can also include ids
-        for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data loading,
-        default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param init_lr: float representing the initial learning for analysis,
-        default=1e-5.
-    :param optim_args: Additional arguments to be passed in to the optimizer as
-        a json object.
-    :param final_lr: The final learning rate to use for the sensitivity
-        analysis.
-    :param steps_per_measurement: The number of steps (batches) to run for each
-        measurement.
-    """
-
-    batch_size: int = field(metadata={"help": "The batch size to use for analysis"})
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list"
-            "(will raise an exception with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for analysis, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights "
-            "for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained is"
-            "set. Default is None which will load the default dataset for "
-            "the architecture. Ex can be set to imagenet, cifar10, etc.",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor,"
-            " should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for "
-            "data parallel), ex:cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will be"
-            "ignored. If using a SparseZoo recipe, can also provide "
-            "'zoo' to load the base weights associated with that recipe"
-        },
-    )
-
-    init_lr: float = field(
-        default=1e-5,
-        metadata={
-            "help": "The initial learning rate to use for the sensitivity analysis"
-        },
-    )
-
-    optim_args: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Additional args to be passed to the optimizer passed in"
-            " as a json object"
-        },
-    )
-
-    final_lr: float = field(
-        default=0.5,
-        metadata={
-            "help": "The final learning rate to use for the sensitivity analysis",
-        },
-    )
-
-    steps_per_measurement: int = field(
-        default=20,
-        metadata={"help": "The number of steps (batches) to run for each measurement"},
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.is_main_process = True
-        self.local_rank = -1
-        self.rank = -1
-
-
-def lr_sensitivity(
-    args,
-    model: Module,
-    train_loader: DataLoader,
-    save_dir: str,
-) -> None:
-    """
-    Utility function to run learning rate sensitivity analysis
-
-    :param args: An LRAnalysisArguments object containing config for current
-        LR analysis task.
-    :param model: loaded model architecture to analyse
-    :param train_loader: A DataLoader for training data
-    :param save_dir: Directory to save results
-    """
-    # optimizer setup
-    optim = SGD(model.parameters(), lr=args.init_lr, **args.optim_args)
-    LOGGER.info(f"created optimizer: {optim}")
-
-    # loss setup
-    loss = utils.get_loss_wrapper(args.arch_key)
-    LOGGER.info(f"created loss: {loss}")
-
-    # device setup
-    model, device, device_ids = model_to_device(model, args.device)
-
-    # learning rate analysis
-    LOGGER.info(f"running analysis: {loss}")
-    analysis = lr_loss_sensitivity(
-        model,
-        train_loader,
-        loss,
-        optim,
-        device,
-        args.steps_per_measurement,
-        check_lrs=default_exponential_check_lrs(args.init_lr, args.final_lr),
-        trainer_loggers=[PythonLogger()],
-    )
-
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info(f"Saving results in {save_dir}")
-    analysis.save_json(os.path.join(save_dir, "lr_sensitivity.json"))
-    analysis.plot(os.path.join(save_dir, "lr_sensitivity.png"))
-    analysis.print_res()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=LRAnalysisArguments,
-        description="Utility script to Run a "
-        "learning rate sensitivity analysis "
-        "for a desired image classification architecture",
-    )
-    args_, _ = _parser.parse_args_into_dataclasses()
-    save_dir, loggers = utils.get_save_dir_and_loggers(
-        args_,
-        task=CURRENT_TASK,
-    )
-
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    # assume shape [C, S, S] where S is the image size
-    image_size = input_shape[1]
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(
-        args_,
-        image_size,
-        task=CURRENT_TASK,
-    )
-
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-    model = utils.create_model(args_, num_classes)
-    lr_sensitivity(args_, model, train_loader, save_dir)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/examples/pytorch/notebooks/classification.ipynb b/integrations/examples/pytorch/notebooks/classification.ipynb
deleted file mode 100644
index 6bdac17c274..00000000000
--- a/integrations/examples/pytorch/notebooks/classification.ipynb
+++ /dev/null
@@ -1,366 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# PyTorch Classification Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML\n",
-    "- Prune the model using the PyTorch+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 15 minutes on a GPU\n",
-    "- 45 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = ModelRegistry.create(...)\n",
-    "- train_dataset = ImagenetteDataset(...)\n",
-    "- val_dataset = ImagenetteDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"resnet50\",\n",
-    "    pretrained=True,\n",
-    "    pretrained_dataset=\"imagenette\",\n",
-    "    num_classes=10,\n",
-    ")\n",
-    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "    total_correct = 0\n",
-    "    total_predictions = 0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
-    "        loss = criterion(outputs, labels)\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "        predictions = outputs.argmax(dim=1)\n",
-    "        total_correct += torch.sum(predictions == labels).item()\n",
-    "        total_predictions += inputs.size(0)\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    accuracy = total_correct / total_predictions\n",
-    "    return loss, accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up PyTorch Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 128\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"classification\",\n",
-    "    architecture=\"resnet_v1\",\n",
-    "    sub_architecture=\"50\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"sparseml\",\n",
-    "    dataset=\"imagenette\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Run model pruning\n",
-    "epoch = manager.min_epochs\n",
-    "while epoch < manager.max_epochs:\n",
-    "    # run training loop\n",
-    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
-    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        \"Training Epoch: {}\\nTraining Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
-    "            epoch_name, train_loss, train_acc\n",
-    "        )\n",
-    "    )\n",
-    "    \n",
-    "    # run validation loop\n",
-    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
-    "    val_loss, val_acc = run_model_one_epoch(\n",
-    "        model, val_loader, criterion, device\n",
-    "    )\n",
-    "    print(\n",
-    "        \"Validation Epoch: {}\\nVal Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
-    "            epoch_name, val_loss, val_acc\n",
-    "        )\n",
-    "    )\n",
-    "    \n",
-    "    epoch += 1\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_classification\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/pytorch/notebooks/detection.ipynb b/integrations/examples/pytorch/notebooks/detection.ipynb
deleted file mode 100644
index 7a76aa223dd..00000000000
--- a/integrations/examples/pytorch/notebooks/detection.ipynb
+++ /dev/null
@@ -1,372 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# PyTorch Detection Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML\n",
-    "- Prune the model using the PyTorch+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 30 minutes on a GPU\n",
-    "- 90 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [SSD](https://arxiv.org/abs/1512.02325) model trained on the [VOC detection dataset](http://host.robots.ox.ac.uk/pascal/VOC/). The model's pretrained weights are downloaded from the SparseZoo model repo. The VOC detection dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "Note, for this notebook, you will use a ResNet18 backbone for the object detector.  This is to save training time and demonstrate the general pruning flow. For better accuracy, you can use a different backbone or model.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = ModelRegistry.create(...)\n",
-    "- train_dataset = VOCDetectionDataset(...)\n",
-    "- val_dataset = VOCDetectionDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import VOCDetectionDataset\n",
-    "from sparseml.pytorch.utils import get_default_boxes_300\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"ssd300_resnet18\",\n",
-    "    pretrained=True,\n",
-    "    pretrained_dataset=\"voc\",\n",
-    "    pretrained_backbone=False,  # no need to download initial weights\n",
-    "    num_classes=21,\n",
-    ")\n",
-    "model_name = model.__class__.__name__\n",
-    "input_shape = ModelRegistry.input_shape(\"ssd300_resnet18\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "default_boxes = get_default_boxes_300(\"voc\")\n",
-    "train_dataset = VOCDetectionDataset(\n",
-    "    train=True, rand_trans=True, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = VOCDetectionDataset(\n",
-    "    train=False, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) to work with our detection model and loss function.  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "from sparseml.pytorch.utils import DEFAULT_LOSS_KEY\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(\n",
-    "    model, data_loader, criterion, device, train=False, optimizer=None\n",
-    "):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = [\n",
-    "            label.to(device) if isinstance(label, torch.Tensor) else label\n",
-    "            for label in labels\n",
-    "        ]\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs = model(inputs)\n",
-    "        loss = criterion((inputs, labels), outputs)[DEFAULT_LOSS_KEY]\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    return loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up Detection Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow.  The loss function and collate function are standard for SSD training and are defined in the sparseml API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "from sparseml.pytorch.datasets import ssd_collate_fn\n",
-    "from sparseml.pytorch.utils import SSDLossWrapper\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 64\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset,\n",
-    "    batch_size,\n",
-    "    shuffle=True,\n",
-    "    pin_memory=True,\n",
-    "    num_workers=12,\n",
-    "    collate_fn=ssd_collate_fn,\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset,\n",
-    "    batch_size,\n",
-    "    shuffle=False,\n",
-    "    pin_memory=True,\n",
-    "    num_workers=12,\n",
-    "    collate_fn=ssd_collate_fn,\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer\n",
-    "criterion = SSDLossWrapper()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning SSD-ResNet18 on the VOC dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"detection\",\n",
-    "    architecture=\"ssd\",\n",
-    "    sub_architecture=\"resnet18_300\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"sparseml\",\n",
-    "    dataset=\"voc\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "epoch = manager.min_epochs\n",
-    "while epoch < manager.max_epochs:\n",
-    "    # run training loop\n",
-    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
-    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
-    "    train_loss = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\"Training Epoch: {}\\nTraining Loss: {}\\n\".format(epoch_name, train_loss))\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
-    "    val_loss = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\"Validation Epoch: {}\\nVal Loss: {}\\n\".format(epoch_name, val_loss))\n",
-    "\n",
-    "    epoch += 1\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_detection\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"ssd_resnet18_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 300, 300), name=\"ssd_resnet18_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb b/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
deleted file mode 100644
index d5d50f63a23..00000000000
--- a/integrations/examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
+++ /dev/null
@@ -1,454 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Sparse-Quantized Transfer Learning in PyTorch using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for creating a performant sparse-quantized model\n",
-    "by transfer learning the pruned structure from an already sparse-quantized model.\n",
-    "\n",
-    "Sparse-quantized models combine [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to reduce both the number of parameters and the precision of the remaining parameters to significantly increase the performance of neural networks. Using these optimizations, your model will obtain significantly better (around 7x vs. unoptimized) performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse).\n",
-    "\n",
-    "Sparse-quantized transfer learning takes two steps. [SparseML](https://github.com/neuralmagic/sparseml) recipes make it easy to perform these optimizations:\n",
-    "- First, fine-tune a pre-trained sparse model for the transfer dataset while maintaining the pre-trained sparsity structure.\n",
-    "- Second, perform [quantization-aware training (QAT)](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) to quantize the now sparse model while still holding the same sparsity structure.\n",
-    "\n",
-    "In this notebook, you will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a generic PyTorch training flow\n",
-    "- Integrate the PyTorch flow with SparseML for transfer learning\n",
-    "- Perform sparse transfer learning and quantization-aware training using the PyTorch and SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/) and convert the model from a QAT\n",
-    "- [Optional] Compare DeepSparse Engine benchmarks of the final sparse-quantized model to an unoptimized model\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow for transfer learning and generically. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 30 minutes on a GPU\n",
-    "- 90 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML, SparseZoo\n",
-    "* PyTorch (>= 1.7.0) and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision\n",
-    "\n",
-    "assert torch.__version__ >= \"1.7\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will transfer learn from a sparse-quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [ImageNet dataset](http://www.image-net.org/) to the much smaller [Imagenette dataset](https://github.com/fastai/imagenette). The transfer learning weights are downloaded from the [SparseZoo](https://github.com/neuralmagic/sparsezoo) model repository.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "When loading weights for transfer learning classification models, it is standard to override the final classifier layer to fit the output shape for the new dataset.  In the example below, this is done by specifying `ignore_error_tensors` as the weights that will be initialzed for the new model.  In other flows this could be accomplished by setting `model.classifier.fc = torch.nn.Linear(...)`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.models import ModelRegistry\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "from sparsezoo import Model\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
-    "zoo_stub_path = (\n",
-    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate\"\n",
-    "    \"?recipe=transfer_learn\"\n",
-    ")\n",
-    "model = ModelRegistry.create(\n",
-    "    key=\"resnet50\",\n",
-    "    pretrained_path=zoo_stub_path,\n",
-    "    pretrained_dataset=\"imagenette\",\n",
-    "    num_classes=10,\n",
-    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
-    ")\n",
-    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
-    "input_size = input_shape[-1]\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
-    ")\n",
-    "print(val_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Creating a PyTorch Training Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune and quantize your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm.auto import tqdm\n",
-    "import math\n",
-    "import torch\n",
-    "\n",
-    "\n",
-    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "    total_correct = 0\n",
-    "    total_predictions = 0\n",
-    "\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
-    "        loss = criterion(outputs, labels)\n",
-    "\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "        predictions = outputs.argmax(dim=1)\n",
-    "        total_correct += torch.sum(predictions == labels).item()\n",
-    "        total_predictions += inputs.size(0)\n",
-    "\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    accuracy = total_correct / total_predictions\n",
-    "    return loss, accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Building PyTorch Training Objects\n",
-    "In this step, you will select hyperparameters, a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import Adam\n",
-    "\n",
-    "# hyperparameters\n",
-    "BATCH_SIZE = 32\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(f\"Using device: {device}\")\n",
-    "\n",
-    "# setup data loaders\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Running Sparse-Quantized Transfer Learning with a SparseML Recipe\n",
-    "\n",
-    "To run sparse-quantized transfer learning with SparseML, you will download a transfer learning recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to maintain the pre-optimized model's sparsity structure while learning weights for the new dataset as well as performing quantization-aware training (QAT).\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, QAT, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe  file to match the new target's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error; to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides preconfigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet-50 on the Imagenette dataset and record its saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model\n",
-    "\n",
-    "zoo_model = Model(zoo_stub_path)\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "# Run model pruning\n",
-    "epoch = manager.min_epochs\n",
-    "for epoch in range(manager.max_epochs):\n",
-    "    # run training loop\n",
-    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
-    "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(\n",
-    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
-    "    )\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
-    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(\n",
-    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
-    "    )\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - Viewing Model Sparsity\n",
-    "To see the effects of sparse-quantized transfer learning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 7 - Exporting to ONNX\n",
-    "\n",
-    "Now that the sparse-quantized transfer learning is complete, it should be prepped for inference.  A common next step for inference is exporting the model to ONNX.  This is also the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) to achieve the sparse-quantized speedups.\n",
-    "\n",
-    "For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Additionally, PyTorch, exports a graph setup for quantization-aware training (QAT) to ONNX. To run a fully quantized graph, you will need to convert these QAT operations to fully quantized INT8 operations.  SparseML provides the `quantize_torch_qat_export` helper function to perform this conversion.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.\n",
-    "\n",
-    "Normally, exporting a QAT model from PyTorch to ONNX will create a graph with \"fake quantized\" operations that represent the QAT graph.  By setting `convert_qat=True` in our exporter, a function will automatically be called to convert this exported model to a fully quantized graph that will contain desired quantized structure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"pytorch_sparse_quantized_transfer_learning\"\n",
-    "quant_onnx_graph_name = \"resnet50_imagenette_pruned_quant.onnx\"\n",
-    "quantized_onnx_path = os.path.join(save_dir, quant_onnx_graph_name)\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned_qat.pth\")\n",
-    "exporter.export_onnx(\n",
-    "    torch.randn(1, 3, 224, 224), name=quant_onnx_graph_name, convert_qat=True\n",
-    ")\n",
-    "\n",
-    "print(f\"Sparse-Quantized ONNX model saved to {quantized_onnx_path}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## [Optional] Step 8 - Benchmarking\n",
-    "\n",
-    "Finally, to see the total effect of these optimizations, you will benchmark an unoptimized, dense ResNet-50 model from SparseZoo against your sparse-quantized model using the `deepsparse` API.\n",
-    "\n",
-    "To run this step `deepsparse` must be installed in your python environment. You can install it with `pip install deepsparse`.\n",
-    "\n",
-    "Note, in order to view speedup from quantization, your CPU must run VNNI instructions.  The benchmarking cell below contains a check for VNNI instructions and will log a warning if they are not detected.  You can learn more about DeepSparse hardware compatibility [here](https://docs.neuralmagic.com/deepsparse/hardware.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "from deepsparse import benchmark_model\n",
-    "from deepsparse.cpu import cpu_architecture\n",
-    "\n",
-    "\n",
-    "# check VNNI\n",
-    "if cpu_architecture()[\"vnni\"]:\n",
-    "    print(\"VNNI extensions detected, model will run with quantized speedups\\n\")\n",
-    "else:\n",
-    "    print(\n",
-    "        \"WARNING: No VNNI extensions detected. Your model will not run with \"\n",
-    "        \"quantized speedups which will affect benchmarking\\n\"\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "BATCH_SIZE = 64\n",
-    "NUM_CORES = None  # maximum number of cores available\n",
-    "NUM_ITERATIONS = 100\n",
-    "NUM_WARMUP_ITERATIONS = 20\n",
-    "\n",
-    "\n",
-    "def benchmark_imagenette_model(model_name, model_path):\n",
-    "    print(\n",
-    "        f\"Benchmarking {model_name} for {NUM_ITERATIONS} iterations at batch \"\n",
-    "        f\"size {BATCH_SIZE} with {NUM_CORES} CPU cores\"\n",
-    "    )\n",
-    "    sample_input = [\n",
-    "        numpy.ascontiguousarray(\n",
-    "            numpy.random.randn(BATCH_SIZE, 3, 224, 224).astype(numpy.float32)\n",
-    "        )\n",
-    "    ]\n",
-    "\n",
-    "    results = benchmark_model(\n",
-    "        model=model_path,\n",
-    "        inp=sample_input,\n",
-    "        batch_size=BATCH_SIZE,\n",
-    "        num_cores=NUM_CORES,\n",
-    "        num_iterations=NUM_ITERATIONS,\n",
-    "        num_warmup_iterations=NUM_WARMUP_ITERATIONS,\n",
-    "        show_progress=True,\n",
-    "    )\n",
-    "    print(f\"results:\\n{results}\")\n",
-    "    return results\n",
-    "\n",
-    "\n",
-    "# base ResNet-50 Imagenette model downloaded from SparseZoo\n",
-    "base_results = benchmark_imagenette_model(\n",
-    "    \"ResNet-50 Imagenette Base\",\n",
-    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none\"\n",
-    ")\n",
-    "\n",
-    "optimized_results = benchmark_imagenette_model(\n",
-    "    \"ResNet-50 Imagenette pruned-quantized\", quantized_onnx_path\n",
-    ")\n",
-    "\n",
-    "speed_up = base_results.ms_per_batch / optimized_results.ms_per_batch\n",
-    "print(f\"Speed-up from sparse-quantized transfer learning: {speed_up}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have created a sparse-quantized model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Transfer learning, pruning, or quantizing different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Benchmarking other models on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/pytorch/notebooks/torchvision.ipynb b/integrations/examples/pytorch/notebooks/torchvision.ipynb
deleted file mode 100644
index 82c2f82147b..00000000000
--- a/integrations/examples/pytorch/notebooks/torchvision.ipynb
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# Torchvision Classification Model Pruning using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning a [torchvision model](https://pytorch.org/docs/stable/torchvision/models.html) using SparseML. You will:\n",
-    "- Download a pre-trained torchvision model and generic dataset\n",
-    "- Define a generic torchvision finetuning flow\n",
-    "- Integrate the torchvision flow with SparseML\n",
-    "- Prune the model using the torchvision+SparseML flow\n",
-    "- Save the model and export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to integrate SparseML with torchvision or more generically a PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 15 minutes on a GPU\n",
-    "- 45 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* PyTorch and torchvision\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import torch\n",
-    "import torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model while finetuning it on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from torchvision. The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "Additionally, we will override the FC layer in the ResNet50 model to have 10 output classes instead of the ImageNet standard 1000.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
-    "- model = resnet50(pretrained=True)\n",
-    "- train_dataset = ImagenetteDataset(...)\n",
-    "- val_dataset = ImagenetteDataset(...)\n",
-    "\n",
-    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision.models import resnet50\n",
-    "from torch.nn import Linear\n",
-    "\n",
-    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
-    "\n",
-    "#######################################################\n",
-    "# Define your model below\n",
-    "#######################################################\n",
-    "print(\"loading model...\")\n",
-    "model = resnet50(pretrained=True)\n",
-    "print(model)\n",
-    "#######################################################\n",
-    "# Define your train and validation datasets below\n",
-    "#######################################################\n",
-    "\n",
-    "print(\"\\nloading train dataset...\")\n",
-    "train_dataset = ImagenetteDataset(\n",
-    "    train=True, dataset_size=ImagenetteSize.s320, image_size=224\n",
-    ")\n",
-    "print(train_dataset)\n",
-    "\n",
-    "print(\"\\nloading val dataset...\")\n",
-    "val_dataset = ImagenetteDataset(\n",
-    "    train=False, dataset_size=ImagenetteSize.s320, image_size=224\n",
-    ")\n",
-    "print(val_dataset)\n",
-    "\n",
-    "# Overriding number of classes\n",
-    "NUM_CLASSES = 10  # number of imagenette classes\n",
-    "model.fc = Linear(in_features=model.fc.in_features, out_features=NUM_CLASSES, bias=True)\n",
-    "print(model.fc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Set Up a Torchvision Finetuning Loop\n",
-    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop taken from the [torchvision finetuning example](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import copy\n",
-    "import torch\n",
-    "\n",
-    "def train_model(\n",
-    "    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False\n",
-    "):\n",
-    "    since = time.time()\n",
-    "\n",
-    "    val_acc_history = []\n",
-    "\n",
-    "    best_acc = 0.0\n",
-    "\n",
-    "    for epoch in range(num_epochs):\n",
-    "        print(\"Epoch {}/{}\".format(epoch, num_epochs - 1))\n",
-    "        print(\"-\" * 10)\n",
-    "\n",
-    "        # Each epoch has a training and validation phase\n",
-    "        for phase in [\"train\", \"val\"]:\n",
-    "            if phase == \"train\":\n",
-    "                model.train()  # Set model to training mode\n",
-    "            else:\n",
-    "                model.eval()  # Set model to evaluate mode\n",
-    "\n",
-    "            running_loss = 0.0\n",
-    "            running_corrects = 0\n",
-    "\n",
-    "            # Iterate over data.\n",
-    "            for inputs, labels in dataloaders[phase]:\n",
-    "                inputs = inputs.to(device)\n",
-    "                labels = labels.to(device)\n",
-    "\n",
-    "                # zero the parameter gradients\n",
-    "                optimizer.zero_grad()\n",
-    "\n",
-    "                # forward\n",
-    "                # track history if only in train\n",
-    "                with torch.set_grad_enabled(phase == \"train\"):\n",
-    "                    # Get model outputs and calculate loss\n",
-    "                    # Special case for inception because in training it has an auxiliary output. In train\n",
-    "                    #   mode we calculate the loss by summing the final output and the auxiliary output\n",
-    "                    #   but in testing we only consider the final output.\n",
-    "                    if is_inception and phase == \"train\":\n",
-    "                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958\n",
-    "                        outputs, aux_outputs = model(inputs)\n",
-    "                        loss1 = criterion(outputs, labels)\n",
-    "                        loss2 = criterion(aux_outputs, labels)\n",
-    "                        loss = loss1 + 0.4 * loss2\n",
-    "                    else:\n",
-    "                        outputs = model(inputs)\n",
-    "                        loss = criterion(outputs, labels)\n",
-    "\n",
-    "                    _, preds = torch.max(outputs, 1)\n",
-    "\n",
-    "                    # backward + optimize only if in training phase\n",
-    "                    if phase == \"train\":\n",
-    "                        loss.backward()\n",
-    "                        optimizer.step()\n",
-    "\n",
-    "                # statistics\n",
-    "                running_loss += loss.item() * inputs.size(0)\n",
-    "                running_corrects += torch.sum(preds == labels.data)\n",
-    "\n",
-    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
-    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
-    "\n",
-    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
-    "\n",
-    "            # deep copy the model\n",
-    "            if phase == \"val\" and epoch_acc > best_acc:\n",
-    "                best_acc = epoch_acc\n",
-    "            if phase == \"val\":\n",
-    "                val_acc_history.append(epoch_acc)\n",
-    "\n",
-    "        print()\n",
-    "\n",
-    "    time_elapsed = time.time() - since\n",
-    "    print(\n",
-    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
-    "            time_elapsed // 60, time_elapsed % 60\n",
-    "        )\n",
-    "    )\n",
-    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
-    "\n",
-    "    # load best model weights\n",
-    "    return model, val_acc_history"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Set Up PyTorch Training Objects\n",
-    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import SGD\n",
-    "\n",
-    "# setup device\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model.to(device)\n",
-    "print(\"Using device: {}\".format(device))\n",
-    "\n",
-    "# setup data loaders\n",
-    "batch_size = 128\n",
-    "train_loader = DataLoader(\n",
-    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "val_loader = DataLoader(\n",
-    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
-    ")\n",
-    "dataloaders = {\"train\": train_loader, \"val\": val_loader}\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Downloading a Recipe from SparseZoo\n",
-    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "zoo_model = search_models(\n",
-    "    domain=\"cv\",\n",
-    "    sub_domain=\"classification\",\n",
-    "    architecture=\"resnet_v1\",\n",
-    "    sub_architecture=\"50\",\n",
-    "    framework=\"pytorch\",\n",
-    "    repo=\"torchvision\",\n",
-    "    dataset=\"imagenette\",\n",
-    "    sparse_name=\"pruned\",\n",
-    ")[0]  # unwrap search result\n",
-    "\n",
-    "recipe_path = zoo_model.recipes.default.path\n",
-    "print(f\"Recipe downloaded to: {recipe_path}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "\n",
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
-    "\n",
-    "\n",
-    "train_model(\n",
-    "    model,\n",
-    "    dataloaders,\n",
-    "    criterion,\n",
-    "    optimizer,\n",
-    "    device,\n",
-    "    num_epochs=manager.max_epochs,\n",
-    "    is_inception=False,\n",
-    ")\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - View Model Sparsity\n",
-    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "# print sparsities of each layer\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 6 - Save Model and Export to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.pytorch.utils import ModuleExporter\n",
-    "\n",
-    "save_dir = \"torchvision_models\"\n",
-    "\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/pytorch/pr_sensitivity.py b/integrations/examples/pytorch/pr_sensitivity.py
deleted file mode 100644
index fad574bd440..00000000000
--- a/integrations/examples/pytorch/pr_sensitivity.py
+++ /dev/null
@@ -1,416 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-#####
-Command help:
-python integrations/pytorch/pr_sensitivity.py --help
-usage: pr_sensitivity.py [-h] --arch-key ARCH_KEY --dataset DATASET
-                         --dataset-path DATASET_PATH
-                         [--pretrained PRETRAINED]
-                         [--pretrained-dataset PRETRAINED_DATASET]
-                         [--model-kwargs MODEL_KWARGS]
-                         [--dataset-kwargs DATASET_KWARGS]
-                         [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                         [--device DEVICE]
-                         [--loader-num-workers LOADER_NUM_WORKERS]
-                         [--no-loader-pin-memory]
-                         [--loader-pin-memory [LOADER_PIN_MEMORY]]
-                         [--checkpoint-path CHECKPOINT_PATH]
-                         [--steps-per-measurement STEPS_PER_MEASUREMENT]
-                         [--batch-size BATCH_SIZE]
-                         [--approximate [APPROXIMATE]]
-
-Utility script to Run a kernel sparsity (pruning) analysis for a
-desired image classification architecture
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list
-                        (will raise an exception with the list)
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder
-                        for a generic dataset setup with an image
-                        folder structure setup like imagenet or
-                        loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default
-                        is true to load the default pretrained weights
-                        for the model. Otherwise should be set to the
-                        desired weights type: [base, optim, optim-
-                        perf]. To not load any weights set to one of
-                        [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will
-                        load the default dataset for the architecture.
-                        Ex can be set to imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model
-                        constructor, should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results
-                        under save-dir, defaults to the model arch and
-                        dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for
-                        data parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the
-                        state from and resume the state for. If
-                        provided, pretrained will be ignored. If using
-                        a SparseZoo recipe, can also provide 'zoo' to
-                        load the base weights associated with that
-                        recipe
-  --steps-per-measurement STEPS_PER_MEASUREMENT
-                        The number of steps (batches) to run for each
-                        measurement
-  --batch-size BATCH_SIZE
-                        The batch size to use for analysis
-  --approximate [APPROXIMATE]
-                        approximate without running data through the
-                        model(uses one shot analysis if --approximate
-                        not passed)
-#########
-EXAMPLES
-#########
-
-##########
-Example command for running one shot KS sens analysis on ssd300_resnet50 for
-coco:
-python integrations/pytorch/lr_analysis.py pr_sensitivity \
-    --arch-key ssd300_resnet50 --dataset coco \
-    --dataset-path ~/datasets/coco-detection
-
-Note: Might need to install pycocotools using pip
-"""
-import json
-import os
-from dataclasses import dataclass, field
-from typing import Any, List, Optional
-
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import (
-    pruning_loss_sens_magnitude,
-    pruning_loss_sens_one_shot,
-)
-from sparseml.pytorch.utils import default_device, model_to_device
-
-
-CURRENT_TASK = utils.Tasks.PR_SENSITIVITY
-LOGGER = get_main_logger()
-
-
-@dataclass
-class PRAnalysisArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    kernel sparsity (pruning) analysis tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for training, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param pretrained: The type of pretrained weights to use default is true to
-        load the default pretrained weights for the model Otherwise should be
-        set to the desired weights type: [base, optim, optim-perf]; To not
-        load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load
-        pretrained weights for if pretrained is set; Default is None which will
-        load the default dataset for the architecture; Ex can be set to
-        imagenet, cifar10, etc".
-    :param model_kwargs: json object containing keyword arguments to be
-        passed to model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be
-        passed to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results
-        under save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on
-        (can also include ids for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data
-        loading, default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with
-        a recipe.
-    :param steps_per_measurement: The number of steps (batches) to run for
-        each measurement
-    :param batch_size: The batch size to use for analysis
-    :param approximate: approximate without running data through the model
-        (uses one shot analysis if --approximate not passed)
-    """
-
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception "
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or "
-            "loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights "
-            "for the model. Otherwise should be set to the desired "
-            "weights type: [base, optim, optim-perf]. To not load any"
-            " weights set  to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained"
-            "is set. Default is None which will load the default "
-            "dataset for the architecture. Ex can be set to imagenet,"
-            "cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, "
-            "should be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under "
-            "save-dir, defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for "
-            "data parallel), ex: cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored. If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    steps_per_measurement: int = field(
-        default=15,
-        metadata={"help": "The number of steps (batches) to run for each measurement"},
-    )
-    batch_size: int = field(
-        default=64, metadata={"help": "The batch size to use for analysis"}
-    )
-
-    approximate: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "approximate without running data through the model"
-            "(uses one shot analysis if --approximate not passed)",
-        },
-    )
-
-    def __post_init__(self):
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        self.is_main_process = True
-        self.local_rank = -1
-        self.rank = -1
-
-
-def pruning_loss_sensitivity(
-    args: PRAnalysisArguments,
-    model,
-    train_loader: DataLoader,
-    save_dir: str,
-    loggers: List[Any],
-) -> None:
-    """
-    Utility function for pruning sensitivity analysis
-
-    :param args : A PRAnalysisArguments object containing config for current
-        analysis
-    :param model: loaded model architecture to analyse
-    :param train_loader: A DataLoader for training data
-    :param save_dir: Directory to save results
-    :param loggers: List of loggers to use during analysis
-    """
-    # loss setup
-    if not args.approximate:
-        loss = utils.get_loss_wrapper(args)
-        LOGGER.info(f"created loss: {loss}")
-    else:
-        loss = None
-
-    # device setup
-    if not args.approximate:
-        module, device, device_ids = model_to_device(model, args.device)
-    else:
-        device = None
-
-    # kernel sparsity analysis
-    if args.approximate:
-        analysis = pruning_loss_sens_magnitude(model)
-    else:
-        analysis = pruning_loss_sens_one_shot(
-            model,
-            train_loader,
-            loss,
-            device,
-            args.steps_per_measurement,
-            tester_loggers=loggers,
-        )
-
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info(f"Saving results in {save_dir}")
-    analysis.save_json(
-        os.path.join(
-            save_dir,
-            "ks_approx_sensitivity.json"
-            if args.approximate
-            else "ks_one_shot_sensitivity.json",
-        )
-    )
-    analysis.plot(
-        os.path.join(
-            save_dir,
-            os.path.join(
-                save_dir,
-                "ks_approx_sensitivity.png"
-                if args.approximate
-                else "ks_one_shot_sensitivity.png",
-            ),
-        ),
-        plot_integral=True,
-    )
-    analysis.print_res()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(
-        dataclass_types=PRAnalysisArguments,
-        description="Utility script to Run a "
-        "kernel sparsity (pruning) analysis "
-        "for a desired image classification architecture",
-    )
-
-    args_, _ = _parser.parse_args_into_dataclasses()
-
-    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
-
-    input_shape = ModelRegistry.input_shape(args_.arch_key)
-    # assume shape [C, S, S] where S is the image size
-    image_size = input_shape[1]
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
-
-    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
-
-    model = utils.create_model(args_, num_classes)
-
-    pruning_loss_sensitivity(args_, model, train_loader, save_dir, loggers)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/examples/pytorch/recipes/.gitkeep b/integrations/examples/pytorch/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md
deleted file mode 100644
index 29479dcdc1f..00000000000
--- a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned.md
+++ /dev/null
@@ -1,98 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# Epoch and Learning-Rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-    
----
-
-# Transfer Learning
-
-This recipe provides a framework for performing sparse transfer learning on
-SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
-
-
-To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned
-```
-
-To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned.md\
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag mobilenet-imagenette-sparse-transfer-learned
-```
-## Other Datasets
-To transfer learn this sparsified model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increase the learning rate when increasing batch size
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md b/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
deleted file mode 100644
index 5820fcf05f3..00000000000
--- a/integrations/examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
+++ /dev/null
@@ -1,106 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# Epoch and Learning-Rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-# quantization variables
-quantization_start_epoch: &quantization_start_epoch 4.0
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-
-# phase 2 apply quantization
-sparse_quantized_transfer_learning_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-
----
-# Transfer Learning
-
-This recipe provides a framework for performing sparse transfer learning on
-SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
-
-
-To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned-quantized
-```
-
-To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
-
-```
-sparseml.image_classification.train \
-    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
-    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag mobilenet-imagenette-sparse-transfer-learned-quantized
-```
-## Other Datasets
-To transfer learn this sparsified model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increase the learning rate when increasing batch size
-* Increasing the number of epochs before starting quantization can give a higher baseline
-* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/mobilenet-pruned.md b/integrations/examples/pytorch/recipes/mobilenet-pruned.md
deleted file mode 100644
index 03dd40b9811..00000000000
--- a/integrations/examples/pytorch/recipes/mobilenet-pruned.md
+++ /dev/null
@@ -1,107 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 90
-init_lr: &init_lr 0.0028
-lr_step_epochs: &lr_step_epochs [43, 60]
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 0.0
-pruning_end_epoch: &pruning_end_epoch 40.0
-pruning_update_frequency: &pruning_update_frequency 0.4
-init_sparsity: &init_sparsity 0.05
-pruning_mask_type: &pruning_mask_type unstructured
-prune_low_target_sparsity: &prune_low_target_sparsity 0.7
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.9
-
-
-training_modifiers:
-  - !EpochRangeModifier
-    end_epoch: *num_epochs
-    start_epoch: 0.0
-
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: -1.0
-    init_lr: *init_lr
-    lr_class: MultiStepLR
-    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
-    start_epoch: 0.0
-    update_frequency: -1.0
-    
-pruning_modifiers:
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_low_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.1.0.point.conv.weight', 'sections.1.1.point.conv.weight', 'sections.2.0.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_mid_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.2.1.point.conv.weight', 'sections.3.0.point.conv.weight', 'sections.3.1.point.conv.weight', 'sections.3.5.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *prune_high_target_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.3.2.point.conv.weight', 'sections.3.3.point.conv.weight', 'sections.3.4.point.conv.weight', 'sections.4.0.point.conv.weight', 'sections.4.1.point.conv.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# MobileNet-V1 ImageNet Moderate Sparse
-
-This recipe creates a sparse [MobileNet-V1](https://arxiv.org/abs/1704.04861) model that
-achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
-Training was done using 4 GPUs using a total training batch size of 1024
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key mobilenet \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 1024 --test-batch-size 2056 \
-    --loader-num-workers 16 \
-    --model-tag mobilenet-imagenet-pruned-moderate
-```
diff --git a/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md b/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md
deleted file mode 100644
index 94f71cf07d8..00000000000
--- a/integrations/examples/pytorch/recipes/mobilenet-pruned_quant.md
+++ /dev/null
@@ -1,160 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 100.0
-init_lr: &init_lr 0.005
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
-pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 90
-disable_quantization_observer_epoch: &disable_quantization_observer_epoch 92.0
-freeze_bn_stats_epoch: &freeze_bn_stats_epoch 92.0
-quantization_init_lr: &quantization_init_lr 0.00001
-quantization_lr_step_milestones: &quantization_lr_step_milestones [91]
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 36.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.55
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.7
-prune_high_target_sparsity: &prune_high_target_sparsity 0.8
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    start_epoch: *pruning_recovery_start_epoch
-    end_epoch: *quantization_start_epoch
-    lr_class: CosineAnnealingWarmRestarts
-    lr_kwargs:
-      lr_min: *pruning_recovery_end_lr
-      cycle_epochs: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_recovery_start_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !LearningRateModifier
-    start_epoch: *quantization_start_epoch
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *quantization_lr_step_milestones
-      gamma: 0.1
-    init_lr: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.point.conv.weight
-      - sections.1.0.point.conv.weight
-      - sections.1.1.point.conv.weight
-      - sections.2.0.point.conv.weight
-      - classifier.fc.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.2.1.point.conv.weight
-      - sections.3.0.point.conv.weight
-      - sections.3.1.point.conv.weight
-      - sections.3.5.point.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.3.2.point.conv.weight
-      - sections.3.3.point.conv.weight
-      - sections.3.4.point.conv.weight
-      - sections.4.0.point.conv.weight
-      - sections.4.1.point.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
-    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
-
-    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
-    # but are unnecessary due to these already being set during the training phase
----
-
-# MobileNet-V1 ImageNet Moderate Sparse Quantization
-
-This recipe creates a sparse quantized [MobileNet-V1](https://arxiv.org/abs/1704.04861) model
-that achieves 99% of its baseline accuracy on the ImageNet dataset.  Training was done using
-4 GPUs using a total training batch size of 96 using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-python -m torch.distributed.launch \
-  --nproc_per_node 4 \
-  src/sparseml/pytorch/image_classification/train.py \
-    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch-sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key mobilenet \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 96 --test-batch-size 256 \
-    --loader-num-workers 16 \
-    --model-tag mobilenet-imagenet-pruned_quant-moderate
-```
diff --git a/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md b/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md
deleted file mode 100644
index 16318a27b99..00000000000
--- a/integrations/examples/pytorch/recipes/resnet50-imagenette-pruned.md
+++ /dev/null
@@ -1,145 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 10
-lr: &lr 0.008
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 8.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.8
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.85
-prune_high_target_sparsity: &prune_high_target_sparsity 0.9
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.conv1.weight
-      - sections.0.0.conv2.weight
-      - sections.0.0.conv3.weight
-      - sections.0.0.identity.conv.weight
-      - sections.0.1.conv1.weight
-      - sections.0.1.conv3.weight
-      - sections.0.2.conv1.weight
-      - sections.0.2.conv3.weight
-      - sections.1.0.conv1.weight
-      - sections.1.0.conv3.weight
-      - sections.1.2.conv3.weight
-      - sections.1.3.conv1.weight
-      - sections.2.0.conv1.weight
-      - sections.3.0.conv1.weight
-      - classifier.fc.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.0.1.conv2.weight
-      - sections.0.2.conv2.weight
-      - sections.1.0.identity.conv.weight
-      - sections.1.1.conv1.weight
-      - sections.1.1.conv2.weight
-      - sections.1.1.conv3.weight
-      - sections.1.2.conv1.weight
-      - sections.1.2.conv2.weight
-      - sections.1.3.conv2.weight
-      - sections.1.3.conv3.weight
-      - sections.2.0.conv3.weight
-      - sections.2.0.identity.conv.weight
-      - sections.2.1.conv1.weight
-      - sections.2.1.conv3.weight
-      - sections.2.2.conv1.weight
-      - sections.2.2.conv3.weight
-      - sections.2.3.conv1.weight
-      - sections.2.3.conv3.weight
-      - sections.2.4.conv1.weight
-      - sections.2.4.conv3.weight
-      - sections.2.5.conv1.weight
-      - sections.2.5.conv3.weight
-      - sections.3.1.conv1.weight
-      - sections.3.2.conv1.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.1.0.conv2.weight
-      - sections.2.0.conv2.weight
-      - sections.2.1.conv2.weight
-      - sections.2.2.conv2.weight
-      - sections.2.3.conv2.weight
-      - sections.2.4.conv2.weight
-      - sections.2.5.conv2.weight
-      - sections.3.0.conv2.weight
-      - sections.3.0.conv3.weight
-      - sections.3.0.identity.conv.weight
-      - sections.3.1.conv2.weight
-      - sections.3.1.conv3.weight
-      - sections.3.2.conv2.weight
-      - sections.3.2.conv3.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# ResNet-50 Imagenette Conservative Sparse
-
-This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
-achieves full recovery of its baseline accuracy on the Imagenette dataset.
-Training was done using 1 GPU with a total training batch size of 128
-using a SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training. 
-Alternatively, a full walk-through notebook is located at `sparseml/integrations/pytorch/notebooks/classification.ipynb`.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 128 --test-batch-size 256 \
-    --loader-num-workers 8 \
-    --model-tag resnet50-imagenette-pruned-conservative
-```
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned.md b/integrations/examples/pytorch/recipes/resnet50-pruned.md
deleted file mode 100644
index 27fe718867b..00000000000
--- a/integrations/examples/pytorch/recipes/resnet50-pruned.md
+++ /dev/null
@@ -1,83 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 90
-init_lr: &init_lr 0.0028
-lr_step_epochs: &lr_step_epochs [43, 60]
-
-# Pruning variables
-pruning_start_epoch: &pruning_start_epoch 0.0
-pruning_end_epoch: &pruning_end_epoch 40.0
-pruning_update_frequency: &pruning_update_frequency 0.4
-init_sparsity: &init_sparsity 0.05
-final_sparsity: &final_sparsity 0.88
-pruning_mask_type: &pruning_mask_type unstructured
-
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    end_epoch: *num_epochs
-    start_epoch: 0.0
-
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: -1.0
-    init_lr: *init_lr
-    lr_class: MultiStepLR
-    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
-    start_epoch: 0.0
-    update_frequency: -1.0
-        
-pruning_modifiers:
-  - !GMPruningModifier
-    end_epoch: *pruning_end_epoch
-    final_sparsity: *final_sparsity
-    init_sparsity: *init_sparsity
-    inter_func: cubic
-    leave_enabled: True
-    mask_type: *pruning_mask_type
-    params: ['sections.0.0.conv1.weight', 'sections.0.0.conv2.weight', 'sections.0.0.conv3.weight', 'sections.0.0.identity.conv.weight', 'sections.0.1.conv1.weight', 'sections.0.1.conv2.weight', 'sections.0.1.conv3.weight', 'sections.0.2.conv1.weight', 'sections.0.2.conv2.weight', 'sections.0.2.conv3.weight', 'sections.1.0.conv1.weight', 'sections.1.0.conv2.weight', 'sections.1.0.conv3.weight', 'sections.1.0.identity.conv.weight', 'sections.1.1.conv1.weight', 'sections.1.1.conv2.weight', 'sections.1.1.conv3.weight', 'sections.1.2.conv1.weight', 'sections.1.2.conv2.weight', 'sections.1.2.conv3.weight', 'sections.1.3.conv1.weight', 'sections.1.3.conv2.weight', 'sections.1.3.conv3.weight', 'sections.2.0.conv1.weight', 'sections.2.0.conv2.weight', 'sections.2.0.conv3.weight', 'sections.2.0.identity.conv.weight', 'sections.2.1.conv1.weight', 'sections.2.1.conv2.weight', 'sections.2.1.conv3.weight', 'sections.2.2.conv1.weight', 'sections.2.2.conv2.weight', 'sections.2.2.conv3.weight', 'sections.2.3.conv1.weight', 'sections.2.3.conv2.weight', 'sections.2.3.conv3.weight', 'sections.2.4.conv1.weight', 'sections.2.4.conv2.weight', 'sections.2.4.conv3.weight', 'sections.2.5.conv1.weight', 'sections.2.5.conv2.weight', 'sections.2.5.conv3.weight', 'sections.3.0.conv1.weight', 'sections.3.0.conv2.weight', 'sections.3.0.conv3.weight', 'sections.3.0.identity.conv.weight', 'sections.3.1.conv1.weight', 'sections.3.1.conv2.weight', 'sections.3.1.conv3.weight', 'sections.3.2.conv1.weight', 'sections.3.2.conv2.weight', 'sections.3.2.conv3.weight']
-    start_epoch: *pruning_start_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# ResNet-50 ImageNet Moderate Sparse
-
-This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
-achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
-Training was done using 4 GPUs using a total training batch size of 1024
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 1024 --test-batch-size 2056 \
-    --loader-num-workers 16 \
-    --model-tag resnet50-imagenet-pruned-moderate
-```
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md b/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
deleted file mode 100644
index 49fba746f53..00000000000
--- a/integrations/examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
+++ /dev/null
@@ -1,100 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# epoch and learning rate variables
-num_epochs: &num_epochs 10.0
-init_lr: &init_lr 0.008
-lr_step_milestones: &lr_step_milestones [5]
-
-# quantization variables
-quantization_start_epoch: &quantization_start_epoch 4.0
-quantize_submodules: &quantize_submodules
-  - input
-  - sections
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateModifier
-    start_epoch: 0.0
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *lr_step_milestones
-      gamma: 0.1
-    init_lr: *init_lr
-
-# phase 1 sparse transfer learning / recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL__
-
-# phase 2 apply quantization
-sparse_quantized_transfer_learning_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-
-  - !SetWeightDecayModifier
-    start_epoch: 0.0
-    weight_decay: 0.0
----
-
-# Transfer Learning ResNet50 ImageNet Pruned-Quantized
-
-This recipe provides a framework for performing pruned-quantized transfer learning on
-SparseML's `pruned_quantized` ResNet50 model.  This recipe was written for transfer learning
-onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
-a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
-for your dataset and training environment.
-
-## Imagenette
-On the default transfer learning dataset, Imagenette, this recipe fully recovered to 99.9% Top-1 accuracy.
-To run this recipe, you may run the example notebook
-`sparseml/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb`
-or can run the example script command below.
-
-```
-sparseml.image_classification.train \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn \
-    --checkpoint-path zoo \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 8 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-pruned_quant-transfer_learned
-```
-
-## Other Datasets
-To transfer learn this ResNet 50 Pruned-Quantized model to other datasets
-you may have to adjust certain hyperparameters in this recipe and/or training script.
-Some considerations:
-* For more complex datasets, increate the number of epochs, adjusting the learning rate step accordingly
-* Adding more learning rate step milestones can lead to more jumps in accuracy
-* Increasing the number of epochs before starting quantization can give a higher baseline
-* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
-* Increase the learning rate when increasing batch size
-* Increase the number of epochs if using SGD instead of the Adam optimizer
-* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md b/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md
deleted file mode 100644
index fe83cb8a3fc..00000000000
--- a/integrations/examples/pytorch/recipes/resnet50-pruned_quant.md
+++ /dev/null
@@ -1,198 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 135.0
-init_lr: &init_lr 0.008
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
-pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
-pruning_recovery_end_lr: &pruning_recovery_end_lr 0.00001
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 100
-disable_quantization_observer_epoch: &disable_quantization_observer_epoch 115.0
-freeze_bn_stats_epoch: &freeze_bn_stats_epoch 116.0
-quantization_init_lr: &quantization_init_lr 0.01
-quantization_lr_step_milestones: &quantization_lr_step_milestones [107, 114, 125]
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 1.0
-pruning_end_epoch: &pruning_end_epoch 36.0
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_low_target_sparsity: &prune_low_target_sparsity 0.7
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.75
-prune_high_target_sparsity: &prune_high_target_sparsity 0.8
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    start_epoch: *pruning_recovery_start_epoch
-    end_epoch: *quantization_start_epoch
-    lr_class: CosineAnnealingWarmRestarts
-    lr_kwargs:
-      lr_min: *pruning_recovery_end_lr
-      cycle_epochs: *pruning_recovery_num_epochs
-    init_lr: *pruning_recovery_start_lr
-
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_recovery_start_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !LearningRateModifier
-    start_epoch: *quantization_start_epoch
-    lr_class: MultiStepLR
-    lr_kwargs:
-      milestones: *quantization_lr_step_milestones
-      gamma: 0.1
-    init_lr: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - sections.0.0.identity.conv.weight
-      - sections.0.0.conv1.weight
-      - sections.0.0.conv2.weight
-      - sections.0.0.conv3.weight
-      - sections.0.1.conv1.weight
-      - sections.0.1.conv3.weight
-      - sections.0.2.conv1.weight
-      - sections.0.2.conv3.weight
-      - sections.1.0.conv1.weight
-      - sections.1.0.conv3.weight
-      - sections.1.2.conv3.weight
-      - sections.1.3.conv1.weight
-      - sections.2.0.conv1.weight
-      - sections.3.0.conv1.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.0.1.conv2.weight
-      - sections.0.2.conv2.weight
-      - sections.1.0.identity.conv.weight
-      - sections.1.1.conv1.weight
-      - sections.1.1.conv2.weight
-      - sections.1.1.conv3.weight
-      - sections.1.2.conv1.weight
-      - sections.1.2.conv2.weight
-      - sections.1.3.conv2.weight
-      - sections.1.3.conv3.weight
-      - sections.2.0.conv3.weight
-      - sections.2.0.identity.conv.weight
-      - sections.2.1.conv1.weight
-      - sections.2.1.conv3.weight
-      - sections.2.2.conv1.weight
-      - sections.2.3.conv1.weight
-      - sections.2.3.conv3.weight
-      - sections.2.4.conv1.weight
-      - sections.2.4.conv3.weight
-      - sections.2.5.conv1.weight
-      - sections.2.5.conv3.weight
-      - sections.3.1.conv1.weight
-      - sections.3.2.conv1.weight
-      - sections.1.0.conv2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - sections.2.0.conv2.weight
-      - sections.2.1.conv2.weight
-      - sections.2.2.conv2.weight
-      - sections.2.3.conv2.weight
-      - sections.2.4.conv2.weight
-      - sections.2.5.conv2.weight
-      - sections.3.0.conv2.weight
-      - sections.3.0.conv3.weight
-      - sections.3.0.identity.conv.weight
-      - sections.3.1.conv2.weight
-      - sections.3.1.conv3.weight
-      - sections.3.2.conv2.weight
-      - sections.3.2.conv3.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - input
-      - sections
-    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
-    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
-
-    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
-    # but are unnecessary due to these already being set during the training phase
----
-
-# ResNet-50 ImageNet Moderate Sparse Quantization
-
-This recipe creates a sparse quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of its baseline accuracy
-on the ImageNet dataset.  Training was done using 4 GPUs using a total training batch size of 96
-using an SGD optimizer.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Training
-
-We can use the `sparseml.image_classification.train` utility for training.
-
-*script command:*
-
-```
-python -m torch.distributed.launch \
-  --nproc_per_node 4 \
-  src/sparseml/pytorch/image_classification/train.py \
-    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenet \
-    --dataset-path /PATH/TO/IMAGENET  \
-    --train-batch-size 96 --test-batch-size 256 \
-    --loader-num-workers 16 \
-    --model-tag resnet50-imagenet-pruned_quant-moderate
-```
diff --git a/integrations/examples/pytorch/torchvision_sparsification.py b/integrations/examples/pytorch/torchvision_sparsification.py
deleted file mode 100644
index 07acd698b19..00000000000
--- a/integrations/examples/pytorch/torchvision_sparsification.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Example script for running training and optimization tasks on torchvision
-classification models with an imagefolder based dataset using SparseML.
-Example tasks include:
-* Model pruning
-* Quantization aware training
-* Sparse transfer learning
-
-More information about torchvision models can be found here:
-https://pytorch.org/docs/stable/torchvision/models.html
-
-##########
-Command help:
-usage: torchvision_sparseml.py [-h] --model MODEL [--recipe-path RECIPE_PATH]
-                               [--image-size IMAGE_SIZE]
-                               [--batch-size BATCH_SIZE]
-                               [--pretrained PRETRAINED]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               --imagefolder-path IMAGEFOLDER_PATH
-                               [--loader-num-workers LOADER_NUM_WORKERS]
-                               [--loader-pin-memory LOADER_PIN_MEMORY]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-
-Train or finetune an image classification model from torchvision.models
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --model MODEL         The torchvision model class to use, ex: inception_v3,
-                        resnet50, mobilenet_v2 model name is fed directly to
-                        torchvision.models, more information can be found here
-                        https://pytorch.org/docs/stable/torchvision/models.htm
-                        l
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the sparseml
-                        modifiers and schedule to apply them with
-  --image-size IMAGE_SIZE
-                        Size of image to use for model input. Default is 224
-                        unless pytorch documentation specifies otherwise
-  --batch-size BATCH_SIZE
-                        Batch size to use when training model. Default is 32
-  --pretrained PRETRAINED
-                        Set True to use torchvisions pretrained weights, to
-                        not set weights, set False. default is true.
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --imagefolder-path IMAGEFOLDER_PATH
-                        Path to root of dataset's generic 'image folder' path.
-                        Should have an image folder structure like imagenet
-                        with subdirectories 'train' and 'val' see https://pyto
-                        rch.org/docs/stable/torchvision/datasets.html#imagefol
-                        der
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --loader-pin-memory LOADER_PIN_MEMORY
-                        Use pinned memory for data loading
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-
-##########
-Example command for pruning resnet50 on an imagefolder dataset:
-python integrations/pytorch/torchvision_sparsification.py \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --model resnet50 \
-    --imagefolder-path ~/datasets/ILSVRC2012 \
-    --batch-size 256
-"""
-
-import argparse
-import os
-import time
-from types import ModuleType
-
-import torch
-from torch.nn import CrossEntropyLoss
-from torch.optim import SGD
-from torch.utils.data import DataLoader
-from torchvision import models
-
-from sparseml.pytorch.datasets.classification import ImageFolderDataset
-from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
-from sparseml.pytorch.utils import ModuleExporter, PythonLogger, load_model
-from sparseml.utils import create_dirs
-
-
-MODEL_IMAGE_SIZES = {
-    "inception_v3": 299,
-}
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Train or finetune an image classification model from "
-        "torchvision.models"
-    )
-
-    # model args
-    parser.add_argument(
-        "--model",
-        type=str,
-        required=True,
-        help=(
-            "The torchvision model class to use, ex: inception_v3, resnet50, "
-            "mobilenet_v2 model name is fed directly to torchvision.models, "
-            "more information can be found here "
-            "https://pytorch.org/docs/stable/torchvision/models.html"
-        ),
-    )
-    parser.add_argument(
-        "--recipe-path",
-        type=str,
-        required=True,
-        help="The path to the yaml file containing the sparseml modifiers and "
-        "schedule to apply them with",
-    )
-    parser.add_argument(
-        "--image-size",
-        type=int,
-        required=False,
-        default=None,
-        help=(
-            "Size of image to use for model input. Default is 224 unless pytorch "
-            "documentation specifies otherwise"
-        ),
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        required=False,
-        default=32,
-        help="Batch size to use when training model. Default is 32",
-    )
-    parser.add_argument(
-        "--pretrained",
-        type=bool,
-        default=True,
-        help="Set True to use torchvisions pretrained weights,"
-        " to not set weights, set False. default is true.",
-    )
-    parser.add_argument(
-        "--checkpoint-path",
-        type=str,
-        default=None,
-        help="A path to a previous checkpoint to load the state from and "
-        "resume the state for. If provided, pretrained will be ignored",
-    )
-
-    # dataset args
-    parser.add_argument(
-        "--imagefolder-path",
-        type=str,
-        required=True,
-        help="Path to root of dataset's generic 'image folder' path. Should have "
-        "an image folder structure like imagenet with subdirectories 'train' and 'val'"
-        " see https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder",
-    )
-    parser.add_argument(
-        "--loader-num-workers",
-        type=int,
-        default=4,
-        help="The number of workers to use for data loading",
-    )
-    parser.add_argument(
-        "--loader-pin-memory",
-        type=bool,
-        default=True,
-        help="Use pinned memory for data loading",
-    )
-
-    # logging and saving
-    parser.add_argument(
-        "--model-tag",
-        type=str,
-        default=None,
-        help="A tag to use for the model for saving results under save-dir, "
-        "defaults to the model arch and dataset used",
-    )
-    parser.add_argument(
-        "--save-dir",
-        type=str,
-        default="torchvision_sparseml_export",
-        help="The path to the directory for saving results",
-    )
-
-    args = parser.parse_args()
-    if args.image_size is None:
-        args.image_size = (
-            MODEL_IMAGE_SIZES[args.model] if args.model in MODEL_IMAGE_SIZES else 224
-        )
-    return args
-
-
-def _get_torchvision_model(name, num_classes, pretrained=True, checkpoint_path=None):
-    model_constructor = getattr(models, name, None)
-    if model_constructor is None or isinstance(model_constructor, ModuleType):
-        # constructor doesn't exist or is a submodule instead of function in torchvision
-        raise ValueError("Torchvision model {} not found".format(name))
-    # build model
-    model = model_constructor(pretrained=False, num_classes=num_classes)
-    if pretrained and not checkpoint_path:
-        pretrained_model = model_constructor(pretrained=True, num_classes=1000)
-        # fix num classes mismatch
-        if num_classes == 1000:
-            model = pretrained_model
-        else:
-            _load_matched_weights(model, pretrained_model)
-        del pretrained_model
-
-    if checkpoint_path is not None:
-        load_model(checkpoint_path, model)
-    return model
-
-
-def _load_matched_weights(base_model, pretrained_model):
-    base_dict = base_model.state_dict()
-    pretrained_dict = pretrained_model.state_dict()
-    for key in base_dict:
-        if (
-            key in pretrained_dict
-            and base_dict[key].shape == pretrained_dict[key].shape
-        ):
-            base_dict[key] = pretrained_dict[key]
-    base_model.load_state_dict(base_dict)
-
-
-def _create_imagefolder_dataloader(args, train=True):
-    dataset = ImageFolderDataset(
-        root=args.imagefolder_path,
-        train=train,
-        rand_trans=train,
-        image_size=args.image_size,
-    )
-    loader = DataLoader(
-        dataset,
-        batch_size=args.batch_size,
-        shuffle=True,
-        num_workers=args.loader_num_workers,
-        pin_memory=args.loader_pin_memory,
-    )
-
-    # return dataloader, number of classes, and input image shape
-    return loader, dataset.num_classes, dataset[0][0].shape
-
-
-######################################################################################
-# torchvision finetuning function from:                                              #
-# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html #
-######################################################################################
-def train_model(
-    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False
-):
-    since = time.time()
-
-    val_acc_history = []
-
-    # not loading best intermediate weights due to sparsity changing
-    # best_model_wts = copy.deepcopy(model.state_dict())
-    best_acc = 0.0
-
-    for epoch in range(num_epochs):
-        print("Epoch {}/{}".format(epoch, num_epochs - 1))
-        print("-" * 10)
-
-        # Each epoch has a training and validation phase
-        for phase in ["train", "val"]:
-            if phase == "train":
-                model.train()  # Set model to training mode
-            else:
-                model.eval()  # Set model to evaluate mode
-
-            running_loss = 0.0
-            running_corrects = 0
-
-            # Iterate over data.
-            for inputs, labels in dataloaders[phase]:
-                inputs = inputs.to(device)
-                labels = labels.to(device)
-
-                # zero the parameter gradients
-                optimizer.zero_grad()
-
-                # forward
-                # track history if only in train
-                with torch.set_grad_enabled(phase == "train"):
-                    # Get model outputs and calculate loss
-                    # Special case for inception because in training it has an
-                    # auxiliary output. In train mode we calculate the loss by summing
-                    # the final output and the auxiliary output but in testing we
-                    # only consider the final output.
-                    if is_inception and phase == "train":
-                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958  # noqa
-                        outputs, aux_outputs = model(inputs)
-                        loss1 = criterion(outputs, labels)
-                        loss2 = criterion(aux_outputs, labels)
-                        loss = loss1 + 0.4 * loss2
-                    else:
-                        outputs = model(inputs)
-                        loss = criterion(outputs, labels)
-
-                    _, preds = torch.max(outputs, 1)
-
-                    # backward + optimize only if in training phase
-                    if phase == "train":
-                        loss.backward()
-                        optimizer.step()
-
-                # statistics
-                running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
-
-            epoch_loss = running_loss / len(dataloaders[phase].dataset)
-            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
-
-            print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc))
-
-            # deep copy the model
-            if phase == "val" and epoch_acc > best_acc:
-                best_acc = epoch_acc
-                # not loading best intermediate weights due to sparsity changing
-                # best_model_wts = copy.deepcopy(model.state_dict())
-            if phase == "val":
-                val_acc_history.append(epoch_acc)
-
-        print()
-
-    time_elapsed = time.time() - since
-    print(
-        "Training complete in {:.0f}m {:.0f}s".format(
-            time_elapsed // 60, time_elapsed % 60
-        )
-    )
-    print("Best val Acc: {:4f}".format(best_acc))
-
-    # load best model weights
-    # not loading best intermediate weights due to sparsity changing
-    # model.load_state_dict(best_model_wts)
-    return model, val_acc_history
-
-
-def _save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    print(f"Saved recipe to {recipe_save_path}")
-
-
-def main(args):
-    ############################
-    # logging and saving setup #
-    ############################
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-
-    # get unique model tag, defaults to '{model_name}'
-    if not args.model_tag:
-        model_tag = args.model.replace("/", ".")
-        model_id = model_tag
-        model_inc = 0
-
-        while os.path.exists(os.path.join(args.save_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-    print("Model id is set to {}".format(model_id))
-
-    ###########################
-    # standard training setup #
-    ###########################
-
-    # create data loaders
-    train_loader, _, _ = _create_imagefolder_dataloader(args, train=True)
-    val_loader, num_classes, image_shape = _create_imagefolder_dataloader(
-        args, train=False
-    )
-    dataloaders = {"train": train_loader, "val": val_loader}
-
-    # create model
-    model = _get_torchvision_model(
-        args.model,
-        num_classes,
-        args.pretrained,
-        args.checkpoint_path,
-    )
-    print("created model: {}".format(model))
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    model.to(device)
-    print("using device: {}".format(device))
-
-    # create standard SGD optimizer and cross entropy loss function
-    criterion = CrossEntropyLoss()
-    optimizer = SGD(
-        model.parameters(), lr=0.001, momentum=0.9
-    )  # lr will be overridden by recipe
-
-    ##########################
-    # add sparseml modifiers #
-    ##########################
-    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
-    optimizer = ScheduledOptimizer(
-        optimizer,
-        model,
-        manager,
-        steps_per_epoch=len(train_loader),
-        loggers=[PythonLogger()],
-    )
-
-    ########################
-    # torchvision training #
-    ########################
-    _save_recipe(recipe_manager=manager, save_dir=save_dir)
-    model, val_acc_history = train_model(
-        model,
-        dataloaders,
-        criterion,
-        optimizer,
-        device,
-        num_epochs=manager.max_epochs,
-        is_inception="inception" in args.model,
-    )
-
-    ########################
-    # export trained model #
-    ########################
-    exporter = ModuleExporter(model, save_dir)
-    sample_input = torch.randn(image_shape).unsqueeze(0)  # sample batch for ONNX export
-    exporter.export_onnx(sample_input, convert_qat=True)
-    exporter.export_pytorch()
-    print("Model ONNX export and PyTorch weights saved to {}".format(save_dir))
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-    main(args_)
diff --git a/integrations/examples/pytorch/train.py b/integrations/examples/pytorch/train.py
deleted file mode 100644
index 0dc86e934af..00000000000
--- a/integrations/examples/pytorch/train.py
+++ /dev/null
@@ -1,703 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-######
-Command help:
-usage: train.py [-h] --train-batch-size TRAIN_BATCH_SIZE --test-batch-size
-                TEST_BATCH_SIZE --arch-key ARCH_KEY --dataset DATASET
-                --dataset-path DATASET_PATH
-                [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
-                [--optim-args OPTIM_ARGS] [--recipe-path RECIPE_PATH]
-                [--sparse-transfer-learn [SPARSE_TRANSFER_LEARN]]
-                [--eval-mode [EVAL_MODE]] [--optim OPTIM]
-                [--logs-dir LOGS_DIR] [--save-best-after SAVE_BEST_AFTER]
-                [--save-epochs SAVE_EPOCHS]
-                [--use-mixed-precision [USE_MIXED_PRECISION]]
-                [--debug-steps DEBUG_STEPS] [--pretrained PRETRAINED]
-                [--pretrained-dataset PRETRAINED_DATASET]
-                [--model-kwargs MODEL_KWARGS]
-                [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
-                [--save-dir SAVE_DIR] [--device DEVICE]
-                [--loader-num-workers LOADER_NUM_WORKERS]
-                [--no-loader-pin-memory]
-                [--loader-pin-memory [LOADER_PIN_MEMORY]]
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.pytorch.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored. If using a SparseZoo recipe, can also
-                        provide 'zoo' to load the base weights associated with
-                        that recipe
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. Can also provide a
-                        SparseZoo stub prefixed with 'zoo:' with an optional
-                        '?recipe_type=' argument
-  --sparse-transfer-learn [SPARSE_TRANSFER_LEARN]
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode [EVAL_MODE]
-                        Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --optim OPTIM         The optimizer type to use, one of [SGD, Adam, RMSprop]
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS
-                        epochs to save checkpoints at
-  --use-mixed-precision [USE_MIXED_PRECISION]
-                        Trains model using mixed precision. Supported
-                        environments are single GPU and multiple GPUs using
-                        DistributedDataParallel with one GPU per process
-  --debug-steps DEBUG_STEPS
-                        Amount of steps to run for training and testing for a
-                        debug mode
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --model-kwargs MODEL_KWARGS
-                        Keyword arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset-kwargs DATASET_KWARGS
-                        Keyword arguments to be passed to dataset constructor,
-                        should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --device DEVICE       The device to run on (can also include ids for data
-                        parallel), ex: cpu, cuda, cuda:0,1
-  --loader-num-workers LOADER_NUM_WORKERS
-                        The number of workers to use for data loading
-  --no-loader-pin-memory
-                        Do not use pinned memory for data loading
-  --loader-pin-memory [LOADER_PIN_MEMORY]
-                        Use pinned memory for data loading
-#########
-EXAMPLES
-#########
-
-##########
-Example command for pruning resnet50 on imagenet dataset:
-python integrations/pytorch/train.py \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
-python integrations/pytorch/train.py \
-    --sparse-transfer-learn \
-    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
-    --arch-key mobilenet_v1 --pretrained pruned-moderate \
-    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Template command for running training with this script on multiple GPUs using
-DistributedDataParallel using mixed precision. Note - DDP support in this script
-only tested for torch==1.7.0.
-python -m torch.distributed.launch \
---nproc_per_node <NUM GPUs> \
-integrations/pytorch/train.py \
---use-mixed-precision \
-<TRAIN.PY ARGUMENTS>
-"""
-import argparse
-import json
-import os
-from dataclasses import dataclass, field
-from typing import Any, List, Optional, Tuple
-
-import torch
-from torch.nn import Module
-from torch.utils.data import DataLoader
-
-import utils
-from argparser_.nm_argparser_ import NmArgumentParser
-from sparseml import get_main_logger
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.utils import (
-    DEFAULT_LOSS_KEY,
-    ModuleDeviceContext,
-    ModuleTester,
-    ModuleTrainer,
-    default_device,
-    get_prunable_layers,
-    model_to_device,
-    set_deterministic_seeds,
-    tensor_sparsity,
-)
-
-
-CURRENT_TASK = utils.Tasks.TRAIN
-LOGGER = get_main_logger()
-
-
-@dataclass
-class TrainingArguments:
-    """
-    Represents the arguments we use in our PyTorch integration scripts for
-    training tasks
-
-    Using :class:`NmArgumentParser` we can turn this class into `argparse
-    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
-    arguments that can be specified on the command line.
-
-    :param train_batch_size: An int representing the training batch size.
-    :param test_batch_size: An int representing the test batch size.
-    :param arch_key: A str key representing the type of model to use,
-        ex:resnet50.
-    :param dataset: The dataset to use for training, ex imagenet, imagenette,
-        etc; Set to `imagefolder` for a custom dataset.
-    :param dataset_path: Root path to dataset location.
-    :param local_rank: DDP argument set by PyTorch in DDP mode, default -1
-    :param checkpoint_path: A path to a previous checkpoint to load the state
-        from and resume the state for; Also works with SparseZoo recipes;
-        Set to zoo to automatically download and load weights associated with a
-        recipe.
-    :param init_lr: float representing the initial learning for training,
-        default=1e-9 .
-    :param optim_args: Additional arguments to be passed in to the optimizer as
-        a json object
-    :param recipe_path: The path to the yaml file containing the modifiers and
-        schedule to apply them with; Can also provide a SparseZoo stub prefixed
-        with 'zoo:'.
-    :param sparse_transfer_learn: Boolean to enable sparse transfer learning
-        modifiers to enforce
-        the sparsity for already sparse layers. The modifiers are added to
-        the ones to be loaded from the recipe-path.
-    :param eval_mode: bool to start evaluation mode so that the model can be
-        evaluated on the desired dataset.
-    :param optim: str respresnting the optimizer type to use, one of
-        [SGD, Adam, RMSprop].
-    :param logs_dir: The path to the directory for saving logs.
-    :param save_best_after: int epoch number to start saving the best
-        validation result after until the end of training.
-    :param save_epochs: int epochs to save checkpoints at.
-    :param use_mixed_precision: bool to train model using mixed precision.
-        Supported environments are single GPU and multiple GPUs using
-        DistributedDataParallel with one GPU per process.
-    :param debug_steps: int represnting amount of steps to run for training and
-        testing for debug mode default=-1.
-    :param pretrained: The type of pretrained weights to use default is true
-        to load the default pretrained weights for the model Otherwise should
-        be set to the desired weights type: [base, optim, optim-perf];
-        To not load any weights set to one of [none, false].
-    :param pretrained_dataset: str representing the dataset to load pretrained
-        weights for if pretrained is set; Default is None which will load the
-        default dataset for the architecture; Ex can be set to imagenet,
-        cifar10, etc".
-    :param model_kwargs: json object containing keyword arguments to be
-        passed to model constructor.
-    :param dataset_kwargs: json object to load keyword arguments to be passed
-        to dataset constructor.
-    :param model_tag: A str tag to use for the model for saving results
-        under save-dir, defaults to the model arch and dataset used.
-    :param save_dir: The path to the directory for saving results,
-        default="pytorch_vision".
-    :param device: str represnting the device to run on (can also include ids
-        for data parallel), ex:{cpu, cuda, cuda:0,1}.
-    :param loader_num_workers: int number of workers to use for data loading,
-        default=4.
-    :param loader_pin_memory: bool to use pinned memory for data loading,
-        default=True.
-    """
-
-    train_batch_size: int = field(
-        metadata={"help": "The batch size to use while training"}
-    )
-
-    test_batch_size: int = field(
-        metadata={"help": "The batch size to use while testing"}
-    )
-    arch_key: str = field(
-        metadata={
-            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception"
-            "with the list)",
-        }
-    )
-
-    dataset: str = field(
-        metadata={
-            "help": "The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or"
-            " loadable by a dataset in sparseml.pytorch.datasets"
-        }
-    )
-
-    dataset_path: str = field(
-        metadata={
-            "help": "The root path to where the dataset is stored",
-        }
-    )
-    local_rank: int = field(
-        default=-1,
-        metadata={
-            "keep_underscores": True,
-            "help": argparse.SUPPRESS,
-        },
-    )
-
-    checkpoint_path: str = field(
-        default=None,
-        metadata={
-            "help": "A path to a previous checkpoint to load the state from "
-            "and resume the state for. If provided, pretrained will "
-            "be ignored . If using a SparseZoo recipe, can also "
-            "provide 'zoo' to load the base weights associated with "
-            "that recipe"
-        },
-    )
-
-    init_lr: float = field(
-        default=1e-9,
-        metadata={
-            "help": "The initial learning rate to use while training, "
-            "the actual initial value used should be set by the"
-            " sparseml recipe"
-        },
-    )
-
-    optim_args: json.loads = field(
-        default_factory=lambda: {
-            "momentum": 0.9,
-            "nesterov": True,
-            "weight_decay": 0.0001,
-        },
-        metadata={
-            "help": "Additional args to be passed to the optimizer passed in"
-            " as a json object",
-        },
-    )
-
-    recipe_path: str = field(
-        default=None,
-        metadata={
-            "help": "The path to the yaml file containing the modifiers and "
-            "schedule to apply them with. Can also provide a "
-            "SparseZoo stub prefixed with 'zoo:' with an optional "
-            "'?recipe_type=' argument"
-        },
-    )
-
-    sparse_transfer_learn: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Enable sparse transfer learning modifiers to enforce the "
-            "sparsity for already sparse layers. The modifiers are "
-            "added to the ones to be loaded from the recipe-path"
-        },
-    )
-
-    eval_mode: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Puts into evaluation mode so that the model can be "
-            "evaluated on the desired dataset"
-        },
-    )
-
-    optim: str = field(
-        default="SGD",
-        metadata={"help": "The optimizer type to use, one of [SGD, Adam, RMSprop]"},
-    )
-
-    logs_dir: str = field(
-        default=os.path.join("pytorch_vision_train", "tensorboard-logs"),
-        metadata={
-            "help": "The path to the directory for saving logs",
-        },
-    )
-
-    save_best_after: int = field(
-        default=-1,
-        metadata={
-            "help": "start saving the best validation result after the given "
-            "epoch completes until the end of training"
-        },
-    )
-    save_epochs: List[int] = field(
-        default_factory=lambda: [], metadata={"help": "epochs to save checkpoints at"}
-    )
-
-    use_mixed_precision: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "Trains model using mixed precision. Supported "
-            "environments are single GPU and multiple GPUs using "
-            "DistributedDataParallel with one GPU per process"
-        },
-    )
-
-    debug_steps: int = field(
-        default=-1,
-        metadata={
-            "help": "Amount of steps to run for training and testing for a "
-            "debug mode"
-        },
-    )
-
-    pretrained: str = field(
-        default=True,
-        metadata={
-            "help": "The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for "
-            "the model. Otherwise should be set to the desired weights "
-            "type: [base, optim, optim-perf]. To not load any weights set"
-            "to one of [none, false]"
-        },
-    )
-
-    pretrained_dataset: str = field(
-        default=None,
-        metadata={
-            "help": "The dataset to load pretrained weights for if pretrained is "
-            "set. Default is None which will load the default dataset for "
-            "the architecture. Ex can be set to imagenet, cifar10, etc",
-        },
-    )
-
-    model_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to model constructor, should "
-            "be given as a json object"
-        },
-    )
-
-    dataset_kwargs: json.loads = field(
-        default_factory=lambda: {},
-        metadata={
-            "help": "Keyword arguments to be passed to dataset constructor, "
-            "should be given as a json object",
-        },
-    )
-
-    model_tag: str = field(
-        default=None,
-        metadata={
-            "help": "A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        },
-    )
-
-    save_dir: str = field(
-        default="pytorch_vision",
-        metadata={
-            "help": "The path to the directory for saving results",
-        },
-    )
-
-    device: str = field(
-        default=default_device(),
-        metadata={
-            "help": "The device to run on (can also include ids for data "
-            "parallel), ex: cpu, cuda, cuda:0,1"
-        },
-    )
-
-    loader_num_workers: int = field(
-        default=4, metadata={"help": "The number of workers to use for data loading"}
-    )
-
-    loader_pin_memory: bool = field(
-        default=True, metadata={"help": "Use pinned memory for data loading"}
-    )
-
-    def __post_init__(self):
-        # add ddp args
-        env_world_size = int(os.environ.get("WORLD_SIZE", 1))
-        self.world_size = env_world_size
-
-        env_rank = int(os.environ.get("RANK", -1))
-        self.rank = env_rank
-
-        self.is_main_process = self.rank in [
-            -1,
-            0,
-        ]  # non DDP execution or 0th DDP process
-
-        # modify training batch size for give world size
-        assert self.train_batch_size % self.world_size == 0, (
-            f"Invalid training batch size for world size {self.world_size} "
-            f"given batch size {self.train_batch_size}. "
-            f"world size must divide training batch size evenly."
-        )
-
-        self.train_batch_size = self.train_batch_size // self.world_size
-
-        if "preprocessing_type" not in self.dataset_kwargs and (
-            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
-        ):
-            if "ssd" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "ssd"
-            elif "yolo" in self.arch_key.lower():
-                self.dataset_kwargs["preprocessing_type"] = "yolo"
-
-        if self.local_rank != -1:
-            torch.distributed.init_process_group(backend="nccl", init_method="env://")
-            set_deterministic_seeds(0)
-
-        self.approximate = False
-
-
-def train(
-    train_args: TrainingArguments,
-    model: Module,
-    train_loader: DataLoader,
-    val_loader: DataLoader,
-    input_shape: Tuple[int, ...],
-    save_dir: str,
-    loggers: List[Any],
-) -> None:
-    """
-    Utility function to drive the training processing
-
-    :param train_args: A TrainingArguments object with
-        arguments for current training task
-    :param model: model architecture to train
-    :param train_loader: A DataLoader for training data
-    :param val_loader: A DataLoader for validation data
-    :param input_shape: A tuple of integers representing the shape of inputs
-    :param save_dir: Directory to store checkpoints at during training process
-    :param loggers: List of loggers to use during training process
-    """
-    # loss setup
-    val_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
-    LOGGER.info(f"created loss for validation: {val_loss}")
-
-    train_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
-    LOGGER.info(f"created loss for training: {train_loss}")
-
-    # training setup
-    if not train_args.eval_mode:
-        epoch, optim, manager = utils.create_scheduled_optimizer(
-            train_args,
-            model,
-            train_loader,
-            loggers,
-        )
-    else:
-        epoch = 0
-        train_loss = None
-        optim = None
-        manager = None
-
-    # device setup
-    if train_args.rank == -1:
-        device = train_args.device
-        ddp = False
-    else:
-        torch.cuda.set_device(train_args.local_rank)
-        device = train_args.local_rank
-        ddp = True
-
-    model, device, device_ids = model_to_device(model, device, ddp=ddp)
-    LOGGER.info(f"running on device {device} for ids {device_ids}")
-
-    trainer = (
-        ModuleTrainer(
-            model,
-            device,
-            train_loss,
-            optim,
-            loggers=loggers,
-            device_context=ModuleDeviceContext(
-                use_mixed_precision=train_args.use_mixed_precision,
-                world_size=train_args.world_size,
-            ),
-        )
-        if not train_args.eval_mode
-        else None
-    )
-
-    if train_args.is_main_process:  # only test on one DDP process if using DDP
-        tester = ModuleTester(model, device, val_loss, loggers=loggers, log_steps=-1)
-
-        # initial baseline eval run
-        tester.run_epoch(
-            val_loader, epoch=epoch - 1, max_epochs=-1, max_steps=train_args.debug_steps
-        )
-
-    if not train_args.eval_mode:
-        utils.save_recipe(recipe_manager=manager, save_dir=save_dir)
-        LOGGER.info(f"starting training from epoch {epoch}")
-
-        if epoch > 0:
-            LOGGER.info("adjusting ScheduledOptimizer to restore point")
-            optim.adjust_current_step(epoch, 0)
-
-        target_metric = (
-            "top1acc" if "top1acc" in tester.loss.available_losses else DEFAULT_LOSS_KEY
-        )
-        best_metric = None
-        val_res = None
-
-        while epoch < manager.max_epochs:
-            if train_args.debug_steps > 0:
-                # correct since all optimizer steps are not
-                # taken in the epochs for debug mode
-                optim.adjust_current_step(epoch, 0)
-
-            if train_args.rank != -1:  # sync DDP dataloaders
-                train_loader.sampler.set_epoch(epoch)
-
-            trainer.run_epoch(
-                train_loader,
-                epoch,
-                max_epochs=manager.max_epochs,
-                max_steps=train_args.debug_steps,
-                show_progress=train_args.is_main_process,
-            )
-
-            # testing steps
-            if train_args.is_main_process:
-                # only test and save on main process
-                val_res = tester.run_epoch(
-                    val_loader,
-                    epoch,
-                    max_epochs=manager.max_epochs,
-                    max_steps=train_args.debug_steps,
-                )
-                val_metric = val_res.result_mean(target_metric).item()
-
-                if epoch >= train_args.save_best_after and (
-                    best_metric is None
-                    or (
-                        val_metric <= best_metric
-                        if target_metric != "top1acc"
-                        else val_metric >= best_metric
-                    )
-                ):
-                    utils.save_model_training(
-                        model,
-                        optim,
-                        input_shape,
-                        "checkpoint-best",
-                        save_dir,
-                        epoch,
-                        val_res,
-                    )
-                    best_metric = val_metric
-
-            # save checkpoints
-            _save_epoch = (
-                train_args.is_main_process
-                and train_args.save_epochs
-                and epoch in train_args.save_epochs
-            )
-            if _save_epoch:
-                utils.save_model_training(
-                    model,
-                    optim,
-                    input_shape,
-                    f"checkpoint-{epoch:04d}-{val_metric:.04f}",
-                    save_dir,
-                    epoch,
-                    val_res,
-                )
-
-            epoch += 1
-
-        # export the final model
-        LOGGER.info("completed...")
-        if train_args.is_main_process:
-            # only convert qat -> quantized ONNX graph for finalized model
-            # TODO: change this to all checkpoints when conversion times improve
-            utils.save_model_training(
-                model, optim, input_shape, "model", save_dir, epoch - 1, val_res, True
-            )
-
-            LOGGER.info("layer sparsities:")
-            for (name, layer) in get_prunable_layers(model):
-                LOGGER.info(
-                    f"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}"
-                )
-
-    # close DDP
-    if train_args.rank != -1:
-        torch.distributed.destroy_process_group()
-
-
-def main():
-    """
-    Driver function for the script
-    """
-    _parser = NmArgumentParser(dataclass_types=TrainingArguments)
-    training_args, _ = _parser.parse_args_into_dataclasses()
-
-    save_dir, loggers = utils.get_save_dir_and_loggers(training_args, task=CURRENT_TASK)
-
-    input_shape = ModelRegistry.input_shape(training_args.arch_key)
-    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
-
-    (
-        train_dataset,
-        train_loader,
-        val_dataset,
-        val_loader,
-    ) = utils.get_train_and_validation_loaders(
-        training_args, image_size, task=CURRENT_TASK
-    )
-
-    num_classes = utils.infer_num_classes(training_args, train_dataset, val_dataset)
-
-    # # model creation
-    model = utils.create_model(training_args, num_classes)
-    train(
-        training_args, model, train_loader, val_loader, input_shape, save_dir, loggers
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/integrations/examples/pytorch/tutorials/.gitkeep b/integrations/examples/pytorch/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md b/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
deleted file mode 100644
index a7962a06f46..00000000000
--- a/integrations/examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
+++ /dev/null
@@ -1,138 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparse Transfer Learning for Image Classification
-
-This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
-
-## Overview
-
-Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo. 
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
-
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process. In this tutorial you will:
-- Download and prepare a pre-sparsified image classification model.
-- Apply a sparse transfer learning recipe on the pre-sparsified model.
-
-The examples listed in this tutorial are all performed on the [Imagenette](https://github.com/fastai/imagenette) dataset.
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues) 
-
-## Setting Up
-
-This tutorial can be run by cloning and installing the `sparseml` repository which contains scripts and recipes for
-this example:
-
-```bash
-pip install "sparseml[torchvision, dev]"
-```
-Note:  make sure to upgrade `pip` using `python -m pip install -U pip`
-## Downloading and Preparing a Pre-Sparsified Model
-
-First, you need to download the sparsified models from the [SparseZoo](https://sparsezoo.neuralmagic.com/). A few image classification models with [SparseZoo](https://sparsezoo.neuralmagic.com/) stubs:
-
-| Model Name     |      Stub      | Description |
-|----------|-------------|-------------|
-| resnet-pruned-moderate | zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate |This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the accuracy of the original baseline model (76.1% top1). Pruned layers achieve 88% sparsity.|
-|resnet-pruned-conservative|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves full recovery original baseline model accuracy (76.1% top1). Pruned layers achieve 80% sparsity.|
-|resnet-pruned-moderate-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate|This model is a sparse, INT8 quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the original baseline accuracy. The average model sparsity is about 72% with pruned layers achieving between 70-80% sparsity. This pruned quantized model achieves 75.46% top1 accuracy on the ImageNet dataset.|
-|mobilenetv1-pruned-moderate|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves 99% of the accuracy of the original baseline model (70.9% top1). Pruned layers achieve between 70-90% sparsity.|
-|mobilenetv1-pruned-conservative|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves the same accuracy as the original baseline model. Pruned layers achieve between 60-86% sparsity. This pruned quantized model achieves 70.9% top1 accuracy on the ImageNet dataset.|
-|mobilenetv1-pruned-moderate-quantized|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate|Pruned [MobileNetV1](https://arxiv.org/abs/1704.04861) architecture with width=1.0 trained on the ImageNet dataset. Recalibrated performance model: within 99% of baseline validation top1 accuracy (70.9%)|
-
-
-A few recipe stubs:
-
-|Recipe Name|Stub|Description|
-|----------|-------------|-------------|
-|resnet50.transfer-learning-pruned-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn|This recipe provides a framework for performing pruned-quantized transfer learning on SparseML's pruned_quantized ResNet50 model. This recipe was written for transfer learning onto the Imagenette dataset using a single GPU, Adam optimizer, and batch size 32. Adjust the hyperparameters at the top of the file accordingly for your dataset and training environment|
-
-
-Note: The models above were originally trained and sparsified on the [ImageNet](https://image-net.org/) dataset.
-
-- After deciding on which model meets your performance requirements for both speed and accuracy, select the SparseZoo stub associated with that model.
-   The stub will be used later with the training script to automatically pull down the desired pre-trained weights.
-
-## Applying a Sparse Transfer Learning Recipe
-
-- After noting respective SparseZoo model stub, [train.py](../train.py) script can be used to download checkpoint and [Imagenette](https://github.com/fastai/imagenette) and kick-start transfer learning. 
-   The transfer learning process itself is guided using recipes; We include example [recipes](../recipes) for classification along with others in the SparseML [GitHub repository](https://github.com/neuralmagic/sparseml). 
-[Learn more about recipes and modifiers](../../../docs/source/recipes.md).
-
-- Run the following example command to kick off transfer learning for [ResNet-50](https://arxiv.org/abs/1512.03385) starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/):
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate" \
-    --arch-key resnet50 \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --model-tag resnet50-imagenette-sparse-transfer-learned
-```
-
-To transfer learn [MobileNet](https://arxiv.org/abs/1704.04861) on [Imagenette](https://github.com/fastai/imagenette), starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/), run the following example command:
-
-```
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
-    --checkpoint-path "zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate" \
-    --arch-key mobilenet \
-    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE  \
-    --train-batch-size 32 --test-batch-size 64 \
-    --loader-num-workers 0 \
-    --optim Adam \
-    --optim-args '{}' \
-    --save-dir sparse-transfer-models
-    --model-tag mobilenet-imagenette-sparse-transfer-learned
-```
-The `--checkpoint-path` argument can take in path to a model checkpoint, or a SpareZoo model stub,
-if a SparseZoo recipe stub is provided in `--recipe-path`, 
-the `checkpoint-path` can be set to `zoo` for auto-downloading corresponding checkpoints from SparseZoo. 
-To learn more about the usage, run script with `-h` option to see help. 
-
-The script automatically saves model checkpoints after each epoch and reports the validation loss along with layer sparsities. 
-
-### ONNX Export
-The model can be saved to [ONNX](https://onnx.ai/) format to be loaded later for inference or other experiments using
-the `sparseml.image_classification.export_onnx` script.
-
-```
-sparseml.image_classification.export_onnx \
-    --arch-key mobilenet \
-    --dataset imagenette \
-    --dataset-path /PATH/TO/IMAGENETTE \
-    --checkpoint-path sparse-transfer-models/mobilenet-imagenette-sparse-transfer-learned/pytorch/model.pth
-```
-
-## Wrap-Up
-
-Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned image classification models. 
-In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and saved the model for future use.
-
-An example for benchmarking and deploying image classification models with DeepSparse [is also available](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md b/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
deleted file mode 100644
index b67881dc79f..00000000000
--- a/integrations/examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
+++ /dev/null
@@ -1,132 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying PyTorch Models Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized PyTorch models.
-
-## Overview
-
-Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of their existing training pipelines.
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others.
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes.
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by pruning a PyTorch [ResNet-50](https://arxiv.org/abs/1512.03385) model.
-
-1. Setting Up
-2. Inspecting a Recipe
-3. Applying a Recipe
-3. Exporting for Inference
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Setting Up
-
-This tutorial can be run by installing `sparseml` which contains scripts for this pruning example:
-
-```bash
-pip install "sparseml[torchvision, dev]"
-```
-
-## Inspecting a Recipe
-
-Recipes are YAML or Markdown files that SparseML uses to easily define and control the sparsification of a model.
-Recipes consist of a series of `Modifiers` that can influence the training process in different ways. A list of
-common modifiers and their uses is provided
-[here](../../../docs/source/recipes.md#modifiers-intro).
-
-SparseML provides a recipe for sparsifying a ResNet-50 model trained on the tiny Imagenette dataset. The recipe can
-be viewed in the browser
-[here](../recipes/resnet50-imagenette-pruned.md)
-or in the command line from the file `sparseml/integrations/pytorch/recipes/resnet50-imagenette-pruned.md`.
-
-The recipe contains three kinds of modifiers:
-
-- an `EpochRangeModifier` that sets the number of epochs used for sparsification
-- a `SetLearningRateModifier` that sets the sparsification learning rate
-- three `GMPruningModifier`s that set various layers of the model to be pruned to different levels.
-
-Notice that each `GMPruningModifier` sets specific parameters of the ResNet-50 model to be pruned. To
-prune a different model, the parameter names should be adjusted to match the new model.  To avoid naming
-individual parameter names, all parameters can be set for pruning by passing `__ALL_PRUNABLE__` instead
-of the parameters list for a single `GMPruningModifier`.
-
-### Applying a Recipe
-
-Recipes can integrated into training flows with a couple of lines of code by using a `ScheduledModifierManager`
-that wraps the PyTorch `Optimizer` step.  An example of how this is done can be found
-[here](../../../docs/source/code.md#pytorch-sparsification).
-
-For this example, we can use the `sparseml.image_classification.train` utility.  This utility runs a
-PyTorch training flow that is modified by a `ScheduledModifierManager` and takes a recipe as an input.
-It natively supports ResNet-50 and other popular image classification models.
-
-To prune a ResNet-50 model trained on the Imagenette dataset with the recipe above, run the following command.
-If the Imagenette dataset has not been downloaded yet, the script will automatically download it to the given
-`--dataset-path`. If you have a local copy of the dataset, `--dataset-path` may be updated.
-
-*Note:* If an out of memory exception occurs, the train batch size should be lowered to fit on your device
-
-```bash
-sparseml.image_classification.train \
-    --recipe-path integrations/pytorch/recipes/resnet50-imagenette-pruned.md \
-    --dataset-path ./data \
-    --pretrained True \
-    --arch-key resnet50 \
-    --dataset imagenette \
-    --train-batch-size 128 \
-    --test-batch-size 256 \
-    --loader-num-workers 8 \
-    --save-dir sparsification_example \
-    --logs-dir sparsification_example \
-    --model-tag resnet50-imagenette-pruned \
-    --save-best-after 8
-```
-
-A full list of arguments and their descriptions can be found by running 
-
-```bash
-sparseml.image_classification.train --help
-```
-
-
-## Exporting for Inference
-
-After, the training pruned model and training log files will be found under 
-`sparsification_example/resnet50-imagenette-pruned`. To run the sparsified model for accelerated inference with
-an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse), the pruned model must be
-exported to the ONNX format.
-
-This step can be completed using `sparseml.image_classification.export_onnx` script which uses the
-`sparseml.pytorch.utils.ModuleExporter` class. Run the script with `--help` option to see usage.
-
-The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in
-inference performance. An example for benchmarking and deploying image classification models with DeepSparse can
-be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate sparsified models.
-In this tutorial, you installed SparseML, downloaded and inspected a pruning recipe, applied it to a ResNet-50 model,
-and exported the sparse model for inference.
-
-Next steps will include creating new recipes and applying them to different models. Check out other
-[SparseML tutorials](https://github.com/neuralmagic/sparseml/tree/main/integrations) to learn more.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/pytorch/utils.py b/integrations/examples/pytorch/utils.py
deleted file mode 100644
index b5dd57c3c41..00000000000
--- a/integrations/examples/pytorch/utils.py
+++ /dev/null
@@ -1,428 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Helper methods for image classification/detection based tasks
-"""
-import os
-from enum import Enum, auto, unique
-from typing import Any, List, Optional, Tuple, Union
-
-import torch
-from torch.nn import Module
-from torch.nn import functional as torch_functional
-from torch.optim import Optimizer
-from torch.utils.data import DataLoader
-
-from sparseml.pytorch.datasets import DatasetRegistry, ssd_collate_fn, yolo_collate_fn
-from sparseml.pytorch.image_classification.utils.helpers import (
-    download_framework_model_by_recipe_type,
-)
-from sparseml.pytorch.models import ModelRegistry
-from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
-from sparseml.pytorch.sparsification import ConstantPruningModifier
-from sparseml.pytorch.utils import (
-    DEFAULT_LOSS_KEY,
-    CrossEntropyLossWrapper,
-    InceptionCrossEntropyLossWrapper,
-    LossWrapper,
-    ModuleExporter,
-    ModuleRunResults,
-    PythonLogger,
-    SSDLossWrapper,
-    TensorBoardLogger,
-    TopKAccuracy,
-    YoloLossWrapper,
-    early_stop_data_loader,
-    torch_distributed_zero_first,
-)
-from sparseml.utils import create_dirs
-from sparsezoo import Model
-
-
-@unique
-class Tasks(Enum):
-    """
-    A class representing supported image classification/detection tasks
-    """
-
-    TRAIN = auto()
-    EXPORT = auto()
-    ANALYSIS = auto()
-    LR_ANALYSIS = auto()
-    PR_SENSITIVITY = auto()
-
-
-# loggers
-def get_save_dir_and_loggers(
-    args: Any, task: Optional[Tasks] = None
-) -> Tuple[Union[str, None], List]:
-    if args.is_main_process:
-        save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-        logs_dir = (
-            os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
-            if task == Tasks.TRAIN
-            else None
-        )
-
-        if not args.model_tag:
-            dataset_name = (
-                f"{args.dataset}-{args.dataset_kwargs['year']}"
-                if "year" in args.dataset_kwargs
-                else args.dataset
-            )
-            model_tag = f"{args.arch_key.replace('/', '.')}_{dataset_name}"
-            model_id = model_tag
-            model_inc = 0
-            # set location to check for models with same name
-            model_main_dir = logs_dir or save_dir
-
-            while os.path.exists(os.path.join(model_main_dir, model_id)):
-                model_inc += 1
-                model_id = f"{model_tag}__{model_inc:02d}"
-        else:
-            model_id = args.model_tag
-
-        save_dir = os.path.join(save_dir, model_id)
-        create_dirs(save_dir)
-
-        # loggers setup
-        loggers = [PythonLogger()]
-        if task == Tasks.TRAIN:
-            logs_dir = os.path.join(logs_dir, model_id)
-            create_dirs(logs_dir)
-            loggers.append(TensorBoardLogger(log_path=logs_dir))
-        print(f"Model id is set to {model_id}")
-    else:
-        # do not log for non main processes
-        save_dir = None
-        loggers = []
-    return save_dir, loggers
-
-
-# data helpers_
-def _get_collate_fn(arch_key: str, task: Optional[Tasks] = None):
-    is_ssd = "ssd" in arch_key.lower()
-    need_collate_function = (
-        is_ssd or "yolo" in arch_key.lower()
-    ) and task != Tasks.EXPORT
-    if need_collate_function:
-        return ssd_collate_fn if is_ssd else yolo_collate_fn
-    return None
-
-
-def _create_train_dataset_and_loader(
-    args: Any,
-    image_size: Tuple[int, ...],
-    task: Optional[Tasks] = None,
-) -> Tuple[Any, Any]:
-    need_train_data = not (
-        task == Tasks.EXPORT
-        or task == Tasks.PR_SENSITIVITY
-        and args.approximate
-        or task == Tasks.TRAIN
-        and args.eval_mode
-    )
-
-    if need_train_data:
-        with torch_distributed_zero_first(
-            args.local_rank,
-        ):  # only download once locally
-            train_dataset = DatasetRegistry.create(
-                args.dataset,
-                root=args.dataset_path,
-                train=True,
-                rand_trans=True,
-                image_size=image_size,
-                **args.dataset_kwargs,
-            )
-        sampler = (
-            torch.utils.data.distributed.DistributedSampler(train_dataset)
-            if args.rank != -1
-            else None
-        )
-        shuffle = True if sampler is None else False
-        batch_size = args.train_batch_size if task == Tasks.TRAIN else args.batch_size
-
-        train_loader = DataLoader(
-            train_dataset,
-            batch_size=batch_size,
-            shuffle=shuffle,
-            num_workers=args.loader_num_workers,
-            pin_memory=args.loader_pin_memory,
-            sampler=sampler,
-            collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
-        )
-        print(f"created train_dataset: {train_dataset}")
-        return train_dataset, train_loader
-
-    return None, None
-
-
-def _create_val_dataset_and_loader(
-    args, image_size: Tuple[int, ...], task: Optional[Tasks] = None
-) -> Tuple[Any, Any]:
-    need_val_data = not (
-        task == Tasks.PR_SENSITIVITY
-        or task == Tasks.LR_ANALYSIS
-        or not (args.is_main_process and args.dataset != "imagefolder")
-    )
-
-    if need_val_data:
-        val_dataset = DatasetRegistry.create(
-            args.dataset,
-            root=args.dataset_path,
-            train=False,
-            rand_trans=False,
-            image_size=image_size,
-            **args.dataset_kwargs,
-        )
-        if args.is_main_process:
-            is_training = task == Tasks.TRAIN
-            val_loader = DataLoader(
-                val_dataset,
-                batch_size=args.test_batch_size if is_training else 1,
-                shuffle=False,
-                num_workers=args.loader_num_workers if is_training else 1,
-                pin_memory=args.loader_pin_memory if is_training else False,
-                collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
-            )
-            if task == Tasks.EXPORT:
-                val_loader = early_stop_data_loader(
-                    val_loader, args.num_samples if args.num_samples > 1 else 1
-                )
-            print(f"created val_dataset: {val_dataset}")
-        else:
-            val_loader = None  # only val dataset needed to get the number of classes
-        return val_dataset, val_loader
-    return None, None  # val dataset not needed
-
-
-def get_train_and_validation_loaders(
-    args: Any, image_size: Tuple[int, ...], task: Optional[Tasks] = None
-):
-    """
-    :param args: Object containing relevant configuration for the task
-    :param image_size: A Tuple of integers representing the shape of input image
-    :param task: The current task being performed
-    :return: 4 element tuple with the following format (train_dataset,
-        train_loader, val_dataset, val_loader)
-    """
-    train_dataset, train_loader = _create_train_dataset_and_loader(
-        args, image_size, task=task
-    )
-    val_dataset, val_loader = _create_val_dataset_and_loader(
-        args, image_size, task=task
-    )
-    return train_dataset, train_loader, val_dataset, val_loader
-
-
-# Model creation Helpers
-
-
-def create_model(args: Any, num_classes: int) -> Module:
-    """
-    :param args: object with configuration for model classes
-    :param num_classes: Integer representing the number of output classes
-    :returns: A Module object representing the created model
-    """
-    with torch_distributed_zero_first(args.local_rank):  # only download once locally
-        if args.checkpoint_path == "zoo":
-            if args.recipe_path and args.recipe_path.startswith("zoo:"):
-                zoo_model = Model(args.recipe_path)
-                args.checkpoint_path = download_framework_model_by_recipe_type(
-                    zoo_model
-                )
-            else:
-                raise ValueError(
-                    "'zoo' provided as --checkpoint-path but a SparseZoo stub"
-                    " prefixed by 'zoo:' not provided as --recipe-path"
-                )
-
-        model = ModelRegistry.create(
-            args.arch_key,
-            args.pretrained,
-            args.checkpoint_path,
-            args.pretrained_dataset,
-            num_classes=num_classes,
-            **args.model_kwargs,
-        )
-    print(f"created model: {model}")
-    return model
-
-
-def infer_num_classes(args: Any, train_dataset, val_dataset):
-    """
-    :param args: Object with configuration settings
-    :param train_dataset: dataset representing training data
-    :param val_dataset: dataset representing validation data
-    :return: An integer representing the number of classes
-    """
-    if "num_classes" in args.model_kwargs:
-        # handle manually overriden num classes
-        num_classes = args.model_kwargs["num_classes"]
-        del args.model_kwargs["num_classes"]
-    elif args.dataset == "imagefolder":
-        dataset = val_dataset or train_dataset  # get non None dataset
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-    return num_classes
-
-
-def get_loss_wrapper(
-    arch_key: str, training: bool = False, task: Optional[Tasks] = None
-):
-    """
-    :param arch_key: The model architecture
-    :param training: True if training task started else False
-    :param task: current task being executed
-    """
-    if "ssd" in arch_key.lower():
-        return SSDLossWrapper()
-    if "yolo" in arch_key.lower():
-        return YoloLossWrapper()
-
-    extras = {"top1acc": TopKAccuracy(1), "top5acc": TopKAccuracy(5)}
-    if task == Tasks.TRAIN:
-        return (
-            CrossEntropyLossWrapper(extras)
-            if training and "inception" not in arch_key
-            else InceptionCrossEntropyLossWrapper(extras)
-        )
-    return LossWrapper(loss_fn=torch_functional.cross_entropy, extras=extras)
-
-
-#  optimizer helper
-def create_scheduled_optimizer(
-    train_args: Any,
-    model: Module,
-    train_loader: DataLoader,
-    loggers: List[Any],
-) -> Tuple[int, ScheduledOptimizer, ScheduledModifierManager]:
-    """
-    :param train_args : An object with task specific config
-    :param model: model architecture to train
-    :param train_loader: A DataLoader for training data
-    :param loggers: List of loggers to use during training process
-    :type train_args: TrainingArguments
-    """
-    # # optimizer setup
-    optim_const = torch.optim.__dict__[train_args.optim]
-    optim = optim_const(
-        model.parameters(), lr=train_args.init_lr, **train_args.optim_args
-    )
-    print(f"created optimizer: {optim}")
-    print(
-        "note, the lr for the optimizer may not reflect the manager yet until "
-        "the recipe config is created and run"
-    )
-
-    # restore from previous check point
-    if train_args.checkpoint_path:
-        # currently optimizer restoring is unsupported
-        # mapping of the restored params to the correct device is not working
-        # load_optimizer(args.checkpoint_path, optim)
-        epoch = 0  # load_epoch(args.checkpoint_path) + 1
-        print(
-            f"restored checkpoint from {train_args.checkpoint_path} for "
-            f"epoch {epoch - 1}"
-        )
-    else:
-        epoch = 0
-
-    # modifier setup
-    add_mods = (
-        ConstantPruningModifier.from_sparse_model(model)
-        if train_args.sparse_transfer_learn
-        else None
-    )
-    manager = ScheduledModifierManager.from_yaml(
-        file_path=train_args.recipe_path, add_modifiers=add_mods
-    )
-    optim = ScheduledOptimizer(
-        optim,
-        model,
-        manager,
-        steps_per_epoch=len(train_loader),
-        loggers=loggers,
-    )
-    print(f"created manager: {manager}")
-    return epoch, optim, manager
-
-
-# saving helpers
-
-
-def save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-    """
-    :param recipe_manager: The ScheduleModified manager to save recipes
-    :param save_dir: The directory to save the recipe
-    """
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    print(f"Saved recipe to {recipe_save_path}")
-
-
-def save_model_training(
-    model: Module,
-    optim: Optimizer,
-    input_shape: Tuple[int, ...],
-    save_name: str,
-    save_dir: str,
-    epoch: int,
-    val_res: Union[ModuleRunResults, None],
-    convert_qat: bool = False,
-):
-    """
-    :param model: model architecture
-    :param optim: The optimizer used
-    :param input_shape: A tuple of integers representing the input shape
-    :param save_name: name to save model to
-    :param save_dir: directory to save results in
-    :param epoch: integer representing umber of epochs to
-    :param val_res: results from validation run
-    :param convert_qat: True if model is to be quantized before saving
-    """
-    has_top1 = "top1acc" in val_res.results
-    metric_name = "top-1 accuracy" if has_top1 else "val_loss"
-    metric = val_res.result_mean("top1acc" if has_top1 else DEFAULT_LOSS_KEY).item()
-    print(
-        f"Saving model for epoch {epoch} and {metric_name} "
-        f"{metric} to {save_dir} for {save_name}"
-    )
-    exporter = ModuleExporter(model, save_dir)
-    exporter.export_pytorch(optim, epoch, f"{save_name}.pth")
-    exporter.export_onnx(
-        torch.randn(1, *input_shape),
-        f"{save_name}.onnx",
-        convert_qat=convert_qat,
-    )
-
-    info_path = os.path.join(save_dir, f"{save_name}.txt")
-
-    with open(info_path, "w") as info_file:
-        info_lines = [
-            f"epoch: {epoch}",
-        ]
-
-        if val_res is not None:
-            for loss in val_res.results.keys():
-                info_lines.append(f"{loss}: {val_res.result_mean(loss).item()}")
-
-        info_file.write("\n".join(info_lines))
diff --git a/integrations/examples/pytorch/vision.py b/integrations/examples/pytorch/vision.py
deleted file mode 100644
index 6ffeb78869c..00000000000
--- a/integrations/examples/pytorch/vision.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Deprecated script kindly use the following task specific scripts for
- optimization tasks on image classification and object detection models:
-
-* Model pruning - sparseml.image_classification.train
-* Quantization aware training - sparseml.image_classification.train
-* Sparse transfer learning - sparseml.image_classification.train
-* pruning sensitivity analysis - sparseml.image_classification.pr_sensitivity
-* learning rate sensitivity analysis - sparseml.image_classification.lr_analysis
-* ONNX export - sparseml.image_classification.export_onnx
-"""
-
-
-if __name__ == "__main__":
-    print(__doc__)
diff --git a/integrations/examples/rwightman-timm/README.md b/integrations/examples/rwightman-timm/README.md
deleted file mode 100644
index 773c10116d5..00000000000
--- a/integrations/examples/rwightman-timm/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML-rwightman/pytorch-image-models integration
-This directory provides a SparseML integrated training script for the popular
-[rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models)
-repository also known as [timm](https://pypi.org/project/timm/).
-
-Using this integration, you will be able to apply SparseML optimizations
-to the powerful training flows of the pytorch-image-models repository.
-
-Some of the tasks you can perform using this integration include, but are not limited to:
-* Pruning
-* Quantization
-* Pruning and Quantization
-* Sparse Transfer Learning
-
-## Installation
-We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
-A virtual environment can be created using the following commands:
-
-```bash
-python3 -m venv venv # create a venv virtual environment
-source venv/bin/activate # activate venv
-pip install --upgrade pip # upgrade pip
-```
-
-To begin, run the following command in the root directory of this integration (`cd integrations/rwightman-timm`):
-```bash
-bash setup_integration.sh
-```
-
-The `setup_integration.sh` file will clone the [pytorch-image-models](https://github.com/neuralmagic/pytorch-image-models.git) repository with the SparseML integration as a subfolder.
-After the repo has successfully cloned, pytorch-image-models will be installed along with any necessary dependencies.
-
-This integration requires `python>=3.7,<3.10`
-
-## Quick Tour
-### Recipes
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration adds a `--recipe` argument to the 
-[`train.py` script](https://github.com/neuralmagic/pytorch-image-models/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-You can learn how to build or download a recipe using the
-[SparseML](https://github.com/neuralmagic/sparseml)
-or [SparseZoo](https://github.com/neuralmagic/sparsezoo)
-documentation, or export one with [Sparsify](https://github.com/neuralmagic/sparsify).
-
-ViT recipes are not yet available on SparseZoo, but are in progress.
-
-Using a local recipe and checkpoint, pruning a model can be done by running the following command from within the root of this integration's folder
-```bash
-python train.py \
-  /PATH/TO/DATASET/imagenet/ \
-  --recipe ../recipes/vit_base.85.recal.config.yaml \
-  --dataset imagenet \
-  --batch-size 64 \
-  --remode pixel --reprob 0.0 --smoothing 0.1 --mixup 0.5 --mixup 0.5 \
-  --output models/optimized \
-  --model vit_base_patch16_224 \
-  --workers 8 
-```  
-
-Documentation on the original script can be found
-[here](https://rwightman.github.io/pytorch-image-models/scripts/).
-The latest commit hash that `train.py` is based on is included in the docstring.
-
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| [recipes](./recipes)              | Typical recipes for sparsifying ViT models along with any downloaded recipes from the SparseZoo.                      |
-| tutorials            | Tutorial walkthroughs for how to sparsify ViT models using recipes (Coming Soon).                                                   |          |
-| [README.md](./README.md)            | Readme file.                                                                                                        |
-| [setup_integration.sh](./setup_integration.sh) | Setup file for the integration run from the command line.   
-
-### Exporting for Inference
-
-After sparsifying a model, to convert the model into an [ONNX](https://onnx.ai/) deployment format run the `export.py script`(https://github.com/neuralmagic/pytorch-image-models/blob/master/export.py).
-
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-For example, the following command can be run from within the integration's folder to export a trained/sparsified model's checkpoint:
-```bash
-python export.py \
-    --checkpoint ./path/to/checkpoint/model.pth.tar \
-    --recipe ../recipes/vit_base.85.recal.config.yaml \
-    --save-dir ./exported-models \
-    --filename vit_base_patch16_224 \
-    --batch-size 1 \
-    --image-shape 3 224 224 \
-    --config ./path/to/checkpoint/args.yaml
-```
-
-The DeepSparse Engine [accepts ONNX formats](https://docs.neuralmagic.com/sparseml/source/onnx_export.html) and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse).
\ No newline at end of file
diff --git a/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml b/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
deleted file mode 100644
index 148060b57dd..00000000000
--- a/integrations/examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
+++ /dev/null
@@ -1,83 +0,0 @@
-version: 1.1.0
-
-modifiers:
-    - !GMPruningModifier
-        end_epoch: 36.0
-        final_sparsity: 0.03
-        global_sparsity: False
-        init_sparsity: 0.25
-        inter_func: cubic
-        leave_enabled: True
-        mask_type: unstructured
-        params: 
-            - blocks.0.attn.qkv.weight
-            - blocks.0.attn.proj.weight
-            - blocks.0.mlp.fc1.weight
-            - blocks.0.mlp.fc2.weight
-            - blocks.1.attn.qkv.weight
-            - blocks.1.attn.proj.weight
-            - blocks.1.mlp.fc1.weight
-            - blocks.1.mlp.fc2.weight
-            - blocks.2.attn.qkv.weight
-            - blocks.2.attn.proj.weight
-            - blocks.2.mlp.fc1.weight
-            - blocks.2.mlp.fc2.weight
-            - blocks.3.attn.qkv.weight
-            - blocks.3.attn.proj.weight
-            - blocks.3.mlp.fc1.weight
-            - blocks.3.mlp.fc2.weight
-            - blocks.4.attn.qkv.weight
-            - blocks.4.attn.proj.weight
-            - blocks.4.mlp.fc1.weight
-            - blocks.4.mlp.fc2.weight
-            - blocks.5.attn.qkv.weight
-            - blocks.5.attn.proj.weight
-            - blocks.5.mlp.fc1.weight
-            - blocks.5.mlp.fc2.weight
-            - blocks.6.attn.qkv.weight
-            - blocks.6.attn.proj.weight
-            - blocks.6.mlp.fc1.weight
-            - blocks.6.mlp.fc2.weight
-            - blocks.7.attn.qkv.weight
-            - blocks.7.attn.proj.weight
-            - blocks.7.mlp.fc1.weight
-            - blocks.7.mlp.fc2.weight
-            - blocks.8.attn.qkv.weight
-            - blocks.8.attn.proj.weight
-            - blocks.8.mlp.fc1.weight
-            - blocks.8.mlp.fc2.weight
-            - blocks.9.attn.qkv.weight
-            - blocks.9.attn.proj.weight
-            - blocks.9.mlp.fc1.weight
-            - blocks.9.mlp.fc2.weight
-            - blocks.10.attn.qkv.weight
-            - blocks.10.attn.proj.weight
-            - blocks.10.mlp.fc1.weight
-            - blocks.10.mlp.fc2.weight
-            - blocks.11.attn.qkv.weight
-            - blocks.11.attn.proj.weight
-            - blocks.11.mlp.fc1.weight
-            - blocks.11.mlp.fc2.weight
-        phased: False
-        score_type: magnitude
-        start_epoch: 1.0
-        update_frequency: 0.5
-
-    - !EpochRangeModifier
-        end_epoch: 100.0
-        start_epoch: 0.0
-
-    - !SetLearningRateModifier
-        constant_logging: False
-        end_epoch: -1
-        learning_rate: 0.002505
-        start_epoch: 0.0
-
-    - !LearningRateModifier
-        constant_logging: False
-        end_epoch: -1.0
-        init_lr: 0.00062625
-        lr_class: StepLR
-        lr_kwargs: {'step_size': 10, 'gamma': 0.25}
-        start_epoch: 40.0
-        update_frequency: -1.0
diff --git a/integrations/examples/rwightman-timm/setup_integration.sh b/integrations/examples/rwightman-timm/setup_integration.sh
deleted file mode 100644
index ed6c1068339..00000000000
--- a/integrations/examples/rwightman-timm/setup_integration.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/bin/bash
-
-# Integration setup command to setup the folder so it is ready to train and sparsify models.
-# Creates a pytorch-image-models folder next to this script with all required dependencies from the rwightman/pytorch-image-models repository.
-# Command: `bash setup_integration.sh`
-
-git clone https://github.com/neuralmagic/pytorch-image-models.git
-cd pytorch-image-models
-git checkout release/1.1
-pip install -r requirements.txt
-pip install sparseml[torch]
diff --git a/integrations/examples/tensorflow_v1/README.md b/integrations/examples/tensorflow_v1/README.md
deleted file mode 100644
index 0471fa009d2..00000000000
--- a/integrations/examples/tensorflow_v1/README.md
+++ /dev/null
@@ -1,93 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML TensorFlow V1 Integration
-
-This directory combines the SparseML recipe-driven approach with standard TensorFlow V1 training flows for models within
-[TF Slim](https://github.com/google-research/tf-slim) and SparseML.
-They are intended as example flows to show how to integrate sparsification techniques with a custom TensorFlow V1 training flow.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Coming soon!
-
-## Tutorials
-
-- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/tensorflow_v1/notebooks/classification.ipynb)
-
-## Installation
-
-To begin, run `pip install sparseml[tf_v1]` or `pip install sparseml[tf_v1_gpu]` if training on a GPU.
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=tensorflow_v1) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=tensorflow_v1) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| notebooks            | Jupyter notebooks to walk through sparsifiation within standard TensorFlow V1 training flows                          |
-| recipes              | Typical recipes for sparsifying TensorFlow V1 models along with any downloaded recipes from the SparseZoo.            |
-| tutorials            | Tutorial walkthroughs for how to sparsify TensorFlow V1 models using recipes.                                         |
-| classification.py    | Utility training script for sparsifying classification models in TensorFlow V1 (loads SparseML models).               |
-| README.md            | Readme file.                                                                                                          |
-
-### Exporting for Inference
-
-All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/examples/tensorflow_v1/classification.py b/integrations/examples/tensorflow_v1/classification.py
deleted file mode 100644
index ea544a0429e..00000000000
--- a/integrations/examples/tensorflow_v1/classification.py
+++ /dev/null
@@ -1,965 +0,0 @@
-# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-Perform optimization tasks on image classification tensorflow_v1 including:
-* Model training
-* Model pruning
-* Sparse transfer learning
-* pruning sensitivity analysis
-* ONNX export
-
-
-##########
-Command help:
-usage: classification.py [-h] {train,export,pruning_sensitivity} ...
-
-Run tasks on classification models and datasets using the sparseml API
-
-positional arguments:
-  {train,export,pruning_sensitivity}
-
-optional arguments:
-  -h, --help            show this help message and exit
-
-
-##########
-train command help:
-usage: classification.py train [-h] --arch-key ARCH_KEY
-                               [--pretrained PRETRAINED]
-                               [--pretrained-dataset PRETRAINED_DATASET]
-                               [--checkpoint-path CHECKPOINT_PATH]
-                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
-                               --dataset-path DATASET_PATH
-                               [--dataset-kwargs DATASET_KWARGS]
-                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
-                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                               [--recipe-path RECIPE_PATH]
-                               [--sparse-transfer-learn] [--eval-mode]
-                               --train-batch-size TRAIN_BATCH_SIZE
-                               --test-batch-size TEST_BATCH_SIZE
-                               [--logs-dir LOGS_DIR]
-                               [--save-best-after SAVE_BEST_AFTER]
-                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
-                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
-
-Train and/or prune an image classification model
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
---dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for dataset loading
-  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for dataset loading
-  --recipe-path RECIPE_PATH
-                        The path to the yaml file containing the modifiers and
-                        schedule to apply them with. If set to
-                        'transfer_learning', then will create a schedule to
-                        enable sparse transfer learning
-  --sparse-transfer-learn
-                        Enable sparse transfer learning modifiers to enforce
-                        the sparsity for already sparse layers. The modifiers
-                        are added to the ones to be loaded from the recipe-
-                        path
-  --eval-mode           Puts into evaluation mode so that the model can be
-                        evaluated on the desired dataset
-  --train-batch-size TRAIN_BATCH_SIZE
-                        The batch size to use while training
-  --test-batch-size TEST_BATCH_SIZE
-                        The batch size to use while testing
-  --logs-dir LOGS_DIR   The path to the directory for saving logs
-  --save-best-after SAVE_BEST_AFTER
-                        start saving the best validation result after the
-                        given epoch completes until the end of training
-  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
-                        epochs to save checkpoints at
-  --init-lr INIT_LR     The initial learning rate to use while training, the
-                        actual initial value used should be set by the
-                        sparseml recipe
-  --optim-args OPTIM_ARGS
-                        Additional args to be passed to the optimizer passed
-                        in as a json object
-
-
-##########
-export command help:
-usage: classification.py export [-h] --arch-key ARCH_KEY
-                                [--pretrained PRETRAINED]
-                                [--pretrained-dataset PRETRAINED_DATASET]
-                                [--checkpoint-path CHECKPOINT_PATH]
-                                [--model-kwargs MODEL_KWARGS] --dataset
-                                DATASET --dataset-path DATASET_PATH
-                                [--dataset-kwargs DATASET_KWARGS]
-                                [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
-                                [--num-samples NUM_SAMPLES]
-                                [--onnx-opset ONNX_OPSET]
-
-Export a model to onnx as well as store sample inputs, outputs, and labels
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --num-samples NUM_SAMPLES
-                        The number of samples to export along with the model
-                        onnx and pth files (sample inputs and labels as well
-                        as the outputs from model execution)
-  --onnx-opset ONNX_OPSET
-                        The onnx opset to use for export. Default is 11
-
-
-##########
-pruning_sensitivity command help:
-usage: classification.py pruning_sensitivity [-h] --arch-key ARCH_KEY
-                                             [--pretrained PRETRAINED]
-                                             [--pretrained-dataset PRETRAINED_DATASET]
-                                             [--checkpoint-path CHECKPOINT_PATH]
-                                             [--model-kwargs MODEL_KWARGS]
-                                             --dataset DATASET --dataset-path
-                                             DATASET_PATH
-                                             [--dataset-kwargs DATASET_KWARGS]
-                                             [--model-tag MODEL_TAG]
-                                             [--save-dir SAVE_DIR]
-                                             [--dataset-parallel-calls
-                                                DATASET_PARALLEL_CALLS]
-                                             [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
-                                             [--approximate]
-                                             [--steps-per-measurement
-                                                STEPS_PER_MEASUREMENT]
-                                             [--batch-size BATCH_SIZE]
-
-Run a kernel sparsity (pruning) analysis for a given model
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
-                        mobilenet put as help to see the full list (will raise
-                        an exception with the list)
-  --pretrained PRETRAINED
-                        The type of pretrained weights to use, default is true
-                        to load the default pretrained weights for the model.
-                        Otherwise should be set to the desired weights type:
-                        [base, optim, optim-perf]. To not load any weights set
-                        to one of [none, false]
-  --pretrained-dataset PRETRAINED_DATASET
-                        The dataset to load pretrained weights for if
-                        pretrained is set. Default is None which will load the
-                        default dataset for the architecture. Ex can be set to
-                        imagenet, cifar10, etc
-  --checkpoint-path CHECKPOINT_PATH
-                        A path to a previous checkpoint to load the state from
-                        and resume the state for. If provided, pretrained will
-                        be ignored
-  --model-kwargs MODEL_KWARGS
-                        kew word arguments to be passed to model constructor,
-                        should be given as a json object
-  --dataset DATASET     The dataset to use for training, ex: imagenet,
-                        imagenette, cifar10, etc. Set to imagefolder for a
-                        generic dataset setup with an image folder structure
-                        setup like imagenet or loadable by a dataset in
-                        sparseml.tensorflow_v1.datasets
-  --dataset-path DATASET_PATH
-                        The root path to where the dataset is stored
-  --dataset-kwargs DATASET_KWARGS
-                        kew word arguments to be passed to dataset
-                        constructor, should be given as a json object
-  --model-tag MODEL_TAG
-                        A tag to use for the model for saving results under
-                        save-dir, defaults to the model arch and dataset used
-  --save-dir SAVE_DIR   The path to the directory for saving results
-  --dataset-parallel-calls DATASET_PARALLEL_CALLS
-                        the number of parallel workers for dataset loading
-  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
-                        Shuffle buffer size for dataset loading
-  --approximate         True to approximate without running data through the
-                        model, otherwise will run a one shot analysis
-  --steps-per-measurement STEPS_PER_MEASUREMENT
-                        The number of steps (batches) to run for each
-                        measurement
-  --batch-size BATCH_SIZE
-                        The batch size to use while performing analysis
-
-
-#########
-EXAMPLES
-#########
-
-##########
-Example command for pruning resnet50 on imagenet dataset:
-python scripts/tensorflow_v1/classification.py train \
-    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
-python scripts/tensorflow_v1/classification.py train \
-    --sparse-transfer-learn \
-    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
-    --arch-key mobilenet_v1 --pretrained optim \
-    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
-    --train-batch-size 256 --test-batch-size 1024
-
-##########
-Example command for exporting ResNet50:
-python scripts/tensorflow_v1/classification.py export \
-    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
-
-##########
-Example command for running approximated KS sensitivity analysis on mobilenet:
-python scripts/tensorflow_v1/classification.py pruning_sensitivity \
-    --approximate \
-    --arch-key mobilenet --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012
-
-##########
-Example command for running one shot KS sensitivity analysis on resnet50 for coco:
-python scripts/tensorflow_v1/classification.py pruning_sensitivity \
-    --arch-key resnet50 --dataset imagenet \
-    --dataset-path ~/datasets/ILSVRC2012
-"""
-
-import argparse
-import json
-import math
-import os
-from typing import Dict, Optional, Tuple
-
-import numpy
-
-from sparseml import get_main_logger
-from sparseml.tensorflow_v1.datasets import (
-    Dataset,
-    DatasetRegistry,
-    create_split_iterators_handle,
-)
-from sparseml.tensorflow_v1.models import ModelRegistry
-from sparseml.tensorflow_v1.optim import (
-    ConstantPruningModifier,
-    ScheduledModifierManager,
-    pruning_loss_sens_magnitude,
-    pruning_loss_sens_one_shot,
-    pruning_loss_sens_op_vars,
-)
-from sparseml.tensorflow_v1.utils import (
-    GraphExporter,
-    accuracy,
-    batch_cross_entropy_loss,
-    tf_compat,
-    write_simple_summary,
-)
-from sparseml.utils import create_dirs
-
-
-LOGGER = get_main_logger()
-TRAIN_COMMAND = "train"
-EXPORT_COMMAND = "export"
-PRUNING_SENSITVITY_COMMAND = "pruning_sensitivity"
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Run tasks on classification models and datasets "
-        "using the sparseml API"
-    )
-
-    subparsers = parser.add_subparsers(dest="command")
-    train_parser = subparsers.add_parser(
-        TRAIN_COMMAND,
-        description="Train and/or prune an image classification model",
-    )
-    export_parser = subparsers.add_parser(
-        EXPORT_COMMAND,
-        description="Export a model to onnx as well as "
-        "store sample inputs, outputs, and labels",
-    )
-    pruning_sensitivity_parser = subparsers.add_parser(
-        PRUNING_SENSITVITY_COMMAND,
-        description="Run a kernel sparsity (pruning) analysis for a given model",
-    )
-
-    parsers = [
-        train_parser,
-        export_parser,
-        pruning_sensitivity_parser,
-    ]
-    for par in parsers:
-        # general arguments
-        # model args
-        par.add_argument(
-            "--arch-key",
-            type=str,
-            required=True,
-            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
-            "put as help to see the full list (will raise an exception with the list)",
-        )
-        par.add_argument(
-            "--pretrained",
-            type=str,
-            default=True,
-            help="The type of pretrained weights to use, "
-            "default is true to load the default pretrained weights for the model. "
-            "Otherwise should be set to the desired weights type: "
-            "[base, optim, optim-perf]. "
-            "To not load any weights set to one of [none, false]",
-        )
-        par.add_argument(
-            "--pretrained-dataset",
-            type=str,
-            default=None,
-            help="The dataset to load pretrained weights for if pretrained is set. "
-            "Default is None which will load the default dataset for the architecture."
-            " Ex can be set to imagenet, cifar10, etc",
-        )
-        par.add_argument(
-            "--checkpoint-path",
-            type=str,
-            default=None,
-            help="A path to a previous checkpoint to load the state from and "
-            "resume the state for. If provided, pretrained will be ignored",
-        )
-        par.add_argument(
-            "--model-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to model constructor, should be "
-            " given as a json object",
-        )
-
-        # dataset args
-        par.add_argument(
-            "--dataset",
-            type=str,
-            required=True,
-            help="The dataset to use for training, "
-            "ex: imagenet, imagenette, cifar10, etc. "
-            "Set to imagefolder for a generic dataset setup "
-            "with an image folder structure setup like imagenet or loadable by a "
-            "dataset in sparseml.tensorflow_v1.datasets",
-        )
-        par.add_argument(
-            "--dataset-path",
-            type=str,
-            required=True,
-            help="The root path to where the dataset is stored",
-        )
-        par.add_argument(
-            "--dataset-kwargs",
-            type=json.loads,
-            default={},
-            help="kew word arguments to be passed to dataset constructor, should be "
-            " given as a json object",
-        )
-
-        # logging and saving
-        par.add_argument(
-            "--model-tag",
-            type=str,
-            default=None,
-            help="A tag to use for the model for saving results under save-dir, "
-            "defaults to the model arch and dataset used",
-        )
-        par.add_argument(
-            "--save-dir",
-            type=str,
-            default="tensorflow_v1_classification",
-            help="The path to the directory for saving results",
-        )
-
-        # task specific arguments
-        if par in [train_parser, pruning_sensitivity_parser]:
-            par.add_argument(
-                "--dataset-parallel-calls",
-                type=int,
-                default=4,
-                help="the number of parallel workers for dataset loading",
-            )
-            par.add_argument(
-                "--shuffle-buffer-size",
-                type=int,
-                default=1000,
-                help="Shuffle buffer size for dataset loading",
-            )
-
-        if par == train_parser:
-            par.add_argument(
-                "--recipe-path",
-                type=str,
-                default=None,
-                help="The path to the yaml file containing the modifiers and "
-                "schedule to apply them with. If set to 'transfer_learning', "
-                "then will create a schedule to enable sparse transfer learning",
-            )
-            par.add_argument(
-                "--sparse-transfer-learn",
-                action="store_true",
-                help=(
-                    "Enable sparse transfer learning modifiers to enforce the sparsity "
-                    "for already sparse layers. The modifiers are added to the "
-                    "ones to be loaded from the recipe-path"
-                ),
-            )
-            par.add_argument(
-                "--eval-mode",
-                action="store_true",
-                help="Puts into evaluation mode so that the model can be "
-                "evaluated on the desired dataset",
-            )
-            par.add_argument(
-                "--train-batch-size",
-                type=int,
-                required=True,
-                help="The batch size to use while training",
-            )
-            par.add_argument(
-                "--test-batch-size",
-                type=int,
-                required=True,
-                help="The batch size to use while testing",
-            )
-            par.add_argument(
-                "--logs-dir",
-                type=str,
-                default=os.path.join(
-                    "tensorflow_v1_classification_train", "tensorboard-logs"
-                ),
-                help="The path to the directory for saving logs",
-            )
-            par.add_argument(
-                "--save-best-after",
-                type=int,
-                default=-1,
-                help="start saving the best validation result after the given "
-                "epoch completes until the end of training",
-            )
-            par.add_argument(
-                "--save-epochs",
-                type=int,
-                default=[],
-                nargs="+",
-                help="epochs to save checkpoints at",
-            )
-            par.add_argument(
-                "--init-lr",
-                type=float,
-                default=1e-9,
-                help="The initial learning rate to use while training, "
-                "the actual initial value used should be set by the sparseml recipe",
-            )
-            par.add_argument(
-                "--optim-args",
-                type=json.loads,
-                default={},
-                help="Additional args to be passed to the optimizer passed in"
-                " as a json object",
-            )
-
-        if par == export_parser:
-            par.add_argument(
-                "--num-samples",
-                type=int,
-                default=100,
-                help="The number of samples to export along with the model onnx "
-                "and pth files (sample inputs and labels as well as the outputs "
-                "from model execution)",
-            )
-            par.add_argument(
-                "--onnx-opset",
-                type=int,
-                default=11,
-                help="The onnx opset to use for export. Default is 11",
-            )
-
-        if par == pruning_sensitivity_parser:
-            par.add_argument(
-                "--approximate",
-                action="store_true",
-                help="True to approximate without running data through the model, "
-                "otherwise will run a one shot analysis",
-            )
-            par.add_argument(
-                "--steps-per-measurement",
-                type=int,
-                default=15,
-                help="The number of steps (batches) to run for each measurement",
-            )
-            par.add_argument(
-                "--batch-size",
-                type=int,
-                default=64,
-                help="The batch size to use while performing analysis",
-            )
-
-    return parser.parse_args()
-
-
-def _setup_save_dirs(args) -> Tuple[str, Optional[str]]:
-    # logging and saving setup
-    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
-    logs_dir = (
-        os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
-        if args.command == TRAIN_COMMAND
-        else None
-    )
-
-    if not args.model_tag:
-        model_tag = "{}_{}".format(args.arch_key.replace("/", "."), args.dataset)
-        model_id = model_tag
-        model_inc = 0
-        # set location to check for models with same name
-        model_main_dir = logs_dir or save_dir
-
-        while os.path.exists(os.path.join(model_main_dir, model_id)):
-            model_inc += 1
-            model_id = "{}__{:02d}".format(model_tag, model_inc)
-    else:
-        model_id = args.model_tag
-
-    save_dir = os.path.join(save_dir, model_id)
-    create_dirs(save_dir)
-
-    # logs dir setup
-    if args.command == TRAIN_COMMAND:
-        logs_dir = os.path.join(logs_dir, model_id)
-        create_dirs(logs_dir)
-    else:
-        logs_dir = None
-    LOGGER.info("Model id is set to {}".format(model_id))
-    return save_dir, logs_dir
-
-
-def _create_dataset(args, train=True, image_size=None) -> Tuple[Dataset, int]:
-    kwargs = args.dataset_kwargs
-    if "image_size" in kwargs:
-        image_size = kwargs["image_size"]
-        del kwargs["image_size"]
-
-    dataset = DatasetRegistry.create(
-        args.dataset,
-        root=args.dataset_path,
-        train=train,
-        image_size=image_size,
-        **kwargs,
-    )
-    LOGGER.info("created {} dataset: {}".format("train" if train else "val", dataset))
-
-    # get num_classes
-    if args.dataset == "imagefolder":
-        num_classes = dataset.num_classes
-    else:
-        dataset_attributes = DatasetRegistry.attributes(args.dataset)
-        num_classes = dataset_attributes["num_classes"]
-
-    return dataset, num_classes
-
-
-def _build_dataset(args, dataset: Dataset, batch_size: int) -> Dataset:
-    return dataset.build(
-        batch_size,
-        shuffle_buffer_size=args.shuffle_buffer_size,
-        prefetch_buffer_size=batch_size,
-        num_parallel_calls=args.dataset_parallel_calls,
-    )
-
-
-def _create_model(args, num_classes, inputs, training=False):
-    outputs = ModelRegistry.create(
-        args.arch_key,
-        inputs,
-        training=training,
-        num_classes=num_classes,
-        **args.model_kwargs,
-    )
-    LOGGER.info("created model {}".format(args.arch_key))
-    return outputs
-
-
-def _load_model(args, sess, checkpoint_path=None):
-    sess.run(
-        [
-            tf_compat.global_variables_initializer(),
-            tf_compat.local_variables_initializer(),
-        ]
-    )
-    checkpoint_path = checkpoint_path or args.checkpoint_path
-    ModelRegistry.load_pretrained(
-        args.arch_key,
-        pretrained=args.pretrained,
-        pretrained_dataset=args.pretrained_dataset,
-        pretrained_path=checkpoint_path,
-        sess=sess,
-    )
-    if checkpoint_path:
-        LOGGER.info("Loaded model weights from checkpoint: {}".format(checkpoint_path))
-
-
-def _save_checkpoint(args, sess, save_dir, checkpoint_name) -> str:
-    checkpoint_path = os.path.join(os.path.join(save_dir, checkpoint_name, "model"))
-    create_dirs(checkpoint_path)
-    saver = ModelRegistry.saver(args.arch_key)
-    saved_name = saver.save(sess, checkpoint_path)
-    checkpoint_path = os.path.join(checkpoint_path, saved_name)
-    LOGGER.info("Checkpoint saved to {}".format(checkpoint_path))
-    return checkpoint_path
-
-
-def _save_recipe(
-    recipe_manager: ScheduledModifierManager,
-    save_dir: str,
-):
-
-    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
-    recipe_manager.save(recipe_save_path)
-    LOGGER.info(f"Saved recipe to {recipe_save_path}")
-
-
-def train(args, save_dir, logs_dir):
-    # setup dataset
-    with tf_compat.device("/cpu:0"):
-        train_dataset, _ = _create_dataset(args, train=True)
-        val_dataset, num_classes = _create_dataset(args, train=False)
-        # calc steps
-        train_steps = math.ceil(len(train_dataset) / args.train_batch_size)
-        val_steps = math.ceil(len(val_dataset) / args.test_batch_size)
-        # build datasets
-        train_dataset = _build_dataset(args, train_dataset, args.train_batch_size)
-        val_dataset = _build_dataset(args, val_dataset, args.test_batch_size)
-    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(
-        [train_dataset, val_dataset]
-    )
-
-    # set up model graph
-    images, labels = iterator.get_next()
-    training = tf_compat.placeholder(dtype=tf_compat.bool, shape=[])
-    outputs = _create_model(args, num_classes, images, training)
-
-    # set up training objects
-    loss = batch_cross_entropy_loss(outputs, labels)
-    acc = accuracy(outputs, labels)
-    global_step = tf_compat.train.get_or_create_global_step()
-    train_op = tf_compat.train.AdamOptimizer(
-        learning_rate=args.init_lr, **args.optim_args
-    ).minimize(loss, global_step=global_step)
-    update_ops = tf_compat.get_collection(tf_compat.GraphKeys.UPDATE_OPS)
-    LOGGER.info("Created update ops for training")
-
-    # set up sparseml modifier ops
-    add_mods = (
-        ConstantPruningModifier(params="__ALL__")
-        if args.sparse_transfer_learn
-        else None
-    )
-    manager = ScheduledModifierManager.from_yaml(
-        file_path=args.recipe_path, add_modifiers=add_mods
-    )
-    mod_ops, mod_extras = manager.create_ops(train_steps, global_step)
-    _save_recipe(recipe_manager=manager, save_dir=save_dir)
-    with tf_compat.Session() as sess:
-        # set up tensorboard logging
-        summary_writer = tf_compat.summary.FileWriter(logs_dir, sess.graph)
-        summaries = tf_compat.summary.merge_all()
-        LOGGER.info("Logging to tensorboard at {}".format(logs_dir))
-
-        # initialize variables, load pretrained weights, initialize modifiers
-        train_iter_handle, val_iter_handle = sess.run(
-            [train_iter.string_handle(), val_iter.string_handle()]
-        )
-        LOGGER.info("Initialized graph variables")
-        _load_model(args, sess)
-        manager.initialize_session()
-        LOGGER.info("Initialized SparseML modifiers")
-
-        best_loss = None
-        for epoch in range(manager.max_epochs):
-            # train
-            LOGGER.info("Training for epoch {}...".format(epoch))
-            sess.run(train_iter.initializer)
-            train_acc, train_loss = [], []
-            for step in range(train_steps):
-                _, __, meas_step, meas_loss, meas_acc, meas_summ = sess.run(
-                    [train_op, update_ops, global_step, loss, acc, summaries],
-                    feed_dict={handle: train_iter_handle, training: True},
-                )
-                if step >= train_steps - 1:
-                    # log the general summaries on the last training step
-                    summary_writer.add_summary(meas_summ, meas_step)
-                # run modifier ops
-                sess.run(mod_ops)
-                # summarize
-                write_simple_summary(summary_writer, "Train/Loss", meas_loss, meas_step)
-                write_simple_summary(
-                    summary_writer, "Train/Acc", meas_acc * 100.0, meas_step
-                )
-                train_acc.append(meas_acc)
-                train_loss.append(meas_loss)
-            LOGGER.info(
-                "Epoch {} - Train Loss: {}, Train Acc: {}".format(
-                    epoch, numpy.mean(train_loss).item(), numpy.mean(train_acc).item()
-                )
-            )
-
-            # val
-            LOGGER.info("Validating for epoch {}...".format(epoch))
-            sess.run(val_iter.initializer)
-            val_acc, val_loss = [], []
-            for step in range(val_steps):
-                meas_loss, meas_acc = sess.run(
-                    [loss, acc],
-                    feed_dict={handle: val_iter_handle, training: False},
-                )
-                val_acc.append(meas_acc)
-                val_loss.append(meas_loss)
-                write_simple_summary(
-                    summary_writer, "Val/Loss", numpy.mean(val_loss).item(), epoch
-                )
-                write_simple_summary(
-                    summary_writer, "Val/Acc", numpy.mean(val_acc).item(), epoch
-                )
-            val_loss = numpy.mean(val_loss).item()
-            LOGGER.info(
-                "Epoch {} - Val Loss: {}, Val Acc: {}".format(
-                    epoch, val_loss, numpy.mean(train_acc).item()
-                )
-            )
-            if epoch >= args.save_best_after and (
-                best_loss is None or val_loss <= best_loss
-            ):
-                _save_checkpoint(args, sess, save_dir, "checkpoint-best")
-                best_loss = val_loss
-            if args.save_epochs and epoch in args.save_epochs:
-                _save_checkpoint(
-                    args, sess, save_dir, "checkpoint-epoch-{}".format(epoch)
-                )
-
-        # cleanup graph and save final checkpoint
-        manager.complete_graph()
-        checkpoint_path = _save_checkpoint(args, sess, save_dir, "final-checkpoint")
-    LOGGER.info("Running ONNX export flow")
-    export(
-        args,
-        save_dir,
-        checkpoint_path=checkpoint_path,
-        skip_samples=True,
-        num_classes=num_classes,
-        opset=11,
-    )
-
-
-def export(
-    args,
-    save_dir,
-    checkpoint_path=None,
-    skip_samples=False,
-    num_classes=None,
-    opset=None,
-):
-    assert not skip_samples or num_classes
-    # dataset creation
-    if not skip_samples:
-        val_dataset, num_classes = _create_dataset(args, train=False)
-
-    with tf_compat.Graph().as_default():
-        input_shape = ModelRegistry.input_shape(args.arch_key)
-        inputs = tf_compat.placeholder(
-            tf_compat.float32, [None] + list(input_shape), name="inputs"
-        )
-        outputs = _create_model(args, num_classes, inputs)
-
-        with tf_compat.Session() as sess:
-            _load_model(
-                args, sess, checkpoint_path=checkpoint_path or args.checkpoint_path
-            )
-
-            exporter = GraphExporter(save_dir)
-
-            if not skip_samples:
-                # Export a batch of samples and expected outputs
-                tf_dataset = val_dataset.build(
-                    args.num_samples, repeat_count=1, num_parallel_calls=1
-                )
-                tf_iter = tf_compat.data.make_one_shot_iterator(tf_dataset)
-                features, _ = tf_iter.get_next()
-                inputs_val = sess.run(features)
-                exporter.export_samples([inputs], [inputs_val], [outputs], sess)
-
-            # Export model to tensorflow checkpoint format
-            LOGGER.info("exporting tensorflow in {}".format(save_dir))
-            exporter.export_checkpoint(sess=sess)
-
-            # Export model to pb format
-            LOGGER.info("exporting pb in {}".format(exporter.pb_path))
-            exporter.export_pb(outputs=[outputs])
-
-    # Export model to onnx format
-    LOGGER.info("exporting onnx in {}".format(exporter.onnx_path))
-    exporter.export_onnx([inputs], [outputs], opset=opset or args.onnx_opset)
-
-
-def pruning_loss_sensitivity(args, save_dir):
-    input_shape = ModelRegistry.input_shape(args.arch_key)
-    train_dataset, num_classes = _create_dataset(
-        args, train=True, image_size=input_shape[1]
-    )
-    with tf_compat.Graph().as_default() as graph:
-        # create model graph
-        inputs = tf_compat.placeholder(
-            tf_compat.float32, [None] + list(input_shape), name="inputs"
-        )
-        outputs = _create_model(args, num_classes, inputs)
-
-        with tf_compat.Session() as sess:
-            _load_model(args, sess, checkpoint_path=args.checkpoint_path)
-            if args.approximate:
-                LOGGER.info("Running weight magnitude loss sensitivity analysis...")
-                analysis = pruning_loss_sens_magnitude(graph, sess)
-            else:
-                op_vars = pruning_loss_sens_op_vars(graph)
-                train_steps = math.ceil(len(train_dataset) / args.batch_size)
-                train_dataset = _build_dataset(args, train_dataset, args.batch_size)
-                handle, iterator, dataset_iter = create_split_iterators_handle(
-                    [train_dataset]
-                )
-                dataset_iter = dataset_iter[0]
-                images, labels = iterator.get_next()
-                loss = batch_cross_entropy_loss(outputs, labels)
-                tensor_names = ["inputs:0", labels.name]
-                sess.run(dataset_iter.initializer)
-
-                def feed_dict_creator(step: int) -> Dict[str, tf_compat.Tensor]:
-                    assert step < train_steps
-                    batch_data = [
-                        tens.eval(session=sess) for tens in dataset_iter.get_next()
-                    ]
-                    return dict(zip(tensor_names, batch_data))
-
-                LOGGER.info("Running one shot loss sensitivity analysis...")
-                analysis = pruning_loss_sens_one_shot(
-                    op_vars=op_vars,
-                    loss_tensor=loss,
-                    steps_per_measurement=args.steps_per_measurement,
-                    feed_dict_creator=feed_dict_creator,
-                    sess=sess,
-                )
-    # saving and printing results
-    LOGGER.info("completed...")
-    LOGGER.info("Saving results in {}".format(save_dir))
-    analysis.save_json(
-        os.path.join(
-            save_dir,
-            "ks_approx_sensitivity.json"
-            if args.approximate
-            else "ks_one_shot_sensitivity.json",
-        )
-    )
-    analysis.plot(
-        os.path.join(
-            save_dir,
-            os.path.join(
-                save_dir,
-                "ks_approx_sensitivity.png"
-                if args.approximate
-                else "ks_one_shot_sensitivity.png",
-            ),
-        ),
-        plot_integral=True,
-    )
-    analysis.print_res()
-
-
-def main(args):
-    # set up saving and logging dirs
-    save_dir, logs_dir = _setup_save_dirs(args)
-
-    # RUN COMMAND SPECIFIC TASTS
-    if args.command == TRAIN_COMMAND:
-        train(args, save_dir, logs_dir)
-    if args.command == EXPORT_COMMAND:
-        export(args, save_dir)
-    if args.command == PRUNING_SENSITVITY_COMMAND:
-        pruning_loss_sensitivity(args, save_dir)
-
-
-if __name__ == "__main__":
-    args_ = parse_args()
-
-    main(args_)
diff --git a/integrations/examples/tensorflow_v1/notebooks/classification.ipynb b/integrations/examples/tensorflow_v1/notebooks/classification.ipynb
deleted file mode 100644
index 158d12aaef0..00000000000
--- a/integrations/examples/tensorflow_v1/notebooks/classification.ipynb
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
-    "\n",
-    "# TensorFlow v1 Classification Model Pruning Using SparseML\n",
-    "\n",
-    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Engine. You will:\n",
-    "- Set up the model and dataset\n",
-    "- Define a TensorFlow training flow with a simple SparseML integration\n",
-    "- Prune the model using the TensorFlow+SparseML flow\n",
-    "- Export to [ONNX](https://onnx.ai/)\n",
-    "\n",
-    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your TensorFlow training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
-    "- 20 minutes on a GPU\n",
-    "- 60 minutes on a laptop CPU"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1 - Requirements\n",
-    "To run this notebook, you will need the following packages already installed:\n",
-    "* SparseML and SparseZoo\n",
-    "* TensorFlow v1 and tf2onnx\n",
-    "\n",
-    "You can install any package that is not already present via `pip`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sparseml\n",
-    "import sparsezoo\n",
-    "import tensorflow as tf\n",
-    "import tf2onnx\n",
-    "\n",
-    "assert tf.__version__ < \"2\"\n",
-    "\n",
-    "# suppress warnings\n",
-    "import warnings\n",
-    "\n",
-    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2 - Setting Up the Model and Dataset\n",
-    "\n",
-    "By default, you will prune a [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
-    "\n",
-    "In the cells below, functions are defined to load the dataset, model, and training objects to be called during training from within the `Graph` context.\n",
-    "\n",
-    "If you would like to try out your model for pruning, modify the appropriate function for to load your model or dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "from sparseml.tensorflow_v1.models import ModelRegistry\n",
-    "from sparseml.tensorflow_v1.datasets import (\n",
-    "    ImagenetteDataset,\n",
-    "    ImagenetteSize,\n",
-    ")\n",
-    "from sparseml.tensorflow_v1.utils import (\n",
-    "    batch_cross_entropy_loss,\n",
-    "    accuracy,\n",
-    ")\n",
-    "\n",
-    "MODEL_NAME = \"resnet50\"\n",
-    "BATCH_SIZE = 128\n",
-    "INPUT_SIZE = 224\n",
-    "NUM_CLASSES = 10\n",
-    "SAVE_DIR = \"tensorflow_v1_classification_pruning\"\n",
-    "\n",
-    "\n",
-    "def load_dataset():\n",
-    "    with tf.device(\"/cpu:0\"):\n",
-    "        print(\"loading datasets\")\n",
-    "        train_dataset = ImagenetteDataset(\n",
-    "            train=True, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
-    "        )\n",
-    "        train_len = len(train_dataset)\n",
-    "        train_steps = math.ceil(train_len / float(BATCH_SIZE))\n",
-    "        train_dataset = train_dataset.build(\n",
-    "            BATCH_SIZE,\n",
-    "            shuffle_buffer_size=1000,\n",
-    "            prefetch_buffer_size=BATCH_SIZE,\n",
-    "            num_parallel_calls=4,\n",
-    "        )\n",
-    "\n",
-    "        val_dataset = ImagenetteDataset(\n",
-    "            train=False, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
-    "        )\n",
-    "        val_len = len(val_dataset)\n",
-    "        val_steps = math.ceil(val_len / float(BATCH_SIZE))\n",
-    "        val_dataset = val_dataset.build(\n",
-    "            BATCH_SIZE,\n",
-    "            shuffle_buffer_size=1000,\n",
-    "            prefetch_buffer_size=BATCH_SIZE,\n",
-    "            num_parallel_calls=4,\n",
-    "        )\n",
-    "\n",
-    "    return train_dataset, val_dataset, (train_steps, val_steps)\n",
-    "\n",
-    "\n",
-    "def create_model(sample_input, training):\n",
-    "    print(\"Creating model graph for {}\".format(MODEL_NAME))\n",
-    "    logits = ModelRegistry.create(\n",
-    "        MODEL_NAME,\n",
-    "        inputs=sample_input,\n",
-    "        training=training,\n",
-    "        num_classes=NUM_CLASSES,\n",
-    "    )\n",
-    "    return logits\n",
-    "\n",
-    "\n",
-    "def create_training_objects(sample_labels):\n",
-    "    print(\"Creating loss, accuracy, and optimizer in graph\")\n",
-    "    loss = batch_cross_entropy_loss(logits, labels)\n",
-    "    acc = accuracy(logits, labels)\n",
-    "    global_step = tf.train.get_or_create_global_step()\n",
-    "    train_op = tf.train.AdamOptimizer(learning_rate=0.00008).minimize(\n",
-    "        loss, global_step=global_step\n",
-    "    )\n",
-    "    return loss, acc, global_step, train_op\n",
-    "\n",
-    "\n",
-    "def load_pretrained():\n",
-    "    print(\"loading pre-trained model weights\")\n",
-    "    ModelRegistry.load_pretrained(\n",
-    "        MODEL_NAME, pretrained=\"base\", remove_dynamic_tl_vars=True,\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3 - Create a SparseML Modifier Manager\n",
-    "\n",
-    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to create operations that modify the training process.\n",
-    "\n",
-    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
-    "\n",
-    "Using the operators generated from this manager object, you will be able to prune your model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.tensorflow_v1.optim import (\n",
-    "    ScheduledModifierManager,\n",
-    ")\n",
-    "from sparsezoo import Model, search_models\n",
-    "\n",
-    "\n",
-    "def create_sparseml_manager():\n",
-    "    model = search_models(\n",
-    "        domain=\"cv\",\n",
-    "        sub_domain=\"classification\",\n",
-    "        architecture=\"resnet_v1\",\n",
-    "        sub_architecture=\"50\",\n",
-    "        framework=\"tensorflow_v1\",\n",
-    "        repo=\"sparseml\",\n",
-    "        dataset=\"imagenette\",\n",
-    "        sparse_name=\"pruned\",\n",
-    "    )[0]  # unwrap search result\n",
-    "\n",
-    "    recipe_path = model.recipes.default.path\n",
-    "    print(f\"Recipe downloaded to: {recipe_path}\")\n",
-    "\n",
-    "    return ScheduledModifierManager.from_yaml(recipe_path)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4 - Prune your model using a TensorFlow training loop\n",
-    "SparseML can plug directly into your existing TensorFlow training flow by creating additional operators to run. To demonstrate this, in the cell below, prune the model using a standard TensorFlow training loop while also running the operators created by the manager object.  To prune your existing models using SparseML, you can use your own training flow with the additional operators added.\n",
-    "\n",
-    "For your convienence the lines needed for integrating with SparseML are preceeded by large comment blocks.\n",
-    "\n",
-    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `BATCH_SIZE` in Step 2 above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy\n",
-    "import os\n",
-    "from tqdm.auto import tqdm\n",
-    "from sparseml.utils import create_unique_dir, create_dirs\n",
-    "from sparseml.tensorflow_v1.datasets import create_split_iterators_handle\n",
-    "\n",
-    "with tf.Graph().as_default() as graph:\n",
-    "    # create dataset\n",
-    "    train_dataset, val_dataset, (train_steps, val_steps) = load_dataset()\n",
-    "    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(\n",
-    "        [train_dataset, val_dataset]\n",
-    "    )\n",
-    "    images, labels = iterator.get_next()\n",
-    "\n",
-    "    # create base training objects\n",
-    "    training = tf.placeholder(dtype=tf.bool, shape=[])\n",
-    "    logits = create_model(images, training)\n",
-    "    loss, acc, global_step, train_op = create_training_objects(labels)\n",
-    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
-    "\n",
-    "    #######################################################\n",
-    "    # create sparseml training manager\n",
-    "    #######################################################\n",
-    "    manager = create_sparseml_manager()\n",
-    "    mod_ops, mod_extras = manager.create_ops(train_steps, global_step, graph=graph)\n",
-    "\n",
-    "    with tf.Session() as sess:\n",
-    "        print(\"initializing session\")\n",
-    "        sess.run(\n",
-    "            [\n",
-    "                tf.global_variables_initializer(),\n",
-    "                tf.local_variables_initializer(),\n",
-    "            ]\n",
-    "        )\n",
-    "        train_iter_handle, val_iter_handle = sess.run(\n",
-    "            [train_iter.string_handle(), val_iter.string_handle()]\n",
-    "        )\n",
-    "\n",
-    "        # initialize sparseml manager after pretrained weights loaded\n",
-    "        load_pretrained()\n",
-    "        manager.initialize_session()\n",
-    "\n",
-    "        num_epochs = manager.max_epochs\n",
-    "        for epoch in tqdm(range(num_epochs), desc=\"pruning\"):\n",
-    "            print(\"training for epoch {}...\".format(epoch))\n",
-    "            sess.run(train_iter.initializer)\n",
-    "            train_losses = []\n",
-    "            train_acc = []\n",
-    "\n",
-    "            for step in range(train_steps):\n",
-    "                _, __, meas_step, meas_loss, meas_acc = sess.run(\n",
-    "                    [train_op, update_ops, global_step, loss, acc],\n",
-    "                    feed_dict={handle: train_iter_handle, training: True},\n",
-    "                )\n",
-    "                train_losses.append(meas_loss)\n",
-    "                train_acc.append(meas_acc)\n",
-    "\n",
-    "                #######################################################\n",
-    "                # Modifier update ops line for transfer learning from a sparse model in TensorFlow\n",
-    "                #######################################################\n",
-    "                sess.run(mod_ops)\n",
-    "            print(\n",
-    "                \"completed epoch {} training with: loss {} / acc {}\".format(\n",
-    "                    epoch,\n",
-    "                    numpy.mean(train_losses).item(),\n",
-    "                    numpy.mean(train_acc).item() * 100,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "            print(\"validating for epoch {}...\".format(epoch))\n",
-    "            sess.run(val_iter.initializer)\n",
-    "            val_losses = []\n",
-    "            val_acc = []\n",
-    "\n",
-    "            for step in range(val_steps):\n",
-    "                meas_loss, meas_acc = sess.run(\n",
-    "                    [loss, acc],\n",
-    "                    feed_dict={handle: val_iter_handle, training: False},\n",
-    "                )\n",
-    "                val_losses.append(meas_loss)\n",
-    "                val_acc.append(meas_acc)\n",
-    "\n",
-    "            print(\n",
-    "                \"completed epoch {} validation with: loss {} / acc {}\".format(\n",
-    "                    epoch,\n",
-    "                    numpy.mean(val_losses).item(),\n",
-    "                    numpy.mean(val_acc).item() * 100,\n",
-    "                )\n",
-    "            )\n",
-    "\n",
-    "        #######################################################\n",
-    "        # Final line for sparseml training in TensorFlow, complete the graph\n",
-    "        #######################################################\n",
-    "        manager.complete_graph()\n",
-    "\n",
-    "        NAME = \"resnet50-imagenette-pruned\"\n",
-    "        checkpoint_path = create_unique_dir(\n",
-    "            os.path.join(\".\", SAVE_DIR, NAME, \"checkpoint\")\n",
-    "        )\n",
-    "        checkpoint_path = os.path.join(checkpoint_path, \"model\")\n",
-    "        create_dirs(checkpoint_path)\n",
-    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
-    "        saver.save(sess, checkpoint_path)\n",
-    "        print(\"saved model checkpoint to {}\".format(checkpoint_path))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 5 - Exporting to ONNX\n",
-    "\n",
-    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For TensorFlow, exporting to ONNX is not natively supported. To add support, you will use the `tf2onnx` Python package. In the cell block below, a convenience class, `GraphExporter()`, is used to handle exporting. It wraps the somewhat complicated API for `tf2onnx` into an easy to use interface.\n",
-    "\n",
-    "Note, for some configurations, the tf2onnx code does not work properly in a Jupyter Notebook. To remedy this, you should run the `exporter.export_onnx()` function call in a Python console or script.\n",
-    "\n",
-    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sparseml.utils import clean_path\n",
-    "from sparseml.tensorflow_v1.utils import GraphExporter\n",
-    "\n",
-    "\n",
-    "export_path = clean_path(os.path.join(\".\", SAVE_DIR, NAME))\n",
-    "exporter = GraphExporter(export_path)\n",
-    "\n",
-    "with tf.Graph().as_default() as graph:\n",
-    "    print(\"Recreating graph...\", flush=True)\n",
-    "\n",
-    "    input_placeholder = tf.placeholder(\n",
-    "        tf.float32, [None, INPUT_SIZE, INPUT_SIZE, 3], name=\"inputs\"\n",
-    "    )\n",
-    "    logits = create_model(input_placeholder, training=False)\n",
-    "\n",
-    "    input_names = [input_placeholder.name]\n",
-    "    output_names = [logits.name]\n",
-    "\n",
-    "    with tf.Session() as sess:\n",
-    "        sess.run(tf.global_variables_initializer())\n",
-    "        print(\"Restoring previous weights...\", flush=True)\n",
-    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
-    "        saver.restore(sess, checkpoint_path)\n",
-    "\n",
-    "        print(\"Exporting to pb...\", flush=True)\n",
-    "        exporter.export_pb(outputs=[logits])\n",
-    "        print(\"Exported pb file to {}\".format(exporter.pb_path), flush=True)\n",
-    "\n",
-    "print(\"Exporting to onnx...\", flush=True)\n",
-    "exporter.export_onnx(inputs=input_names, outputs=output_names)\n",
-    "print(\"Exported onnx file to {}\".format(exporter.onnx_path))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Next Steps\n",
-    "\n",
-    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
-    "* Pruning different models using SparseML\n",
-    "* Trying different pruning and optimization recipes\n",
-    "* Running your model on the DeepSparse Engine"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/integrations/examples/tensorflow_v1/recipes/.gitkeep b/integrations/examples/tensorflow_v1/recipes/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/tensorflow_v1/tutorials/.gitkeep b/integrations/examples/tensorflow_v1/tutorials/.gitkeep
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/integrations/examples/ultralytics-yolov3/README.md b/integrations/examples/ultralytics-yolov3/README.md
deleted file mode 100644
index 1dae432bd4f..00000000000
--- a/integrations/examples/ultralytics-yolov3/README.md
+++ /dev/null
@@ -1,128 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# SparseML Ultralytics YOLOv3 Integration
-
-This directory combines the SparseML recipe-driven approach with the 
-[ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository.
-By integrating the robust training flows in the `yolov3` repository with the SparseML code base,
-we enable model sparsification techniques on the popular [YOLOv3 architecture](https://arxiv.org/abs/1804.02767)
-creating smaller and faster deployable versions.
-The techniques include, but are not limited to:
-- Pruning
-- Quantization
-- Pruning + Quantization
-- Sparse Transfer Learning
-
-## Highlights
-
-- Blog: [YOLOv3 on CPUs: Sparsifying to Achieve GPU-Level Performance](https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-deepsparse/)
-- Example: [DeepSparse YOLOv3 Inference Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo)
-- Video: [DeepSparse YOLOv3 Pruned Quantized Performance](https://youtu.be/o5qIYs47MPw)
-
-## Tutorials
-
-- [Sparsifying YOLOv3 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md)
-- [Sparse Transfer Learning With YOLOv3](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md)
-
-## Installation
-
-To begin, run the following command in the root directory of this integration (`cd integrations/ultralytics-yolov3`):
-```bash
-bash setup_integration.sh
-```
-
-The setup_integration.sh file will clone the yolov3 repository with the SparseML integration as a subfolder.
-After the repo has successfully cloned,  all dependencies from the `yolov3/requirements.txt` file will install in your current environment.
-
-## Quick Tour
-
-### Recipes
-
-Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
-The modifiers can range from pruning and quantization to learning rate and weight decay.
-When appropriately combined, it becomes possible to create highly sparse and accurate models.
-
-This integration adds a `--recipe` argument to the [`train.py` script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
-The argument loads an appropriate recipe while preserving the rest of the training pipeline.
-Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
-Otherwise, all other arguments and functionality remain the same as the original repository.
-
-For example, pruning and quantizing a model on the VOC dataset can be done by running the following command from within the yolov3 repository folder:
-```bash
-python train.py --batch 16 --img 512 --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml --hyp ../data/hyp.pruned_quantized.yaml \
-    --recipe ../recipes/yolov3-spp.pruned_quantized.short.md
-```
-
-### SparseZoo
-
-Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-The following popular stubs can be used to download these models and recipes through the SparseZoo API directly.
-
-| Model                       | Dataset | mAP@50 | Description                                                    | SparseZoo Stub                                                                   |
-|-----------------------------|---------|--------|----------------------------------------------------------------|----------------------------------------------------------------------------------|
-| YOLOv3-SPP Pruned Quantized | COCO    | 60.4   | 83% sparse and INT8 quantized model with LeakyReLU activations | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 |
-| YOLOv3-SPP Pruned           | COCO    | 62.4   | 88% sparse model with LeakyReLU activations                    | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97       |
-| YOLOv3-SPP                  | COCO    | 64.2   | Baseline model with LeakyReLU activations                      | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none                  |
-
-Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/detection?repo=ultralytics) and 
-[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/detection?repo=ultralytics) compatible with this integration as well.
-
-Sample code for retrieving a model from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
-print(model)
-```
-
-Sample code for retrieving a recipe from the SparseZoo:
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
-recipe = model.recipes.default
-print(recipe)
-```
-
-### Structure
-
-The following table lays out the root-level files and folders along with a description for each.
-
-| Folder/File Name     | Description                                                                                                           |
-|----------------------|-----------------------------------------------------------------------------------------------------------------------|
-| data                 | The hyperparameters to use and files to use for training on data.                                                     |
-| models               | Model architecture definitions along with any downloaded checkpoints from the SparseZoo.                              |
-| recipes              | Typical recipes for sparsifying YOLOv3 models along with any downloaded recipes from the SparseZoo.                   |
-| tutorials            | Tutorial walkthroughs for how to sparsify YOLOv3 models using recipes.                                                |
-| yolov3               | Integration repository folder used to train and sparsify YOLOv3 models (setup_integration.sh must run first).         |
-| README.md            | Readme file.                                                                                                          |
-| setup_integration.sh | Setup file for the integration run from the command line.                                                             |
-
-### Exporting for Inference
-
-After sparsifying a model, the [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) 
-converts the model into deployment formats such as [ONNX](https://onnx.ai/).
-The export process is modified such that the quantized and pruned models are corrected and folded properly.
-
-For example, the following command can be run from within the yolov3 repository folder to export a trained/sparsified model's checkpoint:
-```bash
-python models/export.py --weights PATH/TO/weights.pt --img-size 512 512
-```
-
-The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
-Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
diff --git a/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml b/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml
deleted file mode 100644
index 69a20b0f57b..00000000000
--- a/integrations/examples/ultralytics-yolov3/data/hyp.pruned.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml b/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
deleted file mode 100644
index a146f379e9c..00000000000
--- a/integrations/examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml b/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
deleted file mode 100644
index 57edb510de2..00000000000
--- a/integrations/examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
+++ /dev/null
@@ -1,52 +0,0 @@
-# parameters
-nc: 80  # number of classes
-depth_multiple: 1.0  # model depth multiple
-width_multiple: 1.0  # layer channel multiple
-act: nn.LeakyReLU  # activations
-
-# anchors
-anchors:
-  - [10,13, 16,30, 33,23]  # P3/8
-  - [30,61, 62,45, 59,119]  # P4/16
-  - [116,90, 156,198, 373,326]  # P5/32
-
-# darknet53 backbone
-backbone:
-  # [from, number, module, args]
-  [[-1, 1, Conv, [32, 3, 1]],  # 0
-   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
-   [-1, 1, Bottleneck, [64]],
-   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
-   [-1, 2, Bottleneck, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
-   [-1, 8, Bottleneck, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
-   [-1, 8, Bottleneck, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
-   [-1, 4, Bottleneck, [1024]],  # 10
-  ]
-
-# YOLOv3-SPP head
-head:
-  [[-1, 1, Bottleneck, [1024, False]],
-   [-1, 1, SPP, [512, [5, 9, 13]]],
-   [-1, 1, Conv, [1024, 3, 1]],
-   [-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
-
-   [-2, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Bottleneck, [512, False]],
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
-
-   [-2, 1, Conv, [128, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
-   [-1, 1, Bottleneck, [256, False]],
-   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
-
-   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
-  ]
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
deleted file mode 100644
index d5fc87fb2b3..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
+++ /dev/null
@@ -1,208 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 300
-
-# pruning hyperparameters
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 80
-update_frequency: &pruning_update_frequency 0.5
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned
-
-This recipe creates a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 97% recovery of its baseline accuracy on the COCO detection dataset.
-Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
-[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned.yaml \
-    --name yolov3-spp-lrelu-pruned
-```
-
-hyp.pruned.yaml:
-```yaml
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
deleted file mode 100644
index c04ef0a581d..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
+++ /dev/null
@@ -1,207 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 80
-
-# pruning hyperparameters
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 0.5
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned Short
-
-This recipe creates a sparse [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened schedule as compared to the original pruned recipe.
-It will train faster, but will recover slightly worse.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.short.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned.yaml \
-    --name yolov3-spp-lrelu-pruned
-```
-
-hyp.pruned.yaml:
-```yaml
-lr0: 0.005
-lrf: 0.1
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
deleted file mode 100644
index e9108a49440..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
+++ /dev/null
@@ -1,286 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 242
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 240
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 100
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized
-
-This recipe creates a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 94% recovery of its baseline accuracy on the COCO detection dataset.
-Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
-[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
deleted file mode 100644
index c25340e5a41..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
+++ /dev/null
@@ -1,286 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 52
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 50
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 23
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized
-
-This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened shcedule as compared to the original pruned recipe.
-It will train faster, but will recover slightly worse.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.pruned.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.short.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
deleted file mode 100644
index 29b26e134eb..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
+++ /dev/null
@@ -1,285 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR variables
-num_epochs: &num_epochs 5
-init_lr: &init_lr 0.0032
-
-# Pruning Epoch/LR variables
-pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
-pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
-pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
-
-# Quantization Epoch/LR variables
-quantization_start_epoch: &quantization_start_epoch 4
-quantization_init_lr: &quantization_init_lr 0.0032
-
-# Block pruning management variables
-pruning_start_epoch: &pruning_start_epoch 0
-pruning_end_epoch: &pruning_end_epoch 2
-pruning_update_frequency: &pruning_update_frequency 0.5
-init_sparsity: &init_sparsity 0.05
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.85
-  
-# Modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-  # pruning phase
-  - !SetLearningRateModifier
-    start_epoch: 0.0
-    learning_rate: *init_lr
-
-  # pruning recovery phase
-  - !LearningRateModifier
-    constant_logging: False
-    end_epoch: *quantization_start_epoch
-    init_lr: *pruning_recovery_start_lr
-    lr_class: StepLR
-    lr_kwargs: {
-        'step_size': *pruning_recovery_lr_step_size,
-        'gamma': *pruning_recovery_lr_gamma
-      }
-    start_epoch: *pruning_end_epoch
-    update_frequency: -1.0
-    
-  - !SetWeightDecayModifier
-    start_epoch: *pruning_end_epoch
-    weight_decay: 0.0
-    
-  # quantization aware training phase
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !GMPruningModifier
-    params:
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.26.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_none_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.12.cv1.conv.weight
-      - model.14.conv.weight
-      - model.2.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.22.conv.weight
-      - model.26.cv1.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.28.m.0.weight
-      - model.28.m.2.weight
-      - model.4.0.cv1.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.7.cv1.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.1.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.11.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.19.cv2.conv.weight
-      - model.2.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv2.conv.weight
-      - model.28.m.1.weight
-      - model.3.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv2.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_mid_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-  - !GMPruningModifier
-    params:
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv2.conv.weight
-      - model.13.conv.weight
-      - model.15.conv.weight
-      - model.20.cv1.conv.weight
-      - model.21.conv.weight
-      - model.23.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_high_target_sparsity
-    mask_type: *mask_type
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Test Recipe
-
-This is a test recipe useful for quickly evaluating the time and resources needed for pruning and quantizing a model.
-In addition, it offers a quick integration tests pathway.
-This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that will not be accurate.
-Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
-
-Note that half-precision, EMA, and pickling are not supported for quantization.
-Therefore, once quantization is run, all three will be disabled for the training pipeline.
-This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
-
-## Weights and Biases
-
-- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-*script command:*
-
-```
-python train.py \
-    --recipe ../recipes/yolov3-spp.test.md \
-    --weights PRETRAINED_WEIGHTS \
-    --data voc.yaml \
-    --hyp ../data/hyp.pruned_quantized.short.yaml \
-    --name yolov3-spp-lrelu-pruned-quantized
-```
-
-hyp.prune_quantized.yaml:
-```yaml
-lr0: 0.0
-lrf: 0.0
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 40.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-```
\ No newline at end of file
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
deleted file mode 100644
index 2a21412a138..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
+++ /dev/null
@@ -1,169 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR Hyperparams
-num_epochs: &num_epochs 50
-
-# Pruning Hyperparams
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 5
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 1
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.92
-
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params:
-      - model.1.conv.weight
-      - model.2.cv1.conv.weight
-      - model.2.cv2.conv.weight
-      - model.3.conv.weight
-      - model.4.0.cv1.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.3.cv2.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv1.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv1.conv.weight
-      - model.11.cv2.conv.weight
-      - model.12.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.13.conv.weight
-      - model.14.conv.weight
-      - model.15.conv.weight
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.19.cv2.conv.weight
-      - model.20.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.21.conv.weight
-      - model.22.conv.weight
-      - model.23.conv.weight
-      - model.26.cv1.conv.weight
-      - model.26.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.27.1.cv2.conv.weight
-
-  - !GMPruningModifier
-    params:
-      - model.28.m.0.weight
-      - model.28.m.1.weight
-      - model.28.m.2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
----
-
-# YOLOv3-SPP Pruned Transfer Learning
-
-This recipe transfer learns from a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
-It was originally tested on the VOC dataset and achieved 0.84 mAP@0.5.
-
-Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-The training results for this recipe are made available through Weights and Biases for easy viewing.
-
-- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/3kvb4neh)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
- ```python
-from sparsezoo import Model
-
-stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
-model = Model(stub)
-downloaded_path = model.path
-print(f'Model with stub {stub} downloaded to {downloaded_path}.')
-```
-
-*script command:*
-
-```bash
-python train.py \
-    --data voc.yaml \
-    --cfg ../models/yolov3-spp.lrelu.yaml \
-    --weights DOWNLOADED_PATH \
-    --hyp data/hyp.finetune.yaml \
-    --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
-```
diff --git a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md b/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
deleted file mode 100644
index 8cf05b9d59c..00000000000
--- a/integrations/examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+++ /dev/null
@@ -1,238 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
----
-# General Epoch/LR Hyperparams
-num_epochs: &num_epochs 52
-warmup_lr: &warmup_lr 0.0
-warmup_lr_bias: &warmup_lr_bias 0.05
-init_lr: &init_lr 0.0032
-final_lr: &final_lr 0.00038
-
-# Pruning Hyperparams
-init_sparsity: &init_sparsity 0.05
-pruning_start_epoch: &pruning_start_epoch 5
-pruning_end_epoch: &pruning_end_epoch 40
-update_frequency: &pruning_update_frequency 1
-mask_type: &mask_type [1, 4]
-
-prune_none_target_sparsity: &prune_none_target_sparsity 0.4
-prune_low_target_sparsity: &prune_low_target_sparsity 0.75
-prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
-prune_high_target_sparsity: &prune_high_target_sparsity 0.92
-
-# Quantization Hyperparams
-quantization_start_epoch: &quantization_start_epoch 50
-quantization_init_lr: &quantization_init_lr 0.0038
-
-# modifiers
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: *num_epochs
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 100
-    end_epoch: 160
-    lr_func: cosine
-    init_lr: *init_lr
-    final_lr: *final_lr
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *warmup_lr
-    final_lr: *init_lr
-    param_groups: [0, 1]
-    
-  - !LearningRateFunctionModifier
-    start_epoch: 0
-    end_epoch: 3
-    lr_func: linear
-    init_lr: *warmup_lr_bias
-    final_lr: *init_lr
-    param_groups: [2]
-    
-  - !SetLearningRateModifier
-    start_epoch: *quantization_start_epoch
-    learning_rate: *quantization_init_lr
-
-pruning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params:
-      - model.1.conv.weight
-      - model.2.cv1.conv.weight
-      - model.2.cv2.conv.weight
-      - model.3.conv.weight
-      - model.4.0.cv1.conv.weight
-      - model.4.0.cv2.conv.weight
-      - model.4.1.cv1.conv.weight
-      - model.4.1.cv2.conv.weight
-      - model.5.conv.weight
-      - model.6.0.cv1.conv.weight
-      - model.6.0.cv2.conv.weight
-      - model.6.1.cv1.conv.weight
-      - model.6.1.cv2.conv.weight
-      - model.6.2.cv1.conv.weight
-      - model.6.2.cv2.conv.weight
-      - model.6.3.cv1.conv.weight
-      - model.6.3.cv2.conv.weight
-      - model.6.4.cv1.conv.weight
-      - model.6.4.cv2.conv.weight
-      - model.6.5.cv1.conv.weight
-      - model.6.5.cv2.conv.weight
-      - model.6.6.cv1.conv.weight
-      - model.6.6.cv2.conv.weight
-      - model.6.7.cv1.conv.weight
-      - model.6.7.cv2.conv.weight
-      - model.7.conv.weight
-      - model.8.0.cv1.conv.weight
-      - model.8.0.cv2.conv.weight
-      - model.8.1.cv1.conv.weight
-      - model.8.1.cv2.conv.weight
-      - model.8.2.cv1.conv.weight
-      - model.8.2.cv2.conv.weight
-      - model.8.3.cv1.conv.weight
-      - model.8.3.cv2.conv.weight
-      - model.8.4.cv1.conv.weight
-      - model.8.4.cv2.conv.weight
-      - model.8.5.cv1.conv.weight
-      - model.8.5.cv2.conv.weight
-      - model.8.6.cv1.conv.weight
-      - model.8.6.cv2.conv.weight
-      - model.8.7.cv1.conv.weight
-      - model.8.7.cv2.conv.weight
-      - model.9.conv.weight
-      - model.10.0.cv1.conv.weight
-      - model.10.0.cv2.conv.weight
-      - model.10.1.cv1.conv.weight
-      - model.10.1.cv2.conv.weight
-      - model.10.2.cv1.conv.weight
-      - model.10.2.cv2.conv.weight
-      - model.10.3.cv1.conv.weight
-      - model.10.3.cv2.conv.weight
-      - model.11.cv1.conv.weight
-      - model.11.cv2.conv.weight
-      - model.12.cv1.conv.weight
-      - model.12.cv2.conv.weight
-      - model.13.conv.weight
-      - model.14.conv.weight
-      - model.15.conv.weight
-      - model.16.conv.weight
-      - model.19.cv1.conv.weight
-      - model.19.cv2.conv.weight
-      - model.20.cv1.conv.weight
-      - model.20.cv2.conv.weight
-      - model.21.conv.weight
-      - model.22.conv.weight
-      - model.23.conv.weight
-      - model.26.cv1.conv.weight
-      - model.26.cv2.conv.weight
-      - model.27.0.cv1.conv.weight
-      - model.27.0.cv2.conv.weight
-      - model.27.1.cv1.conv.weight
-      - model.27.1.cv2.conv.weight
-
-  - !GMPruningModifier
-    params:
-      - model.28.m.0.weight
-      - model.28.m.1.weight
-      - model.28.m.2.weight
-    init_sparsity: *init_sparsity
-    final_sparsity: *prune_low_target_sparsity
-    start_epoch: *pruning_start_epoch
-    end_epoch: *pruning_end_epoch
-    update_frequency: *pruning_update_frequency
-    mask_type: *mask_type
-        
-quantization_modifiers:
-  - !QuantizationModifier
-    start_epoch: *quantization_start_epoch
-    submodules:
-      - model.0
-      - model.1 
-      - model.2
-      - model.3 
-      - model.4
-      - model.5
-      - model.6
-      - model.7
-      - model.8
-      - model.9
-      - model.10
-      - model.11
-      - model.12
-      - model.13
-      - model.14
-      - model.15
-      - model.16
-      - model.17
-      - model.18
-      - model.19
-      - model.20
-      - model.21
-      - model.22
-      - model.23
-      - model.24
-      - model.25
-      - model.26
-      - model.27
----
-
-# YOLOv3-SPP Pruned-Quantized Transfer Learning
-
-This recipe transfer learns from a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
-It was originally tested on the VOC dataset and achieved 0.838 mAP@0.5.
-
-Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
-
-When running, adjust hyperparameters based on training environment and dataset.
-
-## Weights and Biases
-
-The training results for this recipe are made available through Weights and Biases for easy viewing.
-
-- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/2hbvu7w2)
-
-## Training
-
-To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
-Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
-Adjust the script command for your GPU device setup. 
-Ultralytics supports both DataParallel and DDP.
-
-The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
- ```python
-from sparsezoo import Model
-
-stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
-model = Model(stub)
-downloded_path = model.path
-print(f"Model with stub {stub} downloaded to {downloaded_path}.")
-```
-
-*script command:*
-
-```bash
-python train.py \
-    --data voc.yaml \
-    --cfg ../models/yolov3-spp.lrelu.yaml \
-    --weights DOWNLOADED_PATH \
-    --hyp data/hyp.finetune.yaml \
-    --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
-```
diff --git a/integrations/examples/ultralytics-yolov3/setup_integration.sh b/integrations/examples/ultralytics-yolov3/setup_integration.sh
deleted file mode 100644
index b14482e2979..00000000000
--- a/integrations/examples/ultralytics-yolov3/setup_integration.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/bin/bash
-
-# Integration setup command to setup the folder so it is ready to train and sparsify models.
-# Creates a yolov3 folder next to this script with all required dependencies from the ultralytics/yolov3 repository.
-# Command: `bash setup_integration.sh`
-
-git clone https://github.com/neuralmagic/yolov3.git
-cd yolov3
-git checkout release/1.1
-pip install -r requirements.txt
diff --git a/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg b/integrations/examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg
deleted file mode 100644
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diff --git a/integrations/examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md b/integrations/examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
deleted file mode 100644
index cd9ecbee7df..00000000000
--- a/integrations/examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
+++ /dev/null
@@ -1,247 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparsifying YOLOv3 Using Recipes
-
-This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models.
-
-## Overview
-
-Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of Ultralytics’ robust training pipelines. Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-
-* Creating a pre-trained model to establish a baseline. You will set up your custom data and then train the model.
-* Applying a recipe to select the trade off between the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-The examples listed in this tutorial are all performed on the VOC dataset. Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc).
-
-<div style="width: 100%; display: flex; justify-content: center;">
-    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
-        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
-    </a>
-</div>
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Creating a Pre-trained Model
-
-Before applying one of the recipes, you must first create the pre-trained model to sparsify further. The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
-
-Creating a pre-trained model involves two steps: 1) setting up the data and 2) training the model.
-
-**Note**: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
-
-
-### Setting Up the Data
-
-1. For this tutorial, we run the VOC setup script with the following command from the root of the yolov3 repository:
-```bash
-bash data/scripts/get_voc.sh
-```
-2. Download and validation of the VOC dataset will begin and takes around 10 minutes to finish.
-The script downloads the VOC dataset into a `VOC` folder under the parent directory.
-Notie that, once completed, the data is ready for training with the folder structure in the following state:
-```
-|-- VOC
-|   |-- images
-|   |   |-- train
-|   |   `-- val
-|   `-- labels
-|       |-- train
-|       `-- val
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- utils
-|   |-- weights
-|   |-- Dockerfile
-|   |-- LICENSE
-|   |-- README.md
-|   |-- detect.py
-|   |-- hubconf.py
-|   |-- requirements.txt
-|   |-- test.py
-|   |-- train.py
-|   `-- tutorial.ipynb
-```
-
-You are ready to train the model.
-
-### Training the Model
-
-The training command will take a few hours to complete (~3 hours for a Titan RTX). Afterward, you will have a model that achieves roughly 0.85 mAP on the VOC dataset ready for sparsifying.
-
-1. To expedite the training process, you will transfer learn from weights initially trained on the COCO dataset. These are stored in the SparseZoo and accessed with the following Python code. Enter:
-
-```python
-from sparsezoo import Model
-
-model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none")
-checkpoint_path = model.training.default.path
-print(checkpoint_path)
-```
-
-After running, the code has downloaded the checkpoint file to the local system:
-```bash
-downloading...: 100%|██████████| 126342273/126342273 [00:04<00:00, 28306365.97it/s]
-/Users/markkurtz/.cache/sparsezoo/16fe8358-2e91-4b81-a1f2-df85bd1a9ac3/pytorch/model.pth
-```
-
-2. Next, the checkpoint file provides the source weights for training on VOC. Run the following train command from within the yolov3 repository folder:
-```bash
-python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50
-```
-
-The training command creates a `runs` directory under the yolov3 repository directory.
-This directory will contain the outputs from the training run, including experimental results along with the trained model:
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-You are ready to use the weights at `yolov3/runs/train/exp/weights/best.pt` with the Neural Magic recipes to create a sparsified model.
-
-## Applying a Recipe
-
-In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
-The [`recipes` folder](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes) contains multiple files, each offering certain advantages over others. The table below compares these tradeoffs and shows how to run them on the VOC dataset.
-1. Review this table, which lists recipes, commands, and results.
-
-| Recipe Name                                                                                                                                              | Description                                                                                                                     | Train Command                                                                                                                                                                               | VOC mAP@0.5 | Size on Disk | DeepSparse Performance** | Training Epochs (time) | Weights and Biases                                                       |
-|----------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------------|--------------------------|------------------------|--------------------------------------------------------------------------|
-| Baseline                                                                                                                                                 | The baseline, pretrained model originally transfer learned onto the VOC dataset.                                                | ``` python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 ```                                                                                     | 0.857       | 194 MB       | 19.5 img/sec             | 50 (3.21 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/261llnb1) |
-| [Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md)                                 | Creates a highly sparse, FP32 model that recovers close to the baseline model.                                                  | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.md ```                           | 0.858       | 33.6 MB      | 34.0 img/sec             | 300 (20.45 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts) |
-| [Pruned Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md)                     | Creates a highly sparse, FP32 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.short.md ```                     | 0.854       | 33.6 MB      | 34.0 img/sec             | 80 (5.53 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n) |
-| [Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md)             | Creates a highly sparse, INT8 model that recovers reasonably close to the baseline model.                                       | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.md ```       | 0.827       | 13.4 MB      | 90.4 img/sec             | 242 (18.32 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs) |
-| [Pruned Quantized Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md) | Creates a highly sparse, INT8 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.short.md ``` | 0.808       | 13.4 MB      | 90.4 img/sec             | 52 (4.23 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy) |
-| [Test](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md)                                     | A test recipe to test the training pipeline and device for both pruning and quantization in 5 epochs.                           | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.test.md ```                   | 0.702       | 13.4 MB      | 90.4 img/sec             | 5 (17 minutes)         | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60) |
-
-** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
-
-2. Notice the Weights and Biases information in the table, which is very useful for comparing across these runs. The epochs versus mAP@0.5 graph is:
-
-<img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
-
-**Notes About Recipe Selection**
-
-If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using either the  `pruned` or `pruned short` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
-
-If your hardware does support quantized networks (VNNI instruction set on CPUs, for example), we recommend using the `pruned quantized` or `pruned quantized short` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
-
-When running quantized models, the memory footprint for training will significantly increase (roughly 3x). Therefore it is essential to take this into account when selecting a batch size to train at. To ensure no issues with the longer quantized runs, run the quicker test recipe first to ensure your configurations are correct and the training process will complete successfully.
-
-3. To begin applying one of the recipes, use the `--recipe` argument within the Ultralytics [train script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
-In addition, the hyperparameters are changed slightly to better work with the recipe.
-These hyperparameters are stored in appropriately named files under the [`data` directory](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/data) and are passed into the training script using the `--hyp` argument.
-Both of the arguments are combined with our previous training command and VOC pre-trained weights to run the recipes over the model. For example:
-```bash
-python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 --recipe PATH_TO_SPARSIFICATION_RECIPE
-```
-After applying a recipe, you are ready to export for inference.
-
-## Exporting for Inference
-
-This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-When you applied a recipe in the previous step, the sparsification run created a new `exp` directory under the yolov3 runs directory:
-
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |-- exp1
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-The `best.pt` file contains a checkpoint of the best weights measured on the validation set.
-These weights can be loaded into the `train.py` and `test.py` scripts now. However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-The [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-
-1. Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
-
-```bash
-python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
-```
-
-The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-```
-|-- exp
-|-- exp1
-|-- weights
-|   |-- best.pt
-|   |-- best.onnx
-|   `-- last.pt
-`-- ...
-```
-
-2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
- 
-The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
-
-## Wrap-Up
-
-Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models. In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md b/integrations/examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
deleted file mode 100644
index fbcdea5b80f..00000000000
--- a/integrations/examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
+++ /dev/null
@@ -1,217 +0,0 @@
-<!--
-Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-   http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing,
-software distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-# Sparse Transfer Learning With YOLOv3
-
-This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
-
-## Overview
-
-Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo on top of Ultralytics’ robust training pipelines. 
-Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
-This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
-Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
-Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
-- Downloading and preparing a pre-sparsified model.
-- Setting up your custom data.
-- Applying a sparse transfer learning recipe.
-- Exporting an inference graph to reduce its deployment size and run it in an inference engine such as DeepSparse.
-
-The examples listed in this tutorial are all performed on the VOC dataset. 
-Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning).
-
-<div style="width: 100%; display: flex; justify-content: center;">
-    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
-        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
-    </a>
-</div>
-
-## Need Help?
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
-
-## Downloading and Preparing a Pre-pruned Model
-
-First, you need to download the sparsified models from the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
-These models were originally pruned on the COCO dataset achieving the following metrics:
-
-| Sparsification Type | Description                                                                       | COCO mAP@0.5 | Size on Disk | DeepSparse Performance** |
-|---------------------|-----------------------------------------------------------------------------------|--------------|--------------|--------------------------|
-| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.642        | 194 MB       | 19.5 img/sec             |
-| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.624        | 33.6 MB      | 34.0 img/sec             |
-| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.605        | 13.4 MB      | 90.4 img/sec             |
-** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
-
-1) After deciding on which model meets your performance requirements for both speed and accuracy, the following code is used to download the PyTorch checkpoints for the desired model from the SparseZoo:
-    ```python
-    from sparsezoo import Model
-    
-    BASELINE_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none'
-    PRUNED_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
-    PRUNED_QUANT_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
-    
-    stub = PRUNED_QUANT_STUB
-    model = Model(stub)
-    downloded_path = model.path
-    print(f'Model with stub {stub} downloaded to {downloded_path}.')
-    ```
-
-2) Once the desired checkpoint has downloaded, it must be reset for training again.
-    The `utility.py` script within YOLOv3 repository is used for this.
-    First change into the `yolov3` directory using `cd yolov3`.
-    Next run the following command to prepare the checkpoint for training:
-    ```bash
-    python utility.py strip /PATH/TO/DOWNLOADED/WEIGHTS.pt
-    ```
-   
-You are now ready to set up the data for training.
-
-## Setting Up the Data
-
-Note: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). 
-Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
-
-1) For this tutorial, run the VOC setup script with the following command from the root of the `yolov3` repository:
-    
-    `bash data/scripts/get_voc.sh`
-2) Download and validation of the VOC dataset will begin and take around 10 minutes to finish. 
-    The script downloads the VOC dataset into a VOC folder under the parent directory. 
-    Notice that, once completed, the data is ready for training with the folder structure in the following state:
-    ```
-    |-- VOC
-    |   |-- images
-    |   |   |-- train
-    |   |   `-- val
-    |   `-- labels
-    |       |-- train
-    |       `-- val
-    |-- yolov3
-    |   |-- data
-    |   |-- models
-    |   |-- utils
-    |   |-- weights
-    |   |-- Dockerfile
-    |   |-- LICENSE
-    |   |-- README.md
-    |   |-- detect.py
-    |   |-- hubconf.py
-    |   |-- requirements.txt
-    |   |-- test.py
-    |   |-- train.py
-    |   `-- tutorial.ipynb
-    ```
-
-You are ready to transfer learn the model.
-
-## Transfer Learning the Model
-
-The training command will take a few hours to complete (~3 hours for a Titan RTX). 
-Afterward, you will have a sparse model transfer learned onto the VOC dataset.
-
-The command uses the `--recipe` argument to encode the proper hyperparams such that SparseML will enforce the sparsity for all layers.
-Without the proper recipes, the zeroed weights will slowly become dense as the model is trained further.
-The recipes are specific to the sparsification type, so the training command will differ based on if you are transfer learning the pruned quantized, pruned, or baseline model.
-
-1) Select the proper command to run based on the sparsification type of the model you downloaded earlier.
-   Change directory into the `yolov3` folder to use the `train.py` script.
-   - Pruned transfer learning, achieves 0.84 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
-        ```
-   - Pruned-Quantized transfer learning, achieves 0.838 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
-        ```
-     Note: Running quantization-aware training (QAT) will consume up to three times the memory as FP16 training. 
-     It is recommended to use this recipe with a batch size that fits in memory for FP16 as normal at the start, though.
-     This will keep the training times the same until the quantization portion.
-     Then it will fail with an out of memory exception, but you can use the `last.pt` checkpoint with the `--weights` argument to resume the run with a lower batch size for QAT.
-   - Baseline transfer learning, achieves 0.86 mAP@0.5 on the VOC dataset:
-        ```bash
-        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml
-        ```
-
-    Weights and Biases is very useful for comparing across the different runs; the epochs vs mAP@0.5 graph is supplied below:
-    
-    <img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
-
-Afterward, you will have a sparse model trained on the VOC dataset almost ready for inference.
-The training command creates a `runs` directory under the `yolov3` repository directory. 
-This directory will contain the outputs from the training run, including experimental results along with the trained model:
-```
-|-- VOC
-|-- data
-|-- models
-|-- recipes
-|-- tutorials
-|-- yolov3
-|   |-- data
-|   |-- models
-|   |-- runs
-|   |   `-- train
-|   |       |-- exp
-|   |       |   |-- weights
-|   |       |   |   |-- best.pt
-|   |       |   |   `-- last.pt
-|   |       |   |-- F1_curve.png
-|   |       |   |-- PR_curve.png
-|   |       |   |-- P_curve.png
-|   |       |   |-- R_curve.png
-|   |       |   |-- confusion_matrix.png
-|   |       |   `-- ...
-|   |-- train.py
-|   `-- ...
-|-- README.md
-`-- setup_integration.sh
-```
-
-## Exporting for Inference
-
-This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. 
-Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as DeepSparse.
-
-The `best.pt` file contains a checkpoint of the best weights measured on the validation set. 
-These weights can be loaded into the `train.py` and `test.py` scripts now. 
-However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
-
-The [export.py script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
-1) Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
-    ```bash
-    python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
-    ```
-    
-    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
-    ```
-    |-- exp
-    |-- exp1
-    |-- weights
-    |   |-- best.pt
-    |   |-- best.onnx
-    |   `-- last.pt
-    `-- ...
-    ```
-2) Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
-    The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
-    An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
-
-## Wrap-Up
-
-Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned and pruned-quantized YOLOv3 models. 
-In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and exported to ONNX to run through an inference engine.
-
-Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
-
-For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)

From e9724501b1fa0346dd51f8921cb0d93c1e4752cb Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Tue, 14 Mar 2023 20:51:51 -0400
Subject: [PATCH 17/29] readded old-examples

---
 integrations/old-examples/README.md           |  20 +
 .../old-examples/dbolya-yolact/README.md      | 144 +++
 .../dbolya-yolact/recipes/yolact.pruned.md    | 174 ++++
 .../recipes/yolact.pruned_quant.md            | 204 ++++
 .../dbolya-yolact/recipes/yolact.quantized.md |  74 ++
 .../sparsifying_yolact_using_recipes.md       | 226 ++++
 integrations/old-examples/keras/README.md     |  94 ++
 .../old-examples/keras/classification.py      | 898 ++++++++++++++++
 .../keras/notebooks/classification.ipynb      | 433 ++++++++
 .../old-examples/keras/prune_resnet20.py      | 241 +++++
 .../old-examples/keras/recipes/.gitkeep       |   0
 .../old-examples/keras/tutorials/.gitkeep     |   0
 integrations/old-examples/pytorch/README.md   |  99 ++
 .../pytorch/argparser_/nm_argparser_.py       | 282 +++++
 .../pytorch/argparser_/test_nmargparser.py    | 283 +++++
 integrations/old-examples/pytorch/export.py   | 358 +++++++
 .../old-examples/pytorch/lr_analysis.py       | 408 ++++++++
 .../pytorch/notebooks/classification.ipynb    | 366 +++++++
 .../pytorch/notebooks/detection.ipynb         | 372 +++++++
 .../sparse_quantized_transfer_learning.ipynb  | 454 ++++++++
 .../pytorch/notebooks/torchvision.ipynb       | 399 ++++++++
 .../old-examples/pytorch/pr_sensitivity.py    | 416 ++++++++
 .../old-examples/pytorch/recipes/.gitkeep     |   0
 .../classification.transfer_learn_pruned.md   |  98 ++
 ...ication.transfer_learn_pruned_quantized.md | 106 ++
 .../pytorch/recipes/mobilenet-pruned.md       | 107 ++
 .../pytorch/recipes/mobilenet-pruned_quant.md | 160 +++
 .../recipes/resnet50-imagenette-pruned.md     | 145 +++
 .../pytorch/recipes/resnet50-pruned.md        |  83 ++
 .../resnet50-pruned_quant-transfer_learn.md   | 100 ++
 .../pytorch/recipes/resnet50-pruned_quant.md  | 198 ++++
 .../pytorch/torchvision_sparsification.py     | 455 +++++++++
 integrations/old-examples/pytorch/train.py    | 703 +++++++++++++
 .../old-examples/pytorch/tutorials/.gitkeep   |   0
 ...ation_sparse_transfer_learning_tutorial.md | 138 +++
 ...parsifying_pytorch_models_using_recipes.md | 132 +++
 integrations/old-examples/pytorch/utils.py    | 428 ++++++++
 integrations/old-examples/pytorch/vision.py   |  29 +
 .../old-examples/rwightman-timm/README.md     | 116 +++
 .../recipes/vit_base.85.recal.config.yaml     |  83 ++
 .../rwightman-timm/setup_integration.sh       |  11 +
 .../old-examples/tensorflow_v1/README.md      |  93 ++
 .../tensorflow_v1/classification.py           | 965 ++++++++++++++++++
 .../notebooks/classification.ipynb            | 403 ++++++++
 .../tensorflow_v1/recipes/.gitkeep            |   0
 .../tensorflow_v1/tutorials/.gitkeep          |   0
 .../old-examples/ultralytics-yolov3/README.md | 128 +++
 .../ultralytics-yolov3/data/hyp.pruned.yaml   |  27 +
 .../data/hyp.pruned_quantized.yaml            |  27 +
 .../models/yolov3-spp.lrelu.yaml              |  52 +
 .../recipes/yolov3-spp.pruned.md              | 208 ++++
 .../recipes/yolov3-spp.pruned.short.md        | 207 ++++
 .../recipes/yolov3-spp.pruned_quantized.md    | 286 ++++++
 .../yolov3-spp.pruned_quantized.short.md      | 286 ++++++
 .../recipes/yolov3-spp.test.md                | 285 ++++++
 .../yolov3-spp.transfer_learn_pruned.md       | 169 +++
 ...ov3-spp.transfer_learn_pruned_quantized.md | 238 +++++
 .../ultralytics-yolov3/setup_integration.sh   |  10 +
 .../images/pruned-quantized-result.jpeg       | Bin 0 -> 2071890 bytes
 .../images/pruned-quantized-wandb-chart.png   | Bin 0 -> 915074 bytes
 .../images/transfer-learning-wandb-chart.png  | Bin 0 -> 493088 bytes
 .../sparsifying_yolov3_using_recipes.md       | 247 +++++
 .../yolov3_sparse_transfer_learning.md        | 217 ++++
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diff --git a/integrations/old-examples/README.md b/integrations/old-examples/README.md
new file mode 100644
index 00000000000..25f69459dcf
--- /dev/null
+++ b/integrations/old-examples/README.md
@@ -0,0 +1,20 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Examples
+
+This directory contains old examples of SparseML integrated with other libraries. Note that these pathways
+have not been tested recently, but we have left there here as examples of how to integrate SparseML into a new repository.
\ No newline at end of file
diff --git a/integrations/old-examples/dbolya-yolact/README.md b/integrations/old-examples/dbolya-yolact/README.md
new file mode 100644
index 00000000000..8d0be24b308
--- /dev/null
+++ b/integrations/old-examples/dbolya-yolact/README.md
@@ -0,0 +1,144 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML YOLACT Integration
+
+This directory combines the [SparseML](../../) recipe-driven approach with the 
+[dbolya/yolact](https://github.com/dbolya/yolact) repository.
+By integrating the training flows in the `yolact` repository with the SparseML 
+code base,
+we enable model sparsification techniques on the popular 
+[YOLACT architecture](https://arxiv.org/abs/1804.02767)
+creating smaller and faster deployable versions.
+The techniques include, but are not limited to:
+
+- Pruning
+- Quantization
+- Pruning and Quantization
+- Sparse Transfer Learning
+
+## Installation
+
+We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
+A virtual environment can be created using the following commands:
+
+```bash
+python3 -m venv venv # create a venv virtual environment
+source venv/bin/activate # activate venv
+pip install --upgrade pip # upgrade pip
+```
+To begin, install `sparseml[torchvision]>=1.1`
+
+```bash
+pip install "sparseml[torchvision]>=1.1"
+```
+
+Note: This integration requires `python>=3.7,<3.10`
+
+
+## Quick Tour
+
+### Downloading COCO
+
+The `sparseml.yolact.download` utility provides an easy to use interface to download
+the `COCO` dataset.
+
+Simply invoke:
+```bash
+sparseml.yolact.download
+```
+
+To download  a test `COCO` dataset, run `sparseml.yolact.download --test`. For more information
+on this utility append the `--help` option. (Note: by default, the dataset is downloaded to the code execution directory. )
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+`sparseml.yolact.train` adds a `--recipe` argument to the 
+[`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+Example `train` command;
+be sure to provide the correct dataset and recipe path to the utility based on your local setup:
+
+```bash
+sparseml.yolact.train --resume \
+zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--recipe ./recipes/yolact.pruned.md \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
+
+### SparseZoo
+
+| Sparsification Type | Description                                                                       | Zoo Stub                                                                     | COCO mAP@all | Size on Disk | DeepSparse Performance** |
+|---------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------------------|--------------|--------------|--------------------------|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none           | 0.288        | 170 MB       | -- img/sec               |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned90-none       | 0.286        | 30.1 MB      | -- img/sec               |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/pruned82_quant-none | 0.282        | 9.7 MB       | -- img/sec               |
+
+These models can also be viewed on the [SparseZoo Website](https://sparsezoo.neuralmagic.com/?domain=cv&sub_domain=segmentation&page=1).
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name                                | Description                                                                                                           |
+|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [recipes](./recipes)                            | Typical recipes for sparsifying YOLACT models along with any downloaded recipes from the SparseZoo.                   |
+| [yolact](./yolact)                              | Integration repository folder used to train and sparsify YOLACT models (`setup_integration.sh` must run first).       |
+| [README.md](./README.md)                        | Readme file.                                                                                                          |
+| [tutorials](./tutorials)                        | Easy to follow sparsification tutorials for YOLACT  models.                                                            |
+
+### Exporting for Inference
+
+After sparsifying a model, the 
+`sparseml.yolact.export_onnx` utility
+converts the model into deployment formats such as [ONNX](https://onnx.ai/).
+The export process is modified such that the quantized and pruned models are 
+corrected and folded properly.
+
+For example, the following command can be run to export a trained/sparsified YOLACT
+model's checkpoint:
+
+```bash
+sparseml.yolact.export_onnx --checkpoint ./quantized-yolact/model.pth \
+    --name quantized-yolact.onnx
+```
+
+
+### DeepSparse
+
+The [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) accepts ONNX 
+formats and is engineered to significantly speed up inference on CPUs for 
+the sparsified models from this integration. [Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact) scripts can be found in the DeepSparse repository.
+
+## Citation
+```bibtex
+@inproceedings{yolact-iccv2019,
+  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  title     = {YOLACT: {Real-time} Instance Segmentation},
+  booktitle = {ICCV},
+  year      = {2019},
+}
+```
diff --git a/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned.md b/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned.md
new file mode 100644
index 00000000000..b3435b049d2
--- /dev/null
+++ b/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned.md
@@ -0,0 +1,174 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Hyperparams
+num_epochs: 60
+
+# Pruning Hyperparams
+init_sparsity: 0.35
+pruning_start_target: 0
+pruning_end_target: 0.5
+pruning_update_frequency: 0.2
+mask_type: unstructured
+prune_none_target_sparsity: 0.75
+prune_low_target_sparsity: 0.83
+prune_mid_target_sparsity: 0.87
+prune_high_target_sparsity: 0.92
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0
+    end_epoch: eval(num_epochs)
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+    - backbone.layers.0.1.conv1.0.weight
+    - backbone.layers.0.1.conv2.0.weight
+    - backbone.layers.1.0.0.weight
+    - backbone.layers.1.1.conv1.0.weight
+    - backbone.layers.1.1.conv2.0.weight
+    - backbone.layers.1.2.conv1.0.weight
+    - backbone.layers.1.2.conv2.0.weight
+    - backbone._preconv.0.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_none_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.2.0.0.weight
+      - backbone.layers.2.1.conv1.0.weight
+      - backbone.layers.2.1.conv2.0.weight
+      - backbone.layers.2.2.conv1.0.weight
+      - backbone.layers.2.2.conv2.0.weight
+      - backbone.layers.2.3.conv1.0.weight
+      - backbone.layers.2.3.conv2.0.weight
+      - backbone.layers.2.4.conv1.0.weight
+      - backbone.layers.2.4.conv2.0.weight
+      - backbone.layers.2.5.conv1.0.weight
+      - backbone.layers.2.5.conv2.0.weight
+      - backbone.layers.2.6.conv1.0.weight
+      - backbone.layers.2.6.conv2.0.weight
+      - backbone.layers.2.7.conv1.0.weight
+      - backbone.layers.2.7.conv2.0.weight
+      - backbone.layers.2.8.conv1.0.weight
+      - backbone.layers.2.8.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_low_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - proto_net.0.weight
+      - proto_net.2.weight
+      - proto_net.4.weight
+      - proto_net.8.weight
+      - proto_net.10.weight
+      - fpn.lat_layers.0.weight
+      - fpn.lat_layers.1.weight
+      - fpn.lat_layers.2.weight
+      - fpn.pred_layers.0.weight
+      - fpn.pred_layers.1.weight
+      - fpn.pred_layers.2.weight
+      - fpn.downsample_layers.0.weight
+      - fpn.downsample_layers.1.weight
+      - prediction_layers.0.upfeature.0.weight
+      - prediction_layers.0.bbox_layer.weight
+      - prediction_layers.0.conf_layer.weight
+      - prediction_layers.0.mask_layer.weight
+      - semantic_seg_conv.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_mid_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.3.0.0.weight
+      - backbone.layers.3.1.conv1.0.weight
+      - backbone.layers.3.1.conv2.0.weight
+      - backbone.layers.3.2.conv1.0.weight
+      - backbone.layers.3.2.conv2.0.weight
+      - backbone.layers.3.3.conv1.0.weight
+      - backbone.layers.3.3.conv2.0.weight
+      - backbone.layers.3.4.conv1.0.weight
+      - backbone.layers.3.4.conv2.0.weight
+      - backbone.layers.3.5.conv1.0.weight
+      - backbone.layers.3.5.conv2.0.weight
+      - backbone.layers.3.6.conv1.0.weight
+      - backbone.layers.3.6.conv2.0.weight
+      - backbone.layers.3.7.conv1.0.weight
+      - backbone.layers.3.7.conv2.0.weight
+      - backbone.layers.3.8.conv1.0.weight
+      - backbone.layers.3.8.conv2.0.weight
+      - backbone.layers.4.0.0.weight
+      - backbone.layers.4.1.conv1.0.weight
+      - backbone.layers.4.1.conv2.0.weight
+      - backbone.layers.4.2.conv1.0.weight
+      - backbone.layers.4.2.conv2.0.weight
+      - backbone.layers.4.3.conv1.0.weight
+      - backbone.layers.4.3.conv2.0.weight
+      - backbone.layers.4.4.conv1.0.weight
+      - backbone.layers.4.4.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_high_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_epochs)
+    end_epoch: eval(pruning_end_target * num_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+---
+
+# YOLACT Pruned
+
+
+This recipe creates a sparse, [YOLACT](https://github.com/dbolya/yolact) model 
+that recovers to 99.37% of its baseline accuracy on the COCO dataset 
+(50.16, 46.57 mAP@0.5 baseline Vs 49.36, 46.37 mAP@0.5 for this recipe for bounding box, mask).
+Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup. 
+
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
diff --git a/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned_quant.md b/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned_quant.md
new file mode 100644
index 00000000000..5b2c365710c
--- /dev/null
+++ b/integrations/old-examples/dbolya-yolact/recipes/yolact.pruned_quant.md
@@ -0,0 +1,204 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Hyperparams
+num_epochs: 62
+init_lr: 0.001
+num_quantization_epochs: 2
+num_pruning_epochs: eval(num_epochs - num_quantization_epochs)
+quantization_observer_end_target: 0.5
+
+# Pruning Hyperparams
+init_sparsity: 0.2
+pruning_start_target: 0
+pruning_end_target: 0.5
+pruning_update_frequency: 0.2
+mask_type: [1, 4]
+prune_none_target_sparsity: 0.6
+prune_low_target_sparsity: 0.7
+prune_mid_target_sparsity: 0.8
+prune_high_target_sparsity: 0.85
+
+# quantization hyperparams
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0
+    end_epoch: eval(num_epochs)
+
+  # pruning
+  - !SetWeightDecayModifier
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    weight_decay: 0.0005
+
+  - !LearningRateFunctionModifier
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    lr_func: linear
+    init_lr: eval(init_lr)
+    final_lr: eval(0.01 * init_lr)
+  # quantization
+  - !SetWeightDecayModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    weight_decay: 0.0
+
+  - !LearningRateFunctionModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    end_epoch: eval(num_epochs)
+    lr_func: linear
+    init_lr: eval(0.01 * init_lr)
+    final_lr: eval(0.001 * init_lr)
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+    - backbone.layers.0.1.conv1.0.weight
+    - backbone.layers.0.1.conv2.0.weight
+    - backbone.layers.1.0.0.weight
+    - backbone.layers.1.1.conv1.0.weight
+    - backbone.layers.1.1.conv2.0.weight
+    - backbone.layers.1.2.conv1.0.weight
+    - backbone.layers.1.2.conv2.0.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_none_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.2.0.0.weight
+      - backbone.layers.2.1.conv1.0.weight
+      - backbone.layers.2.1.conv2.0.weight
+      - backbone.layers.2.2.conv1.0.weight
+      - backbone.layers.2.2.conv2.0.weight
+      - backbone.layers.2.3.conv1.0.weight
+      - backbone.layers.2.3.conv2.0.weight
+      - backbone.layers.2.4.conv1.0.weight
+      - backbone.layers.2.4.conv2.0.weight
+      - backbone.layers.2.5.conv1.0.weight
+      - backbone.layers.2.5.conv2.0.weight
+      - backbone.layers.2.6.conv1.0.weight
+      - backbone.layers.2.6.conv2.0.weight
+      - backbone.layers.2.7.conv1.0.weight
+      - backbone.layers.2.7.conv2.0.weight
+      - backbone.layers.2.8.conv1.0.weight
+      - backbone.layers.2.8.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_low_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - proto_net.0.weight
+      - proto_net.2.weight
+      - proto_net.4.weight
+      - proto_net.8.weight
+      - proto_net.10.weight
+      - fpn.lat_layers.0.weight
+      - fpn.lat_layers.1.weight
+      - fpn.lat_layers.2.weight
+      - fpn.pred_layers.0.weight
+      - fpn.pred_layers.1.weight
+      - fpn.pred_layers.2.weight
+      - fpn.downsample_layers.0.weight
+      - fpn.downsample_layers.1.weight
+      - prediction_layers.0.upfeature.0.weight
+      - prediction_layers.0.bbox_layer.weight
+      - prediction_layers.0.conf_layer.weight
+      - prediction_layers.0.mask_layer.weight
+      - semantic_seg_conv.weight
+
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_mid_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+  - !GMPruningModifier
+    params:
+      - backbone.layers.3.0.0.weight
+      - backbone.layers.3.1.conv1.0.weight
+      - backbone.layers.3.1.conv2.0.weight
+      - backbone.layers.3.2.conv1.0.weight
+      - backbone.layers.3.2.conv2.0.weight
+      - backbone.layers.3.3.conv1.0.weight
+      - backbone.layers.3.3.conv2.0.weight
+      - backbone.layers.3.4.conv1.0.weight
+      - backbone.layers.3.4.conv2.0.weight
+      - backbone.layers.3.5.conv1.0.weight
+      - backbone.layers.3.5.conv2.0.weight
+      - backbone.layers.3.6.conv1.0.weight
+      - backbone.layers.3.6.conv2.0.weight
+      - backbone.layers.3.7.conv1.0.weight
+      - backbone.layers.3.7.conv2.0.weight
+      - backbone.layers.3.8.conv1.0.weight
+      - backbone.layers.3.8.conv2.0.weight
+      - backbone.layers.4.0.0.weight
+      - backbone.layers.4.1.conv1.0.weight
+      - backbone.layers.4.1.conv2.0.weight
+      - backbone.layers.4.2.conv1.0.weight
+      - backbone.layers.4.2.conv2.0.weight
+      - backbone.layers.4.3.conv1.0.weight
+      - backbone.layers.4.3.conv2.0.weight
+      - backbone.layers.4.4.conv1.0.weight
+      - backbone.layers.4.4.conv2.0.weight
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(prune_high_target_sparsity)
+    start_epoch: eval(pruning_start_target * num_pruning_epochs)
+    end_epoch: eval(pruning_end_target * num_pruning_epochs)
+    update_frequency: eval(pruning_update_frequency)
+    mask_type: eval(mask_type)
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(num_epochs - num_quantization_epochs)
+    disable_quantization_observer_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
+    freeze_bn_stats_epoch: eval(num_epochs - (quantization_observer_end_target * num_quantization_epochs))
+---
+
+# YOLACT Pruned Quantized
+
+This recipe creates a sparse quantized, [YOLACT](https://github.com/dbolya/yolact) model that achieves about 
+98% recovery on the COCO dataset when compared against baseline ( 30.61, 28.80 mAP@all baseline Vs 29.82, 28.26 mAP@all for this recipe for bounding box, mask).
+Training was done using 4 GPUs with a total batch size of 64 using the [SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup.
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned_quant.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
diff --git a/integrations/old-examples/dbolya-yolact/recipes/yolact.quantized.md b/integrations/old-examples/dbolya-yolact/recipes/yolact.quantized.md
new file mode 100644
index 00000000000..f992d0420c1
--- /dev/null
+++ b/integrations/old-examples/dbolya-yolact/recipes/yolact.quantized.md
@@ -0,0 +1,74 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+num_epochs: 2
+quantization_start_target: 0
+quantization_end_target: 1
+quantization_observer_end_target: 0.5
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: eval(num_epochs)
+
+  - !SetWeightDecayModifier
+    start_epoch: eval(quantization_start_target * num_epochs)
+    weight_decay: 0.0
+
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: eval(quantization_end_target * num_epochs)
+    lr_func: linear
+    init_lr: 0.00001
+    final_lr: 0.000001
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: eval(quantization_start_target * num_epochs)
+    disable_quantization_observer_epoch: eval(quantization_observer_end_target * num_epochs)
+    freeze_bn_stats_epoch: eval(quantization_observer_end_target * num_epochs)
+---
+
+# YOLACT Quantized
+
+This recipe quantizes a [YOLACT](https://github.com/dbolya/yolact) model.
+Training was done using 4 GPUs with a total batch size of 64 using the 
+[SparseML integration with dbolya/yolact](../).
+When running, adjust hyper-parameters based on the training environment and dataset.
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](../README.md).
+The following command can be used to launch this recipe. 
+Adjust the script command for your GPU device setup.
+YOLACT supports DataParallel. Currently, this repo only supports YOLACT models with a Darknet-53 backbone.
+
+*script command:*
+
+```
+sparseml.yolact.train \
+--recipe=../recipes/yolact.quantized.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
\ No newline at end of file
diff --git a/integrations/old-examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md b/integrations/old-examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
new file mode 100644
index 00000000000..daef9205b9c
--- /dev/null
+++ b/integrations/old-examples/dbolya-yolact/tutorials/sparsifying_yolact_using_recipes.md
@@ -0,0 +1,226 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying YOLACT Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters 
+and instructions needed to create highly accurate pruned and pruned-quantized [YOLACT](https://arxiv.org/abs/1904.02689) segmentation models.
+
+## Overview
+
+Neural Magic's ML team creates recipes that allow anyone to plug in their data and leverage SparseML's recipe-driven 
+approach on top of [YOLACT](https://github.com/dbolya/yolact) training pipelines.
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and 
+quantization, among others. This sparsification process results in many benefits for deployment environments, 
+including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the 
+complicated process and number of hyper-parameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+
+* Creating a pre-trained model to establish a baseline. You will set up your data and then train the model.
+* Applying a recipe to select the trade-off between the amount of recovery to the baseline training performance with 
+the amount of sparsification for inference performance.
+* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) 
+through a compression algorithm to reduce its deployment size and run it in an inference engine such as 
+[DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+The examples listed in this tutorial are all performed on the COCO dataset.
+
+Before diving in, be sure to go through the setup as listed out in the [README](../README.md) for this integration.
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Creating a Pre-trained Model
+
+Before applying one of the recipes, you must first create the pre-trained model to sparsify further. 
+The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. 
+Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
+ 
+Creating a pre-trained model involves three steps: 
+1) Setting up the data.
+2) Fetching a model backbone. 
+3) Training the model.
+
+If training with COCO, skip steps 2 and 3 and use the following baseline SparseZoo stub,
+`zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none` with the `--resume` argument
+in the [`train.py` script](https://github.com/neuralmagic/yolact/blob/master/train.py)
+
+**Note**: If using your custom data, the [YOLACT](https://github.com/dbolya/yolact) repo mentions a post for [training custom data](https://github.com/dbolya/yolact/issues/70#issuecomment-504283008). 
+Otherwise, setup scripts for [COCO](https://cocodataset.org/#home) can be found under the [yolact/data/scripts path](https://github.com/neuralmagic/yolact/tree/master/data/scripts).
+
+### Setting Up the Data
+
+1. For this tutorial, run the COCO setup script with the following command from the root of the `yolact` repository:
+    ```bash
+    sparseml.yolact.download
+    ```
+   
+Based on your internet connection, downloading and validation of the COCO dataset will take around 10 minutes to complete.
+    The script downloads the COCO dataset into a `coco` folder under the data directory.
+Once completed, the data is ready for training with the folder structure in the following state (only directories are shown for brevity):
+```
+    └─ data
+       └─ coco
+          ├── annotations
+          └── images
+ ```
+   
+ You are ready to train the model.
+
+### Downloading Model Backbone
+
+1. Training YOLACT from scratch requires a pretrained-backbone model, 
+   currently SparseML supports training with `DarkNet-53` backbone. 
+   - Download ImageNet-pre-trained [`Darknet-53` backbone](https://drive.google.com/file/d/17Y431j4sagFpSReuPNoFcj9h7azDTZFf/view?usp=sharing)
+     and put it in `./weights` directory. The directory structure should look like the following:
+````
+    └── Project_directory
+        ├── data
+        │   └─ coco
+        │      ├── annotations
+        │      └── images
+        │   
+        └── weights
+            └── darknet53.pth
+````
+
+2) Run the following command to kickstart training
+```bash
+sparseml.yolact.train --resume \
+./weights/darknet53.pth \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images \
+--backbone
+```
+The weights are stored in the `./weights` directory by default and use the `<config>_<epoch>_<iter>.pth` naming 
+convention
+
+3) Validate that the training command was completed successfully by checking under the `./weights` directory for the trained weights.
+   Upon success, the resulting directory structure should look like the following (a few directories are missing content for brevity):
+```
+└── Project_Directory
+    ├── data
+    │    └── coco
+    │        ├── annotations
+    │        └── images
+    │   
+    └── weights
+        ├── darknet53.pth
+        └── yolact_darknet53_54_800000.pth
+ ```
+
+You are ready to use the weights at `./weights/yolact_darknet53_54_800000.pth` with the Neural Magic recipes to create a sparsified model.
+You can also download this baseline, pre-trained checkpoint directly from the [SparseZoo UI](https://sparsezoo.neuralmagic.com/models/cv%2Fsegmentation%2Fyolact-darknet53%2Fpytorch%2Fdbolya%2Fcoco%2Fbase-none),
+or pass its model stub directly to the `--resume` argument while invoking the training script.
+## Applying a Recipe
+
+In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+The [`recipes` folder](../recipes) contains multiple files, each offering certain advantages over others. 
+The table below compares these tradeoffs and shows how to run them on the COCO dataset.
+1. Review this table, which lists recipes, commands, and results.
+
+| Sparsification Type |                                    Description                                    | COCO mAP@all | Size on Disk | DeepSparse Performance** |                                        Commands                                        |
+|:-------------------:|:---------------------------------------------------------------------------------:|:------------:|:------------:|:------------------------:|:--------------------------------------------------------------------------------------:|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.288        | 170 MB       | 29.7 img/sec               | `python train.py`                                                                      |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.286        | 30.1 MB      | 61.6 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned.md`       |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.282        | 9.7 MB       | 144.4 img/sec               | `python train.py --resume weights/model.pth --recipe ../recipe/yolact.pruned_quant.md` |
+
+   \*\* DeepSparse Performance measured on an AWS c5.12xlarge instance with 24 cores, batch size 64, and 550x550 input with version 0.12.0 of the DeepSparse Engine i.e. `deepsparse.benchmark --batch_size 64 --scenario sync [model_path]`
+   
+2. Select a recipe to use on top of the pre-trained model you created.
+
+    - Check your CPU hardware support for quantized networks (VNNI instruction set) using the DeepSparse API:
+      ```bash
+      deepsparse.check_hardware
+      ```
+    - If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using the  `pruned` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
+    - If your hardware does support quantized networks, we recommend using the `pruned quantized` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
+    - When running quantized models, the memory footprint for training will significantly increase (roughly 3x). It is recommended to train at a high batch size at first. This will fail with an out-of-memory exception once quantization starts. Once this happens, use the weights from that run to resume training with lower batch size.
+
+3. To begin applying one of the recipes, use the `--recipe` argument within the YOLACT [train script](https://github.com/neuralmagic/yolact/blob/master/train.py).
+   The recipe argument is combined with our previous training command and COCO pre-trained weights to run the recipes over the model. For example, a command for pruning YOLACT would look like this:
+```bash
+sparseml.yolact.train \
+--recipe=../recipes/yolact.pruned.md \
+--resume=zoo:cv/segmentation/yolact-darknet53/pytorch/dbolya/coco/base-none \
+--save_folder=./pruned \
+--train_info ./data/coco/annotations/instances_train2017.json \
+--validation_info ./data/coco/annotations/instances_val2017.json \
+--train_images ./data/coco/images \
+--validation_images ./data/coco/images
+```
+After applying a recipe, you are ready to export for inference.
+
+## Exporting for Inference
+
+This step loads a checkpoint file along with the recipe used if any and converts it into the more common inference formats. 
+Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+When you applied a recipe in the previous step, the sparsification run created a new `./pruned` directory under the `yolact` directory:
+
+```
+└── Project_directory
+    ├── data
+    │   ├── coco
+    │   │   ├── annotations
+    │   │   └── images
+    │   └── scripts
+    ├── pruned
+    │    └── yolact_darknet53_3_29316.pth 
+    └── weights
+        └── yolact_darknet53_54_800000.pth
+```
+
+These weights under `./pruned` can be loaded into the `sparseml.yolact.train` and `sparseml.yolact.export_onnx` utilities now. 
+However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+[`sparseml.yolact.export_onnx`](https://github.com/neuralmagic/yolact/blob/master/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+
+1. Enter the following command to load the PyTorch graph, convert to ONNX, and correct any misformatted pieces of the graph for the pruned and quantized models.
+
+    ```bash
+    sparseml.yolact.export_onnx --checkpoint PATH_TO_SPARSIFIED_WEIGHTS
+    ```
+    
+    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+
+2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+   The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
+   An example for benchmarking and deploying YOLACT models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/dbolya-yolact).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLACT models for image segmentation tasks. 
+In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported it to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/blob/main/src/deepsparse/yolact/README.md) for an example for benchmarking and deploying YOLACT models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Citation
+
+```bibtex
+@inproceedings{yolact-iccv2019,
+  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  title     = {YOLACT: {Real-time} Instance Segmentation},
+  booktitle = {ICCV},
+  year      = {2019},
+}
+```
diff --git a/integrations/old-examples/keras/README.md b/integrations/old-examples/keras/README.md
new file mode 100644
index 00000000000..c3ffdc4d9b3
--- /dev/null
+++ b/integrations/old-examples/keras/README.md
@@ -0,0 +1,94 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML Keras Integration
+
+This directory combines the SparseML recipe-driven approach with standard Keras training flows for models within
+[Keras Applications](https://keras.io/api/applications/) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom Keras training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Coming soon!
+
+## Tutorials
+
+- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/keras/notebooks/classification.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[tf_keras]`
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=keras) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=keras) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| notebooks            | Jupyter notebooks to walk through sparsifiation within standard Keras training flows                                  |
+| recipes              | Typical recipes for sparsifying Keras models along with any downloaded recipes from the SparseZoo.                    |
+| tutorials            | Tutorial walkthroughs for how to sparsify Keras models using recipes.                                                 |
+| classification.py    | Utility training script for sparsifying classification models in Keras (loads SparseML models).                       |
+| prune_resnet20.py    | Simple example walking through pruning a ResNet-20 model on the Cifar dataset.                                        |
+| README.md            | Readme file.                                                                                                          |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/old-examples/keras/classification.py b/integrations/old-examples/keras/classification.py
new file mode 100644
index 00000000000..35644f01e36
--- /dev/null
+++ b/integrations/old-examples/keras/classification.py
@@ -0,0 +1,898 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Perform tasks on image classification models in Keras including:
+* Model training/pruning
+* Model evaluation
+* ONNX export
+
+
+##########
+train command help:
+usage: classification.py train [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                               [--recipe-path RECIPE_PATH]
+                               [--sparse-transfer-learn] [--eval-mode]
+                               --train-batch-size TRAIN_BATCH_SIZE
+                               --test-batch-size TEST_BATCH_SIZE
+                               [--logs-dir LOGS_DIR]
+                               [--save-best-after SAVE_BEST_AFTER]
+                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
+                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
+Train and/or prune an image classification model
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --model-kwargs MODEL_KWARGS
+                        key word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        Key word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --transfer-class-type TRANSFER_CLASS_TYPE
+                        Type of the target class for transfer ("single" for softmax
+                        function or "multi" for sigmoid function); takes effects only
+                        if the specified dataset is different from the pretrained
+                        dataset, implying a transfer learning usecase
+  --transfer-top-layer TRANSFER_TOP_LAYER
+                        Name of the layer which a custom dense layer will be put on;
+                        None by default to imply the layer right under the top of the
+                        original model will be used (in other words, layer[-2])
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --train-dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for train dataset loading
+  --train-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for train dataset loading
+  --train-prefetch-buffer-size PREFETCH_BUFFER_SIZE
+                        Prefetch buffer size for train dataset loading
+  --val-dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for val dataset loading
+  --val-shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for val dataset loading
+  --val-prefetch-buffer-size PREFETCH_BUFFER_SIZE
+                        Prefetch buffer size for val dataset loading
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. If set to
+                        'transfer_learning', then will create a schedule to
+                        enable sparse transfer learning. Recipe from SparseZoo
+                        could be specified using model stub starting with "zoo:"
+  --sparse-transfer-learn
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode           Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
+                        epochs to save checkpoints at
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+################
+Evaluate command help:
+usage: classification.py evaluate [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               --test-batch-size TEST_BATCH_SIZE
+Evaluate an image classification model
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while evaluating
+################
+Export command help:
+usage: classification.py export [-h] --arch-key ARCH_KEY
+                                [--pretrained PRETRAINED]
+                                [--pretrained-dataset PRETRAINED_DATASET]
+                                [--checkpoint-path CHECKPOINT_PATH]
+                                --dataset-path DATASET_PATH
+                                [--dataset-kwargs DATASET_KWARGS]
+                                [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                                [--num-samples NUM_SAMPLES]
+                                [--onnx-opset OPSET]
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. Specify "zoo" to load a model from SparseZoo
+                        based on other information such as arch key and pretrained
+                        dataset.
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.keras.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        key word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --num-samples NUM_SAMPLES
+                        Number of samples to generate, default to 100
+  --onnx-opset OPSET
+                        The ONNX opset to export to, default to 11
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --export-debug-mode EXPORT_DEBUG
+                        The debug mode for ONNX export (passed into keras2onnx).
+                        Default to False.
+
+
+############
+EXAMPLES
+############
+
+#############
+Example command for fine-tuning a ResNet50 model from Keras Application, pretrained on
+Imagenet, for the Imagenette dataset. The recipe could be used to define learning rate
+schedule. Note that there're no pruning performed from this experiment.
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --pretrained-dataset imagenet \
+     --dataset imagenette \
+     --dataset-path <DATASET_DIR> \
+     --train-batch-size 64 \
+     --recipe-path <RECIPE_PATH>
+############
+Example command for pruning a model with a recipe. Assuming that a ResNet50 model
+from Keras Application was fine-tuned for the Imagenette dataset. The following
+command specifies the model checkpoint after fine-tuning, and a recipe that could
+be used next to prune its weights:
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --train-batch-size 64 \
+     --recipe-path <RECIPE_PATH>
+
+In the above command, specifying "zoo" for --checkpoint-path forces loading the
+base model from SparseZoo. The following command prunes a ResNet50 from Keras
+Applications fine-tuned for Imagenette dataset, using a recipe from SparseZoo:
+
+python scripts/keras_classification.py train \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path zoo \
+     --pretrained-dataset imagenette \
+     --dataset imagenette \
+     --dataset-path <DATASET_DIR> \
+     --train-batch-size 64 \
+     --recipe-path zoo:cv/classification/resnet_v1-50/keras/\
+        keras.applications/imagenette/pruned-moderate?recipe_type=original
+############
+Example command for loading a ResNet50 model from a checkpoint, then
+evaluate it on the validation of the Imagenette dataset.
+python scripts/keras_classification.py evaluate \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --test-batch-size 64
+############
+Example command for loading a ResNet50 model from a checkpoint, then export it
+into ONNX format. It also generates a number of inputs, outputs and labels.
+python scripts/keras_classification.py export \
+     --arch-key keras_applications.ResNet50 \
+     --checkpoint-path <MODEL_CHECKPOINT> \
+     --dataset imagenette \
+     --dataset-path /hdd/datasets/imagenette \
+     --num-samples <NUM_SAMPLES>
+     --save-dir <EXPORT_DIR>
+"""
+
+import argparse
+import inspect
+import json
+import math
+import os
+from typing import Optional, Tuple
+
+import numpy
+import tensorflow
+
+from sparseml import get_main_logger
+from sparseml.keras.datasets import Dataset, DatasetRegistry
+from sparseml.keras.models import ModelRegistry
+from sparseml.keras.optim import ScheduledModifierManager, remove_pruning_masks
+from sparseml.keras.utils import (
+    LossesAndMetricsLoggingCallback,
+    ModelExporter,
+    TensorBoardLogger,
+    keras,
+)
+from sparseml.utils import create_dirs
+
+
+LOGGER = get_main_logger()
+TRAIN_COMMAND = "train"
+EVAL_COMMAND = "evaluate"
+EXPORT_COMMAND = "export"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run tasks on classification models and datasets "
+        "using the sparseml API"
+    )
+
+    subparsers = parser.add_subparsers(dest="command")
+    train_parser = subparsers.add_parser(
+        TRAIN_COMMAND,
+        description="Train and/or prune an image classification model",
+    )
+    eval_parser = subparsers.add_parser(
+        EVAL_COMMAND,
+        description="Evaluate an image classification model on a dataset",
+    )
+    export_parser = subparsers.add_parser(
+        EXPORT_COMMAND,
+        description="Export a model to onnx as well as "
+        "store sample inputs, outputs, and labels",
+    )
+
+    parsers = [
+        train_parser,
+        eval_parser,
+        export_parser,
+    ]
+    for par in parsers:
+        # general arguments
+        # model args
+        par.add_argument(
+            "--arch-key",
+            type=str,
+            required=True,
+            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception with the list)",
+        )
+        par.add_argument(
+            "--pretrained",
+            type=str,
+            default=True,
+            help="The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]",
+        )
+        par.add_argument(
+            "--pretrained-dataset",
+            type=str,
+            default=None,
+            help="The dataset to load pretrained weights for if pretrained is set. "
+            "Default is None which will load the default dataset for the architecture."
+            " Ex can be set to imagenet, cifar10, etc",
+        )
+        par.add_argument(
+            "--checkpoint-path",
+            type=str,
+            default=None,
+            help="A path to a previous checkpoint to load the state from and "
+            "resume the state for. If provided, pretrained will be ignored",
+        )
+        par.add_argument(
+            "--model-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to model constructor, should be "
+            " given as a json object",
+        )
+
+        # dataset args
+        par.add_argument(
+            "--dataset",
+            type=str,
+            required=True,
+            help="The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or loadable by a "
+            "dataset in sparseml.keras.datasets",
+        )
+        par.add_argument(
+            "--dataset-path",
+            type=str,
+            required=True,
+            help="The root path to where the dataset is stored",
+        )
+        par.add_argument(
+            "--dataset-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to dataset constructor, should be "
+            " given as a json object",
+        )
+
+        # logging and saving
+        par.add_argument(
+            "--model-tag",
+            type=str,
+            default=None,
+            help="A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        )
+        par.add_argument(
+            "--save-dir",
+            type=str,
+            default="keras_classification",
+            help="The path to the directory for saving results",
+        )
+
+    # task specific arguments: training
+    train_parser.add_argument(
+        "--dataset-parallel-calls",
+        type=int,
+        default=4,
+        help="the number of parallel workers for dataset loading",
+    )
+    train_parser.add_argument(
+        "--train-shuffle-buffer-size",
+        type=int,
+        default=None,
+        help="Shuffle buffer size for dataset loading",
+    )
+    train_parser.add_argument(
+        "--train-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for train dataset loading",
+    )
+    train_parser.add_argument(
+        "--test-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for test dataset loading",
+    )
+    train_parser.add_argument(
+        "--recipe-path",
+        type=str,
+        default=None,
+        help="The path to the yaml file containing the modifiers and "
+        "schedule to apply them with. If set to 'transfer_learning', "
+        "then will create a schedule to enable sparse transfer learning",
+    )
+    train_parser.add_argument(
+        "--transfer-class-type",
+        type=str,
+        default="single",
+        help="Type of target function: 'single' for softmax, 'multi' for sigmoid "
+        "or 'linear' for linear function. This option takes effects only when the "
+        "specified dataset is different from the pretrained dataset, implying a "
+        "transfer learning usecase.",
+    )
+    train_parser.add_argument(
+        "--transfer-top-layer",
+        type=str,
+        default=None,
+        help="Name of the layer which a custom dense layer will be put on. Default "
+        "to None to imply the layer right under the top of the original model "
+        "will be used (in other words, layer[-2])",
+    )
+    train_parser.add_argument(
+        "--train-batch-size",
+        type=int,
+        required=True,
+        help="The batch size to use while training",
+    )
+    train_parser.add_argument(
+        "--test-batch-size",
+        type=int,
+        required=False,
+        help="The batch size to use while testing; default to the train " "batch size",
+    )
+    train_parser.add_argument(
+        "--log-dir",
+        type=str,
+        default=os.path.join("keras_classification_train", "tensorboard-logs"),
+        help="The path to the directory for saving logs",
+    )
+    train_parser.add_argument(
+        "--log-epoch",
+        type=bool,
+        default=True,
+        help="Whether logging should be performed at the end of each epoch",
+    )
+    train_parser.add_argument(
+        "--log-batch",
+        type=bool,
+        default=False,
+        help="Whether logging should be performed at the end of each training " "batch",
+    )
+    train_parser.add_argument(
+        "--log-steps",
+        type=int,
+        default=-1,
+        help="Whether logging should be performed after every specified number "
+        "of steps",
+    )
+    train_parser.add_argument(
+        "--save-best-only",
+        type=bool,
+        default=True,
+        help="Save model only with better monitored metric",
+    )
+    train_parser.add_argument(
+        "--optim",
+        type=str,
+        default="SGD",
+        help="The optimizer type to use, e.g., 'Adam', 'SGD' etc",
+    )
+    train_parser.add_argument(
+        "--optim-args",
+        type=json.loads,
+        default={"momentum": 0.9, "nesterov": True},
+        # default={},
+        help="Additional args to be passed to the optimizer passed in"
+        " as a json object",
+    )
+    train_parser.add_argument(
+        "--run-eagerly",
+        type=bool,
+        default=True,
+        help="Run training in eager execution mode",
+    )
+
+    # task specific arguments: evaluation
+    eval_parser.add_argument(
+        "--test-batch-size",
+        type=int,
+        required=False,
+        help="The batch size to use while testing; default to the train " "batch size",
+    )
+    eval_parser.add_argument(
+        "--dataset-parallel-calls",
+        type=int,
+        default=4,
+        help="the number of parallel workers for dataset loading",
+    )
+    eval_parser.add_argument(
+        "--test-prefetch-buffer-size",
+        type=int,
+        default=None,
+        help="Prefetch buffer size for test dataset loading",
+    )
+
+    # task specific arguments: export
+    export_parser.add_argument(
+        "--num-samples",
+        type=int,
+        default=100,
+        help="The number of samples to export along with the model onnx "
+        "and pth files (sample inputs and labels as well as the outputs "
+        "from model execution)",
+    )
+    export_parser.add_argument(
+        "--onnx-opset",
+        type=int,
+        default=11,
+        help="The onnx opset to use for export. Default is 11",
+    )
+    export_parser.add_argument(
+        "--export-debug-mode",
+        type=bool,
+        default=False,
+        help="The debug mode for ONNX export (passed into keras2onnx). "
+        "Default to False.",
+    )
+
+    return parser.parse_args()
+
+
+def setup_save_and_log_dirs(args) -> Tuple[str, Optional[str]]:
+    # Saving dir setup
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+    if not args.model_tag:
+        model_tag = "{}@{}".format(args.arch_key, args.dataset)
+        model_id = model_tag
+        model_inc = 0
+
+        while os.path.exists(os.path.join(save_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+    LOGGER.info("Model directory is set to {}".format(save_dir))
+
+    # log dir setup
+    log_dir = (
+        os.path.abspath(os.path.expanduser(args.log_dir))
+        if args.command == TRAIN_COMMAND
+        else None
+    )
+    if args.command == TRAIN_COMMAND:
+        log_dir = os.path.join(log_dir, model_id)
+        create_dirs(log_dir)
+        LOGGER.info("Logging directory is set to {}".format(log_dir))
+    else:
+        log_dir = None
+    return save_dir, log_dir
+
+
+def create_dataset(
+    args, train: bool, image_size: Tuple[int, int]
+) -> Tuple[Dataset, int]:
+    kwargs = args.dataset_kwargs
+    dataset = DatasetRegistry.create(
+        args.dataset,
+        root=args.dataset_path,
+        train=train,
+        image_size=image_size,
+        **kwargs,
+    )
+    LOGGER.info(
+        "created {} dataset: {}, images to resize to {}".format(
+            "train" if train else "val", dataset, image_size
+        )
+    )
+
+    # get num_classes
+    if args.dataset == "imagefolder":
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+
+    return dataset, num_classes
+
+
+def build_dataset(
+    args, dataset: Dataset, train: bool = True
+) -> tensorflow.data.Dataset:
+    test_batch_size = (
+        args.test_batch_size if args.test_batch_size else args.train_batch_size
+    )
+    batch_size = args.train_batch_size if train else test_batch_size
+    if train:
+        shuffle_buffer_size = (
+            args.train_shuffle_buffer_size
+            if args.train_shuffle_buffer_size
+            else dataset.num_images
+        )
+    else:
+        shuffle_buffer_size = None
+
+    if train:
+        prefetch_buffer_size = (
+            args.train_prefetch_buffer_size
+            if args.train_prefetch_buffer_size
+            else batch_size * 8
+        )
+    else:
+        prefetch_buffer_size = (
+            args.test_prefetch_buffer_size
+            if args.test_prefetch_buffer_size
+            else batch_size * 8
+        )
+    built_dataset = dataset.build(
+        batch_size,
+        repeat_count=1,
+        shuffle_buffer_size=shuffle_buffer_size,
+        prefetch_buffer_size=prefetch_buffer_size,
+        num_parallel_calls=args.dataset_parallel_calls,
+    )
+    return built_dataset
+
+
+def create_model(args, input_shape, num_classes):
+    kwargs = args.model_kwargs
+
+    if args.checkpoint_path == "zoo":
+        zoo_model = ModelRegistry.create_zoo_model(
+            args.arch_key, args.pretrained, args.pretrained_dataset
+        )
+        model_file_paths = zoo_model.download_framework_files(extensions=[".h5"])
+        if not model_file_paths:
+            model_file_paths = zoo_model.download_framework_files(extensions=[".tf"])
+        if not model_file_paths:
+            raise RuntimeError("Error downloading model from SparseZoo")
+        model_file_path = model_file_paths[0]
+        model = keras.models.load_model(model_file_path)
+    else:
+        model = ModelRegistry.create(
+            args.arch_key,
+            args.pretrained,
+            args.checkpoint_path,
+            args.pretrained_dataset,
+            input_shape=input_shape,
+            **kwargs,
+        )
+
+    if (
+        args.pretrained_dataset is not None
+        and args.dataset is not None
+        and args.pretrained_dataset != args.dataset
+    ):
+        # Set up the model for fine-tuning
+        # The trainablility of model's parameters should be controlled
+        # through recipe; we therefore do not set "model.trainable" to False here.
+        if args.transfer_class_type == "single":
+            activation = "softmax"
+        elif args.transfer_class_type == "multi":
+            activation = "sigmoid"
+        elif args.transfer_class_type == "linear":
+            activation = None
+        else:
+            raise ValueError(
+                "Unknown transfer_class_type; expected: 'single', 'softmax' or 'linear'"
+            )
+        if args.transfer_top_layer is not None:
+            transfer_top_layer = None
+            for i in list(range(len(model.layers)))[::-1]:
+                if model.layers[i].name == args.transfer_top_layer:
+                    transfer_top_layer = args.transfer_top_layer
+                    break
+            if transfer_top_layer is None:
+                raise ValueError(
+                    "Top layer for transfer learning {} not found".format(
+                        args.transfer_top_layer
+                    )
+                )
+        else:
+            transfer_top_layer = model.layers[-2]
+        new_output = keras.layers.Dense(num_classes, activation=activation)(
+            transfer_top_layer.output
+        )
+        model = keras.Model(model.input, new_output)
+
+    return model
+
+
+def create_optimizer(args):
+    optim_const = {}
+    for opt_name in dir(keras.optimizers):
+        opt = getattr(keras.optimizers, opt_name)
+        if inspect.isclass(opt) and opt_name != "Optimizer":
+            optim_const[opt_name] = opt
+    if args.optim not in optim_const:
+        raise ValueError(
+            "Unknown optimizer {}. Supported: {}".format(
+                args.optim, list(optim_const.keys())
+            )
+        )
+    optimizer = optim_const[args.optim](**args.optim_args)
+    return optimizer
+
+
+def get_logging_update_freq(args):
+    if args.log_steps > 0:
+        update_freq = args.log_steps
+    elif args.log_batch:
+        update_freq = "batch"
+    elif args.log_epoch:
+        update_freq = "epoch"
+    else:
+        update_freq = None
+    return update_freq
+
+
+def handle_train_command(args):
+    save_dir, log_dir = setup_save_and_log_dirs(args)
+
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    # Load datasets
+    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
+    num_train_images = train_dataset.num_images
+    train_dataset = build_dataset(args, train_dataset, train=True)
+
+    val_dataset, _ = create_dataset(args, train=False, image_size=image_size)
+    val_dataset = build_dataset(args, val_dataset, train=False)
+
+    # Create model
+    model = create_model(args, input_shape, num_classes=num_classes)
+
+    # Create optimizer
+    optimizer = create_optimizer(args)
+
+    # Logging
+    if log_dir:
+        update_freq = get_logging_update_freq(args)
+        if update_freq is None:
+            raise ValueError(
+                "Logging requires update frequency to take effect; use either "
+                "'log-epoch', 'log-batch' or 'log-steps' option."
+            )
+        loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
+    else:
+        loggers = []
+
+    # Model saving
+    checkpoint_filepath = os.path.join(
+        save_dir, "model.{epoch:02d}-{val_accuracy:.2f}.tf"
+    )
+    checkpoint_callback = keras.callbacks.ModelCheckpoint(
+        filepath=checkpoint_filepath,
+        save_weights_only=False,
+        monitor="val_accuracy",
+        mode="max",
+        save_best_only=True,
+        verbose=1,
+    )
+
+    # Manager
+    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
+
+    # Enhance model
+    steps_per_epoch = math.ceil(num_train_images / args.train_batch_size)
+    model, optimizer, callbacks = manager.modify(
+        model, optimizer, steps_per_epoch, loggers=loggers
+    )
+    if loggers:
+        callbacks.append(LossesAndMetricsLoggingCallback(loggers))
+    callbacks.append(checkpoint_callback)
+
+    model.compile(
+        loss=keras.losses.categorical_crossentropy,
+        optimizer=optimizer,
+        metrics=["accuracy"],
+        run_eagerly=args.run_eagerly,
+    )
+    model.fit(
+        train_dataset,
+        epochs=manager.max_epochs,
+        callbacks=callbacks,
+        validation_data=val_dataset,
+    )
+
+
+def handle_eval_command(args):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    # Load dataset
+    val_dataset, num_classes = create_dataset(args, train=False, image_size=image_size)
+    val_dataset = build_dataset(args, val_dataset, train=False)
+
+    # Create model
+    model = create_model(args, input_shape, num_classes=num_classes)
+
+    # Evaluate
+    result = model.evaluate(val_dataset)
+    print(dict(zip(model.metrics_names, result)))
+
+
+def handle_export_command(args):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    image_size = (input_shape[0], input_shape[1])
+
+    train_dataset, num_classes = create_dataset(args, train=True, image_size=image_size)
+    train_dataset = train_dataset.build(args.num_samples)
+
+    model = create_model(args, input_shape, num_classes=num_classes)
+    model = remove_pruning_masks(model)
+    exporter = ModelExporter(model, args.save_dir)
+    exporter.export_onnx(opset=args.onnx_opset, debug_mode=args.export_debug_mode)
+
+    for img_batch, label_batch in train_dataset.take(1):
+        output_batch = model(img_batch)
+        numpy.save(os.path.join(args.save_dir, "inputs.npy"), img_batch)
+        numpy.save(os.path.join(args.save_dir, "outputs.npy"), output_batch)
+        numpy.save(os.path.join(args.save_dir, "labels.npy"), label_batch)
+
+
+def main(args):
+    if args.command == TRAIN_COMMAND:
+        handle_train_command(args)
+    elif args.command == EVAL_COMMAND:
+        handle_eval_command(args)
+    elif args.command == EXPORT_COMMAND:
+        handle_export_command(args)
+    else:
+        raise ValueError("Unknown command passed in")
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+    main(args_)
diff --git a/integrations/old-examples/keras/notebooks/classification.ipynb b/integrations/old-examples/keras/notebooks/classification.ipynb
new file mode 100644
index 00000000000..f08a5b0ab50
--- /dev/null
+++ b/integrations/old-examples/keras/notebooks/classification.ipynb
@@ -0,0 +1,433 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021 Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Keras Classification Model Pruning Using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Inference Engine. You will:\n",
+    "- Set up the model and dataset\n",
+    "- Integrate the Keras training flow with SparseML\n",
+    "- Prune the model using the Keras+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your Keras training flow.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo;\n",
+    "* Tensorflow >=2.1, which includes Keras and TensorBoard;\n",
+    "* keras2onnx.\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "In this notebook, you will prune a simple convolution neural network model trained on the MNIST dataset. The pretrained model's architecture and weights are downloaded from the SparseZoo model repo. The dataset is downloaded directly from  Keras datasets library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up the Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell defines a procedure to download a model from the SparseZoo; additionally, for convenience it also returns the path to an optimization recipe. You construct a Keras model instance from the pretrained to prune in a later step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from sparseml.keras.utils import keras\n",
+    "from sparsezoo import Model\n",
+    "\n",
+    "# Root directory for the notebook artifacts\n",
+    "root_dir = \"./notebooks/keras\"\n",
+    "\n",
+    "def download_model_and_recipe(root_dir: str):\n",
+    "    \"\"\"\n",
+    "    Download pretrained model and a pruning recipe\n",
+    "    \"\"\"\n",
+    "    model_dir = os.path.join(root_dir, \"mnist\")\n",
+    "    zoo_model = Model(model_dir)\n",
+    "\n",
+    "\n",
+    "    model_file_path = zoo_model.training.default.get_file(\"model.h5\").path\n",
+    "    if not os.path.exists(model_file_path) or not model_file_path.endswith(\".h5\"):\n",
+    "        raise RuntimeError(\"Model file not found: {}\".format(model_file_path))\n",
+    "    recipe_file_path = zoo_model.recipes.default.path\n",
+    "    if not os.path.exists(recipe_file_path):\n",
+    "        raise RuntimeError(\"Recipe file not found: {}\".format(recipe_file_path))\n",
+    "    return model_file_path, recipe_file_path\n",
+    "\n",
+    "model_file_path, recipe_file_path = download_model_and_recipe(root_dir)\n",
+    "\n",
+    "print(\"Loading model {}\".format(model_file_path))\n",
+    "model = keras.models.load_model(model_file_path)\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set Up the Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will download the MNIST dataset from Keras datasets library as follows. You will also normalize the data before using it for training and evaluation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "# Number of classes\n",
+    "num_classes = 10\n",
+    "\n",
+    "# Load MNIST dataset\n",
+    "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
+    "\n",
+    "# Normalize data\n",
+    "x_train = x_train.astype('float32') / 255\n",
+    "x_test = x_test.astype('float32') / 255\n",
+    "\n",
+    "# Add batch dimension (for older TF versions)\n",
+    "x_train = numpy.expand_dims(x_train, -1)\n",
+    "x_test = numpy.expand_dims(x_test, -1)\n",
+    "\n",
+    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+    "\n",
+    "print(\"Dataset loaded and normalized\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before pruning the model, you could run the cell below to verify the accuracy of the model on the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = model.evaluate(x_test, y_test)\n",
+    "print(\"Test loss, accuracy: \", res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Pruning the Pretrained Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section, you will prune the above pretrained Keras model using the SparseML model optimization library. Recall that a common training workflow in Keras is first to compile the model with the appropriate losses, metrics and an optimizer, then to train the model using the `fit()` method of the `Model` class. The SparseML library makes it easy to extend this training workflow to perform gradual pruning based on weight magnitudes.\n",
+    "\n",
+    "Given a pretrained model, the pruning workflow can be summarized as follows:\n",
+    "1. Create a recipe for pruning, which could be done effectively using the Sparsify toolkit\n",
+    "2. Instantiate a Keras optimizer instance (such as SGD or Adam)\n",
+    "3. Instantiate a `ScheduledModifierManager` object from the recipe\n",
+    "4. Enhance the model and optimizer with pruning data structures by calling the manager's `modify` method. At this step, you have options to define the loggers used during the pruning process. The results of this step are a model to be pruned, an optimizer that should be used and a list of callbacks\n",
+    "5. [Optional] Add to the callback list any additional callbacks such as model checkpoint and the SparseML built-in LossesAndMetricsLogging callback\n",
+    "6. Compile and fit the modified model using Keras built-in APIs, using the optimizer and callback list\n",
+    "7. Erase the pruning information in the enhanced model, and get back the original model with pruned weights\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, you will set up a directory path for logging and the frequency for the logging update."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "\n",
+    "# Logging directory\n",
+    "log_dir = \"./tensorboard/mnist:\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
+    "print(\"Logging directory: {}\".format(log_dir))\n",
+    "\n",
+    "# Number of steps before the next logging should take place\n",
+    "# Use \"epoch\" or \"batch\" to log at every training epoch or batch (respectively)\n",
+    "update_freq = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell contains the few steps required for pruning using the SparseML library, ultimately resulting in a modified model, optimizer and a list of callbacks incoporating the optimization logics. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "import math\n",
+    "from tensorflow.keras.optimizers import Adam\n",
+    "\n",
+    "from sparseml.keras.optim import ScheduledModifierManager\n",
+    "from sparseml.keras.utils import LossesAndMetricsLoggingCallback, TensorBoardLogger\n",
+    "\n",
+    "# Training batch size\n",
+    "batch_size = 32\n",
+    "\n",
+    "# Number of steps per epoch\n",
+    "steps_per_epoch = math.ceil(len(x_train) / batch_size)\n",
+    "\n",
+    "# Create a manager from the recipe\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_file_path)\n",
+    "\n",
+    "# Create an optimizer\n",
+    "optimizer = Adam()\n",
+    "\n",
+    "# Optional: Create a TensorBoardLogger instance\n",
+    "loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)\n",
+    "\n",
+    "# Modify the model and optimizer\n",
+    "model_for_pruning, optimizer, callbacks = manager.modify(model, optimizer, steps_per_epoch, loggers=loggers)\n",
+    "\n",
+    "# Include your own callbacks. Here you will use the built-in LossesAndMetricsLoggingCallback\n",
+    "callbacks.append(LossesAndMetricsLoggingCallback(loggers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you are ready to compile the modified model using the losses and metrics of your choice, and the optimizer enhanced by the mananger. The last step is to train the model using its `fit()` method, passing in (among other information) the list of callbacks constructed above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compile the modified model\n",
+    "model_for_pruning.compile(\n",
+    "    loss=keras.losses.categorical_crossentropy,\n",
+    "    optimizer=optimizer,\n",
+    "    metrics=['accuracy'],\n",
+    "    run_eagerly=True\n",
+    ")\n",
+    "\n",
+    "# Prune the model\n",
+    "model_for_pruning.fit(x_train, y_train, batch_size=batch_size, epochs=manager.max_epochs,\n",
+    "                      validation_data=(x_test, y_test), shuffle=True, callbacks=callbacks)\n",
+    "\n",
+    "print(\"Pruning finished\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is time to verify the accuracy of the pruned model. Later on you can also check the sparsity level of the ONNX version of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Verify the pruned model's accuracy\n",
+    "res = model_for_pruning.evaluate(x_test, y_test)\n",
+    "print(\"Validation loss, accuracy: \", res)\n",
+    "\n",
+    "# Erase the enhanced information used for pruning \n",
+    "pruned_model = manager.finalize(model_for_pruning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given that you used a TensorBoardLogger, you can now observe the logging information in TensorBoard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext tensorboard\n",
+    "%tensorboard --logdir ./tensorboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Examine the Pruned Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can observe the layers of the pruned Keras model using its `layers` property and `get_weights()` method. First, print out the list of layers of this model, and recall that in this optimization recipe we pruned all the Conv2D layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pruned_model.layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can observe the weights of any Conv2D that you pruned, and notice that the majority of them are zeros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change the layer index to examine the layers. Choose one among 1, 4, 7, 10 as the layer\n",
+    "# indices of the pruned layers.\n",
+    "layer_index = 10\n",
+    "pruned_model.layers[layer_index].get_weights()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To observe the overall sparsity of the model, as well as the sparsity level of each layer, run the following cell. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.keras.utils import sparsity\n",
+    "from pprint import pprint\n",
+    "\n",
+    "model_sparsity, layer_sparsity_dict = sparsity(pruned_model)\n",
+    "print(\"Model sparsity: {}\".format(model_sparsity))\n",
+    "print(\"Layer sparsities:\")\n",
+    "pprint(layer_sparsity_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For Keras, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to.\n",
+    "\n",
+    "#### Note:\n",
+    "The `keras2onnx` is known to cause issues during the conversion; in particular, if you installed Tensorflow 2.4, you might encounter the `AttributeError: 'KerasTensor' object has no attribute 'graph'`. You might need to downgrade to Tensorflow 2.2 or 2.3 for the ONNX export to work properly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.keras.utils import ModelExporter\n",
+    "\n",
+    "save_dir = \"keras_classification\"\n",
+    "onnx_file_name = \"pruned_mnist.onnx\"\n",
+    "\n",
+    "exporter = ModelExporter(pruned_model, output_dir=save_dir)\n",
+    "exporter.export_onnx(name=onnx_file_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the DeepSparse Engine"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/keras/prune_resnet20.py b/integrations/old-examples/keras/prune_resnet20.py
new file mode 100644
index 00000000000..f4429da738b
--- /dev/null
+++ b/integrations/old-examples/keras/prune_resnet20.py
@@ -0,0 +1,241 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Example of pruning a ResNet20-v1 model pretrained on the Cifar-10 dataset.
+The pretrained model and this pruning script were adapted from:
+https://keras.io/zh/integrations/cifar10_resnet/
+
+Run the following command from the top repo directory:
+
+   python3 integrations/keras/prune_resnet20.py
+
+"""
+
+import math
+import os
+from datetime import datetime
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.callbacks import ModelCheckpoint
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+
+from sparseml.keras.optim import ScheduledModifierManager
+from sparseml.keras.utils.callbacks import LossesAndMetricsLoggingCallback
+from sparseml.keras.utils.exporter import ModelExporter
+from sparseml.keras.utils.logger import TensorBoardLogger
+from sparsezoo import Model
+
+
+# Root directory
+root_dir = "./integrations/keras"
+
+# Logging setup
+log_dir = os.path.join(root_dir, "tensorboard", "resnet20_v1")
+log_dir += ":" + datetime.now().strftime("%Y%m%d-%H%M%S")
+update_freq = 100  # Logging update every this many training steps
+
+# Train/dataset setting
+num_classes = 10
+batch_size = 32
+subtract_pixel_mean = True
+data_augmentation = True
+
+# Pruned model directory
+pruned_model_dir = os.path.join(root_dir, "pruned")
+if not os.path.isdir(pruned_model_dir):
+    os.makedirs(pruned_model_dir)
+
+
+def download_model_and_recipe(root_dir: str):
+    """
+    Download pretrained model and a pruning recipe
+    """
+
+    # Use the recipe stub
+    recipe_file_path = (
+        "zoo:cv/classification/resnet_v1-20/keras/sparseml/cifar_10/pruned-conservative"
+    )
+
+    # Load base model to prune
+    base_zoo_model = Model(recipe_file_path)
+    base_zoo_model.path = os.path.join(root_dir, "resnet20_v1")
+    checkpoint = base_zoo_model.training.default
+    model_file_path = checkpoint.get_file("model.h5").path
+    recipe_file_path = base_zoo_model.recipes.default.path
+    if not os.path.exists(model_file_path) or not model_file_path.endswith(".h5"):
+        raise RuntimeError("Model file not found: {}".format(model_file_path))
+
+    return model_file_path, recipe_file_path
+
+
+def load_and_normalize_cifar10(subtract_pixel_mean: bool = True):
+    """
+    Load and normalize the Cifar-10 dataset
+    """
+    # Load the CIFAR10 data.
+    (X_train, y_train), (X_test, y_test) = cifar10.load_data()
+
+    # Normalize data.
+    X_train = X_train.astype("float32") / 255
+    X_test = X_test.astype("float32") / 255
+
+    # If subtract pixel mean is enabled
+    if subtract_pixel_mean:
+        X_train_mean = np.mean(X_train, axis=0)
+        X_train -= X_train_mean
+        X_test -= X_train_mean
+
+    # Convert class vectors to binary class matrices.
+    y_train = tf.keras.utils.to_categorical(y_train, num_classes)
+    y_test = tf.keras.utils.to_categorical(y_test, num_classes)
+
+    return (X_train, y_train), (X_test, y_test)
+
+
+def model_checkpoint_callback():
+    """
+    Create model checkpoint callback
+    """
+    pruned_model_name = "resnet20_v1.pruned.{epoch:03d}.h5"
+    pruned_filepath = os.path.join(pruned_model_dir, pruned_model_name)
+
+    # Prepare a callback for model saving
+    checkpoint = ModelCheckpoint(
+        filepath=pruned_filepath, monitor="val_accuracy", verbose=1, save_best_only=True
+    )
+    return checkpoint
+
+
+def main():
+    print("Load and normalize Cifar-10 dataset")
+    (X_train, y_train), (X_test, y_test) = load_and_normalize_cifar10()
+
+    model_file_path, recipe_file_path = download_model_and_recipe(root_dir)
+
+    print("Load pretrained model")
+    base_model = tf.keras.models.load_model(model_file_path)
+    base_model.summary()
+
+    scores = base_model.evaluate(X_test, y_test, verbose=1)
+    print("Test loss:", scores[0])
+    print("Test accuracy:", scores[1])
+
+    optimizer = tf.keras.optimizers.Adam()
+
+    steps_per_epoch = math.ceil(len(X_train) / batch_size)
+
+    # Enhance the model and optimizer for pruning using the manager
+    loggers = TensorBoardLogger(log_dir=log_dir, update_freq=update_freq)
+    manager = ScheduledModifierManager.from_yaml(recipe_file_path)
+    model_for_pruning, optimizer, callbacks = manager.modify(
+        base_model, optimizer, steps_per_epoch, loggers=loggers
+    )
+    callbacks.append(LossesAndMetricsLoggingCallback(loggers))
+    callbacks.append(model_checkpoint_callback())
+
+    # Compile the enhanced model
+    model_for_pruning.compile(
+        loss=tf.keras.losses.categorical_crossentropy,
+        optimizer=optimizer,
+        metrics=["accuracy"],
+        run_eagerly=True,
+    )
+
+    # Run training and pruning, with or without data augmentation.
+    if not data_augmentation:
+        print("Not using data augmentation.")
+        model_for_pruning.fit(
+            X_train,
+            y_train,
+            batch_size=batch_size,
+            validation_data=(X_test, y_test),
+            shuffle=True,
+            callbacks=callbacks,
+            epochs=manager.max_epochs,
+        )
+    else:
+        print("Using real-time data augmentation.")
+        # This will do preprocessing and realtime data augmentation:
+        datagen = ImageDataGenerator(
+            # set input mean to 0 over the dataset
+            featurewise_center=False,
+            # set each sample mean to 0
+            samplewise_center=False,
+            # divide inputs by std of dataset
+            featurewise_std_normalization=False,
+            # divide each input by its std
+            samplewise_std_normalization=False,
+            # apply ZCA whitening
+            zca_whitening=False,
+            # epsilon for ZCA whitening
+            zca_epsilon=1e-06,
+            # randomly rotate images in the range (deg 0 to 180)
+            rotation_range=0,
+            # randomly shift images horizontally
+            width_shift_range=0.1,
+            # randomly shift images vertically
+            height_shift_range=0.1,
+            # set range for random shear
+            shear_range=0.0,
+            # set range for random zoom
+            zoom_range=0.0,
+            # set range for random channel shifts
+            channel_shift_range=0.0,
+            # set mode for filling points outside the input boundaries
+            fill_mode="nearest",
+            # value used for fill_mode = "constant"
+            cval=0.0,
+            # randomly flip images
+            horizontal_flip=True,
+            # randomly flip images
+            vertical_flip=False,
+            # set rescaling factor (applied before any other transformation)
+            rescale=None,
+            # set function that will be applied on each input
+            preprocessing_function=None,
+            # image data format, either "channels_first" or "channels_last"
+            data_format=None,
+            # fraction of images reserved for validation (strictly between 0 and 1)
+            validation_split=0.0,
+        )
+
+        # Compute quantities required for featurewise normalization
+        # (std, mean, and principal components if ZCA whitening is applied).
+        datagen.fit(X_train)
+
+        # Fit the model on the batches generated by datagen.flow().
+        model_for_pruning.fit_generator(
+            datagen.flow(X_train, y_train, batch_size=batch_size),
+            validation_data=(X_test, y_test),
+            epochs=manager.max_epochs,
+            verbose=1,
+            workers=4,
+            callbacks=callbacks,
+        )
+
+    # Erase pruning masks and export to ONNX model
+    pruned_model = manager.finalize(model_for_pruning)
+    exporter = ModelExporter(pruned_model, output_dir=pruned_model_dir)
+    onnx_model_name = "pruned_resnet20_v1.onnx"
+    exporter.export_onnx(name=onnx_model_name)
+    print(
+        "Model exported to {}".format(os.path.join(pruned_model_dir, onnx_model_name))
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/old-examples/keras/recipes/.gitkeep b/integrations/old-examples/keras/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/keras/tutorials/.gitkeep b/integrations/old-examples/keras/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/pytorch/README.md b/integrations/old-examples/pytorch/README.md
new file mode 100644
index 00000000000..ca0a38fbb0f
--- /dev/null
+++ b/integrations/old-examples/pytorch/README.md
@@ -0,0 +1,99 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML PyTorch Integration
+
+This directory combines the SparseML recipe-driven approach with standard PyTorch training flows for models within
+[torchvision](https://pytorch.org/vision/stable/index.html) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom PyTorch training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Blog: [ResNet-50 on CPUs: Sparsifying for Better Performance on CPUs](https://neuralmagic.com/blog/benchmark-resnet50-with-deepsparse/)
+
+## Tutorials
+
+- [Classification](./notebooks/classification.ipynb)
+- [Detection](./notebooks/detection.ipynb)
+- [Sparse Quantized Transfer Learning](./notebooks/sparse_quantized_transfer_learning.ipynb)
+- [Torchvision](./notebooks/torchvision.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[torchvision]`
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=pytorch) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=pytorch) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name                                             | Description                                                                                                           |
+|--------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [notebooks](./notebooks)                                     | Jupyter notebooks to walk through sparsifiation within standard PyTorch training flows                                |
+| [recipes](./recipes)                                         | Typical recipes for sparsifying PyTorch models along with any downloaded recipes from the SparseZoo.                  |
+| [tutorials](./tutorials)                                     | Tutorial walkthroughs for how to sparsify PyTorch models using recipes.                                               |
+| [README.md](./README.md)                                     | Readme file.                                                                                                          |
+| [torchvision_sparsification.py](./torchvision_sparsification.py) | Example training script for sparsifying PyTorch torchvision classification models.                                    |
+| `sparseml.image_classification.train`                        | Utility training script for sparsifying classification and detection models in PyTorch (loads SparseML models).       |
+| `sparseml.image_classification.export`                       | Utility script for exporting pytorch model to ONNX file along with sample inputs, outputs and labels.                 |
+| `sparseml.image_classification.lr_analysis`                  |Utility script to Run learning rate sensitivity analysis for a desired image classification architecture |`sparseml.image_classification.pr_sensitivity`                |Utility script to Run a kernel sparsity (pruning) analysis for a desired image classification architecture |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/old-examples/pytorch/argparser_/nm_argparser_.py b/integrations/old-examples/pytorch/argparser_/nm_argparser_.py
new file mode 100644
index 00000000000..f460a012054
--- /dev/null
+++ b/integrations/old-examples/pytorch/argparser_/nm_argparser_.py
@@ -0,0 +1,282 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import dataclasses
+import json
+import re
+import sys
+from argparse import ArgumentParser, ArgumentTypeError
+from copy import copy
+from enum import Enum
+from pathlib import Path
+from typing import Any, Iterable, List, NewType, Optional, Tuple, Union
+
+
+DataClass = NewType("DataClass", Any)
+DataClassType = NewType("DataClassType", Any)
+
+
+# From https://stackoverflow.com/questions/15008758
+# /parsing-boolean-values-with-argparse
+def string_to_bool(v):
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise ArgumentTypeError(
+            f"Truthy value expected: got {v} but expected one of yes/no,"
+            f"true/false, t/f, y/n, 1/0 (case insensitive)."
+        )
+
+
+# Inspired from https://huggingface.co/transformers/_modules
+# /transformers/hf_argparser.html
+class NmArgumentParser(ArgumentParser):
+    """
+    This subclass of `argparse.ArgumentParser` uses type hints on dataclasses
+    to generate arguments.
+
+    The class is designed to play well with the native argparse. In particular,
+    you can add more (non-dataclass backed) arguments to the parser after
+    initialization and you'll get the output back after parsing as an additional
+    namespace. Optional: To create sub argument groups use the
+    `_argument_group_name` attribute in the dataclass.
+
+    Note: __post_init__(...) for specific dataclasses passed is executed only
+    when parse_args_into_dataclasses(...) function is called because it needs
+    actual instantiation of the dataclass.
+    """
+
+    dataclass_types: Iterable[DataClassType]
+
+    def __init__(
+        self, dataclass_types: Union[DataClassType, Iterable[DataClassType]], **kwargs
+    ):
+        """
+        :param dataclass_types: Dataclass type, or list of dataclass types
+            for which we will "fill" instances with the parsed args.
+        :param kwargs: (Optional) Passed to `argparse.ArgumentParser()` in
+            the regular way.
+        """
+        super().__init__(**kwargs)
+        if dataclasses.is_dataclass(dataclass_types):
+            dataclass_types = [dataclass_types]
+        self.dataclass_types = dataclass_types
+        for dataclass_ in self.dataclass_types:
+            self._add_dataclass_arguments(dataclass_)
+
+    def _add_dataclass_arguments(self, dataclass_: DataClassType):
+        if hasattr(dataclass_, "_argument_group_name"):
+            parser = self.add_argument_group(dataclass_._argument_group_name)
+        else:
+            parser = self
+        for field in dataclasses.fields(dataclass_):
+            if not field.init:
+                continue
+
+            name, kwargs = field.name, field.metadata.copy()
+
+            keep_underscores_key = "keep_underscores"
+            keep_underscores = kwargs.get(keep_underscores_key)
+            _field_name = name if keep_underscores else name.replace("_", "-")
+
+            # cleanup
+            if keep_underscores_key in kwargs:
+                del kwargs[keep_underscores_key]
+
+            # field.metadata is not used at all by Data Classes,
+            # it is provided as a third-party extension mechanism.
+            if isinstance(field.type, str):
+                raise ImportError(
+                    "This implementation is not compatible with Postponed "
+                    "Evaluation of Annotations (PEP 563),"
+                    "which can be opted in from Python 3.7 with "
+                    "`from __future__ import annotations`."
+                )
+            typestring = str(field.type)
+            for prim_type in (int, float, str):
+                for collection in (List,):
+                    if (
+                        typestring == f"typing.Union["
+                        f"{collection[prim_type]}, NoneType]"
+                        or typestring == f"typing.Optional" f"[{collection[prim_type]}]"
+                    ):
+                        field.type = collection[prim_type]
+                if (
+                    typestring == f"typing.Union[" f"{prim_type.__name__}, NoneType]"
+                    or typestring == f"typing.Optional[" f"{prim_type.__name__}]"
+                ):
+                    field.type = prim_type
+
+            if isinstance(field.type, type) and issubclass(field.type, Enum):
+                kwargs["choices"] = [x.value for x in field.type]
+                kwargs["type"] = type(kwargs["choices"][0])
+                if field.default is not dataclasses.MISSING:
+                    kwargs["default"] = field.default
+                else:
+                    kwargs["required"] = True
+            elif field.type is bool or field.type == Optional[bool]:
+                if field.default is True:
+                    kwargs_copy = copy(kwargs)
+                    if "help" in kwargs_copy:
+                        kwargs_copy["help"] = f"Do not {kwargs_copy['help'].lower()}"
+                    parser.add_argument(
+                        f"--no-{_field_name}",
+                        action="store_false",
+                        dest=_field_name,
+                        **kwargs_copy,
+                    )
+
+                # Hack because type=bool in argparse does not behave as we want.
+                kwargs["type"] = string_to_bool
+                if field.type is bool or (
+                    field.default is not None
+                    and field.default is not dataclasses.MISSING
+                ):
+                    # Default value is False if we have no default
+                    # when of type bool.
+                    if field.default is dataclasses.MISSING:
+                        default = False
+                    else:
+                        default = field.default
+                    # This is the value that will get picked if
+                    # we don't include --field_name in any way
+                    kwargs["default"] = default
+
+                    # This tells argparse we accept 0 or 1
+                    # value after --field_name
+                    kwargs["nargs"] = "?"
+                    # This is the value that will get picked
+                    # if we do --field_name (without value)
+                    kwargs["const"] = True
+            elif (
+                hasattr(field.type, "__origin__")
+                and re.search(r"^typing\.List\[(.*)\]$", str(field.type)) is not None
+            ):
+                kwargs["nargs"] = "+"
+                kwargs["type"] = field.type.__args__[0]
+                assert all(
+                    x == kwargs["type"] for x in field.type.__args__
+                ), f"{field.name} cannot be a List of mixed types"
+                if field.default_factory is not dataclasses.MISSING:
+                    kwargs["default"] = field.default_factory()
+                elif field.default is dataclasses.MISSING:
+                    kwargs["required"] = True
+            else:
+                kwargs["type"] = field.type
+                if field.default is not dataclasses.MISSING:
+                    kwargs["default"] = field.default
+                elif field.default_factory is not dataclasses.MISSING:
+                    kwargs["default"] = field.default_factory()
+                else:
+                    kwargs["required"] = True
+            parser.add_argument(f"--{_field_name}", **kwargs)
+
+    def parse_args_into_dataclasses(
+        self,
+        args=None,
+        return_remaining_strings=False,
+        look_for_args_file=True,
+        args_filename=None,
+    ) -> Tuple[DataClass, ...]:
+        """
+        Parse command-line args into instances of the specified dataclass types.
+
+        This relies on argparse's `ArgumentParser.parse_known_args`.
+        See the doc at:
+        docs.python.org/3.7/library/argparse.html#argparse.ArgumentParser.
+        parse_args
+
+
+        :param args: List of strings to parse. The default is taken from
+            sys.argv. (same as argparse.ArgumentParser)
+        :param return_remaining_strings: If true, also return a list of
+            remaining argument strings.
+        :param look_for_args_file: If true, will look for a ".args" file with
+            the same base name as the entry point script for this process,
+            and will append its potential content to the command line args.
+        :param args_filename: If not None, will uses this file instead of the
+            ".args" file specified in the previous argument.
+        :returns: Tuple consisting of:
+                    - the dataclass instances in the same order as they were
+                    passed to the initializer.abspath
+                    - if applicable, an additional namespace for more
+                    (non-dataclass backed) arguments added to the parser
+                    after initialization.
+                    - The potential list of remaining argument strings.
+                    (same as argparse.ArgumentParser.parse_known_args)
+        """
+        if args_filename or (look_for_args_file and len(sys.argv)):
+            if args_filename:
+                args_file = Path(args_filename)
+            else:
+                args_file = Path(sys.argv[0]).with_suffix(".args")
+
+            if args_file.exists():
+                fargs = args_file.read_text().split()
+                args = fargs + args if args is not None else fargs + sys.argv[1:]
+                # in case of duplicate arguments the first one has precedence
+                # so we append rather than prepend.
+        namespace, remaining_args = self.parse_known_args(args=args)
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in vars(namespace).items() if k in keys}
+            for k in keys:
+                delattr(namespace, k)
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        if len(namespace.__dict__) > 0:
+            # additional namespace.
+            outputs.append(namespace)
+        if return_remaining_strings:
+            return (*outputs, remaining_args)
+        else:
+            if remaining_args:
+                raise ValueError(
+                    f"Some specified arguments are not used by the "
+                    f"NmArgumentParser: {remaining_args}"
+                )
+
+            return (*outputs,)
+
+    def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
+        """
+        Alternative helper method that does not use `argparse` at all,
+        instead loading a json file and populating the dataclass types.
+        """
+        data = json.loads(Path(json_file).read_text())
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in data.items() if k in keys}
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        return (*outputs,)
+
+    def parse_dict(self, args: dict) -> Tuple[DataClass, ...]:
+        """
+        Alternative helper method that does not use `argparse` at all,
+        instead uses a dict and populating the dataclass types.
+        """
+        outputs = []
+        for dtype in self.dataclass_types:
+            keys = {f.name for f in dataclasses.fields(dtype) if f.init}
+            inputs = {k: v for k, v in args.items() if k in keys}
+            obj = dtype(**inputs)
+            outputs.append(obj)
+        return (*outputs,)
diff --git a/integrations/old-examples/pytorch/argparser_/test_nmargparser.py b/integrations/old-examples/pytorch/argparser_/test_nmargparser.py
new file mode 100644
index 00000000000..a7d18a66b79
--- /dev/null
+++ b/integrations/old-examples/pytorch/argparser_/test_nmargparser.py
@@ -0,0 +1,283 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import unittest
+from argparse import Namespace
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import List, Optional
+
+from nm_argparser_ import NmArgumentParser, string_to_bool
+
+
+def list_field(default=None, metadata=None):
+    return field(default_factory=lambda: default, metadata=metadata)
+
+
+@dataclass
+class BasicExample:
+    foo: int
+    bar: float
+    baz: str
+    flag: bool
+
+
+@dataclass
+class WithDefaultExample:
+    foo: int = 42
+    baz: str = field(default="toto", metadata={"help": "help message"})
+
+
+@dataclass
+class WithDefaultBoolExample:
+    foo: bool = False
+    baz: bool = True
+    opt: Optional[bool] = None
+
+
+class BasicEnum(Enum):
+    titi = "titi"
+    toto = "toto"
+
+
+@dataclass
+class EnumExample:
+    foo: BasicEnum = "toto"
+
+    def __post_init__(self):
+        self.foo = BasicEnum(self.foo)
+
+
+@dataclass
+class OptionalExample:
+    foo: Optional[int] = None
+    bar: Optional[float] = field(default=None, metadata={"help": "help message"})
+    baz: Optional[str] = None
+    ces: Optional[List[str]] = list_field(default=[])
+    des: Optional[List[int]] = list_field(default=[])
+
+
+@dataclass
+class ListExample:
+    foo_int: List[int] = list_field(default=[])
+    bar_int: List[int] = list_field(default=[1, 2, 3])
+    foo_str: List[str] = list_field(default=["Hallo", "Bonjour", "Hello"])
+    foo_float: List[float] = list_field(default=[0.1, 0.2, 0.3])
+
+
+@dataclass
+class RequiredExample:
+    required_list: List[int] = field()
+    required_str: str = field()
+    required_enum: BasicEnum = field()
+
+    def __post_init__(self):
+        self.required_enum = BasicEnum(self.required_enum)
+
+
+@dataclass
+class NonHyphenatedExample:
+    non_hyphenated: int = field(
+        metadata={
+            "keep_underscores": True,
+        }
+    )
+
+
+class NmArgumentParserTest(unittest.TestCase):
+    def argparsersEqual(
+        self, parser: argparse.ArgumentParser, expected_parser: argparse.ArgumentParser
+    ) -> bool:
+        """
+        Small helper to check pseudo-equality of parsed arguments on
+        `ArgumentParser` instances.
+        """
+        self.assertEqual(len(parser._actions), len(expected_parser._actions))
+        for x, y in zip(parser._actions, expected_parser._actions):
+            xx = {k: v for k, v in vars(x).items() if k != "container"}
+            yy = {k: v for k, v in vars(y).items() if k != "container"}
+            self.assertEqual(xx, yy)
+
+    def test_basic(self):
+        parser = NmArgumentParser(BasicExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", type=int, required=True)
+        expected.add_argument("--bar", type=float, required=True)
+        expected.add_argument("--baz", type=str, required=True)
+        expected.add_argument(
+            "--flag", type=string_to_bool, default=False, const=True, nargs="?"
+        )
+        self.argparsersEqual(parser, expected)
+
+        args = ["--foo", "1", "--baz", "quux", "--bar", "0.5"]
+        (example,) = parser.parse_args_into_dataclasses(args, look_for_args_file=False)
+        self.assertFalse(example.flag)
+
+    def test_with_default(self):
+        parser = NmArgumentParser(WithDefaultExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", default=42, type=int)
+        expected.add_argument("--baz", default="toto", type=str, help="help message")
+        self.argparsersEqual(parser, expected)
+
+    def test_with_default_bool(self):
+        parser = NmArgumentParser(WithDefaultBoolExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument(
+            "--foo", type=string_to_bool, default=False, const=True, nargs="?"
+        )
+        expected.add_argument("--no-baz", action="store_false", dest="baz")
+        expected.add_argument(
+            "--baz", type=string_to_bool, default=True, const=True, nargs="?"
+        )
+        expected.add_argument("--opt", type=string_to_bool, default=None)
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args, Namespace(foo=False, baz=True, opt=None))
+
+        args = parser.parse_args(["--foo", "--no-baz"])
+        self.assertEqual(args, Namespace(foo=True, baz=False, opt=None))
+
+        args = parser.parse_args(["--foo", "--baz"])
+        self.assertEqual(args, Namespace(foo=True, baz=True, opt=None))
+
+        args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"])
+        self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
+
+        args = parser.parse_args(["--foo", "False", "--baz", "False", "--opt", "False"])
+        self.assertEqual(args, Namespace(foo=False, baz=False, opt=False))
+
+    def test_with_enum(self):
+        parser = NmArgumentParser(EnumExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument(
+            "--foo", default="toto", choices=["titi", "toto"], type=str
+        )
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args.foo, "toto")
+        enum_ex = parser.parse_args_into_dataclasses([])[0]
+        self.assertEqual(enum_ex.foo, BasicEnum.toto)
+
+        args = parser.parse_args(["--foo", "titi"])
+        self.assertEqual(args.foo, "titi")
+        enum_ex = parser.parse_args_into_dataclasses(["--foo", "titi"])[0]
+        self.assertEqual(enum_ex.foo, BasicEnum.titi)
+
+    def test_with_list(self):
+        parser = NmArgumentParser(ListExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo-int", nargs="+", default=[], type=int)
+        expected.add_argument("--bar-int", nargs="+", default=[1, 2, 3], type=int)
+        expected.add_argument(
+            "--foo-str", nargs="+", default=["Hallo", "Bonjour", "Hello"], type=str
+        )
+        expected.add_argument(
+            "--foo-float", nargs="+", default=[0.1, 0.2, 0.3], type=float
+        )
+
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(
+            args,
+            Namespace(
+                foo_int=[],
+                bar_int=[1, 2, 3],
+                foo_str=["Hallo", "Bonjour", "Hello"],
+                foo_float=[0.1, 0.2, 0.3],
+            ),
+        )
+
+        args = parser.parse_args(
+            "--foo-int 1 --bar-int 2 3 --foo-str parser expected_parser "
+            "c --foo-float 0.1 0.7".split()
+        )
+        self.assertEqual(
+            args,
+            Namespace(
+                foo_int=[1],
+                bar_int=[2, 3],
+                foo_str=["parser", "expected_parser", "c"],
+                foo_float=[0.1, 0.7],
+            ),
+        )
+
+    def test_with_optional(self):
+        parser = NmArgumentParser(OptionalExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--foo", default=None, type=int)
+        expected.add_argument("--bar", default=None, type=float, help="help message")
+        expected.add_argument("--baz", default=None, type=str)
+        expected.add_argument("--ces", nargs="+", default=[], type=str)
+        expected.add_argument("--des", nargs="+", default=[], type=int)
+        self.argparsersEqual(parser, expected)
+
+        args = parser.parse_args([])
+        self.assertEqual(args, Namespace(foo=None, bar=None, baz=None, ces=[], des=[]))
+
+        args = parser.parse_args(
+            "--foo 12 --bar 3.14 --baz 42 --ces parser expected_parser "
+            "c --des 1 2 3".split()
+        )
+        self.assertEqual(
+            args,
+            Namespace(
+                foo=12,
+                bar=3.14,
+                baz="42",
+                ces=["parser", "expected_parser", "c"],
+                des=[1, 2, 3],
+            ),
+        )
+
+    def test_with_required(self):
+        parser = NmArgumentParser(RequiredExample)
+
+        expected = argparse.ArgumentParser()
+        expected.add_argument("--required-list", nargs="+", type=int, required=True)
+        expected.add_argument("--required-str", type=str, required=True)
+        expected.add_argument(
+            "--required-enum", type=str, choices=["titi", "toto"], required=True
+        )
+        self.argparsersEqual(parser, expected)
+
+    def test_parse_dict(self):
+        parser = NmArgumentParser(BasicExample)
+
+        args_dict = {
+            "foo": 12,
+            "bar": 3.14,
+            "baz": "42",
+            "flag": True,
+        }
+
+        parsed_args = parser.parse_dict(args_dict)[0]
+        args = BasicExample(**args_dict)
+        self.assertEqual(parsed_args, args)
+
+    def test_non_hyphenated(self):
+        parser = NmArgumentParser(dataclass_types=NonHyphenatedExample)
+        args = parser.parse_args("--non_hyphenated 1".split())
+
+        self.assertEqual(args, Namespace(non_hyphenated=1))
diff --git a/integrations/old-examples/pytorch/export.py b/integrations/old-examples/pytorch/export.py
new file mode 100644
index 00000000000..230304b6fc4
--- /dev/null
+++ b/integrations/old-examples/pytorch/export.py
@@ -0,0 +1,358 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+usage: export.py [-h] --arch-key ARCH_KEY --dataset DATASET
+                 --dataset-path DATASET_PATH
+                 [--checkpoint-path CHECKPOINT_PATH]
+                 [--num-samples NUM_SAMPLES] [--onnx-opset ONNX_OPSET]
+                 [--use-zipfile-serialization-if-available
+                 USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE]
+                 [--pretrained PRETRAINED]
+                 [--pretrained-dataset PRETRAINED_DATASET]
+                 [--model-kwargs MODEL_KWARGS]
+                 [--dataset-kwargs DATASET_KWARGS]
+                 [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+
+Utility script to export a model to onnx and also store sample
+inputs/outputs
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list
+                        (will raise an exception with the list)
+  --dataset DATASET     The dataset to use for exporting, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder
+                        for a generic dataset setup with an image
+                        folder structure setup like imagenet or
+                        loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the
+                        state from and resume the state for. If
+                        provided, pretrained will be ignored . If using
+                        a SparseZoo recipe, can also provide 'zoo' to
+                        load the base weights associated with that
+                        recipe
+  --num-samples NUM_SAMPLES
+                        The number of samples to export along with the
+                        model onnx and pth files (sample inputs and
+                        labels as well as the outputs from model
+                        execution)
+  --onnx-opset ONNX_OPSET
+                        The onnx opset to use for export.
+  --use-zipfile-serialization-if-available
+                        USE_ZIPFILE_SERIALIZATION_IF_AVAILABLE
+                        for torch >= 1.6.0 only exports the Module's
+                        state dict using the new zipfile serialization.
+                        Default is True, has no affect on lower torch
+                        versions
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default
+                        is true to load the default pretrained weights
+                        for the model. Otherwise should be set to the
+                        desired weights type: [base, optim, optim-
+                        perf]. To not load any weights set to one of
+                        [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will
+                        load the default dataset for the architecture.
+                        Ex can be set to imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model
+                        constructor, should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results
+                        under save-dir, defaults to the model arch and
+                        dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+##########
+Example command for exporting ResNet50:
+python integrations/pytorch/export.py \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
+"""
+import json
+from dataclasses import dataclass, field
+from typing import Any, Optional, Tuple
+
+from torch.nn import Module
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.opset import TORCH_DEFAULT_ONNX_OPSET
+from sparseml.pytorch.utils import ModuleExporter
+from sparseml.utils import convert_to_bool
+
+
+CURRENT_TASK = utils.Tasks.EXPORT
+LOGGER = get_main_logger()
+
+
+@dataclass
+class ExportArgs:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    exporting tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for analysis, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param num_samples: The number of samples to export along with the model
+        onnx and pth files (sample inputs and labels as well as the outputs
+        from model execution). Default is 100.
+    :param onnx_opset: The onnx opset to use for export. Default is 11.
+    :param use_zipfile_serialization_if_available: for torch >= 1.6.0 only
+        exports the Module's state dict using the new zipfile serialization.
+        Default is True, has no affect on lower torch versions.
+    :param pretrained: The type of pretrained weights to use,
+        default is true to load the default pretrained weights for the model.
+        Otherwise should be set to the desired weights type: [base, optim,
+        optim-perf]. To not load any weights set to one of [none, false].
+    :param pretrained_dataset: The dataset to load pretrained weights for if
+        pretrained is set. Default is None which will load the default
+        dataset for the architecture. Ex can be set to imagenet, cifar10, etc.
+    :param model_kwargs: Keyword arguments to be passed to model constructor,
+        should be given as a json object.
+    :param dataset_kwargs: Keyword arguments to be passed to dataset
+        constructor, should be given as a json object.
+    :param model_tag: A tag to use for the model for saving results under
+        save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results.
+    """
+
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception "
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for exporting, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored . If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    num_samples: int = field(
+        default=100,
+        metadata={
+            "help": "The number of samples to export along with the model onnx "
+            "and pth files (sample inputs and labels as well as the "
+            "outputs from model execution)"
+        },
+    )
+
+    onnx_opset: int = field(
+        default=TORCH_DEFAULT_ONNX_OPSET,
+        metadata={
+            "help": "The onnx opset to use for export. "
+            f"Default is {TORCH_DEFAULT_ONNX_OPSET}"
+        },
+    )
+
+    use_zipfile_serialization_if_available: convert_to_bool = field(
+        default=True,
+        metadata={
+            "help": "for torch >= 1.6.0 only exports the Module's state dict "
+            "using the new zipfile serialization. Default is True, "
+            "has no affect on lower torch versions"
+        },
+    )
+
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for "
+            "the model. Otherwise should be set to the desired weights "
+            "type: [base, optim, optim-perf]. To not load any weights "
+            "set to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained "
+            "is set. Default is None which will load the default "
+            "dataset for the architecture. Ex can be set to imagenet, "
+            "cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be  given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.local_rank: int = -1
+        self.is_main_process: bool = True
+
+
+def export(
+    args: ExportArgs, model: Module, val_loader: DataLoader, save_dir: str
+) -> None:
+    """
+    Utility method to export the model and data
+
+    :param args : An ExportArgs object containing config for export task.
+    :param model: loaded model architecture to export
+    :param val_loader: A DataLoader for validation data
+    :param save_dir: Directory to store checkpoints at during exporting process
+    """
+    exporter = ModuleExporter(model, save_dir)
+
+    # export PyTorch state dict
+    LOGGER.info(f"exporting pytorch in {save_dir}")
+
+    exporter.export_pytorch(
+        use_zipfile_serialization_if_available=(
+            args.use_zipfile_serialization_if_available
+        )
+    )
+    onnx_exported = False
+
+    for batch, data in tqdm(
+        enumerate(val_loader),
+        desc="Exporting samples",
+        total=args.num_samples if args.num_samples > 1 else 1,
+    ):
+        if not onnx_exported:
+            # export onnx file using first sample for graph freezing
+            LOGGER.info(f"exporting onnx in {save_dir}")
+            exporter.export_onnx(data[0], opset=args.onnx_opset, convert_qat=True)
+            onnx_exported = True
+
+        if args.num_samples > 0:
+            exporter.export_samples(
+                sample_batches=[data[0]], sample_labels=[data[1]], exp_counter=batch
+            )
+
+
+def export_setup(args_: ExportArgs) -> Tuple[Module, Optional[str], Any]:
+    """
+    Pre-export setup
+
+    :param args_ : An ExportArgs object containing config for export task.
+    """
+    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
+
+    # model creation
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+    model = utils.create_model(args_, num_classes)
+    return model, save_dir, val_loader
+
+
+def main():
+    """
+    Driver function
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=ExportArgs,
+        description="Utility script to export a model to onnx "
+        "and also store sample inputs/outputs",
+    )
+    (args_,) = _parser.parse_args_into_dataclasses()
+    model, save_dir, val_loader = export_setup(args_)
+    export(args_, model, val_loader, save_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/old-examples/pytorch/lr_analysis.py b/integrations/old-examples/pytorch/lr_analysis.py
new file mode 100644
index 00000000000..a9dc0e94058
--- /dev/null
+++ b/integrations/old-examples/pytorch/lr_analysis.py
@@ -0,0 +1,408 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+usage: lr_analysis.py [-h] --batch-size BATCH_SIZE --arch-key ARCH_KEY
+                        --dataset DATASET --dataset-path DATASET_PATH [
+                        --pretrained PRETRAINED]
+                      [--pretrained-dataset PRETRAINED_DATASET] [
+                      --model-kwargs MODEL_KWARGS]
+                      [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
+                      [--save-dir SAVE_DIR] [--device DEVICE]
+                      [--loader-num-workers LOADER_NUM_WORKERS] [
+                      --no-loader-pin-memory]
+                      [--loader-pin-memory [LOADER_PIN_MEMORY]]
+                      [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
+                      [--optim-args OPTIM_ARGS] [--final-lr FINAL_LR]
+                      [--steps-per-measurement STEPS_PER_MEASUREMENT]
+
+Utility script to Run a learning rate sensitivity analysis for a desired
+image classification architecture
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --batch-size BATCH_SIZE
+                        The batch size to use for analysis
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16, mobilenet
+                        put as help to see the full list (will raise an
+                        exception with the list)
+  --dataset DATASET     The dataset to use for analysis, ex: imagenet,
+                        imagenette, cifar10,
+                        etc. Set to imagefolder for a generic dataset setup
+                        with an imagefolder structure setup like imagenet or
+                        loadable by a dataset in sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is
+                        true to load the default pretrained weights for the
+                        model. Otherwise should be set to the desired weights
+                        type: [base, optim, optim-perf]. To not load any
+                        weights set to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if pretrained
+                        is set. Default is None which will load the default
+                        dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for
+                        data parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained
+                        will be ignored. If using a SparseZoo recipe,
+                        can also provide 'zoo' to load the base weights
+                        associated with that recipe
+  --init-lr INIT_LR     The initial learning rate to use for the sensitivity
+                        analysis
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed in
+                        as a json object
+  --final-lr FINAL_LR   The final learning rate to use for the sensitivity
+                        analysis
+  --steps-per-measurement STEPS_PER_MEASUREMENT The number of steps (batches)
+                        to run for each measurement
+#########
+EXAMPLES
+#########
+
+##########
+Example command for running LR sensitivity analysis on mobilenet:
+python integrations/pytorch/lr_analysis.py \
+    --arch-key mobilenet --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012 --batch-size 2
+
+Example command for running LR sensitivity analysis on mobilenet using
+imagenette:
+python integrations/pytorch/lr_analysis.py \
+    --arch-key mobilenet --dataset imagenette \
+    --dataset-path ~/datasets/ --batch-size 2
+"""
+
+import json
+import os
+from dataclasses import dataclass, field
+
+from torch.nn import Module
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import default_exponential_check_lrs, lr_loss_sensitivity
+from sparseml.pytorch.utils import PythonLogger, default_device, model_to_device
+
+
+CURRENT_TASK = utils.Tasks.LR_ANALYSIS
+LOGGER = get_main_logger()
+
+
+@dataclass
+class LRAnalysisArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    learning rate analysis.
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`
+    arguments that can be specified on the command line.
+
+    :param batch_size: The batch size to use for analysis.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for analysis, ex imagenet,
+        imagenette, etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param pretrained: The type of pretrained weights to use default is true to
+        load the default pretrained weights for the model. Otherwise should be
+        set to the desired weights type: [base, optim, optim-perf];
+        To not load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load pretrained
+        weights for; if pretrained is set; Default is None which will load the
+        default dataset for the architecture; Ex can be set to imagenet,
+        cifar10, etc.
+    :param model_kwargs: json object containing keyword arguments to be passed
+        to the model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be passed
+        to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results under
+        save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on (can also include ids
+        for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data loading,
+        default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param init_lr: float representing the initial learning for analysis,
+        default=1e-5.
+    :param optim_args: Additional arguments to be passed in to the optimizer as
+        a json object.
+    :param final_lr: The final learning rate to use for the sensitivity
+        analysis.
+    :param steps_per_measurement: The number of steps (batches) to run for each
+        measurement.
+    """
+
+    batch_size: int = field(metadata={"help": "The batch size to use for analysis"})
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list"
+            "(will raise an exception with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for analysis, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights "
+            "for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained is"
+            "set. Default is None which will load the default dataset for "
+            "the architecture. Ex can be set to imagenet, cifar10, etc.",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor,"
+            " should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for "
+            "data parallel), ex:cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will be"
+            "ignored. If using a SparseZoo recipe, can also provide "
+            "'zoo' to load the base weights associated with that recipe"
+        },
+    )
+
+    init_lr: float = field(
+        default=1e-5,
+        metadata={
+            "help": "The initial learning rate to use for the sensitivity analysis"
+        },
+    )
+
+    optim_args: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Additional args to be passed to the optimizer passed in"
+            " as a json object"
+        },
+    )
+
+    final_lr: float = field(
+        default=0.5,
+        metadata={
+            "help": "The final learning rate to use for the sensitivity analysis",
+        },
+    )
+
+    steps_per_measurement: int = field(
+        default=20,
+        metadata={"help": "The number of steps (batches) to run for each measurement"},
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.is_main_process = True
+        self.local_rank = -1
+        self.rank = -1
+
+
+def lr_sensitivity(
+    args,
+    model: Module,
+    train_loader: DataLoader,
+    save_dir: str,
+) -> None:
+    """
+    Utility function to run learning rate sensitivity analysis
+
+    :param args: An LRAnalysisArguments object containing config for current
+        LR analysis task.
+    :param model: loaded model architecture to analyse
+    :param train_loader: A DataLoader for training data
+    :param save_dir: Directory to save results
+    """
+    # optimizer setup
+    optim = SGD(model.parameters(), lr=args.init_lr, **args.optim_args)
+    LOGGER.info(f"created optimizer: {optim}")
+
+    # loss setup
+    loss = utils.get_loss_wrapper(args.arch_key)
+    LOGGER.info(f"created loss: {loss}")
+
+    # device setup
+    model, device, device_ids = model_to_device(model, args.device)
+
+    # learning rate analysis
+    LOGGER.info(f"running analysis: {loss}")
+    analysis = lr_loss_sensitivity(
+        model,
+        train_loader,
+        loss,
+        optim,
+        device,
+        args.steps_per_measurement,
+        check_lrs=default_exponential_check_lrs(args.init_lr, args.final_lr),
+        trainer_loggers=[PythonLogger()],
+    )
+
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info(f"Saving results in {save_dir}")
+    analysis.save_json(os.path.join(save_dir, "lr_sensitivity.json"))
+    analysis.plot(os.path.join(save_dir, "lr_sensitivity.png"))
+    analysis.print_res()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=LRAnalysisArguments,
+        description="Utility script to Run a "
+        "learning rate sensitivity analysis "
+        "for a desired image classification architecture",
+    )
+    args_, _ = _parser.parse_args_into_dataclasses()
+    save_dir, loggers = utils.get_save_dir_and_loggers(
+        args_,
+        task=CURRENT_TASK,
+    )
+
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    # assume shape [C, S, S] where S is the image size
+    image_size = input_shape[1]
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(
+        args_,
+        image_size,
+        task=CURRENT_TASK,
+    )
+
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+    model = utils.create_model(args_, num_classes)
+    lr_sensitivity(args_, model, train_loader, save_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/old-examples/pytorch/notebooks/classification.ipynb b/integrations/old-examples/pytorch/notebooks/classification.ipynb
new file mode 100644
index 00000000000..6bdac17c274
--- /dev/null
+++ b/integrations/old-examples/pytorch/notebooks/classification.ipynb
@@ -0,0 +1,366 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# PyTorch Classification Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML\n",
+    "- Prune the model using the PyTorch+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 15 minutes on a GPU\n",
+    "- 45 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = ModelRegistry.create(...)\n",
+    "- train_dataset = ImagenetteDataset(...)\n",
+    "- val_dataset = ImagenetteDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"resnet50\",\n",
+    "    pretrained=True,\n",
+    "    pretrained_dataset=\"imagenette\",\n",
+    "    num_classes=10,\n",
+    ")\n",
+    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
+    "        loss = criterion(outputs, labels)\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up PyTorch Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 128\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"classification\",\n",
+    "    architecture=\"resnet_v1\",\n",
+    "    sub_architecture=\"50\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"sparseml\",\n",
+    "    dataset=\"imagenette\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Run model pruning\n",
+    "epoch = manager.min_epochs\n",
+    "while epoch < manager.max_epochs:\n",
+    "    # run training loop\n",
+    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
+    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Training Epoch: {}\\nTraining Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
+    "            epoch_name, train_loss, train_acc\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    # run validation loop\n",
+    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
+    "    val_loss, val_acc = run_model_one_epoch(\n",
+    "        model, val_loader, criterion, device\n",
+    "    )\n",
+    "    print(\n",
+    "        \"Validation Epoch: {}\\nVal Loss: {}\\nTop 1 Acc: {}\\n\".format(\n",
+    "            epoch_name, val_loss, val_acc\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    epoch += 1\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_classification\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/pytorch/notebooks/detection.ipynb b/integrations/old-examples/pytorch/notebooks/detection.ipynb
new file mode 100644
index 00000000000..7a76aa223dd
--- /dev/null
+++ b/integrations/old-examples/pytorch/notebooks/detection.ipynb
@@ -0,0 +1,372 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# PyTorch Detection Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML\n",
+    "- Prune the model using the PyTorch+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 30 minutes on a GPU\n",
+    "- 90 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [SSD](https://arxiv.org/abs/1512.02325) model trained on the [VOC detection dataset](http://host.robots.ox.ac.uk/pascal/VOC/). The model's pretrained weights are downloaded from the SparseZoo model repo. The VOC detection dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "Note, for this notebook, you will use a ResNet18 backbone for the object detector.  This is to save training time and demonstrate the general pruning flow. For better accuracy, you can use a different backbone or model.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = ModelRegistry.create(...)\n",
+    "- train_dataset = VOCDetectionDataset(...)\n",
+    "- val_dataset = VOCDetectionDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import VOCDetectionDataset\n",
+    "from sparseml.pytorch.utils import get_default_boxes_300\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"ssd300_resnet18\",\n",
+    "    pretrained=True,\n",
+    "    pretrained_dataset=\"voc\",\n",
+    "    pretrained_backbone=False,  # no need to download initial weights\n",
+    "    num_classes=21,\n",
+    ")\n",
+    "model_name = model.__class__.__name__\n",
+    "input_shape = ModelRegistry.input_shape(\"ssd300_resnet18\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "default_boxes = get_default_boxes_300(\"voc\")\n",
+    "train_dataset = VOCDetectionDataset(\n",
+    "    train=True, rand_trans=True, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = VOCDetectionDataset(\n",
+    "    train=False, preprocessing_type=\"ssd\", default_boxes=default_boxes\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) to work with our detection model and loss function.  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "from sparseml.pytorch.utils import DEFAULT_LOSS_KEY\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(\n",
+    "    model, data_loader, criterion, device, train=False, optimizer=None\n",
+    "):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = [\n",
+    "            label.to(device) if isinstance(label, torch.Tensor) else label\n",
+    "            for label in labels\n",
+    "        ]\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion((inputs, labels), outputs)[DEFAULT_LOSS_KEY]\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    return loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up Detection Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow.  The loss function and collate function are standard for SSD training and are defined in the sparseml API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "from sparseml.pytorch.datasets import ssd_collate_fn\n",
+    "from sparseml.pytorch.utils import SSDLossWrapper\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 64\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset,\n",
+    "    batch_size,\n",
+    "    shuffle=True,\n",
+    "    pin_memory=True,\n",
+    "    num_workers=12,\n",
+    "    collate_fn=ssd_collate_fn,\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset,\n",
+    "    batch_size,\n",
+    "    shuffle=False,\n",
+    "    pin_memory=True,\n",
+    "    num_workers=12,\n",
+    "    collate_fn=ssd_collate_fn,\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer\n",
+    "criterion = SSDLossWrapper()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning SSD-ResNet18 on the VOC dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"detection\",\n",
+    "    architecture=\"ssd\",\n",
+    "    sub_architecture=\"resnet18_300\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"sparseml\",\n",
+    "    dataset=\"voc\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "epoch = manager.min_epochs\n",
+    "while epoch < manager.max_epochs:\n",
+    "    # run training loop\n",
+    "    epoch_name = \"{}/{}\".format(epoch + 1, manager.max_epochs)\n",
+    "    print(\"Running Training Epoch {}\".format(epoch_name))\n",
+    "    train_loss = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\"Training Epoch: {}\\nTraining Loss: {}\\n\".format(epoch_name, train_loss))\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(\"Running Validation Epoch {}\".format(epoch_name))\n",
+    "    val_loss = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\"Validation Epoch: {}\\nVal Loss: {}\\n\".format(epoch_name, val_loss))\n",
+    "\n",
+    "    epoch += 1\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_detection\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"ssd_resnet18_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 300, 300), name=\"ssd_resnet18_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb b/integrations/old-examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
new file mode 100644
index 00000000000..d5d50f63a23
--- /dev/null
+++ b/integrations/old-examples/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb
@@ -0,0 +1,454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Sparse-Quantized Transfer Learning in PyTorch using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for creating a performant sparse-quantized model\n",
+    "by transfer learning the pruned structure from an already sparse-quantized model.\n",
+    "\n",
+    "Sparse-quantized models combine [pruning](https://neuralmagic.com/blog/pruning-overview/) and [quantization](https://arxiv.org/abs/1609.07061) to reduce both the number of parameters and the precision of the remaining parameters to significantly increase the performance of neural networks. Using these optimizations, your model will obtain significantly better (around 7x vs. unoptimized) performance at inference time using the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse).\n",
+    "\n",
+    "Sparse-quantized transfer learning takes two steps. [SparseML](https://github.com/neuralmagic/sparseml) recipes make it easy to perform these optimizations:\n",
+    "- First, fine-tune a pre-trained sparse model for the transfer dataset while maintaining the pre-trained sparsity structure.\n",
+    "- Second, perform [quantization-aware training (QAT)](https://pytorch.org/blog/introduction-to-quantization-on-pytorch/#quantization-aware-training) to quantize the now sparse model while still holding the same sparsity structure.\n",
+    "\n",
+    "In this notebook, you will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a generic PyTorch training flow\n",
+    "- Integrate the PyTorch flow with SparseML for transfer learning\n",
+    "- Perform sparse transfer learning and quantization-aware training using the PyTorch and SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/) and convert the model from a QAT\n",
+    "- [Optional] Compare DeepSparse Engine benchmarks of the final sparse-quantized model to an unoptimized model\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your PyTorch training flow for transfer learning and generically. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 30 minutes on a GPU\n",
+    "- 90 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML, SparseZoo\n",
+    "* PyTorch (>= 1.7.0) and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision\n",
+    "\n",
+    "assert torch.__version__ >= \"1.7\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will transfer learn from a sparse-quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [ImageNet dataset](http://www.image-net.org/) to the much smaller [Imagenette dataset](https://github.com/fastai/imagenette). The transfer learning weights are downloaded from the [SparseZoo](https://github.com/neuralmagic/sparsezoo) model repository.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "When loading weights for transfer learning classification models, it is standard to override the final classifier layer to fit the output shape for the new dataset.  In the example below, this is done by specifying `ignore_error_tensors` as the weights that will be initialzed for the new model.  In other flows this could be accomplished by setting `model.classifier.fc = torch.nn.Linear(...)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.models import ModelRegistry\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "from sparsezoo import Model\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "# SparseZoo stub to pre-trained sparse-quantized ResNet-50 for imagenet dataset\n",
+    "zoo_stub_path = (\n",
+    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate\"\n",
+    "    \"?recipe=transfer_learn\"\n",
+    ")\n",
+    "model = ModelRegistry.create(\n",
+    "    key=\"resnet50\",\n",
+    "    pretrained_path=zoo_stub_path,\n",
+    "    pretrained_dataset=\"imagenette\",\n",
+    "    num_classes=10,\n",
+    "    ignore_error_tensors=[\"classifier.fc.weight\", \"classifier.fc.bias\"],\n",
+    ")\n",
+    "input_shape = ModelRegistry.input_shape(\"resnet50\")\n",
+    "input_size = input_shape[-1]\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=input_size\n",
+    ")\n",
+    "print(val_dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Creating a PyTorch Training Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop adapted from [here](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html).  To prune and quantize your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm\n",
+    "import math\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
+    "    if train:\n",
+    "        model.train()\n",
+    "    else:\n",
+    "        model.eval()\n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    total_correct = 0\n",
+    "    total_predictions = 0\n",
+    "\n",
+    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
+    "        inputs = inputs.to(device)\n",
+    "        labels = labels.to(device)\n",
+    "\n",
+    "        if train:\n",
+    "            optimizer.zero_grad()\n",
+    "\n",
+    "        outputs, _ = model(inputs)  # model returns logits and softmax as a tuple\n",
+    "        loss = criterion(outputs, labels)\n",
+    "\n",
+    "        if train:\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        predictions = outputs.argmax(dim=1)\n",
+    "        total_correct += torch.sum(predictions == labels).item()\n",
+    "        total_predictions += inputs.size(0)\n",
+    "\n",
+    "    loss = running_loss / (step + 1.0)\n",
+    "    accuracy = total_correct / total_predictions\n",
+    "    return loss, accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Building PyTorch Training Objects\n",
+    "In this step, you will select hyperparameters, a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import Adam\n",
+    "\n",
+    "# hyperparameters\n",
+    "BATCH_SIZE = 32\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(f\"Using device: {device}\")\n",
+    "\n",
+    "# setup data loaders\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = Adam(model.parameters(), lr=8e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Running Sparse-Quantized Transfer Learning with a SparseML Recipe\n",
+    "\n",
+    "To run sparse-quantized transfer learning with SparseML, you will download a transfer learning recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to maintain the pre-optimized model's sparsity structure while learning weights for the new dataset as well as performing quantization-aware training (QAT).\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, QAT, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe  file to match the new target's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error; to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides preconfigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet-50 on the Imagenette dataset and record its saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model\n",
+    "\n",
+    "zoo_model = Model(zoo_stub_path)\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "# Run model pruning\n",
+    "epoch = manager.min_epochs\n",
+    "for epoch in range(manager.max_epochs):\n",
+    "    # run training loop\n",
+    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
+    "    print(f\"Running Training Epoch {epoch_name}\")\n",
+    "    train_loss, train_acc = run_model_one_epoch(\n",
+    "        model, train_loader, criterion, device, train=True, optimizer=optimizer\n",
+    "    )\n",
+    "    print(\n",
+    "        f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "    # run validation loop\n",
+    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
+    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
+    "    print(\n",
+    "        f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\"\n",
+    "    )\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Viewing Model Sparsity\n",
+    "To see the effects of sparse-quantized transfer learning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 7 - Exporting to ONNX\n",
+    "\n",
+    "Now that the sparse-quantized transfer learning is complete, it should be prepped for inference.  A common next step for inference is exporting the model to ONNX.  This is also the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse) to achieve the sparse-quantized speedups.\n",
+    "\n",
+    "For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Additionally, PyTorch, exports a graph setup for quantization-aware training (QAT) to ONNX. To run a fully quantized graph, you will need to convert these QAT operations to fully quantized INT8 operations.  SparseML provides the `quantize_torch_qat_export` helper function to perform this conversion.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.\n",
+    "\n",
+    "Normally, exporting a QAT model from PyTorch to ONNX will create a graph with \"fake quantized\" operations that represent the QAT graph.  By setting `convert_qat=True` in our exporter, a function will automatically be called to convert this exported model to a fully quantized graph that will contain desired quantized structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"pytorch_sparse_quantized_transfer_learning\"\n",
+    "quant_onnx_graph_name = \"resnet50_imagenette_pruned_quant.onnx\"\n",
+    "quantized_onnx_path = os.path.join(save_dir, quant_onnx_graph_name)\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned_qat.pth\")\n",
+    "exporter.export_onnx(\n",
+    "    torch.randn(1, 3, 224, 224), name=quant_onnx_graph_name, convert_qat=True\n",
+    ")\n",
+    "\n",
+    "print(f\"Sparse-Quantized ONNX model saved to {quantized_onnx_path}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## [Optional] Step 8 - Benchmarking\n",
+    "\n",
+    "Finally, to see the total effect of these optimizations, you will benchmark an unoptimized, dense ResNet-50 model from SparseZoo against your sparse-quantized model using the `deepsparse` API.\n",
+    "\n",
+    "To run this step `deepsparse` must be installed in your python environment. You can install it with `pip install deepsparse`.\n",
+    "\n",
+    "Note, in order to view speedup from quantization, your CPU must run VNNI instructions.  The benchmarking cell below contains a check for VNNI instructions and will log a warning if they are not detected.  You can learn more about DeepSparse hardware compatibility [here](https://docs.neuralmagic.com/deepsparse/hardware.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "from deepsparse import benchmark_model\n",
+    "from deepsparse.cpu import cpu_architecture\n",
+    "\n",
+    "\n",
+    "# check VNNI\n",
+    "if cpu_architecture()[\"vnni\"]:\n",
+    "    print(\"VNNI extensions detected, model will run with quantized speedups\\n\")\n",
+    "else:\n",
+    "    print(\n",
+    "        \"WARNING: No VNNI extensions detected. Your model will not run with \"\n",
+    "        \"quantized speedups which will affect benchmarking\\n\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "BATCH_SIZE = 64\n",
+    "NUM_CORES = None  # maximum number of cores available\n",
+    "NUM_ITERATIONS = 100\n",
+    "NUM_WARMUP_ITERATIONS = 20\n",
+    "\n",
+    "\n",
+    "def benchmark_imagenette_model(model_name, model_path):\n",
+    "    print(\n",
+    "        f\"Benchmarking {model_name} for {NUM_ITERATIONS} iterations at batch \"\n",
+    "        f\"size {BATCH_SIZE} with {NUM_CORES} CPU cores\"\n",
+    "    )\n",
+    "    sample_input = [\n",
+    "        numpy.ascontiguousarray(\n",
+    "            numpy.random.randn(BATCH_SIZE, 3, 224, 224).astype(numpy.float32)\n",
+    "        )\n",
+    "    ]\n",
+    "\n",
+    "    results = benchmark_model(\n",
+    "        model=model_path,\n",
+    "        inp=sample_input,\n",
+    "        batch_size=BATCH_SIZE,\n",
+    "        num_cores=NUM_CORES,\n",
+    "        num_iterations=NUM_ITERATIONS,\n",
+    "        num_warmup_iterations=NUM_WARMUP_ITERATIONS,\n",
+    "        show_progress=True,\n",
+    "    )\n",
+    "    print(f\"results:\\n{results}\")\n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "# base ResNet-50 Imagenette model downloaded from SparseZoo\n",
+    "base_results = benchmark_imagenette_model(\n",
+    "    \"ResNet-50 Imagenette Base\",\n",
+    "    \"zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none\"\n",
+    ")\n",
+    "\n",
+    "optimized_results = benchmark_imagenette_model(\n",
+    "    \"ResNet-50 Imagenette pruned-quantized\", quantized_onnx_path\n",
+    ")\n",
+    "\n",
+    "speed_up = base_results.ms_per_batch / optimized_results.ms_per_batch\n",
+    "print(f\"Speed-up from sparse-quantized transfer learning: {speed_up}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have created a sparse-quantized model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Transfer learning, pruning, or quantizing different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Benchmarking other models on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/pytorch/notebooks/torchvision.ipynb b/integrations/old-examples/pytorch/notebooks/torchvision.ipynb
new file mode 100644
index 00000000000..82c2f82147b
--- /dev/null
+++ b/integrations/old-examples/pytorch/notebooks/torchvision.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# Torchvision Classification Model Pruning using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning a [torchvision model](https://pytorch.org/docs/stable/torchvision/models.html) using SparseML. You will:\n",
+    "- Download a pre-trained torchvision model and generic dataset\n",
+    "- Define a generic torchvision finetuning flow\n",
+    "- Integrate the torchvision flow with SparseML\n",
+    "- Prune the model using the torchvision+SparseML flow\n",
+    "- Save the model and export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to integrate SparseML with torchvision or more generically a PyTorch training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 15 minutes on a GPU\n",
+    "- 45 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* PyTorch and torchvision\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import torch\n",
+    "import torchvision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet50](https://arxiv.org/abs/1512.03385) model while finetuning it on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from torchvision. The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "Additionally, we will override the FC layer in the ResNet50 model to have 10 output classes instead of the ImageNet standard 1000.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate lines for your model and dataset, specifically:\n",
+    "- model = resnet50(pretrained=True)\n",
+    "- train_dataset = ImagenetteDataset(...)\n",
+    "- val_dataset = ImagenetteDataset(...)\n",
+    "\n",
+    "Take care to keep the variable names the same, as the rest of the notebook is set up according to those and update any parts of the training flow as needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.models import resnet50\n",
+    "from torch.nn import Linear\n",
+    "\n",
+    "from sparseml.pytorch.datasets import ImagenetteDataset, ImagenetteSize\n",
+    "\n",
+    "#######################################################\n",
+    "# Define your model below\n",
+    "#######################################################\n",
+    "print(\"loading model...\")\n",
+    "model = resnet50(pretrained=True)\n",
+    "print(model)\n",
+    "#######################################################\n",
+    "# Define your train and validation datasets below\n",
+    "#######################################################\n",
+    "\n",
+    "print(\"\\nloading train dataset...\")\n",
+    "train_dataset = ImagenetteDataset(\n",
+    "    train=True, dataset_size=ImagenetteSize.s320, image_size=224\n",
+    ")\n",
+    "print(train_dataset)\n",
+    "\n",
+    "print(\"\\nloading val dataset...\")\n",
+    "val_dataset = ImagenetteDataset(\n",
+    "    train=False, dataset_size=ImagenetteSize.s320, image_size=224\n",
+    ")\n",
+    "print(val_dataset)\n",
+    "\n",
+    "# Overriding number of classes\n",
+    "NUM_CLASSES = 10  # number of imagenette classes\n",
+    "model.fc = Linear(in_features=model.fc.in_features, out_features=NUM_CLASSES, bias=True)\n",
+    "print(model.fc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Set Up a Torchvision Finetuning Loop\n",
+    "SparseML can plug directly into your existing PyTorch training flow by overriding the Optimizer object. To demonstrate this, in the cell below, we define a simple PyTorch training loop taken from the [torchvision finetuning example](https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html).  To prune your existing models using SparseML, you can use your own training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import copy\n",
+    "import torch\n",
+    "\n",
+    "def train_model(\n",
+    "    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False\n",
+    "):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    val_acc_history = []\n",
+    "\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        print(\"Epoch {}/{}\".format(epoch, num_epochs - 1))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # forward\n",
+    "                # track history if only in train\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    # Get model outputs and calculate loss\n",
+    "                    # Special case for inception because in training it has an auxiliary output. In train\n",
+    "                    #   mode we calculate the loss by summing the final output and the auxiliary output\n",
+    "                    #   but in testing we only consider the final output.\n",
+    "                    if is_inception and phase == \"train\":\n",
+    "                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958\n",
+    "                        outputs, aux_outputs = model(inputs)\n",
+    "                        loss1 = criterion(outputs, labels)\n",
+    "                        loss2 = criterion(aux_outputs, labels)\n",
+    "                        loss = loss1 + 0.4 * loss2\n",
+    "                    else:\n",
+    "                        outputs = model(inputs)\n",
+    "                        loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "                    # backward + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # statistics\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / len(dataloaders[phase].dataset)\n",
+    "            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            # deep copy the model\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "            if phase == \"val\":\n",
+    "                val_acc_history.append(epoch_acc)\n",
+    "\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
+    "    )\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # load best model weights\n",
+    "    return model, val_acc_history"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Set Up PyTorch Training Objects\n",
+    "In this step, you will select a device to train your model with, set up DataLoader objects, a loss function, and optimizer.  All of these variables and objects can be replaced to fit your training flow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import CrossEntropyLoss\n",
+    "from torch.optim import SGD\n",
+    "\n",
+    "# setup device\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "model.to(device)\n",
+    "print(\"Using device: {}\".format(device))\n",
+    "\n",
+    "# setup data loaders\n",
+    "batch_size = 128\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, batch_size, shuffle=True, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "val_loader = DataLoader(\n",
+    "    val_dataset, batch_size, shuffle=False, pin_memory=True, num_workers=8\n",
+    ")\n",
+    "dataloaders = {\"train\": train_loader, \"val\": val_loader}\n",
+    "\n",
+    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
+    "criterion = CrossEntropyLoss()\n",
+    "optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Apply a SparseML Recipe and Prune Model\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to wrap the optimizer object to gradually prune the model using unstructured weight magnitude pruning after each optimizer step.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the wrapped optimizer object, you will call the training function to prune your model. Finalize the model after training by making a call to manager's `finalize(...)` method.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `batch_size` in the cell above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Downloading a Recipe from SparseZoo\n",
+    "The [SparseZoo](https://github.com/neuralmagic/sparsezoo) API provides precofigured recipes for its optimized model.  In the cell below, you will download a recipe for pruning ResNet50 on the Imagenette dataset and record it's saved path."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "zoo_model = search_models(\n",
+    "    domain=\"cv\",\n",
+    "    sub_domain=\"classification\",\n",
+    "    architecture=\"resnet_v1\",\n",
+    "    sub_architecture=\"50\",\n",
+    "    framework=\"pytorch\",\n",
+    "    repo=\"torchvision\",\n",
+    "    dataset=\"imagenette\",\n",
+    "    sparse_name=\"pruned\",\n",
+    ")[0]  # unwrap search result\n",
+    "\n",
+    "recipe_path = zoo_model.recipes.default.path\n",
+    "print(f\"Recipe downloaded to: {recipe_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "\n",
+    "# create ScheduledModifierManager and Optimizer wrapper\n",
+    "manager = ScheduledModifierManager.from_yaml(recipe_path)\n",
+    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))\n",
+    "\n",
+    "\n",
+    "train_model(\n",
+    "    model,\n",
+    "    dataloaders,\n",
+    "    criterion,\n",
+    "    optimizer,\n",
+    "    device,\n",
+    "    num_epochs=manager.max_epochs,\n",
+    "    is_inception=False,\n",
+    ")\n",
+    "\n",
+    "manager.finalize(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - View Model Sparsity\n",
+    "To see the effects of the model pruning, in this step, you will print out the sparsities of each Conv and FC layer in your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import get_prunable_layers, tensor_sparsity\n",
+    "\n",
+    "# print sparsities of each layer\n",
+    "for (name, layer) in get_prunable_layers(model):\n",
+    "    print(\"{}.weight: {:.4f}\".format(name, tensor_sparsity(layer.weight).item()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6 - Save Model and Export to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse). For PyTorch, exporting to ONNX is natively supported. In the cell block below, a convenience class, ModuleExporter(), is used to handle exporting.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine.  For saving a custom model, you can override the sample batch for ONNX graph freezing and locations to save to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.pytorch.utils import ModuleExporter\n",
+    "\n",
+    "save_dir = \"torchvision_models\"\n",
+    "\n",
+    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
+    "exporter.export_pytorch(name=\"resnet50_imagenette_pruned.pth\")\n",
+    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"resnet50_imagenette_pruned.onnx\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the [DeepSparse Engine](https://github.com/neuralmagic/deepsparse)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/pytorch/pr_sensitivity.py b/integrations/old-examples/pytorch/pr_sensitivity.py
new file mode 100644
index 00000000000..fad574bd440
--- /dev/null
+++ b/integrations/old-examples/pytorch/pr_sensitivity.py
@@ -0,0 +1,416 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+#####
+Command help:
+python integrations/pytorch/pr_sensitivity.py --help
+usage: pr_sensitivity.py [-h] --arch-key ARCH_KEY --dataset DATASET
+                         --dataset-path DATASET_PATH
+                         [--pretrained PRETRAINED]
+                         [--pretrained-dataset PRETRAINED_DATASET]
+                         [--model-kwargs MODEL_KWARGS]
+                         [--dataset-kwargs DATASET_KWARGS]
+                         [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                         [--device DEVICE]
+                         [--loader-num-workers LOADER_NUM_WORKERS]
+                         [--no-loader-pin-memory]
+                         [--loader-pin-memory [LOADER_PIN_MEMORY]]
+                         [--checkpoint-path CHECKPOINT_PATH]
+                         [--steps-per-measurement STEPS_PER_MEASUREMENT]
+                         [--batch-size BATCH_SIZE]
+                         [--approximate [APPROXIMATE]]
+
+Utility script to Run a kernel sparsity (pruning) analysis for a
+desired image classification architecture
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list
+                        (will raise an exception with the list)
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder
+                        for a generic dataset setup with an image
+                        folder structure setup like imagenet or
+                        loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default
+                        is true to load the default pretrained weights
+                        for the model. Otherwise should be set to the
+                        desired weights type: [base, optim, optim-
+                        perf]. To not load any weights set to one of
+                        [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will
+                        load the default dataset for the architecture.
+                        Ex can be set to imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model
+                        constructor, should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results
+                        under save-dir, defaults to the model arch and
+                        dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for
+                        data parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the
+                        state from and resume the state for. If
+                        provided, pretrained will be ignored. If using
+                        a SparseZoo recipe, can also provide 'zoo' to
+                        load the base weights associated with that
+                        recipe
+  --steps-per-measurement STEPS_PER_MEASUREMENT
+                        The number of steps (batches) to run for each
+                        measurement
+  --batch-size BATCH_SIZE
+                        The batch size to use for analysis
+  --approximate [APPROXIMATE]
+                        approximate without running data through the
+                        model(uses one shot analysis if --approximate
+                        not passed)
+#########
+EXAMPLES
+#########
+
+##########
+Example command for running one shot KS sens analysis on ssd300_resnet50 for
+coco:
+python integrations/pytorch/lr_analysis.py pr_sensitivity \
+    --arch-key ssd300_resnet50 --dataset coco \
+    --dataset-path ~/datasets/coco-detection
+
+Note: Might need to install pycocotools using pip
+"""
+import json
+import os
+from dataclasses import dataclass, field
+from typing import Any, List, Optional
+
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import (
+    pruning_loss_sens_magnitude,
+    pruning_loss_sens_one_shot,
+)
+from sparseml.pytorch.utils import default_device, model_to_device
+
+
+CURRENT_TASK = utils.Tasks.PR_SENSITIVITY
+LOGGER = get_main_logger()
+
+
+@dataclass
+class PRAnalysisArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    kernel sparsity (pruning) analysis tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for training, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param pretrained: The type of pretrained weights to use default is true to
+        load the default pretrained weights for the model Otherwise should be
+        set to the desired weights type: [base, optim, optim-perf]; To not
+        load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load
+        pretrained weights for if pretrained is set; Default is None which will
+        load the default dataset for the architecture; Ex can be set to
+        imagenet, cifar10, etc".
+    :param model_kwargs: json object containing keyword arguments to be
+        passed to model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be
+        passed to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results
+        under save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on
+        (can also include ids for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data
+        loading, default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with
+        a recipe.
+    :param steps_per_measurement: The number of steps (batches) to run for
+        each measurement
+    :param batch_size: The batch size to use for analysis
+    :param approximate: approximate without running data through the model
+        (uses one shot analysis if --approximate not passed)
+    """
+
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception "
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or "
+            "loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights "
+            "for the model. Otherwise should be set to the desired "
+            "weights type: [base, optim, optim-perf]. To not load any"
+            " weights set  to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained"
+            "is set. Default is None which will load the default "
+            "dataset for the architecture. Ex can be set to imagenet,"
+            "cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, "
+            "should be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under "
+            "save-dir, defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for "
+            "data parallel), ex: cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored. If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    steps_per_measurement: int = field(
+        default=15,
+        metadata={"help": "The number of steps (batches) to run for each measurement"},
+    )
+    batch_size: int = field(
+        default=64, metadata={"help": "The batch size to use for analysis"}
+    )
+
+    approximate: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "approximate without running data through the model"
+            "(uses one shot analysis if --approximate not passed)",
+        },
+    )
+
+    def __post_init__(self):
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        self.is_main_process = True
+        self.local_rank = -1
+        self.rank = -1
+
+
+def pruning_loss_sensitivity(
+    args: PRAnalysisArguments,
+    model,
+    train_loader: DataLoader,
+    save_dir: str,
+    loggers: List[Any],
+) -> None:
+    """
+    Utility function for pruning sensitivity analysis
+
+    :param args : A PRAnalysisArguments object containing config for current
+        analysis
+    :param model: loaded model architecture to analyse
+    :param train_loader: A DataLoader for training data
+    :param save_dir: Directory to save results
+    :param loggers: List of loggers to use during analysis
+    """
+    # loss setup
+    if not args.approximate:
+        loss = utils.get_loss_wrapper(args)
+        LOGGER.info(f"created loss: {loss}")
+    else:
+        loss = None
+
+    # device setup
+    if not args.approximate:
+        module, device, device_ids = model_to_device(model, args.device)
+    else:
+        device = None
+
+    # kernel sparsity analysis
+    if args.approximate:
+        analysis = pruning_loss_sens_magnitude(model)
+    else:
+        analysis = pruning_loss_sens_one_shot(
+            model,
+            train_loader,
+            loss,
+            device,
+            args.steps_per_measurement,
+            tester_loggers=loggers,
+        )
+
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info(f"Saving results in {save_dir}")
+    analysis.save_json(
+        os.path.join(
+            save_dir,
+            "ks_approx_sensitivity.json"
+            if args.approximate
+            else "ks_one_shot_sensitivity.json",
+        )
+    )
+    analysis.plot(
+        os.path.join(
+            save_dir,
+            os.path.join(
+                save_dir,
+                "ks_approx_sensitivity.png"
+                if args.approximate
+                else "ks_one_shot_sensitivity.png",
+            ),
+        ),
+        plot_integral=True,
+    )
+    analysis.print_res()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(
+        dataclass_types=PRAnalysisArguments,
+        description="Utility script to Run a "
+        "kernel sparsity (pruning) analysis "
+        "for a desired image classification architecture",
+    )
+
+    args_, _ = _parser.parse_args_into_dataclasses()
+
+    save_dir, loggers = utils.get_save_dir_and_loggers(args_, task=CURRENT_TASK)
+
+    input_shape = ModelRegistry.input_shape(args_.arch_key)
+    # assume shape [C, S, S] where S is the image size
+    image_size = input_shape[1]
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(args_, image_size, task=CURRENT_TASK)
+
+    num_classes = utils.infer_num_classes(args_, train_dataset, val_dataset)
+
+    model = utils.create_model(args_, num_classes)
+
+    pruning_loss_sensitivity(args_, model, train_loader, save_dir, loggers)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/old-examples/pytorch/recipes/.gitkeep b/integrations/old-examples/pytorch/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned.md b/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned.md
new file mode 100644
index 00000000000..29479dcdc1f
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned.md
@@ -0,0 +1,98 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# Epoch and Learning-Rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+    
+---
+
+# Transfer Learning
+
+This recipe provides a framework for performing sparse transfer learning on
+SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+
+To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned
+```
+
+To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned.md\
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag mobilenet-imagenette-sparse-transfer-learned
+```
+## Other Datasets
+To transfer learn this sparsified model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increase the learning rate when increasing batch size
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md b/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
new file mode 100644
index 00000000000..5820fcf05f3
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/classification.transfer_learn_pruned_quantized.md
@@ -0,0 +1,106 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# Epoch and Learning-Rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+# quantization variables
+quantization_start_epoch: &quantization_start_epoch 4.0
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# Phase 1 Sparse Transfer Learning / Recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL_PRUNABLE__
+
+# phase 2 apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+
+---
+# Transfer Learning
+
+This recipe provides a framework for performing sparse transfer learning on
+SparseML's `sparsified` ResNet-50, or MobileNet model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+[Imagenette](https://github.com/fastai/imagenette) is a subset of 10 easily classified classes from ImageNet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
+
+
+To transfer learn ResNet-50 on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned-quantized
+```
+
+To transfer learn MobileNet on [Imagenette](https://github.com/fastai/imagenette) with this recipe, run the following example command.
+
+```
+sparseml.image_classification.train \
+    --recipe-path  integrations/pytorch/recipes/classification.transfer_learn_pruned_quantized.md \
+    --checkpoint-path /PATH/TO/MODEL_CHECKPOINT \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag mobilenet-imagenette-sparse-transfer-learned-quantized
+```
+## Other Datasets
+To transfer learn this sparsified model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increase the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increase the learning rate when increasing batch size
+* Increasing the number of epochs before starting quantization can give a higher baseline
+* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/old-examples/pytorch/recipes/mobilenet-pruned.md b/integrations/old-examples/pytorch/recipes/mobilenet-pruned.md
new file mode 100644
index 00000000000..03dd40b9811
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/mobilenet-pruned.md
@@ -0,0 +1,107 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 90
+init_lr: &init_lr 0.0028
+lr_step_epochs: &lr_step_epochs [43, 60]
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 0.0
+pruning_end_epoch: &pruning_end_epoch 40.0
+pruning_update_frequency: &pruning_update_frequency 0.4
+init_sparsity: &init_sparsity 0.05
+pruning_mask_type: &pruning_mask_type unstructured
+prune_low_target_sparsity: &prune_low_target_sparsity 0.7
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.9
+
+
+training_modifiers:
+  - !EpochRangeModifier
+    end_epoch: *num_epochs
+    start_epoch: 0.0
+
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: -1.0
+    init_lr: *init_lr
+    lr_class: MultiStepLR
+    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
+    start_epoch: 0.0
+    update_frequency: -1.0
+    
+pruning_modifiers:
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_low_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.1.0.point.conv.weight', 'sections.1.1.point.conv.weight', 'sections.2.0.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_mid_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.2.1.point.conv.weight', 'sections.3.0.point.conv.weight', 'sections.3.1.point.conv.weight', 'sections.3.5.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *prune_high_target_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.3.2.point.conv.weight', 'sections.3.3.point.conv.weight', 'sections.3.4.point.conv.weight', 'sections.4.0.point.conv.weight', 'sections.4.1.point.conv.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# MobileNet-V1 ImageNet Moderate Sparse
+
+This recipe creates a sparse [MobileNet-V1](https://arxiv.org/abs/1704.04861) model that
+achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
+Training was done using 4 GPUs using a total training batch size of 1024
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key mobilenet \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 1024 --test-batch-size 2056 \
+    --loader-num-workers 16 \
+    --model-tag mobilenet-imagenet-pruned-moderate
+```
diff --git a/integrations/old-examples/pytorch/recipes/mobilenet-pruned_quant.md b/integrations/old-examples/pytorch/recipes/mobilenet-pruned_quant.md
new file mode 100644
index 00000000000..94f71cf07d8
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/mobilenet-pruned_quant.md
@@ -0,0 +1,160 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 100.0
+init_lr: &init_lr 0.005
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
+pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 90
+disable_quantization_observer_epoch: &disable_quantization_observer_epoch 92.0
+freeze_bn_stats_epoch: &freeze_bn_stats_epoch 92.0
+quantization_init_lr: &quantization_init_lr 0.00001
+quantization_lr_step_milestones: &quantization_lr_step_milestones [91]
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 36.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.55
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.7
+prune_high_target_sparsity: &prune_high_target_sparsity 0.8
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    start_epoch: *pruning_recovery_start_epoch
+    end_epoch: *quantization_start_epoch
+    lr_class: CosineAnnealingWarmRestarts
+    lr_kwargs:
+      lr_min: *pruning_recovery_end_lr
+      cycle_epochs: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_recovery_start_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !LearningRateModifier
+    start_epoch: *quantization_start_epoch
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *quantization_lr_step_milestones
+      gamma: 0.1
+    init_lr: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.point.conv.weight
+      - sections.1.0.point.conv.weight
+      - sections.1.1.point.conv.weight
+      - sections.2.0.point.conv.weight
+      - classifier.fc.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.2.1.point.conv.weight
+      - sections.3.0.point.conv.weight
+      - sections.3.1.point.conv.weight
+      - sections.3.5.point.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.3.2.point.conv.weight
+      - sections.3.3.point.conv.weight
+      - sections.3.4.point.conv.weight
+      - sections.4.0.point.conv.weight
+      - sections.4.1.point.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
+    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
+
+    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
+    # but are unnecessary due to these already being set during the training phase
+---
+
+# MobileNet-V1 ImageNet Moderate Sparse Quantization
+
+This recipe creates a sparse quantized [MobileNet-V1](https://arxiv.org/abs/1704.04861) model
+that achieves 99% of its baseline accuracy on the ImageNet dataset.  Training was done using
+4 GPUs using a total training batch size of 96 using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+python -m torch.distributed.launch \
+  --nproc_per_node 4 \
+  src/sparseml/pytorch/image_classification/train.py \
+    --recipe-path zoo:cv/classification/mobilenet_v1-1.0/pytorch-sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key mobilenet \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 96 --test-batch-size 256 \
+    --loader-num-workers 16 \
+    --model-tag mobilenet-imagenet-pruned_quant-moderate
+```
diff --git a/integrations/old-examples/pytorch/recipes/resnet50-imagenette-pruned.md b/integrations/old-examples/pytorch/recipes/resnet50-imagenette-pruned.md
new file mode 100644
index 00000000000..16318a27b99
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/resnet50-imagenette-pruned.md
@@ -0,0 +1,145 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 10
+lr: &lr 0.008
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 8.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.8
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.85
+prune_high_target_sparsity: &prune_high_target_sparsity 0.9
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.conv1.weight
+      - sections.0.0.conv2.weight
+      - sections.0.0.conv3.weight
+      - sections.0.0.identity.conv.weight
+      - sections.0.1.conv1.weight
+      - sections.0.1.conv3.weight
+      - sections.0.2.conv1.weight
+      - sections.0.2.conv3.weight
+      - sections.1.0.conv1.weight
+      - sections.1.0.conv3.weight
+      - sections.1.2.conv3.weight
+      - sections.1.3.conv1.weight
+      - sections.2.0.conv1.weight
+      - sections.3.0.conv1.weight
+      - classifier.fc.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.0.1.conv2.weight
+      - sections.0.2.conv2.weight
+      - sections.1.0.identity.conv.weight
+      - sections.1.1.conv1.weight
+      - sections.1.1.conv2.weight
+      - sections.1.1.conv3.weight
+      - sections.1.2.conv1.weight
+      - sections.1.2.conv2.weight
+      - sections.1.3.conv2.weight
+      - sections.1.3.conv3.weight
+      - sections.2.0.conv3.weight
+      - sections.2.0.identity.conv.weight
+      - sections.2.1.conv1.weight
+      - sections.2.1.conv3.weight
+      - sections.2.2.conv1.weight
+      - sections.2.2.conv3.weight
+      - sections.2.3.conv1.weight
+      - sections.2.3.conv3.weight
+      - sections.2.4.conv1.weight
+      - sections.2.4.conv3.weight
+      - sections.2.5.conv1.weight
+      - sections.2.5.conv3.weight
+      - sections.3.1.conv1.weight
+      - sections.3.2.conv1.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.1.0.conv2.weight
+      - sections.2.0.conv2.weight
+      - sections.2.1.conv2.weight
+      - sections.2.2.conv2.weight
+      - sections.2.3.conv2.weight
+      - sections.2.4.conv2.weight
+      - sections.2.5.conv2.weight
+      - sections.3.0.conv2.weight
+      - sections.3.0.conv3.weight
+      - sections.3.0.identity.conv.weight
+      - sections.3.1.conv2.weight
+      - sections.3.1.conv3.weight
+      - sections.3.2.conv2.weight
+      - sections.3.2.conv3.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# ResNet-50 Imagenette Conservative Sparse
+
+This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
+achieves full recovery of its baseline accuracy on the Imagenette dataset.
+Training was done using 1 GPU with a total training batch size of 128
+using a SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training. 
+Alternatively, a full walk-through notebook is located at `sparseml/integrations/pytorch/notebooks/classification.ipynb`.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 128 --test-batch-size 256 \
+    --loader-num-workers 8 \
+    --model-tag resnet50-imagenette-pruned-conservative
+```
diff --git a/integrations/old-examples/pytorch/recipes/resnet50-pruned.md b/integrations/old-examples/pytorch/recipes/resnet50-pruned.md
new file mode 100644
index 00000000000..27fe718867b
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/resnet50-pruned.md
@@ -0,0 +1,83 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 90
+init_lr: &init_lr 0.0028
+lr_step_epochs: &lr_step_epochs [43, 60]
+
+# Pruning variables
+pruning_start_epoch: &pruning_start_epoch 0.0
+pruning_end_epoch: &pruning_end_epoch 40.0
+pruning_update_frequency: &pruning_update_frequency 0.4
+init_sparsity: &init_sparsity 0.05
+final_sparsity: &final_sparsity 0.88
+pruning_mask_type: &pruning_mask_type unstructured
+
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    end_epoch: *num_epochs
+    start_epoch: 0.0
+
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: -1.0
+    init_lr: *init_lr
+    lr_class: MultiStepLR
+    lr_kwargs: {'milestones': *lr_step_epochs, 'gamma': 0.1}
+    start_epoch: 0.0
+    update_frequency: -1.0
+        
+pruning_modifiers:
+  - !GMPruningModifier
+    end_epoch: *pruning_end_epoch
+    final_sparsity: *final_sparsity
+    init_sparsity: *init_sparsity
+    inter_func: cubic
+    leave_enabled: True
+    mask_type: *pruning_mask_type
+    params: ['sections.0.0.conv1.weight', 'sections.0.0.conv2.weight', 'sections.0.0.conv3.weight', 'sections.0.0.identity.conv.weight', 'sections.0.1.conv1.weight', 'sections.0.1.conv2.weight', 'sections.0.1.conv3.weight', 'sections.0.2.conv1.weight', 'sections.0.2.conv2.weight', 'sections.0.2.conv3.weight', 'sections.1.0.conv1.weight', 'sections.1.0.conv2.weight', 'sections.1.0.conv3.weight', 'sections.1.0.identity.conv.weight', 'sections.1.1.conv1.weight', 'sections.1.1.conv2.weight', 'sections.1.1.conv3.weight', 'sections.1.2.conv1.weight', 'sections.1.2.conv2.weight', 'sections.1.2.conv3.weight', 'sections.1.3.conv1.weight', 'sections.1.3.conv2.weight', 'sections.1.3.conv3.weight', 'sections.2.0.conv1.weight', 'sections.2.0.conv2.weight', 'sections.2.0.conv3.weight', 'sections.2.0.identity.conv.weight', 'sections.2.1.conv1.weight', 'sections.2.1.conv2.weight', 'sections.2.1.conv3.weight', 'sections.2.2.conv1.weight', 'sections.2.2.conv2.weight', 'sections.2.2.conv3.weight', 'sections.2.3.conv1.weight', 'sections.2.3.conv2.weight', 'sections.2.3.conv3.weight', 'sections.2.4.conv1.weight', 'sections.2.4.conv2.weight', 'sections.2.4.conv3.weight', 'sections.2.5.conv1.weight', 'sections.2.5.conv2.weight', 'sections.2.5.conv3.weight', 'sections.3.0.conv1.weight', 'sections.3.0.conv2.weight', 'sections.3.0.conv3.weight', 'sections.3.0.identity.conv.weight', 'sections.3.1.conv1.weight', 'sections.3.1.conv2.weight', 'sections.3.1.conv3.weight', 'sections.3.2.conv1.weight', 'sections.3.2.conv2.weight', 'sections.3.2.conv3.weight']
+    start_epoch: *pruning_start_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# ResNet-50 ImageNet Moderate Sparse
+
+This recipe creates a sparse [ResNet-50](https://arxiv.org/abs/1512.03385) model that
+achieves 99% recovery of its baseline accuracy on the ImageNet dataset.
+Training was done using 4 GPUs using a total training batch size of 1024
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 1024 --test-batch-size 2056 \
+    --loader-num-workers 16 \
+    --model-tag resnet50-imagenet-pruned-moderate
+```
diff --git a/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md b/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
new file mode 100644
index 00000000000..49fba746f53
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant-transfer_learn.md
@@ -0,0 +1,100 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# epoch and learning rate variables
+num_epochs: &num_epochs 10.0
+init_lr: &init_lr 0.008
+lr_step_milestones: &lr_step_milestones [5]
+
+# quantization variables
+quantization_start_epoch: &quantization_start_epoch 4.0
+quantize_submodules: &quantize_submodules
+  - input
+  - sections
+
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateModifier
+    start_epoch: 0.0
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *lr_step_milestones
+      gamma: 0.1
+    init_lr: *init_lr
+
+# phase 1 sparse transfer learning / recovery
+sparse_transfer_learning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params: __ALL__
+
+# phase 2 apply quantization
+sparse_quantized_transfer_learning_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+
+  - !SetWeightDecayModifier
+    start_epoch: 0.0
+    weight_decay: 0.0
+---
+
+# Transfer Learning ResNet50 ImageNet Pruned-Quantized
+
+This recipe provides a framework for performing pruned-quantized transfer learning on
+SparseML's `pruned_quantized` ResNet50 model.  This recipe was written for transfer learning
+onto the [Imagenette](https://github.com/fastai/imagenette) dataset using
+a single GPU, Adam optimizer, and batch size 32.  Adjust the hyperparameters at the top of the file accordingly
+for your dataset and training environment.
+
+## Imagenette
+On the default transfer learning dataset, Imagenette, this recipe fully recovered to 99.9% Top-1 accuracy.
+To run this recipe, you may run the example notebook
+`sparseml/integrations/pytorch/notebooks/sparse_quantized_transfer_learning.ipynb`
+or can run the example script command below.
+
+```
+sparseml.image_classification.train \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn \
+    --checkpoint-path zoo \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 8 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-pruned_quant-transfer_learned
+```
+
+## Other Datasets
+To transfer learn this ResNet 50 Pruned-Quantized model to other datasets
+you may have to adjust certain hyperparameters in this recipe and/or training script.
+Some considerations:
+* For more complex datasets, increate the number of epochs, adjusting the learning rate step accordingly
+* Adding more learning rate step milestones can lead to more jumps in accuracy
+* Increasing the number of epochs before starting quantization can give a higher baseline
+* If accuracy is not recovering in the quantization phase, the total number of epochs should be increased as well
+* Increase the learning rate when increasing batch size
+* Increase the number of epochs if using SGD instead of the Adam optimizer
+* Update the base learning rate based on the number of steps needed to train your dataset 
diff --git a/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant.md b/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant.md
new file mode 100644
index 00000000000..fe83cb8a3fc
--- /dev/null
+++ b/integrations/old-examples/pytorch/recipes/resnet50-pruned_quant.md
@@ -0,0 +1,198 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 135.0
+init_lr: &init_lr 0.008
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_epoch: &pruning_recovery_start_epoch 40.0
+pruning_recovery_num_epochs: &pruning_recovery_num_epochs 60
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.005
+pruning_recovery_end_lr: &pruning_recovery_end_lr 0.00001
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 100
+disable_quantization_observer_epoch: &disable_quantization_observer_epoch 115.0
+freeze_bn_stats_epoch: &freeze_bn_stats_epoch 116.0
+quantization_init_lr: &quantization_init_lr 0.01
+quantization_lr_step_milestones: &quantization_lr_step_milestones [107, 114, 125]
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 1.0
+pruning_end_epoch: &pruning_end_epoch 36.0
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_low_target_sparsity: &prune_low_target_sparsity 0.7
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.75
+prune_high_target_sparsity: &prune_high_target_sparsity 0.8
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    start_epoch: *pruning_recovery_start_epoch
+    end_epoch: *quantization_start_epoch
+    lr_class: CosineAnnealingWarmRestarts
+    lr_kwargs:
+      lr_min: *pruning_recovery_end_lr
+      cycle_epochs: *pruning_recovery_num_epochs
+    init_lr: *pruning_recovery_start_lr
+
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_recovery_start_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !LearningRateModifier
+    start_epoch: *quantization_start_epoch
+    lr_class: MultiStepLR
+    lr_kwargs:
+      milestones: *quantization_lr_step_milestones
+      gamma: 0.1
+    init_lr: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - sections.0.0.identity.conv.weight
+      - sections.0.0.conv1.weight
+      - sections.0.0.conv2.weight
+      - sections.0.0.conv3.weight
+      - sections.0.1.conv1.weight
+      - sections.0.1.conv3.weight
+      - sections.0.2.conv1.weight
+      - sections.0.2.conv3.weight
+      - sections.1.0.conv1.weight
+      - sections.1.0.conv3.weight
+      - sections.1.2.conv3.weight
+      - sections.1.3.conv1.weight
+      - sections.2.0.conv1.weight
+      - sections.3.0.conv1.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.0.1.conv2.weight
+      - sections.0.2.conv2.weight
+      - sections.1.0.identity.conv.weight
+      - sections.1.1.conv1.weight
+      - sections.1.1.conv2.weight
+      - sections.1.1.conv3.weight
+      - sections.1.2.conv1.weight
+      - sections.1.2.conv2.weight
+      - sections.1.3.conv2.weight
+      - sections.1.3.conv3.weight
+      - sections.2.0.conv3.weight
+      - sections.2.0.identity.conv.weight
+      - sections.2.1.conv1.weight
+      - sections.2.1.conv3.weight
+      - sections.2.2.conv1.weight
+      - sections.2.3.conv1.weight
+      - sections.2.3.conv3.weight
+      - sections.2.4.conv1.weight
+      - sections.2.4.conv3.weight
+      - sections.2.5.conv1.weight
+      - sections.2.5.conv3.weight
+      - sections.3.1.conv1.weight
+      - sections.3.2.conv1.weight
+      - sections.1.0.conv2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - sections.2.0.conv2.weight
+      - sections.2.1.conv2.weight
+      - sections.2.2.conv2.weight
+      - sections.2.3.conv2.weight
+      - sections.2.4.conv2.weight
+      - sections.2.5.conv2.weight
+      - sections.3.0.conv2.weight
+      - sections.3.0.conv3.weight
+      - sections.3.0.identity.conv.weight
+      - sections.3.1.conv2.weight
+      - sections.3.1.conv3.weight
+      - sections.3.2.conv2.weight
+      - sections.3.2.conv3.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - input
+      - sections
+    disable_quantization_observer_epoch: *disable_quantization_observer_epoch
+    freeze_bn_stats_epoch: *freeze_bn_stats_epoch
+
+    # normally a ConstantPruningModifier and SetWeightDecayModifer=0 are also added here
+    # but are unnecessary due to these already being set during the training phase
+---
+
+# ResNet-50 ImageNet Moderate Sparse Quantization
+
+This recipe creates a sparse quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of its baseline accuracy
+on the ImageNet dataset.  Training was done using 4 GPUs using a total training batch size of 96
+using an SGD optimizer.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Training
+
+We can use the `sparseml.image_classification.train` utility for training.
+
+*script command:*
+
+```
+python -m torch.distributed.launch \
+  --nproc_per_node 4 \
+  src/sparseml/pytorch/image_classification/train.py \
+    --recipe-path zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenet \
+    --dataset-path /PATH/TO/IMAGENET  \
+    --train-batch-size 96 --test-batch-size 256 \
+    --loader-num-workers 16 \
+    --model-tag resnet50-imagenet-pruned_quant-moderate
+```
diff --git a/integrations/old-examples/pytorch/torchvision_sparsification.py b/integrations/old-examples/pytorch/torchvision_sparsification.py
new file mode 100644
index 00000000000..07acd698b19
--- /dev/null
+++ b/integrations/old-examples/pytorch/torchvision_sparsification.py
@@ -0,0 +1,455 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Example script for running training and optimization tasks on torchvision
+classification models with an imagefolder based dataset using SparseML.
+Example tasks include:
+* Model pruning
+* Quantization aware training
+* Sparse transfer learning
+
+More information about torchvision models can be found here:
+https://pytorch.org/docs/stable/torchvision/models.html
+
+##########
+Command help:
+usage: torchvision_sparseml.py [-h] --model MODEL [--recipe-path RECIPE_PATH]
+                               [--image-size IMAGE_SIZE]
+                               [--batch-size BATCH_SIZE]
+                               [--pretrained PRETRAINED]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               --imagefolder-path IMAGEFOLDER_PATH
+                               [--loader-num-workers LOADER_NUM_WORKERS]
+                               [--loader-pin-memory LOADER_PIN_MEMORY]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+
+Train or finetune an image classification model from torchvision.models
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --model MODEL         The torchvision model class to use, ex: inception_v3,
+                        resnet50, mobilenet_v2 model name is fed directly to
+                        torchvision.models, more information can be found here
+                        https://pytorch.org/docs/stable/torchvision/models.htm
+                        l
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the sparseml
+                        modifiers and schedule to apply them with
+  --image-size IMAGE_SIZE
+                        Size of image to use for model input. Default is 224
+                        unless pytorch documentation specifies otherwise
+  --batch-size BATCH_SIZE
+                        Batch size to use when training model. Default is 32
+  --pretrained PRETRAINED
+                        Set True to use torchvisions pretrained weights, to
+                        not set weights, set False. default is true.
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --imagefolder-path IMAGEFOLDER_PATH
+                        Path to root of dataset's generic 'image folder' path.
+                        Should have an image folder structure like imagenet
+                        with subdirectories 'train' and 'val' see https://pyto
+                        rch.org/docs/stable/torchvision/datasets.html#imagefol
+                        der
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --loader-pin-memory LOADER_PIN_MEMORY
+                        Use pinned memory for data loading
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+
+##########
+Example command for pruning resnet50 on an imagefolder dataset:
+python integrations/pytorch/torchvision_sparsification.py \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --model resnet50 \
+    --imagefolder-path ~/datasets/ILSVRC2012 \
+    --batch-size 256
+"""
+
+import argparse
+import os
+import time
+from types import ModuleType
+
+import torch
+from torch.nn import CrossEntropyLoss
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+from torchvision import models
+
+from sparseml.pytorch.datasets.classification import ImageFolderDataset
+from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
+from sparseml.pytorch.utils import ModuleExporter, PythonLogger, load_model
+from sparseml.utils import create_dirs
+
+
+MODEL_IMAGE_SIZES = {
+    "inception_v3": 299,
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Train or finetune an image classification model from "
+        "torchvision.models"
+    )
+
+    # model args
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help=(
+            "The torchvision model class to use, ex: inception_v3, resnet50, "
+            "mobilenet_v2 model name is fed directly to torchvision.models, "
+            "more information can be found here "
+            "https://pytorch.org/docs/stable/torchvision/models.html"
+        ),
+    )
+    parser.add_argument(
+        "--recipe-path",
+        type=str,
+        required=True,
+        help="The path to the yaml file containing the sparseml modifiers and "
+        "schedule to apply them with",
+    )
+    parser.add_argument(
+        "--image-size",
+        type=int,
+        required=False,
+        default=None,
+        help=(
+            "Size of image to use for model input. Default is 224 unless pytorch "
+            "documentation specifies otherwise"
+        ),
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        required=False,
+        default=32,
+        help="Batch size to use when training model. Default is 32",
+    )
+    parser.add_argument(
+        "--pretrained",
+        type=bool,
+        default=True,
+        help="Set True to use torchvisions pretrained weights,"
+        " to not set weights, set False. default is true.",
+    )
+    parser.add_argument(
+        "--checkpoint-path",
+        type=str,
+        default=None,
+        help="A path to a previous checkpoint to load the state from and "
+        "resume the state for. If provided, pretrained will be ignored",
+    )
+
+    # dataset args
+    parser.add_argument(
+        "--imagefolder-path",
+        type=str,
+        required=True,
+        help="Path to root of dataset's generic 'image folder' path. Should have "
+        "an image folder structure like imagenet with subdirectories 'train' and 'val'"
+        " see https://pytorch.org/docs/stable/torchvision/datasets.html#imagefolder",
+    )
+    parser.add_argument(
+        "--loader-num-workers",
+        type=int,
+        default=4,
+        help="The number of workers to use for data loading",
+    )
+    parser.add_argument(
+        "--loader-pin-memory",
+        type=bool,
+        default=True,
+        help="Use pinned memory for data loading",
+    )
+
+    # logging and saving
+    parser.add_argument(
+        "--model-tag",
+        type=str,
+        default=None,
+        help="A tag to use for the model for saving results under save-dir, "
+        "defaults to the model arch and dataset used",
+    )
+    parser.add_argument(
+        "--save-dir",
+        type=str,
+        default="torchvision_sparseml_export",
+        help="The path to the directory for saving results",
+    )
+
+    args = parser.parse_args()
+    if args.image_size is None:
+        args.image_size = (
+            MODEL_IMAGE_SIZES[args.model] if args.model in MODEL_IMAGE_SIZES else 224
+        )
+    return args
+
+
+def _get_torchvision_model(name, num_classes, pretrained=True, checkpoint_path=None):
+    model_constructor = getattr(models, name, None)
+    if model_constructor is None or isinstance(model_constructor, ModuleType):
+        # constructor doesn't exist or is a submodule instead of function in torchvision
+        raise ValueError("Torchvision model {} not found".format(name))
+    # build model
+    model = model_constructor(pretrained=False, num_classes=num_classes)
+    if pretrained and not checkpoint_path:
+        pretrained_model = model_constructor(pretrained=True, num_classes=1000)
+        # fix num classes mismatch
+        if num_classes == 1000:
+            model = pretrained_model
+        else:
+            _load_matched_weights(model, pretrained_model)
+        del pretrained_model
+
+    if checkpoint_path is not None:
+        load_model(checkpoint_path, model)
+    return model
+
+
+def _load_matched_weights(base_model, pretrained_model):
+    base_dict = base_model.state_dict()
+    pretrained_dict = pretrained_model.state_dict()
+    for key in base_dict:
+        if (
+            key in pretrained_dict
+            and base_dict[key].shape == pretrained_dict[key].shape
+        ):
+            base_dict[key] = pretrained_dict[key]
+    base_model.load_state_dict(base_dict)
+
+
+def _create_imagefolder_dataloader(args, train=True):
+    dataset = ImageFolderDataset(
+        root=args.imagefolder_path,
+        train=train,
+        rand_trans=train,
+        image_size=args.image_size,
+    )
+    loader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        num_workers=args.loader_num_workers,
+        pin_memory=args.loader_pin_memory,
+    )
+
+    # return dataloader, number of classes, and input image shape
+    return loader, dataset.num_classes, dataset[0][0].shape
+
+
+######################################################################################
+# torchvision finetuning function from:                                              #
+# https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html #
+######################################################################################
+def train_model(
+    model, dataloaders, criterion, optimizer, device, num_epochs=25, is_inception=False
+):
+    since = time.time()
+
+    val_acc_history = []
+
+    # not loading best intermediate weights due to sparsity changing
+    # best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+
+    for epoch in range(num_epochs):
+        print("Epoch {}/{}".format(epoch, num_epochs - 1))
+        print("-" * 10)
+
+        # Each epoch has a training and validation phase
+        for phase in ["train", "val"]:
+            if phase == "train":
+                model.train()  # Set model to training mode
+            else:
+                model.eval()  # Set model to evaluate mode
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            # Iterate over data.
+            for inputs, labels in dataloaders[phase]:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                # zero the parameter gradients
+                optimizer.zero_grad()
+
+                # forward
+                # track history if only in train
+                with torch.set_grad_enabled(phase == "train"):
+                    # Get model outputs and calculate loss
+                    # Special case for inception because in training it has an
+                    # auxiliary output. In train mode we calculate the loss by summing
+                    # the final output and the auxiliary output but in testing we
+                    # only consider the final output.
+                    if is_inception and phase == "train":
+                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958  # noqa
+                        outputs, aux_outputs = model(inputs)
+                        loss1 = criterion(outputs, labels)
+                        loss2 = criterion(aux_outputs, labels)
+                        loss = loss1 + 0.4 * loss2
+                    else:
+                        outputs = model(inputs)
+                        loss = criterion(outputs, labels)
+
+                    _, preds = torch.max(outputs, 1)
+
+                    # backward + optimize only if in training phase
+                    if phase == "train":
+                        loss.backward()
+                        optimizer.step()
+
+                # statistics
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / len(dataloaders[phase].dataset)
+            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
+
+            print("{} Loss: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, epoch_acc))
+
+            # deep copy the model
+            if phase == "val" and epoch_acc > best_acc:
+                best_acc = epoch_acc
+                # not loading best intermediate weights due to sparsity changing
+                # best_model_wts = copy.deepcopy(model.state_dict())
+            if phase == "val":
+                val_acc_history.append(epoch_acc)
+
+        print()
+
+    time_elapsed = time.time() - since
+    print(
+        "Training complete in {:.0f}m {:.0f}s".format(
+            time_elapsed // 60, time_elapsed % 60
+        )
+    )
+    print("Best val Acc: {:4f}".format(best_acc))
+
+    # load best model weights
+    # not loading best intermediate weights due to sparsity changing
+    # model.load_state_dict(best_model_wts)
+    return model, val_acc_history
+
+
+def _save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    print(f"Saved recipe to {recipe_save_path}")
+
+
+def main(args):
+    ############################
+    # logging and saving setup #
+    ############################
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+
+    # get unique model tag, defaults to '{model_name}'
+    if not args.model_tag:
+        model_tag = args.model.replace("/", ".")
+        model_id = model_tag
+        model_inc = 0
+
+        while os.path.exists(os.path.join(args.save_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+    print("Model id is set to {}".format(model_id))
+
+    ###########################
+    # standard training setup #
+    ###########################
+
+    # create data loaders
+    train_loader, _, _ = _create_imagefolder_dataloader(args, train=True)
+    val_loader, num_classes, image_shape = _create_imagefolder_dataloader(
+        args, train=False
+    )
+    dataloaders = {"train": train_loader, "val": val_loader}
+
+    # create model
+    model = _get_torchvision_model(
+        args.model,
+        num_classes,
+        args.pretrained,
+        args.checkpoint_path,
+    )
+    print("created model: {}".format(model))
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+    print("using device: {}".format(device))
+
+    # create standard SGD optimizer and cross entropy loss function
+    criterion = CrossEntropyLoss()
+    optimizer = SGD(
+        model.parameters(), lr=0.001, momentum=0.9
+    )  # lr will be overridden by recipe
+
+    ##########################
+    # add sparseml modifiers #
+    ##########################
+    manager = ScheduledModifierManager.from_yaml(args.recipe_path)
+    optimizer = ScheduledOptimizer(
+        optimizer,
+        model,
+        manager,
+        steps_per_epoch=len(train_loader),
+        loggers=[PythonLogger()],
+    )
+
+    ########################
+    # torchvision training #
+    ########################
+    _save_recipe(recipe_manager=manager, save_dir=save_dir)
+    model, val_acc_history = train_model(
+        model,
+        dataloaders,
+        criterion,
+        optimizer,
+        device,
+        num_epochs=manager.max_epochs,
+        is_inception="inception" in args.model,
+    )
+
+    ########################
+    # export trained model #
+    ########################
+    exporter = ModuleExporter(model, save_dir)
+    sample_input = torch.randn(image_shape).unsqueeze(0)  # sample batch for ONNX export
+    exporter.export_onnx(sample_input, convert_qat=True)
+    exporter.export_pytorch()
+    print("Model ONNX export and PyTorch weights saved to {}".format(save_dir))
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+    main(args_)
diff --git a/integrations/old-examples/pytorch/train.py b/integrations/old-examples/pytorch/train.py
new file mode 100644
index 00000000000..0dc86e934af
--- /dev/null
+++ b/integrations/old-examples/pytorch/train.py
@@ -0,0 +1,703 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+######
+Command help:
+usage: train.py [-h] --train-batch-size TRAIN_BATCH_SIZE --test-batch-size
+                TEST_BATCH_SIZE --arch-key ARCH_KEY --dataset DATASET
+                --dataset-path DATASET_PATH
+                [--checkpoint-path CHECKPOINT_PATH] [--init-lr INIT_LR]
+                [--optim-args OPTIM_ARGS] [--recipe-path RECIPE_PATH]
+                [--sparse-transfer-learn [SPARSE_TRANSFER_LEARN]]
+                [--eval-mode [EVAL_MODE]] [--optim OPTIM]
+                [--logs-dir LOGS_DIR] [--save-best-after SAVE_BEST_AFTER]
+                [--save-epochs SAVE_EPOCHS]
+                [--use-mixed-precision [USE_MIXED_PRECISION]]
+                [--debug-steps DEBUG_STEPS] [--pretrained PRETRAINED]
+                [--pretrained-dataset PRETRAINED_DATASET]
+                [--model-kwargs MODEL_KWARGS]
+                [--dataset-kwargs DATASET_KWARGS] [--model-tag MODEL_TAG]
+                [--save-dir SAVE_DIR] [--device DEVICE]
+                [--loader-num-workers LOADER_NUM_WORKERS]
+                [--no-loader-pin-memory]
+                [--loader-pin-memory [LOADER_PIN_MEMORY]]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.pytorch.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored. If using a SparseZoo recipe, can also
+                        provide 'zoo' to load the base weights associated with
+                        that recipe
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. Can also provide a
+                        SparseZoo stub prefixed with 'zoo:' with an optional
+                        '?recipe_type=' argument
+  --sparse-transfer-learn [SPARSE_TRANSFER_LEARN]
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode [EVAL_MODE]
+                        Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --optim OPTIM         The optimizer type to use, one of [SGD, Adam, RMSprop]
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS
+                        epochs to save checkpoints at
+  --use-mixed-precision [USE_MIXED_PRECISION]
+                        Trains model using mixed precision. Supported
+                        environments are single GPU and multiple GPUs using
+                        DistributedDataParallel with one GPU per process
+  --debug-steps DEBUG_STEPS
+                        Amount of steps to run for training and testing for a
+                        debug mode
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --model-kwargs MODEL_KWARGS
+                        Keyword arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset-kwargs DATASET_KWARGS
+                        Keyword arguments to be passed to dataset constructor,
+                        should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --device DEVICE       The device to run on (can also include ids for data
+                        parallel), ex: cpu, cuda, cuda:0,1
+  --loader-num-workers LOADER_NUM_WORKERS
+                        The number of workers to use for data loading
+  --no-loader-pin-memory
+                        Do not use pinned memory for data loading
+  --loader-pin-memory [LOADER_PIN_MEMORY]
+                        Use pinned memory for data loading
+#########
+EXAMPLES
+#########
+
+##########
+Example command for pruning resnet50 on imagenet dataset:
+python integrations/pytorch/train.py \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
+python integrations/pytorch/train.py \
+    --sparse-transfer-learn \
+    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
+    --arch-key mobilenet_v1 --pretrained pruned-moderate \
+    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Template command for running training with this script on multiple GPUs using
+DistributedDataParallel using mixed precision. Note - DDP support in this script
+only tested for torch==1.7.0.
+python -m torch.distributed.launch \
+--nproc_per_node <NUM GPUs> \
+integrations/pytorch/train.py \
+--use-mixed-precision \
+<TRAIN.PY ARGUMENTS>
+"""
+import argparse
+import json
+import os
+from dataclasses import dataclass, field
+from typing import Any, List, Optional, Tuple
+
+import torch
+from torch.nn import Module
+from torch.utils.data import DataLoader
+
+import utils
+from argparser_.nm_argparser_ import NmArgumentParser
+from sparseml import get_main_logger
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.utils import (
+    DEFAULT_LOSS_KEY,
+    ModuleDeviceContext,
+    ModuleTester,
+    ModuleTrainer,
+    default_device,
+    get_prunable_layers,
+    model_to_device,
+    set_deterministic_seeds,
+    tensor_sparsity,
+)
+
+
+CURRENT_TASK = utils.Tasks.TRAIN
+LOGGER = get_main_logger()
+
+
+@dataclass
+class TrainingArguments:
+    """
+    Represents the arguments we use in our PyTorch integration scripts for
+    training tasks
+
+    Using :class:`NmArgumentParser` we can turn this class into `argparse
+    <https://docs.python.org/3/library/argparse.html#module-argparse>`__
+    arguments that can be specified on the command line.
+
+    :param train_batch_size: An int representing the training batch size.
+    :param test_batch_size: An int representing the test batch size.
+    :param arch_key: A str key representing the type of model to use,
+        ex:resnet50.
+    :param dataset: The dataset to use for training, ex imagenet, imagenette,
+        etc; Set to `imagefolder` for a custom dataset.
+    :param dataset_path: Root path to dataset location.
+    :param local_rank: DDP argument set by PyTorch in DDP mode, default -1
+    :param checkpoint_path: A path to a previous checkpoint to load the state
+        from and resume the state for; Also works with SparseZoo recipes;
+        Set to zoo to automatically download and load weights associated with a
+        recipe.
+    :param init_lr: float representing the initial learning for training,
+        default=1e-9 .
+    :param optim_args: Additional arguments to be passed in to the optimizer as
+        a json object
+    :param recipe_path: The path to the yaml file containing the modifiers and
+        schedule to apply them with; Can also provide a SparseZoo stub prefixed
+        with 'zoo:'.
+    :param sparse_transfer_learn: Boolean to enable sparse transfer learning
+        modifiers to enforce
+        the sparsity for already sparse layers. The modifiers are added to
+        the ones to be loaded from the recipe-path.
+    :param eval_mode: bool to start evaluation mode so that the model can be
+        evaluated on the desired dataset.
+    :param optim: str respresnting the optimizer type to use, one of
+        [SGD, Adam, RMSprop].
+    :param logs_dir: The path to the directory for saving logs.
+    :param save_best_after: int epoch number to start saving the best
+        validation result after until the end of training.
+    :param save_epochs: int epochs to save checkpoints at.
+    :param use_mixed_precision: bool to train model using mixed precision.
+        Supported environments are single GPU and multiple GPUs using
+        DistributedDataParallel with one GPU per process.
+    :param debug_steps: int represnting amount of steps to run for training and
+        testing for debug mode default=-1.
+    :param pretrained: The type of pretrained weights to use default is true
+        to load the default pretrained weights for the model Otherwise should
+        be set to the desired weights type: [base, optim, optim-perf];
+        To not load any weights set to one of [none, false].
+    :param pretrained_dataset: str representing the dataset to load pretrained
+        weights for if pretrained is set; Default is None which will load the
+        default dataset for the architecture; Ex can be set to imagenet,
+        cifar10, etc".
+    :param model_kwargs: json object containing keyword arguments to be
+        passed to model constructor.
+    :param dataset_kwargs: json object to load keyword arguments to be passed
+        to dataset constructor.
+    :param model_tag: A str tag to use for the model for saving results
+        under save-dir, defaults to the model arch and dataset used.
+    :param save_dir: The path to the directory for saving results,
+        default="pytorch_vision".
+    :param device: str represnting the device to run on (can also include ids
+        for data parallel), ex:{cpu, cuda, cuda:0,1}.
+    :param loader_num_workers: int number of workers to use for data loading,
+        default=4.
+    :param loader_pin_memory: bool to use pinned memory for data loading,
+        default=True.
+    """
+
+    train_batch_size: int = field(
+        metadata={"help": "The batch size to use while training"}
+    )
+
+    test_batch_size: int = field(
+        metadata={"help": "The batch size to use while testing"}
+    )
+    arch_key: str = field(
+        metadata={
+            "help": "The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception"
+            "with the list)",
+        }
+    )
+
+    dataset: str = field(
+        metadata={
+            "help": "The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or"
+            " loadable by a dataset in sparseml.pytorch.datasets"
+        }
+    )
+
+    dataset_path: str = field(
+        metadata={
+            "help": "The root path to where the dataset is stored",
+        }
+    )
+    local_rank: int = field(
+        default=-1,
+        metadata={
+            "keep_underscores": True,
+            "help": argparse.SUPPRESS,
+        },
+    )
+
+    checkpoint_path: str = field(
+        default=None,
+        metadata={
+            "help": "A path to a previous checkpoint to load the state from "
+            "and resume the state for. If provided, pretrained will "
+            "be ignored . If using a SparseZoo recipe, can also "
+            "provide 'zoo' to load the base weights associated with "
+            "that recipe"
+        },
+    )
+
+    init_lr: float = field(
+        default=1e-9,
+        metadata={
+            "help": "The initial learning rate to use while training, "
+            "the actual initial value used should be set by the"
+            " sparseml recipe"
+        },
+    )
+
+    optim_args: json.loads = field(
+        default_factory=lambda: {
+            "momentum": 0.9,
+            "nesterov": True,
+            "weight_decay": 0.0001,
+        },
+        metadata={
+            "help": "Additional args to be passed to the optimizer passed in"
+            " as a json object",
+        },
+    )
+
+    recipe_path: str = field(
+        default=None,
+        metadata={
+            "help": "The path to the yaml file containing the modifiers and "
+            "schedule to apply them with. Can also provide a "
+            "SparseZoo stub prefixed with 'zoo:' with an optional "
+            "'?recipe_type=' argument"
+        },
+    )
+
+    sparse_transfer_learn: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Enable sparse transfer learning modifiers to enforce the "
+            "sparsity for already sparse layers. The modifiers are "
+            "added to the ones to be loaded from the recipe-path"
+        },
+    )
+
+    eval_mode: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Puts into evaluation mode so that the model can be "
+            "evaluated on the desired dataset"
+        },
+    )
+
+    optim: str = field(
+        default="SGD",
+        metadata={"help": "The optimizer type to use, one of [SGD, Adam, RMSprop]"},
+    )
+
+    logs_dir: str = field(
+        default=os.path.join("pytorch_vision_train", "tensorboard-logs"),
+        metadata={
+            "help": "The path to the directory for saving logs",
+        },
+    )
+
+    save_best_after: int = field(
+        default=-1,
+        metadata={
+            "help": "start saving the best validation result after the given "
+            "epoch completes until the end of training"
+        },
+    )
+    save_epochs: List[int] = field(
+        default_factory=lambda: [], metadata={"help": "epochs to save checkpoints at"}
+    )
+
+    use_mixed_precision: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Trains model using mixed precision. Supported "
+            "environments are single GPU and multiple GPUs using "
+            "DistributedDataParallel with one GPU per process"
+        },
+    )
+
+    debug_steps: int = field(
+        default=-1,
+        metadata={
+            "help": "Amount of steps to run for training and testing for a "
+            "debug mode"
+        },
+    )
+
+    pretrained: str = field(
+        default=True,
+        metadata={
+            "help": "The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for "
+            "the model. Otherwise should be set to the desired weights "
+            "type: [base, optim, optim-perf]. To not load any weights set"
+            "to one of [none, false]"
+        },
+    )
+
+    pretrained_dataset: str = field(
+        default=None,
+        metadata={
+            "help": "The dataset to load pretrained weights for if pretrained is "
+            "set. Default is None which will load the default dataset for "
+            "the architecture. Ex can be set to imagenet, cifar10, etc",
+        },
+    )
+
+    model_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to model constructor, should "
+            "be given as a json object"
+        },
+    )
+
+    dataset_kwargs: json.loads = field(
+        default_factory=lambda: {},
+        metadata={
+            "help": "Keyword arguments to be passed to dataset constructor, "
+            "should be given as a json object",
+        },
+    )
+
+    model_tag: str = field(
+        default=None,
+        metadata={
+            "help": "A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        },
+    )
+
+    save_dir: str = field(
+        default="pytorch_vision",
+        metadata={
+            "help": "The path to the directory for saving results",
+        },
+    )
+
+    device: str = field(
+        default=default_device(),
+        metadata={
+            "help": "The device to run on (can also include ids for data "
+            "parallel), ex: cpu, cuda, cuda:0,1"
+        },
+    )
+
+    loader_num_workers: int = field(
+        default=4, metadata={"help": "The number of workers to use for data loading"}
+    )
+
+    loader_pin_memory: bool = field(
+        default=True, metadata={"help": "Use pinned memory for data loading"}
+    )
+
+    def __post_init__(self):
+        # add ddp args
+        env_world_size = int(os.environ.get("WORLD_SIZE", 1))
+        self.world_size = env_world_size
+
+        env_rank = int(os.environ.get("RANK", -1))
+        self.rank = env_rank
+
+        self.is_main_process = self.rank in [
+            -1,
+            0,
+        ]  # non DDP execution or 0th DDP process
+
+        # modify training batch size for give world size
+        assert self.train_batch_size % self.world_size == 0, (
+            f"Invalid training batch size for world size {self.world_size} "
+            f"given batch size {self.train_batch_size}. "
+            f"world size must divide training batch size evenly."
+        )
+
+        self.train_batch_size = self.train_batch_size // self.world_size
+
+        if "preprocessing_type" not in self.dataset_kwargs and (
+            "coco" in self.dataset.lower() or "voc" in self.dataset.lower()
+        ):
+            if "ssd" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "ssd"
+            elif "yolo" in self.arch_key.lower():
+                self.dataset_kwargs["preprocessing_type"] = "yolo"
+
+        if self.local_rank != -1:
+            torch.distributed.init_process_group(backend="nccl", init_method="env://")
+            set_deterministic_seeds(0)
+
+        self.approximate = False
+
+
+def train(
+    train_args: TrainingArguments,
+    model: Module,
+    train_loader: DataLoader,
+    val_loader: DataLoader,
+    input_shape: Tuple[int, ...],
+    save_dir: str,
+    loggers: List[Any],
+) -> None:
+    """
+    Utility function to drive the training processing
+
+    :param train_args: A TrainingArguments object with
+        arguments for current training task
+    :param model: model architecture to train
+    :param train_loader: A DataLoader for training data
+    :param val_loader: A DataLoader for validation data
+    :param input_shape: A tuple of integers representing the shape of inputs
+    :param save_dir: Directory to store checkpoints at during training process
+    :param loggers: List of loggers to use during training process
+    """
+    # loss setup
+    val_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
+    LOGGER.info(f"created loss for validation: {val_loss}")
+
+    train_loss = utils.get_loss_wrapper(arch_key=train_args.arch_key, training=True)
+    LOGGER.info(f"created loss for training: {train_loss}")
+
+    # training setup
+    if not train_args.eval_mode:
+        epoch, optim, manager = utils.create_scheduled_optimizer(
+            train_args,
+            model,
+            train_loader,
+            loggers,
+        )
+    else:
+        epoch = 0
+        train_loss = None
+        optim = None
+        manager = None
+
+    # device setup
+    if train_args.rank == -1:
+        device = train_args.device
+        ddp = False
+    else:
+        torch.cuda.set_device(train_args.local_rank)
+        device = train_args.local_rank
+        ddp = True
+
+    model, device, device_ids = model_to_device(model, device, ddp=ddp)
+    LOGGER.info(f"running on device {device} for ids {device_ids}")
+
+    trainer = (
+        ModuleTrainer(
+            model,
+            device,
+            train_loss,
+            optim,
+            loggers=loggers,
+            device_context=ModuleDeviceContext(
+                use_mixed_precision=train_args.use_mixed_precision,
+                world_size=train_args.world_size,
+            ),
+        )
+        if not train_args.eval_mode
+        else None
+    )
+
+    if train_args.is_main_process:  # only test on one DDP process if using DDP
+        tester = ModuleTester(model, device, val_loss, loggers=loggers, log_steps=-1)
+
+        # initial baseline eval run
+        tester.run_epoch(
+            val_loader, epoch=epoch - 1, max_epochs=-1, max_steps=train_args.debug_steps
+        )
+
+    if not train_args.eval_mode:
+        utils.save_recipe(recipe_manager=manager, save_dir=save_dir)
+        LOGGER.info(f"starting training from epoch {epoch}")
+
+        if epoch > 0:
+            LOGGER.info("adjusting ScheduledOptimizer to restore point")
+            optim.adjust_current_step(epoch, 0)
+
+        target_metric = (
+            "top1acc" if "top1acc" in tester.loss.available_losses else DEFAULT_LOSS_KEY
+        )
+        best_metric = None
+        val_res = None
+
+        while epoch < manager.max_epochs:
+            if train_args.debug_steps > 0:
+                # correct since all optimizer steps are not
+                # taken in the epochs for debug mode
+                optim.adjust_current_step(epoch, 0)
+
+            if train_args.rank != -1:  # sync DDP dataloaders
+                train_loader.sampler.set_epoch(epoch)
+
+            trainer.run_epoch(
+                train_loader,
+                epoch,
+                max_epochs=manager.max_epochs,
+                max_steps=train_args.debug_steps,
+                show_progress=train_args.is_main_process,
+            )
+
+            # testing steps
+            if train_args.is_main_process:
+                # only test and save on main process
+                val_res = tester.run_epoch(
+                    val_loader,
+                    epoch,
+                    max_epochs=manager.max_epochs,
+                    max_steps=train_args.debug_steps,
+                )
+                val_metric = val_res.result_mean(target_metric).item()
+
+                if epoch >= train_args.save_best_after and (
+                    best_metric is None
+                    or (
+                        val_metric <= best_metric
+                        if target_metric != "top1acc"
+                        else val_metric >= best_metric
+                    )
+                ):
+                    utils.save_model_training(
+                        model,
+                        optim,
+                        input_shape,
+                        "checkpoint-best",
+                        save_dir,
+                        epoch,
+                        val_res,
+                    )
+                    best_metric = val_metric
+
+            # save checkpoints
+            _save_epoch = (
+                train_args.is_main_process
+                and train_args.save_epochs
+                and epoch in train_args.save_epochs
+            )
+            if _save_epoch:
+                utils.save_model_training(
+                    model,
+                    optim,
+                    input_shape,
+                    f"checkpoint-{epoch:04d}-{val_metric:.04f}",
+                    save_dir,
+                    epoch,
+                    val_res,
+                )
+
+            epoch += 1
+
+        # export the final model
+        LOGGER.info("completed...")
+        if train_args.is_main_process:
+            # only convert qat -> quantized ONNX graph for finalized model
+            # TODO: change this to all checkpoints when conversion times improve
+            utils.save_model_training(
+                model, optim, input_shape, "model", save_dir, epoch - 1, val_res, True
+            )
+
+            LOGGER.info("layer sparsities:")
+            for (name, layer) in get_prunable_layers(model):
+                LOGGER.info(
+                    f"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}"
+                )
+
+    # close DDP
+    if train_args.rank != -1:
+        torch.distributed.destroy_process_group()
+
+
+def main():
+    """
+    Driver function for the script
+    """
+    _parser = NmArgumentParser(dataclass_types=TrainingArguments)
+    training_args, _ = _parser.parse_args_into_dataclasses()
+
+    save_dir, loggers = utils.get_save_dir_and_loggers(training_args, task=CURRENT_TASK)
+
+    input_shape = ModelRegistry.input_shape(training_args.arch_key)
+    image_size = input_shape[1]  # assume shape [C, S, S] where S is the image size
+
+    (
+        train_dataset,
+        train_loader,
+        val_dataset,
+        val_loader,
+    ) = utils.get_train_and_validation_loaders(
+        training_args, image_size, task=CURRENT_TASK
+    )
+
+    num_classes = utils.infer_num_classes(training_args, train_dataset, val_dataset)
+
+    # # model creation
+    model = utils.create_model(training_args, num_classes)
+    train(
+        training_args, model, train_loader, val_loader, input_shape, save_dir, loggers
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/integrations/old-examples/pytorch/tutorials/.gitkeep b/integrations/old-examples/pytorch/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md b/integrations/old-examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
new file mode 100644
index 00000000000..a7962a06f46
--- /dev/null
+++ b/integrations/old-examples/pytorch/tutorials/classification_sparse_transfer_learning_tutorial.md
@@ -0,0 +1,138 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparse Transfer Learning for Image Classification
+
+This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
+
+## Overview
+
+Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo. 
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
+
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process. In this tutorial you will:
+- Download and prepare a pre-sparsified image classification model.
+- Apply a sparse transfer learning recipe on the pre-sparsified model.
+
+The examples listed in this tutorial are all performed on the [Imagenette](https://github.com/fastai/imagenette) dataset.
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues) 
+
+## Setting Up
+
+This tutorial can be run by cloning and installing the `sparseml` repository which contains scripts and recipes for
+this example:
+
+```bash
+pip install "sparseml[torchvision, dev]"
+```
+Note:  make sure to upgrade `pip` using `python -m pip install -U pip`
+## Downloading and Preparing a Pre-Sparsified Model
+
+First, you need to download the sparsified models from the [SparseZoo](https://sparsezoo.neuralmagic.com/). A few image classification models with [SparseZoo](https://sparsezoo.neuralmagic.com/) stubs:
+
+| Model Name     |      Stub      | Description |
+|----------|-------------|-------------|
+| resnet-pruned-moderate | zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate |This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the accuracy of the original baseline model (76.1% top1). Pruned layers achieve 88% sparsity.|
+|resnet-pruned-conservative|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves full recovery original baseline model accuracy (76.1% top1). Pruned layers achieve 80% sparsity.|
+|resnet-pruned-moderate-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate|This model is a sparse, INT8 quantized [ResNet-50](https://arxiv.org/abs/1512.03385) model that achieves 99% of the original baseline accuracy. The average model sparsity is about 72% with pruned layers achieving between 70-80% sparsity. This pruned quantized model achieves 75.46% top1 accuracy on the ImageNet dataset.|
+|mobilenetv1-pruned-moderate|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves 99% of the accuracy of the original baseline model (70.9% top1). Pruned layers achieve between 70-90% sparsity.|
+|mobilenetv1-pruned-conservative|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative|This model is a sparse, [MobileNetV1](https://arxiv.org/abs/1704.04861) model that achieves the same accuracy as the original baseline model. Pruned layers achieve between 60-86% sparsity. This pruned quantized model achieves 70.9% top1 accuracy on the ImageNet dataset.|
+|mobilenetv1-pruned-moderate-quantized|zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate|Pruned [MobileNetV1](https://arxiv.org/abs/1704.04861) architecture with width=1.0 trained on the ImageNet dataset. Recalibrated performance model: within 99% of baseline validation top1 accuracy (70.9%)|
+
+
+A few recipe stubs:
+
+|Recipe Name|Stub|Description|
+|----------|-------------|-------------|
+|resnet50.transfer-learning-pruned-quantized|zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=transfer_learn|This recipe provides a framework for performing pruned-quantized transfer learning on SparseML's pruned_quantized ResNet50 model. This recipe was written for transfer learning onto the Imagenette dataset using a single GPU, Adam optimizer, and batch size 32. Adjust the hyperparameters at the top of the file accordingly for your dataset and training environment|
+
+
+Note: The models above were originally trained and sparsified on the [ImageNet](https://image-net.org/) dataset.
+
+- After deciding on which model meets your performance requirements for both speed and accuracy, select the SparseZoo stub associated with that model.
+   The stub will be used later with the training script to automatically pull down the desired pre-trained weights.
+
+## Applying a Sparse Transfer Learning Recipe
+
+- After noting respective SparseZoo model stub, [train.py](../train.py) script can be used to download checkpoint and [Imagenette](https://github.com/fastai/imagenette) and kick-start transfer learning. 
+   The transfer learning process itself is guided using recipes; We include example [recipes](../recipes) for classification along with others in the SparseML [GitHub repository](https://github.com/neuralmagic/sparseml). 
+[Learn more about recipes and modifiers](../../../docs/source/recipes.md).
+
+- Run the following example command to kick off transfer learning for [ResNet-50](https://arxiv.org/abs/1512.03385) starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/):
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path "zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate" \
+    --arch-key resnet50 \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --model-tag resnet50-imagenette-sparse-transfer-learned
+```
+
+To transfer learn [MobileNet](https://arxiv.org/abs/1704.04861) on [Imagenette](https://github.com/fastai/imagenette), starting from a moderately pruned checkpoint from [SparseZoo](https://sparsezoo.neuralmagic.com/), run the following example command:
+
+```
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/classification.transfer_learn_pruned.md \
+    --checkpoint-path "zoo:cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate" \
+    --arch-key mobilenet \
+    --model-kwargs '{"ignore_error_tensors": ["classifier.fc.weight", "classifier.fc.bias"]}' \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE  \
+    --train-batch-size 32 --test-batch-size 64 \
+    --loader-num-workers 0 \
+    --optim Adam \
+    --optim-args '{}' \
+    --save-dir sparse-transfer-models
+    --model-tag mobilenet-imagenette-sparse-transfer-learned
+```
+The `--checkpoint-path` argument can take in path to a model checkpoint, or a SpareZoo model stub,
+if a SparseZoo recipe stub is provided in `--recipe-path`, 
+the `checkpoint-path` can be set to `zoo` for auto-downloading corresponding checkpoints from SparseZoo. 
+To learn more about the usage, run script with `-h` option to see help. 
+
+The script automatically saves model checkpoints after each epoch and reports the validation loss along with layer sparsities. 
+
+### ONNX Export
+The model can be saved to [ONNX](https://onnx.ai/) format to be loaded later for inference or other experiments using
+the `sparseml.image_classification.export_onnx` script.
+
+```
+sparseml.image_classification.export_onnx \
+    --arch-key mobilenet \
+    --dataset imagenette \
+    --dataset-path /PATH/TO/IMAGENETTE \
+    --checkpoint-path sparse-transfer-models/mobilenet-imagenette-sparse-transfer-learned/pytorch/model.pth
+```
+
+## Wrap-Up
+
+Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned image classification models. 
+In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and saved the model for future use.
+
+An example for benchmarking and deploying image classification models with DeepSparse [is also available](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/old-examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md b/integrations/old-examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
new file mode 100644
index 00000000000..b67881dc79f
--- /dev/null
+++ b/integrations/old-examples/pytorch/tutorials/sparsifying_pytorch_models_using_recipes.md
@@ -0,0 +1,132 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying PyTorch Models Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized PyTorch models.
+
+## Overview
+
+Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of their existing training pipelines.
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others.
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes.
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by pruning a PyTorch [ResNet-50](https://arxiv.org/abs/1512.03385) model.
+
+1. Setting Up
+2. Inspecting a Recipe
+3. Applying a Recipe
+3. Exporting for Inference
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Setting Up
+
+This tutorial can be run by installing `sparseml` which contains scripts for this pruning example:
+
+```bash
+pip install "sparseml[torchvision, dev]"
+```
+
+## Inspecting a Recipe
+
+Recipes are YAML or Markdown files that SparseML uses to easily define and control the sparsification of a model.
+Recipes consist of a series of `Modifiers` that can influence the training process in different ways. A list of
+common modifiers and their uses is provided
+[here](../../../docs/source/recipes.md#modifiers-intro).
+
+SparseML provides a recipe for sparsifying a ResNet-50 model trained on the tiny Imagenette dataset. The recipe can
+be viewed in the browser
+[here](../recipes/resnet50-imagenette-pruned.md)
+or in the command line from the file `sparseml/integrations/pytorch/recipes/resnet50-imagenette-pruned.md`.
+
+The recipe contains three kinds of modifiers:
+
+- an `EpochRangeModifier` that sets the number of epochs used for sparsification
+- a `SetLearningRateModifier` that sets the sparsification learning rate
+- three `GMPruningModifier`s that set various layers of the model to be pruned to different levels.
+
+Notice that each `GMPruningModifier` sets specific parameters of the ResNet-50 model to be pruned. To
+prune a different model, the parameter names should be adjusted to match the new model.  To avoid naming
+individual parameter names, all parameters can be set for pruning by passing `__ALL_PRUNABLE__` instead
+of the parameters list for a single `GMPruningModifier`.
+
+### Applying a Recipe
+
+Recipes can integrated into training flows with a couple of lines of code by using a `ScheduledModifierManager`
+that wraps the PyTorch `Optimizer` step.  An example of how this is done can be found
+[here](../../../docs/source/code.md#pytorch-sparsification).
+
+For this example, we can use the `sparseml.image_classification.train` utility.  This utility runs a
+PyTorch training flow that is modified by a `ScheduledModifierManager` and takes a recipe as an input.
+It natively supports ResNet-50 and other popular image classification models.
+
+To prune a ResNet-50 model trained on the Imagenette dataset with the recipe above, run the following command.
+If the Imagenette dataset has not been downloaded yet, the script will automatically download it to the given
+`--dataset-path`. If you have a local copy of the dataset, `--dataset-path` may be updated.
+
+*Note:* If an out of memory exception occurs, the train batch size should be lowered to fit on your device
+
+```bash
+sparseml.image_classification.train \
+    --recipe-path integrations/pytorch/recipes/resnet50-imagenette-pruned.md \
+    --dataset-path ./data \
+    --pretrained True \
+    --arch-key resnet50 \
+    --dataset imagenette \
+    --train-batch-size 128 \
+    --test-batch-size 256 \
+    --loader-num-workers 8 \
+    --save-dir sparsification_example \
+    --logs-dir sparsification_example \
+    --model-tag resnet50-imagenette-pruned \
+    --save-best-after 8
+```
+
+A full list of arguments and their descriptions can be found by running 
+
+```bash
+sparseml.image_classification.train --help
+```
+
+
+## Exporting for Inference
+
+After, the training pruned model and training log files will be found under 
+`sparsification_example/resnet50-imagenette-pruned`. To run the sparsified model for accelerated inference with
+an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse), the pruned model must be
+exported to the ONNX format.
+
+This step can be completed using `sparseml.image_classification.export_onnx` script which uses the
+`sparseml.pytorch.utils.ModuleExporter` class. Run the script with `--help` option to see usage.
+
+The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in
+inference performance. An example for benchmarking and deploying image classification models with DeepSparse can
+be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/classification).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate sparsified models.
+In this tutorial, you installed SparseML, downloaded and inspected a pruning recipe, applied it to a ResNet-50 model,
+and exported the sparse model for inference.
+
+Next steps will include creating new recipes and applying them to different models. Check out other
+[SparseML tutorials](https://github.com/neuralmagic/sparseml/tree/main/integrations) to learn more.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/old-examples/pytorch/utils.py b/integrations/old-examples/pytorch/utils.py
new file mode 100644
index 00000000000..b5dd57c3c41
--- /dev/null
+++ b/integrations/old-examples/pytorch/utils.py
@@ -0,0 +1,428 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Helper methods for image classification/detection based tasks
+"""
+import os
+from enum import Enum, auto, unique
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+from torch.nn import Module
+from torch.nn import functional as torch_functional
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader
+
+from sparseml.pytorch.datasets import DatasetRegistry, ssd_collate_fn, yolo_collate_fn
+from sparseml.pytorch.image_classification.utils.helpers import (
+    download_framework_model_by_recipe_type,
+)
+from sparseml.pytorch.models import ModelRegistry
+from sparseml.pytorch.optim import ScheduledModifierManager, ScheduledOptimizer
+from sparseml.pytorch.sparsification import ConstantPruningModifier
+from sparseml.pytorch.utils import (
+    DEFAULT_LOSS_KEY,
+    CrossEntropyLossWrapper,
+    InceptionCrossEntropyLossWrapper,
+    LossWrapper,
+    ModuleExporter,
+    ModuleRunResults,
+    PythonLogger,
+    SSDLossWrapper,
+    TensorBoardLogger,
+    TopKAccuracy,
+    YoloLossWrapper,
+    early_stop_data_loader,
+    torch_distributed_zero_first,
+)
+from sparseml.utils import create_dirs
+from sparsezoo import Model
+
+
+@unique
+class Tasks(Enum):
+    """
+    A class representing supported image classification/detection tasks
+    """
+
+    TRAIN = auto()
+    EXPORT = auto()
+    ANALYSIS = auto()
+    LR_ANALYSIS = auto()
+    PR_SENSITIVITY = auto()
+
+
+# loggers
+def get_save_dir_and_loggers(
+    args: Any, task: Optional[Tasks] = None
+) -> Tuple[Union[str, None], List]:
+    if args.is_main_process:
+        save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+        logs_dir = (
+            os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
+            if task == Tasks.TRAIN
+            else None
+        )
+
+        if not args.model_tag:
+            dataset_name = (
+                f"{args.dataset}-{args.dataset_kwargs['year']}"
+                if "year" in args.dataset_kwargs
+                else args.dataset
+            )
+            model_tag = f"{args.arch_key.replace('/', '.')}_{dataset_name}"
+            model_id = model_tag
+            model_inc = 0
+            # set location to check for models with same name
+            model_main_dir = logs_dir or save_dir
+
+            while os.path.exists(os.path.join(model_main_dir, model_id)):
+                model_inc += 1
+                model_id = f"{model_tag}__{model_inc:02d}"
+        else:
+            model_id = args.model_tag
+
+        save_dir = os.path.join(save_dir, model_id)
+        create_dirs(save_dir)
+
+        # loggers setup
+        loggers = [PythonLogger()]
+        if task == Tasks.TRAIN:
+            logs_dir = os.path.join(logs_dir, model_id)
+            create_dirs(logs_dir)
+            loggers.append(TensorBoardLogger(log_path=logs_dir))
+        print(f"Model id is set to {model_id}")
+    else:
+        # do not log for non main processes
+        save_dir = None
+        loggers = []
+    return save_dir, loggers
+
+
+# data helpers_
+def _get_collate_fn(arch_key: str, task: Optional[Tasks] = None):
+    is_ssd = "ssd" in arch_key.lower()
+    need_collate_function = (
+        is_ssd or "yolo" in arch_key.lower()
+    ) and task != Tasks.EXPORT
+    if need_collate_function:
+        return ssd_collate_fn if is_ssd else yolo_collate_fn
+    return None
+
+
+def _create_train_dataset_and_loader(
+    args: Any,
+    image_size: Tuple[int, ...],
+    task: Optional[Tasks] = None,
+) -> Tuple[Any, Any]:
+    need_train_data = not (
+        task == Tasks.EXPORT
+        or task == Tasks.PR_SENSITIVITY
+        and args.approximate
+        or task == Tasks.TRAIN
+        and args.eval_mode
+    )
+
+    if need_train_data:
+        with torch_distributed_zero_first(
+            args.local_rank,
+        ):  # only download once locally
+            train_dataset = DatasetRegistry.create(
+                args.dataset,
+                root=args.dataset_path,
+                train=True,
+                rand_trans=True,
+                image_size=image_size,
+                **args.dataset_kwargs,
+            )
+        sampler = (
+            torch.utils.data.distributed.DistributedSampler(train_dataset)
+            if args.rank != -1
+            else None
+        )
+        shuffle = True if sampler is None else False
+        batch_size = args.train_batch_size if task == Tasks.TRAIN else args.batch_size
+
+        train_loader = DataLoader(
+            train_dataset,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            num_workers=args.loader_num_workers,
+            pin_memory=args.loader_pin_memory,
+            sampler=sampler,
+            collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
+        )
+        print(f"created train_dataset: {train_dataset}")
+        return train_dataset, train_loader
+
+    return None, None
+
+
+def _create_val_dataset_and_loader(
+    args, image_size: Tuple[int, ...], task: Optional[Tasks] = None
+) -> Tuple[Any, Any]:
+    need_val_data = not (
+        task == Tasks.PR_SENSITIVITY
+        or task == Tasks.LR_ANALYSIS
+        or not (args.is_main_process and args.dataset != "imagefolder")
+    )
+
+    if need_val_data:
+        val_dataset = DatasetRegistry.create(
+            args.dataset,
+            root=args.dataset_path,
+            train=False,
+            rand_trans=False,
+            image_size=image_size,
+            **args.dataset_kwargs,
+        )
+        if args.is_main_process:
+            is_training = task == Tasks.TRAIN
+            val_loader = DataLoader(
+                val_dataset,
+                batch_size=args.test_batch_size if is_training else 1,
+                shuffle=False,
+                num_workers=args.loader_num_workers if is_training else 1,
+                pin_memory=args.loader_pin_memory if is_training else False,
+                collate_fn=_get_collate_fn(arch_key=args.arch_key, task=task),
+            )
+            if task == Tasks.EXPORT:
+                val_loader = early_stop_data_loader(
+                    val_loader, args.num_samples if args.num_samples > 1 else 1
+                )
+            print(f"created val_dataset: {val_dataset}")
+        else:
+            val_loader = None  # only val dataset needed to get the number of classes
+        return val_dataset, val_loader
+    return None, None  # val dataset not needed
+
+
+def get_train_and_validation_loaders(
+    args: Any, image_size: Tuple[int, ...], task: Optional[Tasks] = None
+):
+    """
+    :param args: Object containing relevant configuration for the task
+    :param image_size: A Tuple of integers representing the shape of input image
+    :param task: The current task being performed
+    :return: 4 element tuple with the following format (train_dataset,
+        train_loader, val_dataset, val_loader)
+    """
+    train_dataset, train_loader = _create_train_dataset_and_loader(
+        args, image_size, task=task
+    )
+    val_dataset, val_loader = _create_val_dataset_and_loader(
+        args, image_size, task=task
+    )
+    return train_dataset, train_loader, val_dataset, val_loader
+
+
+# Model creation Helpers
+
+
+def create_model(args: Any, num_classes: int) -> Module:
+    """
+    :param args: object with configuration for model classes
+    :param num_classes: Integer representing the number of output classes
+    :returns: A Module object representing the created model
+    """
+    with torch_distributed_zero_first(args.local_rank):  # only download once locally
+        if args.checkpoint_path == "zoo":
+            if args.recipe_path and args.recipe_path.startswith("zoo:"):
+                zoo_model = Model(args.recipe_path)
+                args.checkpoint_path = download_framework_model_by_recipe_type(
+                    zoo_model
+                )
+            else:
+                raise ValueError(
+                    "'zoo' provided as --checkpoint-path but a SparseZoo stub"
+                    " prefixed by 'zoo:' not provided as --recipe-path"
+                )
+
+        model = ModelRegistry.create(
+            args.arch_key,
+            args.pretrained,
+            args.checkpoint_path,
+            args.pretrained_dataset,
+            num_classes=num_classes,
+            **args.model_kwargs,
+        )
+    print(f"created model: {model}")
+    return model
+
+
+def infer_num_classes(args: Any, train_dataset, val_dataset):
+    """
+    :param args: Object with configuration settings
+    :param train_dataset: dataset representing training data
+    :param val_dataset: dataset representing validation data
+    :return: An integer representing the number of classes
+    """
+    if "num_classes" in args.model_kwargs:
+        # handle manually overriden num classes
+        num_classes = args.model_kwargs["num_classes"]
+        del args.model_kwargs["num_classes"]
+    elif args.dataset == "imagefolder":
+        dataset = val_dataset or train_dataset  # get non None dataset
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+    return num_classes
+
+
+def get_loss_wrapper(
+    arch_key: str, training: bool = False, task: Optional[Tasks] = None
+):
+    """
+    :param arch_key: The model architecture
+    :param training: True if training task started else False
+    :param task: current task being executed
+    """
+    if "ssd" in arch_key.lower():
+        return SSDLossWrapper()
+    if "yolo" in arch_key.lower():
+        return YoloLossWrapper()
+
+    extras = {"top1acc": TopKAccuracy(1), "top5acc": TopKAccuracy(5)}
+    if task == Tasks.TRAIN:
+        return (
+            CrossEntropyLossWrapper(extras)
+            if training and "inception" not in arch_key
+            else InceptionCrossEntropyLossWrapper(extras)
+        )
+    return LossWrapper(loss_fn=torch_functional.cross_entropy, extras=extras)
+
+
+#  optimizer helper
+def create_scheduled_optimizer(
+    train_args: Any,
+    model: Module,
+    train_loader: DataLoader,
+    loggers: List[Any],
+) -> Tuple[int, ScheduledOptimizer, ScheduledModifierManager]:
+    """
+    :param train_args : An object with task specific config
+    :param model: model architecture to train
+    :param train_loader: A DataLoader for training data
+    :param loggers: List of loggers to use during training process
+    :type train_args: TrainingArguments
+    """
+    # # optimizer setup
+    optim_const = torch.optim.__dict__[train_args.optim]
+    optim = optim_const(
+        model.parameters(), lr=train_args.init_lr, **train_args.optim_args
+    )
+    print(f"created optimizer: {optim}")
+    print(
+        "note, the lr for the optimizer may not reflect the manager yet until "
+        "the recipe config is created and run"
+    )
+
+    # restore from previous check point
+    if train_args.checkpoint_path:
+        # currently optimizer restoring is unsupported
+        # mapping of the restored params to the correct device is not working
+        # load_optimizer(args.checkpoint_path, optim)
+        epoch = 0  # load_epoch(args.checkpoint_path) + 1
+        print(
+            f"restored checkpoint from {train_args.checkpoint_path} for "
+            f"epoch {epoch - 1}"
+        )
+    else:
+        epoch = 0
+
+    # modifier setup
+    add_mods = (
+        ConstantPruningModifier.from_sparse_model(model)
+        if train_args.sparse_transfer_learn
+        else None
+    )
+    manager = ScheduledModifierManager.from_yaml(
+        file_path=train_args.recipe_path, add_modifiers=add_mods
+    )
+    optim = ScheduledOptimizer(
+        optim,
+        model,
+        manager,
+        steps_per_epoch=len(train_loader),
+        loggers=loggers,
+    )
+    print(f"created manager: {manager}")
+    return epoch, optim, manager
+
+
+# saving helpers
+
+
+def save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+    """
+    :param recipe_manager: The ScheduleModified manager to save recipes
+    :param save_dir: The directory to save the recipe
+    """
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    print(f"Saved recipe to {recipe_save_path}")
+
+
+def save_model_training(
+    model: Module,
+    optim: Optimizer,
+    input_shape: Tuple[int, ...],
+    save_name: str,
+    save_dir: str,
+    epoch: int,
+    val_res: Union[ModuleRunResults, None],
+    convert_qat: bool = False,
+):
+    """
+    :param model: model architecture
+    :param optim: The optimizer used
+    :param input_shape: A tuple of integers representing the input shape
+    :param save_name: name to save model to
+    :param save_dir: directory to save results in
+    :param epoch: integer representing umber of epochs to
+    :param val_res: results from validation run
+    :param convert_qat: True if model is to be quantized before saving
+    """
+    has_top1 = "top1acc" in val_res.results
+    metric_name = "top-1 accuracy" if has_top1 else "val_loss"
+    metric = val_res.result_mean("top1acc" if has_top1 else DEFAULT_LOSS_KEY).item()
+    print(
+        f"Saving model for epoch {epoch} and {metric_name} "
+        f"{metric} to {save_dir} for {save_name}"
+    )
+    exporter = ModuleExporter(model, save_dir)
+    exporter.export_pytorch(optim, epoch, f"{save_name}.pth")
+    exporter.export_onnx(
+        torch.randn(1, *input_shape),
+        f"{save_name}.onnx",
+        convert_qat=convert_qat,
+    )
+
+    info_path = os.path.join(save_dir, f"{save_name}.txt")
+
+    with open(info_path, "w") as info_file:
+        info_lines = [
+            f"epoch: {epoch}",
+        ]
+
+        if val_res is not None:
+            for loss in val_res.results.keys():
+                info_lines.append(f"{loss}: {val_res.result_mean(loss).item()}")
+
+        info_file.write("\n".join(info_lines))
diff --git a/integrations/old-examples/pytorch/vision.py b/integrations/old-examples/pytorch/vision.py
new file mode 100644
index 00000000000..6ffeb78869c
--- /dev/null
+++ b/integrations/old-examples/pytorch/vision.py
@@ -0,0 +1,29 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Deprecated script kindly use the following task specific scripts for
+ optimization tasks on image classification and object detection models:
+
+* Model pruning - sparseml.image_classification.train
+* Quantization aware training - sparseml.image_classification.train
+* Sparse transfer learning - sparseml.image_classification.train
+* pruning sensitivity analysis - sparseml.image_classification.pr_sensitivity
+* learning rate sensitivity analysis - sparseml.image_classification.lr_analysis
+* ONNX export - sparseml.image_classification.export_onnx
+"""
+
+
+if __name__ == "__main__":
+    print(__doc__)
diff --git a/integrations/old-examples/rwightman-timm/README.md b/integrations/old-examples/rwightman-timm/README.md
new file mode 100644
index 00000000000..773c10116d5
--- /dev/null
+++ b/integrations/old-examples/rwightman-timm/README.md
@@ -0,0 +1,116 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML-rwightman/pytorch-image-models integration
+This directory provides a SparseML integrated training script for the popular
+[rwightman/pytorch-image-models](https://github.com/rwightman/pytorch-image-models)
+repository also known as [timm](https://pypi.org/project/timm/).
+
+Using this integration, you will be able to apply SparseML optimizations
+to the powerful training flows of the pytorch-image-models repository.
+
+Some of the tasks you can perform using this integration include, but are not limited to:
+* Pruning
+* Quantization
+* Pruning and Quantization
+* Sparse Transfer Learning
+
+## Installation
+We recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your project dependencies isolated.
+A virtual environment can be created using the following commands:
+
+```bash
+python3 -m venv venv # create a venv virtual environment
+source venv/bin/activate # activate venv
+pip install --upgrade pip # upgrade pip
+```
+
+To begin, run the following command in the root directory of this integration (`cd integrations/rwightman-timm`):
+```bash
+bash setup_integration.sh
+```
+
+The `setup_integration.sh` file will clone the [pytorch-image-models](https://github.com/neuralmagic/pytorch-image-models.git) repository with the SparseML integration as a subfolder.
+After the repo has successfully cloned, pytorch-image-models will be installed along with any necessary dependencies.
+
+This integration requires `python>=3.7,<3.10`
+
+## Quick Tour
+### Recipes
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration adds a `--recipe` argument to the 
+[`train.py` script](https://github.com/neuralmagic/pytorch-image-models/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+You can learn how to build or download a recipe using the
+[SparseML](https://github.com/neuralmagic/sparseml)
+or [SparseZoo](https://github.com/neuralmagic/sparsezoo)
+documentation, or export one with [Sparsify](https://github.com/neuralmagic/sparsify).
+
+ViT recipes are not yet available on SparseZoo, but are in progress.
+
+Using a local recipe and checkpoint, pruning a model can be done by running the following command from within the root of this integration's folder
+```bash
+python train.py \
+  /PATH/TO/DATASET/imagenet/ \
+  --recipe ../recipes/vit_base.85.recal.config.yaml \
+  --dataset imagenet \
+  --batch-size 64 \
+  --remode pixel --reprob 0.0 --smoothing 0.1 --mixup 0.5 --mixup 0.5 \
+  --output models/optimized \
+  --model vit_base_patch16_224 \
+  --workers 8 
+```  
+
+Documentation on the original script can be found
+[here](https://rwightman.github.io/pytorch-image-models/scripts/).
+The latest commit hash that `train.py` is based on is included in the docstring.
+
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| [recipes](./recipes)              | Typical recipes for sparsifying ViT models along with any downloaded recipes from the SparseZoo.                      |
+| tutorials            | Tutorial walkthroughs for how to sparsify ViT models using recipes (Coming Soon).                                                   |          |
+| [README.md](./README.md)            | Readme file.                                                                                                        |
+| [setup_integration.sh](./setup_integration.sh) | Setup file for the integration run from the command line.   
+
+### Exporting for Inference
+
+After sparsifying a model, to convert the model into an [ONNX](https://onnx.ai/) deployment format run the `export.py script`(https://github.com/neuralmagic/pytorch-image-models/blob/master/export.py).
+
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+For example, the following command can be run from within the integration's folder to export a trained/sparsified model's checkpoint:
+```bash
+python export.py \
+    --checkpoint ./path/to/checkpoint/model.pth.tar \
+    --recipe ../recipes/vit_base.85.recal.config.yaml \
+    --save-dir ./exported-models \
+    --filename vit_base_patch16_224 \
+    --batch-size 1 \
+    --image-shape 3 224 224 \
+    --config ./path/to/checkpoint/args.yaml
+```
+
+The DeepSparse Engine [accepts ONNX formats](https://docs.neuralmagic.com/sparseml/source/onnx_export.html) and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse).
\ No newline at end of file
diff --git a/integrations/old-examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml b/integrations/old-examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
new file mode 100644
index 00000000000..148060b57dd
--- /dev/null
+++ b/integrations/old-examples/rwightman-timm/recipes/vit_base.85.recal.config.yaml
@@ -0,0 +1,83 @@
+version: 1.1.0
+
+modifiers:
+    - !GMPruningModifier
+        end_epoch: 36.0
+        final_sparsity: 0.03
+        global_sparsity: False
+        init_sparsity: 0.25
+        inter_func: cubic
+        leave_enabled: True
+        mask_type: unstructured
+        params: 
+            - blocks.0.attn.qkv.weight
+            - blocks.0.attn.proj.weight
+            - blocks.0.mlp.fc1.weight
+            - blocks.0.mlp.fc2.weight
+            - blocks.1.attn.qkv.weight
+            - blocks.1.attn.proj.weight
+            - blocks.1.mlp.fc1.weight
+            - blocks.1.mlp.fc2.weight
+            - blocks.2.attn.qkv.weight
+            - blocks.2.attn.proj.weight
+            - blocks.2.mlp.fc1.weight
+            - blocks.2.mlp.fc2.weight
+            - blocks.3.attn.qkv.weight
+            - blocks.3.attn.proj.weight
+            - blocks.3.mlp.fc1.weight
+            - blocks.3.mlp.fc2.weight
+            - blocks.4.attn.qkv.weight
+            - blocks.4.attn.proj.weight
+            - blocks.4.mlp.fc1.weight
+            - blocks.4.mlp.fc2.weight
+            - blocks.5.attn.qkv.weight
+            - blocks.5.attn.proj.weight
+            - blocks.5.mlp.fc1.weight
+            - blocks.5.mlp.fc2.weight
+            - blocks.6.attn.qkv.weight
+            - blocks.6.attn.proj.weight
+            - blocks.6.mlp.fc1.weight
+            - blocks.6.mlp.fc2.weight
+            - blocks.7.attn.qkv.weight
+            - blocks.7.attn.proj.weight
+            - blocks.7.mlp.fc1.weight
+            - blocks.7.mlp.fc2.weight
+            - blocks.8.attn.qkv.weight
+            - blocks.8.attn.proj.weight
+            - blocks.8.mlp.fc1.weight
+            - blocks.8.mlp.fc2.weight
+            - blocks.9.attn.qkv.weight
+            - blocks.9.attn.proj.weight
+            - blocks.9.mlp.fc1.weight
+            - blocks.9.mlp.fc2.weight
+            - blocks.10.attn.qkv.weight
+            - blocks.10.attn.proj.weight
+            - blocks.10.mlp.fc1.weight
+            - blocks.10.mlp.fc2.weight
+            - blocks.11.attn.qkv.weight
+            - blocks.11.attn.proj.weight
+            - blocks.11.mlp.fc1.weight
+            - blocks.11.mlp.fc2.weight
+        phased: False
+        score_type: magnitude
+        start_epoch: 1.0
+        update_frequency: 0.5
+
+    - !EpochRangeModifier
+        end_epoch: 100.0
+        start_epoch: 0.0
+
+    - !SetLearningRateModifier
+        constant_logging: False
+        end_epoch: -1
+        learning_rate: 0.002505
+        start_epoch: 0.0
+
+    - !LearningRateModifier
+        constant_logging: False
+        end_epoch: -1.0
+        init_lr: 0.00062625
+        lr_class: StepLR
+        lr_kwargs: {'step_size': 10, 'gamma': 0.25}
+        start_epoch: 40.0
+        update_frequency: -1.0
diff --git a/integrations/old-examples/rwightman-timm/setup_integration.sh b/integrations/old-examples/rwightman-timm/setup_integration.sh
new file mode 100644
index 00000000000..ed6c1068339
--- /dev/null
+++ b/integrations/old-examples/rwightman-timm/setup_integration.sh
@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Integration setup command to setup the folder so it is ready to train and sparsify models.
+# Creates a pytorch-image-models folder next to this script with all required dependencies from the rwightman/pytorch-image-models repository.
+# Command: `bash setup_integration.sh`
+
+git clone https://github.com/neuralmagic/pytorch-image-models.git
+cd pytorch-image-models
+git checkout release/1.1
+pip install -r requirements.txt
+pip install sparseml[torch]
diff --git a/integrations/old-examples/tensorflow_v1/README.md b/integrations/old-examples/tensorflow_v1/README.md
new file mode 100644
index 00000000000..0471fa009d2
--- /dev/null
+++ b/integrations/old-examples/tensorflow_v1/README.md
@@ -0,0 +1,93 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML TensorFlow V1 Integration
+
+This directory combines the SparseML recipe-driven approach with standard TensorFlow V1 training flows for models within
+[TF Slim](https://github.com/google-research/tf-slim) and SparseML.
+They are intended as example flows to show how to integrate sparsification techniques with a custom TensorFlow V1 training flow.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Coming soon!
+
+## Tutorials
+
+- [Classification](https://github.com/neuralmagic/sparseml/blob/main/integrations/tensorflow_v1/notebooks/classification.ipynb)
+
+## Installation
+
+To begin, run `pip install sparseml[tf_v1]` or `pip install sparseml[tf_v1_gpu]` if training on a GPU.
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration contains `--recipe` arguments appended to the Python scripts and `recipe` variables in the appropriate notebooks.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/classification?repo=sparseml&framework=tensorflow_v1) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/classification?repo=sparseml&framework=tensorflow_v1) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/classification/resnet_v1-50/tensorflow_v1/sparseml/imagenette/pruned-moderate")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| notebooks            | Jupyter notebooks to walk through sparsifiation within standard TensorFlow V1 training flows                          |
+| recipes              | Typical recipes for sparsifying TensorFlow V1 models along with any downloaded recipes from the SparseZoo.            |
+| tutorials            | Tutorial walkthroughs for how to sparsify TensorFlow V1 models using recipes.                                         |
+| classification.py    | Utility training script for sparsifying classification models in TensorFlow V1 (loads SparseML models).               |
+| README.md            | Readme file.                                                                                                          |
+
+### Exporting for Inference
+
+All scripts and notebooks end with an export to the [ONNX](https://onnx.ai/) file format.
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/).
diff --git a/integrations/old-examples/tensorflow_v1/classification.py b/integrations/old-examples/tensorflow_v1/classification.py
new file mode 100644
index 00000000000..ea544a0429e
--- /dev/null
+++ b/integrations/old-examples/tensorflow_v1/classification.py
@@ -0,0 +1,965 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Perform optimization tasks on image classification tensorflow_v1 including:
+* Model training
+* Model pruning
+* Sparse transfer learning
+* pruning sensitivity analysis
+* ONNX export
+
+
+##########
+Command help:
+usage: classification.py [-h] {train,export,pruning_sensitivity} ...
+
+Run tasks on classification models and datasets using the sparseml API
+
+positional arguments:
+  {train,export,pruning_sensitivity}
+
+optional arguments:
+  -h, --help            show this help message and exit
+
+
+##########
+train command help:
+usage: classification.py train [-h] --arch-key ARCH_KEY
+                               [--pretrained PRETRAINED]
+                               [--pretrained-dataset PRETRAINED_DATASET]
+                               [--checkpoint-path CHECKPOINT_PATH]
+                               [--model-kwargs MODEL_KWARGS] --dataset DATASET
+                               --dataset-path DATASET_PATH
+                               [--dataset-kwargs DATASET_KWARGS]
+                               [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                               [--dataset-parallel-calls DATASET_PARALLEL_CALLS]
+                               [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                               [--recipe-path RECIPE_PATH]
+                               [--sparse-transfer-learn] [--eval-mode]
+                               --train-batch-size TRAIN_BATCH_SIZE
+                               --test-batch-size TEST_BATCH_SIZE
+                               [--logs-dir LOGS_DIR]
+                               [--save-best-after SAVE_BEST_AFTER]
+                               [--save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]]
+                               [--init-lr INIT_LR] [--optim-args OPTIM_ARGS]
+
+Train and/or prune an image classification model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+--dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for dataset loading
+  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for dataset loading
+  --recipe-path RECIPE_PATH
+                        The path to the yaml file containing the modifiers and
+                        schedule to apply them with. If set to
+                        'transfer_learning', then will create a schedule to
+                        enable sparse transfer learning
+  --sparse-transfer-learn
+                        Enable sparse transfer learning modifiers to enforce
+                        the sparsity for already sparse layers. The modifiers
+                        are added to the ones to be loaded from the recipe-
+                        path
+  --eval-mode           Puts into evaluation mode so that the model can be
+                        evaluated on the desired dataset
+  --train-batch-size TRAIN_BATCH_SIZE
+                        The batch size to use while training
+  --test-batch-size TEST_BATCH_SIZE
+                        The batch size to use while testing
+  --logs-dir LOGS_DIR   The path to the directory for saving logs
+  --save-best-after SAVE_BEST_AFTER
+                        start saving the best validation result after the
+                        given epoch completes until the end of training
+  --save-epochs SAVE_EPOCHS [SAVE_EPOCHS ...]
+                        epochs to save checkpoints at
+  --init-lr INIT_LR     The initial learning rate to use while training, the
+                        actual initial value used should be set by the
+                        sparseml recipe
+  --optim-args OPTIM_ARGS
+                        Additional args to be passed to the optimizer passed
+                        in as a json object
+
+
+##########
+export command help:
+usage: classification.py export [-h] --arch-key ARCH_KEY
+                                [--pretrained PRETRAINED]
+                                [--pretrained-dataset PRETRAINED_DATASET]
+                                [--checkpoint-path CHECKPOINT_PATH]
+                                [--model-kwargs MODEL_KWARGS] --dataset
+                                DATASET --dataset-path DATASET_PATH
+                                [--dataset-kwargs DATASET_KWARGS]
+                                [--model-tag MODEL_TAG] [--save-dir SAVE_DIR]
+                                [--num-samples NUM_SAMPLES]
+                                [--onnx-opset ONNX_OPSET]
+
+Export a model to onnx as well as store sample inputs, outputs, and labels
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --num-samples NUM_SAMPLES
+                        The number of samples to export along with the model
+                        onnx and pth files (sample inputs and labels as well
+                        as the outputs from model execution)
+  --onnx-opset ONNX_OPSET
+                        The onnx opset to use for export. Default is 11
+
+
+##########
+pruning_sensitivity command help:
+usage: classification.py pruning_sensitivity [-h] --arch-key ARCH_KEY
+                                             [--pretrained PRETRAINED]
+                                             [--pretrained-dataset PRETRAINED_DATASET]
+                                             [--checkpoint-path CHECKPOINT_PATH]
+                                             [--model-kwargs MODEL_KWARGS]
+                                             --dataset DATASET --dataset-path
+                                             DATASET_PATH
+                                             [--dataset-kwargs DATASET_KWARGS]
+                                             [--model-tag MODEL_TAG]
+                                             [--save-dir SAVE_DIR]
+                                             [--dataset-parallel-calls
+                                                DATASET_PARALLEL_CALLS]
+                                             [--shuffle-buffer-size SHUFFLE_BUFFER_SIZE]
+                                             [--approximate]
+                                             [--steps-per-measurement
+                                                STEPS_PER_MEASUREMENT]
+                                             [--batch-size BATCH_SIZE]
+
+Run a kernel sparsity (pruning) analysis for a given model
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --arch-key ARCH_KEY   The type of model to use, ex: resnet50, vgg16,
+                        mobilenet put as help to see the full list (will raise
+                        an exception with the list)
+  --pretrained PRETRAINED
+                        The type of pretrained weights to use, default is true
+                        to load the default pretrained weights for the model.
+                        Otherwise should be set to the desired weights type:
+                        [base, optim, optim-perf]. To not load any weights set
+                        to one of [none, false]
+  --pretrained-dataset PRETRAINED_DATASET
+                        The dataset to load pretrained weights for if
+                        pretrained is set. Default is None which will load the
+                        default dataset for the architecture. Ex can be set to
+                        imagenet, cifar10, etc
+  --checkpoint-path CHECKPOINT_PATH
+                        A path to a previous checkpoint to load the state from
+                        and resume the state for. If provided, pretrained will
+                        be ignored
+  --model-kwargs MODEL_KWARGS
+                        kew word arguments to be passed to model constructor,
+                        should be given as a json object
+  --dataset DATASET     The dataset to use for training, ex: imagenet,
+                        imagenette, cifar10, etc. Set to imagefolder for a
+                        generic dataset setup with an image folder structure
+                        setup like imagenet or loadable by a dataset in
+                        sparseml.tensorflow_v1.datasets
+  --dataset-path DATASET_PATH
+                        The root path to where the dataset is stored
+  --dataset-kwargs DATASET_KWARGS
+                        kew word arguments to be passed to dataset
+                        constructor, should be given as a json object
+  --model-tag MODEL_TAG
+                        A tag to use for the model for saving results under
+                        save-dir, defaults to the model arch and dataset used
+  --save-dir SAVE_DIR   The path to the directory for saving results
+  --dataset-parallel-calls DATASET_PARALLEL_CALLS
+                        the number of parallel workers for dataset loading
+  --shuffle-buffer-size SHUFFLE_BUFFER_SIZE
+                        Shuffle buffer size for dataset loading
+  --approximate         True to approximate without running data through the
+                        model, otherwise will run a one shot analysis
+  --steps-per-measurement STEPS_PER_MEASUREMENT
+                        The number of steps (batches) to run for each
+                        measurement
+  --batch-size BATCH_SIZE
+                        The batch size to use while performing analysis
+
+
+#########
+EXAMPLES
+#########
+
+##########
+Example command for pruning resnet50 on imagenet dataset:
+python scripts/tensorflow_v1/classification.py train \
+    --recipe-path ~/sparseml_recipes/pruning_resnet50.yaml \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012 \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for transfer learning sparse mobilenet_v1 on an image folder dataset:
+python scripts/tensorflow_v1/classification.py train \
+    --sparse-transfer-learn \
+    --recipe-path  ~/sparseml_recipes/pruning_mobilenet.yaml \
+    --arch-key mobilenet_v1 --pretrained optim \
+    --dataset imagefolder --dataset-path ~/datasets/my_imagefolder_dataset \
+    --train-batch-size 256 --test-batch-size 1024
+
+##########
+Example command for exporting ResNet50:
+python scripts/tensorflow_v1/classification.py export \
+    --arch-key resnet50 --dataset imagenet --dataset-path ~/datasets/ILSVRC2012
+
+##########
+Example command for running approximated KS sensitivity analysis on mobilenet:
+python scripts/tensorflow_v1/classification.py pruning_sensitivity \
+    --approximate \
+    --arch-key mobilenet --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012
+
+##########
+Example command for running one shot KS sensitivity analysis on resnet50 for coco:
+python scripts/tensorflow_v1/classification.py pruning_sensitivity \
+    --arch-key resnet50 --dataset imagenet \
+    --dataset-path ~/datasets/ILSVRC2012
+"""
+
+import argparse
+import json
+import math
+import os
+from typing import Dict, Optional, Tuple
+
+import numpy
+
+from sparseml import get_main_logger
+from sparseml.tensorflow_v1.datasets import (
+    Dataset,
+    DatasetRegistry,
+    create_split_iterators_handle,
+)
+from sparseml.tensorflow_v1.models import ModelRegistry
+from sparseml.tensorflow_v1.optim import (
+    ConstantPruningModifier,
+    ScheduledModifierManager,
+    pruning_loss_sens_magnitude,
+    pruning_loss_sens_one_shot,
+    pruning_loss_sens_op_vars,
+)
+from sparseml.tensorflow_v1.utils import (
+    GraphExporter,
+    accuracy,
+    batch_cross_entropy_loss,
+    tf_compat,
+    write_simple_summary,
+)
+from sparseml.utils import create_dirs
+
+
+LOGGER = get_main_logger()
+TRAIN_COMMAND = "train"
+EXPORT_COMMAND = "export"
+PRUNING_SENSITVITY_COMMAND = "pruning_sensitivity"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Run tasks on classification models and datasets "
+        "using the sparseml API"
+    )
+
+    subparsers = parser.add_subparsers(dest="command")
+    train_parser = subparsers.add_parser(
+        TRAIN_COMMAND,
+        description="Train and/or prune an image classification model",
+    )
+    export_parser = subparsers.add_parser(
+        EXPORT_COMMAND,
+        description="Export a model to onnx as well as "
+        "store sample inputs, outputs, and labels",
+    )
+    pruning_sensitivity_parser = subparsers.add_parser(
+        PRUNING_SENSITVITY_COMMAND,
+        description="Run a kernel sparsity (pruning) analysis for a given model",
+    )
+
+    parsers = [
+        train_parser,
+        export_parser,
+        pruning_sensitivity_parser,
+    ]
+    for par in parsers:
+        # general arguments
+        # model args
+        par.add_argument(
+            "--arch-key",
+            type=str,
+            required=True,
+            help="The type of model to use, ex: resnet50, vgg16, mobilenet "
+            "put as help to see the full list (will raise an exception with the list)",
+        )
+        par.add_argument(
+            "--pretrained",
+            type=str,
+            default=True,
+            help="The type of pretrained weights to use, "
+            "default is true to load the default pretrained weights for the model. "
+            "Otherwise should be set to the desired weights type: "
+            "[base, optim, optim-perf]. "
+            "To not load any weights set to one of [none, false]",
+        )
+        par.add_argument(
+            "--pretrained-dataset",
+            type=str,
+            default=None,
+            help="The dataset to load pretrained weights for if pretrained is set. "
+            "Default is None which will load the default dataset for the architecture."
+            " Ex can be set to imagenet, cifar10, etc",
+        )
+        par.add_argument(
+            "--checkpoint-path",
+            type=str,
+            default=None,
+            help="A path to a previous checkpoint to load the state from and "
+            "resume the state for. If provided, pretrained will be ignored",
+        )
+        par.add_argument(
+            "--model-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to model constructor, should be "
+            " given as a json object",
+        )
+
+        # dataset args
+        par.add_argument(
+            "--dataset",
+            type=str,
+            required=True,
+            help="The dataset to use for training, "
+            "ex: imagenet, imagenette, cifar10, etc. "
+            "Set to imagefolder for a generic dataset setup "
+            "with an image folder structure setup like imagenet or loadable by a "
+            "dataset in sparseml.tensorflow_v1.datasets",
+        )
+        par.add_argument(
+            "--dataset-path",
+            type=str,
+            required=True,
+            help="The root path to where the dataset is stored",
+        )
+        par.add_argument(
+            "--dataset-kwargs",
+            type=json.loads,
+            default={},
+            help="kew word arguments to be passed to dataset constructor, should be "
+            " given as a json object",
+        )
+
+        # logging and saving
+        par.add_argument(
+            "--model-tag",
+            type=str,
+            default=None,
+            help="A tag to use for the model for saving results under save-dir, "
+            "defaults to the model arch and dataset used",
+        )
+        par.add_argument(
+            "--save-dir",
+            type=str,
+            default="tensorflow_v1_classification",
+            help="The path to the directory for saving results",
+        )
+
+        # task specific arguments
+        if par in [train_parser, pruning_sensitivity_parser]:
+            par.add_argument(
+                "--dataset-parallel-calls",
+                type=int,
+                default=4,
+                help="the number of parallel workers for dataset loading",
+            )
+            par.add_argument(
+                "--shuffle-buffer-size",
+                type=int,
+                default=1000,
+                help="Shuffle buffer size for dataset loading",
+            )
+
+        if par == train_parser:
+            par.add_argument(
+                "--recipe-path",
+                type=str,
+                default=None,
+                help="The path to the yaml file containing the modifiers and "
+                "schedule to apply them with. If set to 'transfer_learning', "
+                "then will create a schedule to enable sparse transfer learning",
+            )
+            par.add_argument(
+                "--sparse-transfer-learn",
+                action="store_true",
+                help=(
+                    "Enable sparse transfer learning modifiers to enforce the sparsity "
+                    "for already sparse layers. The modifiers are added to the "
+                    "ones to be loaded from the recipe-path"
+                ),
+            )
+            par.add_argument(
+                "--eval-mode",
+                action="store_true",
+                help="Puts into evaluation mode so that the model can be "
+                "evaluated on the desired dataset",
+            )
+            par.add_argument(
+                "--train-batch-size",
+                type=int,
+                required=True,
+                help="The batch size to use while training",
+            )
+            par.add_argument(
+                "--test-batch-size",
+                type=int,
+                required=True,
+                help="The batch size to use while testing",
+            )
+            par.add_argument(
+                "--logs-dir",
+                type=str,
+                default=os.path.join(
+                    "tensorflow_v1_classification_train", "tensorboard-logs"
+                ),
+                help="The path to the directory for saving logs",
+            )
+            par.add_argument(
+                "--save-best-after",
+                type=int,
+                default=-1,
+                help="start saving the best validation result after the given "
+                "epoch completes until the end of training",
+            )
+            par.add_argument(
+                "--save-epochs",
+                type=int,
+                default=[],
+                nargs="+",
+                help="epochs to save checkpoints at",
+            )
+            par.add_argument(
+                "--init-lr",
+                type=float,
+                default=1e-9,
+                help="The initial learning rate to use while training, "
+                "the actual initial value used should be set by the sparseml recipe",
+            )
+            par.add_argument(
+                "--optim-args",
+                type=json.loads,
+                default={},
+                help="Additional args to be passed to the optimizer passed in"
+                " as a json object",
+            )
+
+        if par == export_parser:
+            par.add_argument(
+                "--num-samples",
+                type=int,
+                default=100,
+                help="The number of samples to export along with the model onnx "
+                "and pth files (sample inputs and labels as well as the outputs "
+                "from model execution)",
+            )
+            par.add_argument(
+                "--onnx-opset",
+                type=int,
+                default=11,
+                help="The onnx opset to use for export. Default is 11",
+            )
+
+        if par == pruning_sensitivity_parser:
+            par.add_argument(
+                "--approximate",
+                action="store_true",
+                help="True to approximate without running data through the model, "
+                "otherwise will run a one shot analysis",
+            )
+            par.add_argument(
+                "--steps-per-measurement",
+                type=int,
+                default=15,
+                help="The number of steps (batches) to run for each measurement",
+            )
+            par.add_argument(
+                "--batch-size",
+                type=int,
+                default=64,
+                help="The batch size to use while performing analysis",
+            )
+
+    return parser.parse_args()
+
+
+def _setup_save_dirs(args) -> Tuple[str, Optional[str]]:
+    # logging and saving setup
+    save_dir = os.path.abspath(os.path.expanduser(args.save_dir))
+    logs_dir = (
+        os.path.abspath(os.path.expanduser(os.path.join(args.logs_dir)))
+        if args.command == TRAIN_COMMAND
+        else None
+    )
+
+    if not args.model_tag:
+        model_tag = "{}_{}".format(args.arch_key.replace("/", "."), args.dataset)
+        model_id = model_tag
+        model_inc = 0
+        # set location to check for models with same name
+        model_main_dir = logs_dir or save_dir
+
+        while os.path.exists(os.path.join(model_main_dir, model_id)):
+            model_inc += 1
+            model_id = "{}__{:02d}".format(model_tag, model_inc)
+    else:
+        model_id = args.model_tag
+
+    save_dir = os.path.join(save_dir, model_id)
+    create_dirs(save_dir)
+
+    # logs dir setup
+    if args.command == TRAIN_COMMAND:
+        logs_dir = os.path.join(logs_dir, model_id)
+        create_dirs(logs_dir)
+    else:
+        logs_dir = None
+    LOGGER.info("Model id is set to {}".format(model_id))
+    return save_dir, logs_dir
+
+
+def _create_dataset(args, train=True, image_size=None) -> Tuple[Dataset, int]:
+    kwargs = args.dataset_kwargs
+    if "image_size" in kwargs:
+        image_size = kwargs["image_size"]
+        del kwargs["image_size"]
+
+    dataset = DatasetRegistry.create(
+        args.dataset,
+        root=args.dataset_path,
+        train=train,
+        image_size=image_size,
+        **kwargs,
+    )
+    LOGGER.info("created {} dataset: {}".format("train" if train else "val", dataset))
+
+    # get num_classes
+    if args.dataset == "imagefolder":
+        num_classes = dataset.num_classes
+    else:
+        dataset_attributes = DatasetRegistry.attributes(args.dataset)
+        num_classes = dataset_attributes["num_classes"]
+
+    return dataset, num_classes
+
+
+def _build_dataset(args, dataset: Dataset, batch_size: int) -> Dataset:
+    return dataset.build(
+        batch_size,
+        shuffle_buffer_size=args.shuffle_buffer_size,
+        prefetch_buffer_size=batch_size,
+        num_parallel_calls=args.dataset_parallel_calls,
+    )
+
+
+def _create_model(args, num_classes, inputs, training=False):
+    outputs = ModelRegistry.create(
+        args.arch_key,
+        inputs,
+        training=training,
+        num_classes=num_classes,
+        **args.model_kwargs,
+    )
+    LOGGER.info("created model {}".format(args.arch_key))
+    return outputs
+
+
+def _load_model(args, sess, checkpoint_path=None):
+    sess.run(
+        [
+            tf_compat.global_variables_initializer(),
+            tf_compat.local_variables_initializer(),
+        ]
+    )
+    checkpoint_path = checkpoint_path or args.checkpoint_path
+    ModelRegistry.load_pretrained(
+        args.arch_key,
+        pretrained=args.pretrained,
+        pretrained_dataset=args.pretrained_dataset,
+        pretrained_path=checkpoint_path,
+        sess=sess,
+    )
+    if checkpoint_path:
+        LOGGER.info("Loaded model weights from checkpoint: {}".format(checkpoint_path))
+
+
+def _save_checkpoint(args, sess, save_dir, checkpoint_name) -> str:
+    checkpoint_path = os.path.join(os.path.join(save_dir, checkpoint_name, "model"))
+    create_dirs(checkpoint_path)
+    saver = ModelRegistry.saver(args.arch_key)
+    saved_name = saver.save(sess, checkpoint_path)
+    checkpoint_path = os.path.join(checkpoint_path, saved_name)
+    LOGGER.info("Checkpoint saved to {}".format(checkpoint_path))
+    return checkpoint_path
+
+
+def _save_recipe(
+    recipe_manager: ScheduledModifierManager,
+    save_dir: str,
+):
+
+    recipe_save_path = os.path.join(save_dir, "recipe.yaml")
+    recipe_manager.save(recipe_save_path)
+    LOGGER.info(f"Saved recipe to {recipe_save_path}")
+
+
+def train(args, save_dir, logs_dir):
+    # setup dataset
+    with tf_compat.device("/cpu:0"):
+        train_dataset, _ = _create_dataset(args, train=True)
+        val_dataset, num_classes = _create_dataset(args, train=False)
+        # calc steps
+        train_steps = math.ceil(len(train_dataset) / args.train_batch_size)
+        val_steps = math.ceil(len(val_dataset) / args.test_batch_size)
+        # build datasets
+        train_dataset = _build_dataset(args, train_dataset, args.train_batch_size)
+        val_dataset = _build_dataset(args, val_dataset, args.test_batch_size)
+    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(
+        [train_dataset, val_dataset]
+    )
+
+    # set up model graph
+    images, labels = iterator.get_next()
+    training = tf_compat.placeholder(dtype=tf_compat.bool, shape=[])
+    outputs = _create_model(args, num_classes, images, training)
+
+    # set up training objects
+    loss = batch_cross_entropy_loss(outputs, labels)
+    acc = accuracy(outputs, labels)
+    global_step = tf_compat.train.get_or_create_global_step()
+    train_op = tf_compat.train.AdamOptimizer(
+        learning_rate=args.init_lr, **args.optim_args
+    ).minimize(loss, global_step=global_step)
+    update_ops = tf_compat.get_collection(tf_compat.GraphKeys.UPDATE_OPS)
+    LOGGER.info("Created update ops for training")
+
+    # set up sparseml modifier ops
+    add_mods = (
+        ConstantPruningModifier(params="__ALL__")
+        if args.sparse_transfer_learn
+        else None
+    )
+    manager = ScheduledModifierManager.from_yaml(
+        file_path=args.recipe_path, add_modifiers=add_mods
+    )
+    mod_ops, mod_extras = manager.create_ops(train_steps, global_step)
+    _save_recipe(recipe_manager=manager, save_dir=save_dir)
+    with tf_compat.Session() as sess:
+        # set up tensorboard logging
+        summary_writer = tf_compat.summary.FileWriter(logs_dir, sess.graph)
+        summaries = tf_compat.summary.merge_all()
+        LOGGER.info("Logging to tensorboard at {}".format(logs_dir))
+
+        # initialize variables, load pretrained weights, initialize modifiers
+        train_iter_handle, val_iter_handle = sess.run(
+            [train_iter.string_handle(), val_iter.string_handle()]
+        )
+        LOGGER.info("Initialized graph variables")
+        _load_model(args, sess)
+        manager.initialize_session()
+        LOGGER.info("Initialized SparseML modifiers")
+
+        best_loss = None
+        for epoch in range(manager.max_epochs):
+            # train
+            LOGGER.info("Training for epoch {}...".format(epoch))
+            sess.run(train_iter.initializer)
+            train_acc, train_loss = [], []
+            for step in range(train_steps):
+                _, __, meas_step, meas_loss, meas_acc, meas_summ = sess.run(
+                    [train_op, update_ops, global_step, loss, acc, summaries],
+                    feed_dict={handle: train_iter_handle, training: True},
+                )
+                if step >= train_steps - 1:
+                    # log the general summaries on the last training step
+                    summary_writer.add_summary(meas_summ, meas_step)
+                # run modifier ops
+                sess.run(mod_ops)
+                # summarize
+                write_simple_summary(summary_writer, "Train/Loss", meas_loss, meas_step)
+                write_simple_summary(
+                    summary_writer, "Train/Acc", meas_acc * 100.0, meas_step
+                )
+                train_acc.append(meas_acc)
+                train_loss.append(meas_loss)
+            LOGGER.info(
+                "Epoch {} - Train Loss: {}, Train Acc: {}".format(
+                    epoch, numpy.mean(train_loss).item(), numpy.mean(train_acc).item()
+                )
+            )
+
+            # val
+            LOGGER.info("Validating for epoch {}...".format(epoch))
+            sess.run(val_iter.initializer)
+            val_acc, val_loss = [], []
+            for step in range(val_steps):
+                meas_loss, meas_acc = sess.run(
+                    [loss, acc],
+                    feed_dict={handle: val_iter_handle, training: False},
+                )
+                val_acc.append(meas_acc)
+                val_loss.append(meas_loss)
+                write_simple_summary(
+                    summary_writer, "Val/Loss", numpy.mean(val_loss).item(), epoch
+                )
+                write_simple_summary(
+                    summary_writer, "Val/Acc", numpy.mean(val_acc).item(), epoch
+                )
+            val_loss = numpy.mean(val_loss).item()
+            LOGGER.info(
+                "Epoch {} - Val Loss: {}, Val Acc: {}".format(
+                    epoch, val_loss, numpy.mean(train_acc).item()
+                )
+            )
+            if epoch >= args.save_best_after and (
+                best_loss is None or val_loss <= best_loss
+            ):
+                _save_checkpoint(args, sess, save_dir, "checkpoint-best")
+                best_loss = val_loss
+            if args.save_epochs and epoch in args.save_epochs:
+                _save_checkpoint(
+                    args, sess, save_dir, "checkpoint-epoch-{}".format(epoch)
+                )
+
+        # cleanup graph and save final checkpoint
+        manager.complete_graph()
+        checkpoint_path = _save_checkpoint(args, sess, save_dir, "final-checkpoint")
+    LOGGER.info("Running ONNX export flow")
+    export(
+        args,
+        save_dir,
+        checkpoint_path=checkpoint_path,
+        skip_samples=True,
+        num_classes=num_classes,
+        opset=11,
+    )
+
+
+def export(
+    args,
+    save_dir,
+    checkpoint_path=None,
+    skip_samples=False,
+    num_classes=None,
+    opset=None,
+):
+    assert not skip_samples or num_classes
+    # dataset creation
+    if not skip_samples:
+        val_dataset, num_classes = _create_dataset(args, train=False)
+
+    with tf_compat.Graph().as_default():
+        input_shape = ModelRegistry.input_shape(args.arch_key)
+        inputs = tf_compat.placeholder(
+            tf_compat.float32, [None] + list(input_shape), name="inputs"
+        )
+        outputs = _create_model(args, num_classes, inputs)
+
+        with tf_compat.Session() as sess:
+            _load_model(
+                args, sess, checkpoint_path=checkpoint_path or args.checkpoint_path
+            )
+
+            exporter = GraphExporter(save_dir)
+
+            if not skip_samples:
+                # Export a batch of samples and expected outputs
+                tf_dataset = val_dataset.build(
+                    args.num_samples, repeat_count=1, num_parallel_calls=1
+                )
+                tf_iter = tf_compat.data.make_one_shot_iterator(tf_dataset)
+                features, _ = tf_iter.get_next()
+                inputs_val = sess.run(features)
+                exporter.export_samples([inputs], [inputs_val], [outputs], sess)
+
+            # Export model to tensorflow checkpoint format
+            LOGGER.info("exporting tensorflow in {}".format(save_dir))
+            exporter.export_checkpoint(sess=sess)
+
+            # Export model to pb format
+            LOGGER.info("exporting pb in {}".format(exporter.pb_path))
+            exporter.export_pb(outputs=[outputs])
+
+    # Export model to onnx format
+    LOGGER.info("exporting onnx in {}".format(exporter.onnx_path))
+    exporter.export_onnx([inputs], [outputs], opset=opset or args.onnx_opset)
+
+
+def pruning_loss_sensitivity(args, save_dir):
+    input_shape = ModelRegistry.input_shape(args.arch_key)
+    train_dataset, num_classes = _create_dataset(
+        args, train=True, image_size=input_shape[1]
+    )
+    with tf_compat.Graph().as_default() as graph:
+        # create model graph
+        inputs = tf_compat.placeholder(
+            tf_compat.float32, [None] + list(input_shape), name="inputs"
+        )
+        outputs = _create_model(args, num_classes, inputs)
+
+        with tf_compat.Session() as sess:
+            _load_model(args, sess, checkpoint_path=args.checkpoint_path)
+            if args.approximate:
+                LOGGER.info("Running weight magnitude loss sensitivity analysis...")
+                analysis = pruning_loss_sens_magnitude(graph, sess)
+            else:
+                op_vars = pruning_loss_sens_op_vars(graph)
+                train_steps = math.ceil(len(train_dataset) / args.batch_size)
+                train_dataset = _build_dataset(args, train_dataset, args.batch_size)
+                handle, iterator, dataset_iter = create_split_iterators_handle(
+                    [train_dataset]
+                )
+                dataset_iter = dataset_iter[0]
+                images, labels = iterator.get_next()
+                loss = batch_cross_entropy_loss(outputs, labels)
+                tensor_names = ["inputs:0", labels.name]
+                sess.run(dataset_iter.initializer)
+
+                def feed_dict_creator(step: int) -> Dict[str, tf_compat.Tensor]:
+                    assert step < train_steps
+                    batch_data = [
+                        tens.eval(session=sess) for tens in dataset_iter.get_next()
+                    ]
+                    return dict(zip(tensor_names, batch_data))
+
+                LOGGER.info("Running one shot loss sensitivity analysis...")
+                analysis = pruning_loss_sens_one_shot(
+                    op_vars=op_vars,
+                    loss_tensor=loss,
+                    steps_per_measurement=args.steps_per_measurement,
+                    feed_dict_creator=feed_dict_creator,
+                    sess=sess,
+                )
+    # saving and printing results
+    LOGGER.info("completed...")
+    LOGGER.info("Saving results in {}".format(save_dir))
+    analysis.save_json(
+        os.path.join(
+            save_dir,
+            "ks_approx_sensitivity.json"
+            if args.approximate
+            else "ks_one_shot_sensitivity.json",
+        )
+    )
+    analysis.plot(
+        os.path.join(
+            save_dir,
+            os.path.join(
+                save_dir,
+                "ks_approx_sensitivity.png"
+                if args.approximate
+                else "ks_one_shot_sensitivity.png",
+            ),
+        ),
+        plot_integral=True,
+    )
+    analysis.print_res()
+
+
+def main(args):
+    # set up saving and logging dirs
+    save_dir, logs_dir = _setup_save_dirs(args)
+
+    # RUN COMMAND SPECIFIC TASTS
+    if args.command == TRAIN_COMMAND:
+        train(args, save_dir, logs_dir)
+    if args.command == EXPORT_COMMAND:
+        export(args, save_dir)
+    if args.command == PRUNING_SENSITVITY_COMMAND:
+        pruning_loss_sensitivity(args, save_dir)
+
+
+if __name__ == "__main__":
+    args_ = parse_args()
+
+    main(args_)
diff --git a/integrations/old-examples/tensorflow_v1/notebooks/classification.ipynb b/integrations/old-examples/tensorflow_v1/notebooks/classification.ipynb
new file mode 100644
index 00000000000..158d12aaef0
--- /dev/null
+++ b/integrations/old-examples/tensorflow_v1/notebooks/classification.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<sub>&copy; 2021-present Neuralmagic, Inc. // [Neural Magic Legal](https://neuralmagic.com/legal)</sub> \n",
+    "\n",
+    "# TensorFlow v1 Classification Model Pruning Using SparseML\n",
+    "\n",
+    "This notebook provides a step-by-step walkthrough for pruning an already trained (dense) model to enable better performance at inference time using the DeepSparse Engine. You will:\n",
+    "- Set up the model and dataset\n",
+    "- Define a TensorFlow training flow with a simple SparseML integration\n",
+    "- Prune the model using the TensorFlow+SparseML flow\n",
+    "- Export to [ONNX](https://onnx.ai/)\n",
+    "\n",
+    "Reading through this notebook will be reasonably quick to gain an intuition for how to plug SparseML into your TensorFlow training flow. Rough time estimates for fully pruning the default model are given. Note that training with the PyTorch CPU implementation will be much slower than a GPU:\n",
+    "- 20 minutes on a GPU\n",
+    "- 60 minutes on a laptop CPU"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1 - Requirements\n",
+    "To run this notebook, you will need the following packages already installed:\n",
+    "* SparseML and SparseZoo\n",
+    "* TensorFlow v1 and tf2onnx\n",
+    "\n",
+    "You can install any package that is not already present via `pip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sparseml\n",
+    "import sparsezoo\n",
+    "import tensorflow as tf\n",
+    "import tf2onnx\n",
+    "\n",
+    "assert tf.__version__ < \"2\"\n",
+    "\n",
+    "# suppress warnings\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2 - Setting Up the Model and Dataset\n",
+    "\n",
+    "By default, you will prune a [ResNet-50](https://arxiv.org/abs/1512.03385) model trained on the [Imagenette dataset](https://github.com/fastai/imagenette). The model's pretrained weights are downloaded from the SparseZoo model repo.   The Imagenette dataset is downloaded from its repository via a helper class from SparseML.\n",
+    "\n",
+    "In the cells below, functions are defined to load the dataset, model, and training objects to be called during training from within the `Graph` context.\n",
+    "\n",
+    "If you would like to try out your model for pruning, modify the appropriate function for to load your model or dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "from sparseml.tensorflow_v1.models import ModelRegistry\n",
+    "from sparseml.tensorflow_v1.datasets import (\n",
+    "    ImagenetteDataset,\n",
+    "    ImagenetteSize,\n",
+    ")\n",
+    "from sparseml.tensorflow_v1.utils import (\n",
+    "    batch_cross_entropy_loss,\n",
+    "    accuracy,\n",
+    ")\n",
+    "\n",
+    "MODEL_NAME = \"resnet50\"\n",
+    "BATCH_SIZE = 128\n",
+    "INPUT_SIZE = 224\n",
+    "NUM_CLASSES = 10\n",
+    "SAVE_DIR = \"tensorflow_v1_classification_pruning\"\n",
+    "\n",
+    "\n",
+    "def load_dataset():\n",
+    "    with tf.device(\"/cpu:0\"):\n",
+    "        print(\"loading datasets\")\n",
+    "        train_dataset = ImagenetteDataset(\n",
+    "            train=True, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
+    "        )\n",
+    "        train_len = len(train_dataset)\n",
+    "        train_steps = math.ceil(train_len / float(BATCH_SIZE))\n",
+    "        train_dataset = train_dataset.build(\n",
+    "            BATCH_SIZE,\n",
+    "            shuffle_buffer_size=1000,\n",
+    "            prefetch_buffer_size=BATCH_SIZE,\n",
+    "            num_parallel_calls=4,\n",
+    "        )\n",
+    "\n",
+    "        val_dataset = ImagenetteDataset(\n",
+    "            train=False, dataset_size=ImagenetteSize.s320, image_size=INPUT_SIZE\n",
+    "        )\n",
+    "        val_len = len(val_dataset)\n",
+    "        val_steps = math.ceil(val_len / float(BATCH_SIZE))\n",
+    "        val_dataset = val_dataset.build(\n",
+    "            BATCH_SIZE,\n",
+    "            shuffle_buffer_size=1000,\n",
+    "            prefetch_buffer_size=BATCH_SIZE,\n",
+    "            num_parallel_calls=4,\n",
+    "        )\n",
+    "\n",
+    "    return train_dataset, val_dataset, (train_steps, val_steps)\n",
+    "\n",
+    "\n",
+    "def create_model(sample_input, training):\n",
+    "    print(\"Creating model graph for {}\".format(MODEL_NAME))\n",
+    "    logits = ModelRegistry.create(\n",
+    "        MODEL_NAME,\n",
+    "        inputs=sample_input,\n",
+    "        training=training,\n",
+    "        num_classes=NUM_CLASSES,\n",
+    "    )\n",
+    "    return logits\n",
+    "\n",
+    "\n",
+    "def create_training_objects(sample_labels):\n",
+    "    print(\"Creating loss, accuracy, and optimizer in graph\")\n",
+    "    loss = batch_cross_entropy_loss(logits, labels)\n",
+    "    acc = accuracy(logits, labels)\n",
+    "    global_step = tf.train.get_or_create_global_step()\n",
+    "    train_op = tf.train.AdamOptimizer(learning_rate=0.00008).minimize(\n",
+    "        loss, global_step=global_step\n",
+    "    )\n",
+    "    return loss, acc, global_step, train_op\n",
+    "\n",
+    "\n",
+    "def load_pretrained():\n",
+    "    print(\"loading pre-trained model weights\")\n",
+    "    ModelRegistry.load_pretrained(\n",
+    "        MODEL_NAME, pretrained=\"base\", remove_dynamic_tl_vars=True,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3 - Create a SparseML Modifier Manager\n",
+    "\n",
+    "To prune a model with SparseML, you will download a recipe from SparseZoo and use it to create a `ScheduledModifierManager` object.  This manager will be used to create operations that modify the training process.\n",
+    "\n",
+    "You can create SparseML recipes to perform various model pruning schedules, quantization aware training, sparse transfer learning, and more.  If you are using a different model than the default, you will have to modify the recipe YAML file to target the new model's parameters.\n",
+    "\n",
+    "Using the operators generated from this manager object, you will be able to prune your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.tensorflow_v1.optim import (\n",
+    "    ScheduledModifierManager,\n",
+    ")\n",
+    "from sparsezoo import Model, search_models\n",
+    "\n",
+    "\n",
+    "def create_sparseml_manager():\n",
+    "    model = search_models(\n",
+    "        domain=\"cv\",\n",
+    "        sub_domain=\"classification\",\n",
+    "        architecture=\"resnet_v1\",\n",
+    "        sub_architecture=\"50\",\n",
+    "        framework=\"tensorflow_v1\",\n",
+    "        repo=\"sparseml\",\n",
+    "        dataset=\"imagenette\",\n",
+    "        sparse_name=\"pruned\",\n",
+    "    )[0]  # unwrap search result\n",
+    "\n",
+    "    recipe_path = model.recipes.default.path\n",
+    "    print(f\"Recipe downloaded to: {recipe_path}\")\n",
+    "\n",
+    "    return ScheduledModifierManager.from_yaml(recipe_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4 - Prune your model using a TensorFlow training loop\n",
+    "SparseML can plug directly into your existing TensorFlow training flow by creating additional operators to run. To demonstrate this, in the cell below, prune the model using a standard TensorFlow training loop while also running the operators created by the manager object.  To prune your existing models using SparseML, you can use your own training flow with the additional operators added.\n",
+    "\n",
+    "For your convienence the lines needed for integrating with SparseML are preceeded by large comment blocks.\n",
+    "\n",
+    "If the kernel shuts down during training, this may be an out of memory error, to resolve this, try lowering the `BATCH_SIZE` in Step 2 above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy\n",
+    "import os\n",
+    "from tqdm.auto import tqdm\n",
+    "from sparseml.utils import create_unique_dir, create_dirs\n",
+    "from sparseml.tensorflow_v1.datasets import create_split_iterators_handle\n",
+    "\n",
+    "with tf.Graph().as_default() as graph:\n",
+    "    # create dataset\n",
+    "    train_dataset, val_dataset, (train_steps, val_steps) = load_dataset()\n",
+    "    handle, iterator, (train_iter, val_iter) = create_split_iterators_handle(\n",
+    "        [train_dataset, val_dataset]\n",
+    "    )\n",
+    "    images, labels = iterator.get_next()\n",
+    "\n",
+    "    # create base training objects\n",
+    "    training = tf.placeholder(dtype=tf.bool, shape=[])\n",
+    "    logits = create_model(images, training)\n",
+    "    loss, acc, global_step, train_op = create_training_objects(labels)\n",
+    "    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
+    "\n",
+    "    #######################################################\n",
+    "    # create sparseml training manager\n",
+    "    #######################################################\n",
+    "    manager = create_sparseml_manager()\n",
+    "    mod_ops, mod_extras = manager.create_ops(train_steps, global_step, graph=graph)\n",
+    "\n",
+    "    with tf.Session() as sess:\n",
+    "        print(\"initializing session\")\n",
+    "        sess.run(\n",
+    "            [\n",
+    "                tf.global_variables_initializer(),\n",
+    "                tf.local_variables_initializer(),\n",
+    "            ]\n",
+    "        )\n",
+    "        train_iter_handle, val_iter_handle = sess.run(\n",
+    "            [train_iter.string_handle(), val_iter.string_handle()]\n",
+    "        )\n",
+    "\n",
+    "        # initialize sparseml manager after pretrained weights loaded\n",
+    "        load_pretrained()\n",
+    "        manager.initialize_session()\n",
+    "\n",
+    "        num_epochs = manager.max_epochs\n",
+    "        for epoch in tqdm(range(num_epochs), desc=\"pruning\"):\n",
+    "            print(\"training for epoch {}...\".format(epoch))\n",
+    "            sess.run(train_iter.initializer)\n",
+    "            train_losses = []\n",
+    "            train_acc = []\n",
+    "\n",
+    "            for step in range(train_steps):\n",
+    "                _, __, meas_step, meas_loss, meas_acc = sess.run(\n",
+    "                    [train_op, update_ops, global_step, loss, acc],\n",
+    "                    feed_dict={handle: train_iter_handle, training: True},\n",
+    "                )\n",
+    "                train_losses.append(meas_loss)\n",
+    "                train_acc.append(meas_acc)\n",
+    "\n",
+    "                #######################################################\n",
+    "                # Modifier update ops line for transfer learning from a sparse model in TensorFlow\n",
+    "                #######################################################\n",
+    "                sess.run(mod_ops)\n",
+    "            print(\n",
+    "                \"completed epoch {} training with: loss {} / acc {}\".format(\n",
+    "                    epoch,\n",
+    "                    numpy.mean(train_losses).item(),\n",
+    "                    numpy.mean(train_acc).item() * 100,\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "            print(\"validating for epoch {}...\".format(epoch))\n",
+    "            sess.run(val_iter.initializer)\n",
+    "            val_losses = []\n",
+    "            val_acc = []\n",
+    "\n",
+    "            for step in range(val_steps):\n",
+    "                meas_loss, meas_acc = sess.run(\n",
+    "                    [loss, acc],\n",
+    "                    feed_dict={handle: val_iter_handle, training: False},\n",
+    "                )\n",
+    "                val_losses.append(meas_loss)\n",
+    "                val_acc.append(meas_acc)\n",
+    "\n",
+    "            print(\n",
+    "                \"completed epoch {} validation with: loss {} / acc {}\".format(\n",
+    "                    epoch,\n",
+    "                    numpy.mean(val_losses).item(),\n",
+    "                    numpy.mean(val_acc).item() * 100,\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "        #######################################################\n",
+    "        # Final line for sparseml training in TensorFlow, complete the graph\n",
+    "        #######################################################\n",
+    "        manager.complete_graph()\n",
+    "\n",
+    "        NAME = \"resnet50-imagenette-pruned\"\n",
+    "        checkpoint_path = create_unique_dir(\n",
+    "            os.path.join(\".\", SAVE_DIR, NAME, \"checkpoint\")\n",
+    "        )\n",
+    "        checkpoint_path = os.path.join(checkpoint_path, \"model\")\n",
+    "        create_dirs(checkpoint_path)\n",
+    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
+    "        saver.save(sess, checkpoint_path)\n",
+    "        print(\"saved model checkpoint to {}\".format(checkpoint_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5 - Exporting to ONNX\n",
+    "\n",
+    "Now that the model is fully recalibrated, you need to export it to an ONNX format, which is the format used by the DeepSparse Engine. For TensorFlow, exporting to ONNX is not natively supported. To add support, you will use the `tf2onnx` Python package. In the cell block below, a convenience class, `GraphExporter()`, is used to handle exporting. It wraps the somewhat complicated API for `tf2onnx` into an easy to use interface.\n",
+    "\n",
+    "Note, for some configurations, the tf2onnx code does not work properly in a Jupyter Notebook. To remedy this, you should run the `exporter.export_onnx()` function call in a Python console or script.\n",
+    "\n",
+    "Once the model is saved as an ONNX file, it is ready to be used for inference with the DeepSparse Engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sparseml.utils import clean_path\n",
+    "from sparseml.tensorflow_v1.utils import GraphExporter\n",
+    "\n",
+    "\n",
+    "export_path = clean_path(os.path.join(\".\", SAVE_DIR, NAME))\n",
+    "exporter = GraphExporter(export_path)\n",
+    "\n",
+    "with tf.Graph().as_default() as graph:\n",
+    "    print(\"Recreating graph...\", flush=True)\n",
+    "\n",
+    "    input_placeholder = tf.placeholder(\n",
+    "        tf.float32, [None, INPUT_SIZE, INPUT_SIZE, 3], name=\"inputs\"\n",
+    "    )\n",
+    "    logits = create_model(input_placeholder, training=False)\n",
+    "\n",
+    "    input_names = [input_placeholder.name]\n",
+    "    output_names = [logits.name]\n",
+    "\n",
+    "    with tf.Session() as sess:\n",
+    "        sess.run(tf.global_variables_initializer())\n",
+    "        print(\"Restoring previous weights...\", flush=True)\n",
+    "        saver = ModelRegistry.saver(MODEL_NAME)\n",
+    "        saver.restore(sess, checkpoint_path)\n",
+    "\n",
+    "        print(\"Exporting to pb...\", flush=True)\n",
+    "        exporter.export_pb(outputs=[logits])\n",
+    "        print(\"Exported pb file to {}\".format(exporter.pb_path), flush=True)\n",
+    "\n",
+    "print(\"Exporting to onnx...\", flush=True)\n",
+    "exporter.export_onnx(inputs=input_names, outputs=output_names)\n",
+    "print(\"Exported onnx file to {}\".format(exporter.onnx_path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Congratulations, you have pruned a model and exported it to ONNX for inference!  Next steps you can pursue include:\n",
+    "* Pruning different models using SparseML\n",
+    "* Trying different pruning and optimization recipes\n",
+    "* Running your model on the DeepSparse Engine"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/integrations/old-examples/tensorflow_v1/recipes/.gitkeep b/integrations/old-examples/tensorflow_v1/recipes/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/tensorflow_v1/tutorials/.gitkeep b/integrations/old-examples/tensorflow_v1/tutorials/.gitkeep
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/integrations/old-examples/ultralytics-yolov3/README.md b/integrations/old-examples/ultralytics-yolov3/README.md
new file mode 100644
index 00000000000..1dae432bd4f
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/README.md
@@ -0,0 +1,128 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# SparseML Ultralytics YOLOv3 Integration
+
+This directory combines the SparseML recipe-driven approach with the 
+[ultralytics/yolov3](https://github.com/ultralytics/yolov3) repository.
+By integrating the robust training flows in the `yolov3` repository with the SparseML code base,
+we enable model sparsification techniques on the popular [YOLOv3 architecture](https://arxiv.org/abs/1804.02767)
+creating smaller and faster deployable versions.
+The techniques include, but are not limited to:
+- Pruning
+- Quantization
+- Pruning + Quantization
+- Sparse Transfer Learning
+
+## Highlights
+
+- Blog: [YOLOv3 on CPUs: Sparsifying to Achieve GPU-Level Performance](https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-deepsparse/)
+- Example: [DeepSparse YOLOv3 Inference Example](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo)
+- Video: [DeepSparse YOLOv3 Pruned Quantized Performance](https://youtu.be/o5qIYs47MPw)
+
+## Tutorials
+
+- [Sparsifying YOLOv3 Using Recipes](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md)
+- [Sparse Transfer Learning With YOLOv3](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md)
+
+## Installation
+
+To begin, run the following command in the root directory of this integration (`cd integrations/ultralytics-yolov3`):
+```bash
+bash setup_integration.sh
+```
+
+The setup_integration.sh file will clone the yolov3 repository with the SparseML integration as a subfolder.
+After the repo has successfully cloned,  all dependencies from the `yolov3/requirements.txt` file will install in your current environment.
+
+## Quick Tour
+
+### Recipes
+
+Recipes encode the instructions and hyperparameters for sparsifying a model using modifiers to the training process.
+The modifiers can range from pruning and quantization to learning rate and weight decay.
+When appropriately combined, it becomes possible to create highly sparse and accurate models.
+
+This integration adds a `--recipe` argument to the [`train.py` script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
+The argument loads an appropriate recipe while preserving the rest of the training pipeline.
+Popular recipes used with this argument are found in the [`recipes` folder](./recipes).
+Otherwise, all other arguments and functionality remain the same as the original repository.
+
+For example, pruning and quantizing a model on the VOC dataset can be done by running the following command from within the yolov3 repository folder:
+```bash
+python train.py --batch 16 --img 512 --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml --hyp ../data/hyp.pruned_quantized.yaml \
+    --recipe ../recipes/yolov3-spp.pruned_quantized.short.md
+```
+
+### SparseZoo
+
+Pre-sparsified models and recipes can be downloaded through the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+The following popular stubs can be used to download these models and recipes through the SparseZoo API directly.
+
+| Model                       | Dataset | mAP@50 | Description                                                    | SparseZoo Stub                                                                   |
+|-----------------------------|---------|--------|----------------------------------------------------------------|----------------------------------------------------------------------------------|
+| YOLOv3-SPP Pruned Quantized | COCO    | 60.4   | 83% sparse and INT8 quantized model with LeakyReLU activations | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 |
+| YOLOv3-SPP Pruned           | COCO    | 62.4   | 88% sparse model with LeakyReLU activations                    | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97       |
+| YOLOv3-SPP                  | COCO    | 64.2   | Baseline model with LeakyReLU activations                      | zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none                  |
+
+Complete lists are available online for all [models](https://sparsezoo.neuralmagic.com/tables/models/cv/detection?repo=ultralytics) and 
+[recipes](https://sparsezoo.neuralmagic.com/tables/recipes/cv/detection?repo=ultralytics) compatible with this integration as well.
+
+Sample code for retrieving a model from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
+print(model)
+```
+
+Sample code for retrieving a recipe from the SparseZoo:
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94")
+recipe = model.recipes.default
+print(recipe)
+```
+
+### Structure
+
+The following table lays out the root-level files and folders along with a description for each.
+
+| Folder/File Name     | Description                                                                                                           |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------|
+| data                 | The hyperparameters to use and files to use for training on data.                                                     |
+| models               | Model architecture definitions along with any downloaded checkpoints from the SparseZoo.                              |
+| recipes              | Typical recipes for sparsifying YOLOv3 models along with any downloaded recipes from the SparseZoo.                   |
+| tutorials            | Tutorial walkthroughs for how to sparsify YOLOv3 models using recipes.                                                |
+| yolov3               | Integration repository folder used to train and sparsify YOLOv3 models (setup_integration.sh must run first).         |
+| README.md            | Readme file.                                                                                                          |
+| setup_integration.sh | Setup file for the integration run from the command line.                                                             |
+
+### Exporting for Inference
+
+After sparsifying a model, the [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) 
+converts the model into deployment formats such as [ONNX](https://onnx.ai/).
+The export process is modified such that the quantized and pruned models are corrected and folded properly.
+
+For example, the following command can be run from within the yolov3 repository folder to export a trained/sparsified model's checkpoint:
+```bash
+python models/export.py --weights PATH/TO/weights.pt --img-size 512 512
+```
+
+The DeepSparse Engine accepts ONNX formats and is engineered to significantly speed up inference on CPUs for the sparsified models from this integration.
+Examples for loading, benchmarking, and deploying can be found in the [DeepSparse repository here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
diff --git a/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned.yaml b/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned.yaml
new file mode 100644
index 00000000000..69a20b0f57b
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned.yaml
@@ -0,0 +1,27 @@
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml b/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
new file mode 100644
index 00000000000..a146f379e9c
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/data/hyp.pruned_quantized.yaml
@@ -0,0 +1,27 @@
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml b/integrations/old-examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
new file mode 100644
index 00000000000..57edb510de2
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/models/yolov3-spp.lrelu.yaml
@@ -0,0 +1,52 @@
+# parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+act: nn.LeakyReLU  # activations
+
+# anchors
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [32, 3, 1]],  # 0
+   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2
+   [-1, 1, Bottleneck, [64]],
+   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4
+   [-1, 2, Bottleneck, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 5-P3/8
+   [-1, 8, Bottleneck, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 7-P4/16
+   [-1, 8, Bottleneck, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P5/32
+   [-1, 4, Bottleneck, [1024]],  # 10
+  ]
+
+# YOLOv3-SPP head
+head:
+  [[-1, 1, Bottleneck, [1024, False]],
+   [-1, 1, SPP, [512, [5, 9, 13]]],
+   [-1, 1, Conv, [1024, 3, 1]],
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)
+
+   [-2, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P4
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Bottleneck, [512, False]],
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, Conv, [512, 3, 1]],  # 22 (P4/16-medium)
+
+   [-2, 1, Conv, [128, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P3
+   [-1, 1, Bottleneck, [256, False]],
+   [-1, 2, Bottleneck, [256, False]],  # 27 (P3/8-small)
+
+   [[27, 22, 15], 1, Detect, [nc, anchors]],   # Detect(P3, P4, P5)
+  ]
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
new file mode 100644
index 00000000000..d5fc87fb2b3
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.md
@@ -0,0 +1,208 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 300
+
+# pruning hyperparameters
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 80
+update_frequency: &pruning_update_frequency 0.5
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned
+
+This recipe creates a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 97% recovery of its baseline accuracy on the COCO detection dataset.
+Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
+[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned.yaml \
+    --name yolov3-spp-lrelu-pruned
+```
+
+hyp.pruned.yaml:
+```yaml
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
new file mode 100644
index 00000000000..c04ef0a581d
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md
@@ -0,0 +1,207 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 80
+
+# pruning hyperparameters
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 0.5
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned Short
+
+This recipe creates a sparse [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened schedule as compared to the original pruned recipe.
+It will train faster, but will recover slightly worse.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.short.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned.yaml \
+    --name yolov3-spp-lrelu-pruned
+```
+
+hyp.pruned.yaml:
+```yaml
+lr0: 0.005
+lrf: 0.1
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
new file mode 100644
index 00000000000..e9108a49440
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md
@@ -0,0 +1,286 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 242
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 240
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 100
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized
+
+This recipe creates a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that achieves 94% recovery of its baseline accuracy on the COCO detection dataset.
+Training was done using 4 GPUs at half precision using a total training batch size of 256 with the
+[SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
new file mode 100644
index 00000000000..c25340e5a41
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md
@@ -0,0 +1,286 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 52
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 50
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 23
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized
+
+This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model in a shortened shcedule as compared to the original pruned recipe.
+It will train faster, but will recover slightly worse.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.pruned.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.short.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.test.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
new file mode 100644
index 00000000000..29b26e134eb
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.test.md
@@ -0,0 +1,285 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR variables
+num_epochs: &num_epochs 5
+init_lr: &init_lr 0.0032
+
+# Pruning Epoch/LR variables
+pruning_recovery_start_lr: &pruning_recovery_start_lr 0.0032
+pruning_recovery_lr_step_size: &pruning_recovery_lr_step_size 1
+pruning_recovery_lr_gamma: &pruning_recovery_lr_gamma 0.985
+
+# Quantization Epoch/LR variables
+quantization_start_epoch: &quantization_start_epoch 4
+quantization_init_lr: &quantization_init_lr 0.0032
+
+# Block pruning management variables
+pruning_start_epoch: &pruning_start_epoch 0
+pruning_end_epoch: &pruning_end_epoch 2
+pruning_update_frequency: &pruning_update_frequency 0.5
+init_sparsity: &init_sparsity 0.05
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.85
+  
+# Modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+  # pruning phase
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: *init_lr
+
+  # pruning recovery phase
+  - !LearningRateModifier
+    constant_logging: False
+    end_epoch: *quantization_start_epoch
+    init_lr: *pruning_recovery_start_lr
+    lr_class: StepLR
+    lr_kwargs: {
+        'step_size': *pruning_recovery_lr_step_size,
+        'gamma': *pruning_recovery_lr_gamma
+      }
+    start_epoch: *pruning_end_epoch
+    update_frequency: -1.0
+    
+  - !SetWeightDecayModifier
+    start_epoch: *pruning_end_epoch
+    weight_decay: 0.0
+    
+  # quantization aware training phase
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !GMPruningModifier
+    params:
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.26.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_none_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.12.cv1.conv.weight
+      - model.14.conv.weight
+      - model.2.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.22.conv.weight
+      - model.26.cv1.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.28.m.0.weight
+      - model.28.m.2.weight
+      - model.4.0.cv1.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.7.cv1.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.1.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.11.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.19.cv2.conv.weight
+      - model.2.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv2.conv.weight
+      - model.28.m.1.weight
+      - model.3.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv2.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_mid_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+  - !GMPruningModifier
+    params:
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv2.conv.weight
+      - model.13.conv.weight
+      - model.15.conv.weight
+      - model.20.cv1.conv.weight
+      - model.21.conv.weight
+      - model.23.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_high_target_sparsity
+    mask_type: *mask_type
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Test Recipe
+
+This is a test recipe useful for quickly evaluating the time and resources needed for pruning and quantizing a model.
+In addition, it offers a quick integration tests pathway.
+This recipe creates a sparse-quantized [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model that will not be accurate.
+Use the following [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3) to run.
+
+Note that half-precision, EMA, and pickling are not supported for quantization.
+Therefore, once quantization is run, all three will be disabled for the training pipeline.
+This additionally means that the checkpoints are saved using state_dicts rather than pickels of the model.
+
+## Weights and Biases
+
+- [YOLOv3-SPP LeakyReLU on VOC](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+The contents of the `hyp.pruned_quantized.yaml` hyperparameters file is given below.
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+*script command:*
+
+```
+python train.py \
+    --recipe ../recipes/yolov3-spp.test.md \
+    --weights PRETRAINED_WEIGHTS \
+    --data voc.yaml \
+    --hyp ../data/hyp.pruned_quantized.short.yaml \
+    --name yolov3-spp-lrelu-pruned-quantized
+```
+
+hyp.prune_quantized.yaml:
+```yaml
+lr0: 0.0
+lrf: 0.0
+momentum: 0.843
+weight_decay: 0.00036
+warmup_epochs: 40.0
+warmup_momentum: 0.5
+warmup_bias_lr: 0.05
+box: 0.0296
+cls: 0.243
+cls_pw: 0.631
+obj: 0.301
+obj_pw: 0.911
+iou_t: 0.2
+anchor_t: 2.91
+fl_gamma: 0.0
+hsv_h: 0.0138
+hsv_s: 0.664
+hsv_v: 0.464
+degrees: 0.373
+translate: 0.245
+scale: 0.898
+shear: 0.602
+perspective: 0.0
+flipud: 0.00856
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.243
+```
\ No newline at end of file
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
new file mode 100644
index 00000000000..2a21412a138
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned.md
@@ -0,0 +1,169 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR Hyperparams
+num_epochs: &num_epochs 50
+
+# Pruning Hyperparams
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 5
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 1
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.92
+
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params:
+      - model.1.conv.weight
+      - model.2.cv1.conv.weight
+      - model.2.cv2.conv.weight
+      - model.3.conv.weight
+      - model.4.0.cv1.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.3.cv2.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv1.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv1.conv.weight
+      - model.11.cv2.conv.weight
+      - model.12.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.13.conv.weight
+      - model.14.conv.weight
+      - model.15.conv.weight
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.19.cv2.conv.weight
+      - model.20.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.21.conv.weight
+      - model.22.conv.weight
+      - model.23.conv.weight
+      - model.26.cv1.conv.weight
+      - model.26.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.27.1.cv2.conv.weight
+
+  - !GMPruningModifier
+    params:
+      - model.28.m.0.weight
+      - model.28.m.1.weight
+      - model.28.m.2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+---
+
+# YOLOv3-SPP Pruned Transfer Learning
+
+This recipe transfer learns from a sparse, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
+It was originally tested on the VOC dataset and achieved 0.84 mAP@0.5.
+
+Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+The training results for this recipe are made available through Weights and Biases for easy viewing.
+
+- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/3kvb4neh)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
+ ```python
+from sparsezoo import Model
+
+stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
+model = Model(stub)
+downloaded_path = model.path
+print(f'Model with stub {stub} downloaded to {downloaded_path}.')
+```
+
+*script command:*
+
+```bash
+python train.py \
+    --data voc.yaml \
+    --cfg ../models/yolov3-spp.lrelu.yaml \
+    --weights DOWNLOADED_PATH \
+    --hyp data/hyp.finetune.yaml \
+    --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
+```
diff --git a/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
new file mode 100644
index 00000000000..8cf05b9d59c
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/recipes/yolov3-spp.transfer_learn_pruned_quantized.md
@@ -0,0 +1,238 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+---
+# General Epoch/LR Hyperparams
+num_epochs: &num_epochs 52
+warmup_lr: &warmup_lr 0.0
+warmup_lr_bias: &warmup_lr_bias 0.05
+init_lr: &init_lr 0.0032
+final_lr: &final_lr 0.00038
+
+# Pruning Hyperparams
+init_sparsity: &init_sparsity 0.05
+pruning_start_epoch: &pruning_start_epoch 5
+pruning_end_epoch: &pruning_end_epoch 40
+update_frequency: &pruning_update_frequency 1
+mask_type: &mask_type [1, 4]
+
+prune_none_target_sparsity: &prune_none_target_sparsity 0.4
+prune_low_target_sparsity: &prune_low_target_sparsity 0.75
+prune_mid_target_sparsity: &prune_mid_target_sparsity 0.8
+prune_high_target_sparsity: &prune_high_target_sparsity 0.92
+
+# Quantization Hyperparams
+quantization_start_epoch: &quantization_start_epoch 50
+quantization_init_lr: &quantization_init_lr 0.0038
+
+# modifiers
+training_modifiers:
+  - !EpochRangeModifier
+    start_epoch: 0.0
+    end_epoch: *num_epochs
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 100
+    end_epoch: 160
+    lr_func: cosine
+    init_lr: *init_lr
+    final_lr: *final_lr
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: 3
+    lr_func: linear
+    init_lr: *warmup_lr
+    final_lr: *init_lr
+    param_groups: [0, 1]
+    
+  - !LearningRateFunctionModifier
+    start_epoch: 0
+    end_epoch: 3
+    lr_func: linear
+    init_lr: *warmup_lr_bias
+    final_lr: *init_lr
+    param_groups: [2]
+    
+  - !SetLearningRateModifier
+    start_epoch: *quantization_start_epoch
+    learning_rate: *quantization_init_lr
+
+pruning_modifiers:
+  - !ConstantPruningModifier
+    start_epoch: 0.0
+    params:
+      - model.1.conv.weight
+      - model.2.cv1.conv.weight
+      - model.2.cv2.conv.weight
+      - model.3.conv.weight
+      - model.4.0.cv1.conv.weight
+      - model.4.0.cv2.conv.weight
+      - model.4.1.cv1.conv.weight
+      - model.4.1.cv2.conv.weight
+      - model.5.conv.weight
+      - model.6.0.cv1.conv.weight
+      - model.6.0.cv2.conv.weight
+      - model.6.1.cv1.conv.weight
+      - model.6.1.cv2.conv.weight
+      - model.6.2.cv1.conv.weight
+      - model.6.2.cv2.conv.weight
+      - model.6.3.cv1.conv.weight
+      - model.6.3.cv2.conv.weight
+      - model.6.4.cv1.conv.weight
+      - model.6.4.cv2.conv.weight
+      - model.6.5.cv1.conv.weight
+      - model.6.5.cv2.conv.weight
+      - model.6.6.cv1.conv.weight
+      - model.6.6.cv2.conv.weight
+      - model.6.7.cv1.conv.weight
+      - model.6.7.cv2.conv.weight
+      - model.7.conv.weight
+      - model.8.0.cv1.conv.weight
+      - model.8.0.cv2.conv.weight
+      - model.8.1.cv1.conv.weight
+      - model.8.1.cv2.conv.weight
+      - model.8.2.cv1.conv.weight
+      - model.8.2.cv2.conv.weight
+      - model.8.3.cv1.conv.weight
+      - model.8.3.cv2.conv.weight
+      - model.8.4.cv1.conv.weight
+      - model.8.4.cv2.conv.weight
+      - model.8.5.cv1.conv.weight
+      - model.8.5.cv2.conv.weight
+      - model.8.6.cv1.conv.weight
+      - model.8.6.cv2.conv.weight
+      - model.8.7.cv1.conv.weight
+      - model.8.7.cv2.conv.weight
+      - model.9.conv.weight
+      - model.10.0.cv1.conv.weight
+      - model.10.0.cv2.conv.weight
+      - model.10.1.cv1.conv.weight
+      - model.10.1.cv2.conv.weight
+      - model.10.2.cv1.conv.weight
+      - model.10.2.cv2.conv.weight
+      - model.10.3.cv1.conv.weight
+      - model.10.3.cv2.conv.weight
+      - model.11.cv1.conv.weight
+      - model.11.cv2.conv.weight
+      - model.12.cv1.conv.weight
+      - model.12.cv2.conv.weight
+      - model.13.conv.weight
+      - model.14.conv.weight
+      - model.15.conv.weight
+      - model.16.conv.weight
+      - model.19.cv1.conv.weight
+      - model.19.cv2.conv.weight
+      - model.20.cv1.conv.weight
+      - model.20.cv2.conv.weight
+      - model.21.conv.weight
+      - model.22.conv.weight
+      - model.23.conv.weight
+      - model.26.cv1.conv.weight
+      - model.26.cv2.conv.weight
+      - model.27.0.cv1.conv.weight
+      - model.27.0.cv2.conv.weight
+      - model.27.1.cv1.conv.weight
+      - model.27.1.cv2.conv.weight
+
+  - !GMPruningModifier
+    params:
+      - model.28.m.0.weight
+      - model.28.m.1.weight
+      - model.28.m.2.weight
+    init_sparsity: *init_sparsity
+    final_sparsity: *prune_low_target_sparsity
+    start_epoch: *pruning_start_epoch
+    end_epoch: *pruning_end_epoch
+    update_frequency: *pruning_update_frequency
+    mask_type: *mask_type
+        
+quantization_modifiers:
+  - !QuantizationModifier
+    start_epoch: *quantization_start_epoch
+    submodules:
+      - model.0
+      - model.1 
+      - model.2
+      - model.3 
+      - model.4
+      - model.5
+      - model.6
+      - model.7
+      - model.8
+      - model.9
+      - model.10
+      - model.11
+      - model.12
+      - model.13
+      - model.14
+      - model.15
+      - model.16
+      - model.17
+      - model.18
+      - model.19
+      - model.20
+      - model.21
+      - model.22
+      - model.23
+      - model.24
+      - model.25
+      - model.26
+      - model.27
+---
+
+# YOLOv3-SPP Pruned-Quantized Transfer Learning
+
+This recipe transfer learns from a sparse-quantized, [YOLOv3-SPP](https://arxiv.org/abs/1804.02767) model.
+It was originally tested on the VOC dataset and achieved 0.838 mAP@0.5.
+
+Training was done using 4 GPUs at half precision with the [SparseML integration with ultralytics/yolov3](https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov3).
+
+When running, adjust hyperparameters based on training environment and dataset.
+
+## Weights and Biases
+
+The training results for this recipe are made available through Weights and Biases for easy viewing.
+
+- [YOLOv3-SPP LeakyReLU VOC Transfer Learning](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning/runs/2hbvu7w2)
+
+## Training
+
+To set up the training environment, follow the instructions on the [integration README](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/README.md).
+Using the given training script from the `yolov3` directory the following command can be used to launch this recipe.  
+Adjust the script command for your GPU device setup. 
+Ultralytics supports both DataParallel and DDP.
+
+The sparse weights used with this recipe are stored in the SparseZoo and can be retrieved with the following code:
+ ```python
+from sparsezoo import Model
+
+stub = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
+model = Model(stub)
+downloded_path = model.path
+print(f"Model with stub {stub} downloaded to {downloaded_path}.")
+```
+
+*script command:*
+
+```bash
+python train.py \
+    --data voc.yaml \
+    --cfg ../models/yolov3-spp.lrelu.yaml \
+    --weights DOWNLOADED_PATH \
+    --hyp data/hyp.finetune.yaml \
+    --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+```
diff --git a/integrations/old-examples/ultralytics-yolov3/setup_integration.sh b/integrations/old-examples/ultralytics-yolov3/setup_integration.sh
new file mode 100644
index 00000000000..b14482e2979
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/setup_integration.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Integration setup command to setup the folder so it is ready to train and sparsify models.
+# Creates a yolov3 folder next to this script with all required dependencies from the ultralytics/yolov3 repository.
+# Command: `bash setup_integration.sh`
+
+git clone https://github.com/neuralmagic/yolov3.git
+cd yolov3
+git checkout release/1.1
+pip install -r requirements.txt
diff --git a/integrations/old-examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg b/integrations/old-examples/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg
new file mode 100644
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diff --git a/integrations/old-examples/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png b/integrations/old-examples/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png
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diff --git a/integrations/old-examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md b/integrations/old-examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
new file mode 100644
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--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/tutorials/sparsifying_yolov3_using_recipes.md
@@ -0,0 +1,247 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparsifying YOLOv3 Using Recipes
+
+This tutorial shows how Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models.
+
+## Overview
+
+Neural Magic’s ML team creates recipes that allow anyone to plug in their data and leverage SparseML’s recipe-driven approach on top of Ultralytics’ robust training pipelines. Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+
+* Creating a pre-trained model to establish a baseline. You will set up your custom data and then train the model.
+* Applying a recipe to select the trade off between the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+* Exporting for inference to run a file (that contains a checkpoint of the best weights measured on the validation set) through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+The examples listed in this tutorial are all performed on the VOC dataset. Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc).
+
+<div style="width: 100%; display: flex; justify-content: center;">
+    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
+        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
+    </a>
+</div>
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Creating a Pre-trained Model
+
+Before applying one of the recipes, you must first create the pre-trained model to sparsify further. The pre-trained model enables pruning and other algorithms to remove the correct redundant information in place of random information. Your goal after this is to create a smaller, faster model that recovers to the pre-trained baseline.
+
+Creating a pre-trained model involves two steps: 1) setting up the data and 2) training the model.
+
+**Note**: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
+
+
+### Setting Up the Data
+
+1. For this tutorial, we run the VOC setup script with the following command from the root of the yolov3 repository:
+```bash
+bash data/scripts/get_voc.sh
+```
+2. Download and validation of the VOC dataset will begin and takes around 10 minutes to finish.
+The script downloads the VOC dataset into a `VOC` folder under the parent directory.
+Notie that, once completed, the data is ready for training with the folder structure in the following state:
+```
+|-- VOC
+|   |-- images
+|   |   |-- train
+|   |   `-- val
+|   `-- labels
+|       |-- train
+|       `-- val
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- utils
+|   |-- weights
+|   |-- Dockerfile
+|   |-- LICENSE
+|   |-- README.md
+|   |-- detect.py
+|   |-- hubconf.py
+|   |-- requirements.txt
+|   |-- test.py
+|   |-- train.py
+|   `-- tutorial.ipynb
+```
+
+You are ready to train the model.
+
+### Training the Model
+
+The training command will take a few hours to complete (~3 hours for a Titan RTX). Afterward, you will have a model that achieves roughly 0.85 mAP on the VOC dataset ready for sparsifying.
+
+1. To expedite the training process, you will transfer learn from weights initially trained on the COCO dataset. These are stored in the SparseZoo and accessed with the following Python code. Enter:
+
+```python
+from sparsezoo import Model
+
+model = Model("zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none")
+checkpoint_path = model.training.default.path
+print(checkpoint_path)
+```
+
+After running, the code has downloaded the checkpoint file to the local system:
+```bash
+downloading...: 100%|██████████| 126342273/126342273 [00:04<00:00, 28306365.97it/s]
+/Users/markkurtz/.cache/sparsezoo/16fe8358-2e91-4b81-a1f2-df85bd1a9ac3/pytorch/model.pth
+```
+
+2. Next, the checkpoint file provides the source weights for training on VOC. Run the following train command from within the yolov3 repository folder:
+```bash
+python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50
+```
+
+The training command creates a `runs` directory under the yolov3 repository directory.
+This directory will contain the outputs from the training run, including experimental results along with the trained model:
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+You are ready to use the weights at `yolov3/runs/train/exp/weights/best.pt` with the Neural Magic recipes to create a sparsified model.
+
+## Applying a Recipe
+
+In general, recipes trade off the amount of recovery to the baseline training performance with the amount of sparsification for inference performance.
+The [`recipes` folder](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes) contains multiple files, each offering certain advantages over others. The table below compares these tradeoffs and shows how to run them on the VOC dataset.
+1. Review this table, which lists recipes, commands, and results.
+
+| Recipe Name                                                                                                                                              | Description                                                                                                                     | Train Command                                                                                                                                                                               | VOC mAP@0.5 | Size on Disk | DeepSparse Performance** | Training Epochs (time) | Weights and Biases                                                       |
+|----------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|--------------|--------------------------|------------------------|--------------------------------------------------------------------------|
+| Baseline                                                                                                                                                 | The baseline, pretrained model originally transfer learned onto the VOC dataset.                                                | ``` python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 ```                                                                                     | 0.857       | 194 MB       | 19.5 img/sec             | 50 (3.21 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/261llnb1) |
+| [Pruned](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.md)                                 | Creates a highly sparse, FP32 model that recovers close to the baseline model.                                                  | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.md ```                           | 0.858       | 33.6 MB      | 34.0 img/sec             | 300 (20.45 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2jeadrts) |
+| [Pruned Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned.short.md)                     | Creates a highly sparse, FP32 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned.yaml --recipe ../recipes/yolov3-spp.pruned.short.md ```                     | 0.854       | 33.6 MB      | 34.0 img/sec             | 80 (5.53 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/jktw650n) |
+| [Pruned Quantized](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.md)             | Creates a highly sparse, INT8 model that recovers reasonably close to the baseline model.                                       | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.md ```       | 0.827       | 13.4 MB      | 90.4 img/sec             | 242 (18.32 hours)      | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/2dfy3rgs) |
+| [Pruned Quantized Short](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.pruned_quantized.short.md) | Creates a highly sparse, INT8 model in a shortened schedule to prioritize quicker training while sacrificing a bit on recovery. | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.pruned_quantized.short.md ``` | 0.808       | 13.4 MB      | 90.4 img/sec             | 52 (4.23 hours)        | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/1picfimy) |
+| [Test](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/recipes/yolov3-spp.test.md)                                     | A test recipe to test the training pipeline and device for both pruning and quantization in 5 epochs.                           | ``` python train.py --weights PATH_TO_VOC_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --hyp ../data/hyp.pruned_quantized.yaml --recipe ../recipes/yolov3-spp.test.md ```                   | 0.702       | 13.4 MB      | 90.4 img/sec             | 5 (17 minutes)         | [wandb](https://wandb.ai/neuralmagic/yolov3-spp-lrelu-voc/runs/3bkw6c60) |
+
+** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
+
+2. Notice the Weights and Biases information in the table, which is very useful for comparing across these runs. The epochs versus mAP@0.5 graph is:
+
+<img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
+
+**Notes About Recipe Selection**
+
+If your hardware does not support quantized networks for inference speedup or complete recovery is very important, then Neural Magic recommends using either the  `pruned` or `pruned short` recipe. The recipe to use depends on how long you are willing to train and how vital full recovery is. Consult the table above for this comparison.
+
+If your hardware does support quantized networks (VNNI instruction set on CPUs, for example), we recommend using the `pruned quantized` or `pruned quantized short` recipe. The recipe to use depends on how long you are willing to train and how crucial full recovery is. Consult the table for this comparison.
+
+When running quantized models, the memory footprint for training will significantly increase (roughly 3x). Therefore it is essential to take this into account when selecting a batch size to train at. To ensure no issues with the longer quantized runs, run the quicker test recipe first to ensure your configurations are correct and the training process will complete successfully.
+
+3. To begin applying one of the recipes, use the `--recipe` argument within the Ultralytics [train script](https://github.com/neuralmagic/yolov3/blob/master/train.py).
+In addition, the hyperparameters are changed slightly to better work with the recipe.
+These hyperparameters are stored in appropriately named files under the [`data` directory](https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov3/data) and are passed into the training script using the `--hyp` argument.
+Both of the arguments are combined with our previous training command and VOC pre-trained weights to run the recipes over the model. For example:
+```bash
+python train.py --weights PATH_TO_COCO_PRETRAINED_WEIGHTS --data voc.yaml --img 512 --epochs 50 --recipe PATH_TO_SPARSIFICATION_RECIPE
+```
+After applying a recipe, you are ready to export for inference.
+
+## Exporting for Inference
+
+This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+When you applied a recipe in the previous step, the sparsification run created a new `exp` directory under the yolov3 runs directory:
+
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |-- exp1
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+The `best.pt` file contains a checkpoint of the best weights measured on the validation set.
+These weights can be loaded into the `train.py` and `test.py` scripts now. However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+The [`export.py` script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+
+1. Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
+
+```bash
+python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
+```
+
+The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+```
+|-- exp
+|-- exp1
+|-- weights
+|   |-- best.pt
+|   |-- best.onnx
+|   `-- last.pt
+`-- ...
+```
+
+2. Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+ 
+The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
+
+## Wrap-Up
+
+Neural Magic recipes simplify the sparsification process by encoding the hyperparameters and instructions needed to create highly accurate pruned and pruned-quantized YOLOv3 models. In this tutorial, you created a pre-trained model to establish a baseline, applied a Neural Magic recipe for sparsification, and exported to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
diff --git a/integrations/old-examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md b/integrations/old-examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
new file mode 100644
index 00000000000..fbcdea5b80f
--- /dev/null
+++ b/integrations/old-examples/ultralytics-yolov3/tutorials/yolov3_sparse_transfer_learning.md
@@ -0,0 +1,217 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Sparse Transfer Learning With YOLOv3
+
+This tutorial shows how Neural Magic sparse models simplify the sparsification process by offering pre-sparsified models for transfer learning onto other datasets.
+
+## Overview
+
+Neural Magic’s ML team creates sparsified models that allow anyone to plug in their data and leverage pre-sparsified models from the SparseZoo on top of Ultralytics’ robust training pipelines. 
+Sparsifying involves removing redundant information from neural networks using algorithms such as pruning and quantization, among others. 
+This sparsification process results in many benefits for deployment environments, including faster inference and smaller file sizes. 
+Unfortunately, many have not realized the benefits due to the complicated process and number of hyperparameters involved.
+Working through this tutorial, you will experience how Neural Magic recipes simplify the sparsification process by:
+- Downloading and preparing a pre-sparsified model.
+- Setting up your custom data.
+- Applying a sparse transfer learning recipe.
+- Exporting an inference graph to reduce its deployment size and run it in an inference engine such as DeepSparse.
+
+The examples listed in this tutorial are all performed on the VOC dataset. 
+Additionally, the results listed in this tutorial are available publicly through a [Weights and Biases project](https://wandb.ai/neuralmagic/yolov3-spp-voc-sparse-transfer-learning).
+
+<div style="width: 100%; display: flex; justify-content: center;">
+    <a href="https://youtu.be/o5qIYs47MPw" target="_blank">
+        <img alt="Example YOLOv3 Inference Video" src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/pruned-quantized-result.jpeg" width="560px" style="border: 2px solid #000000;" />
+    </a>
+</div>
+
+## Need Help?
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)
+
+## Downloading and Preparing a Pre-pruned Model
+
+First, you need to download the sparsified models from the [SparseZoo](https://github.com/neuralmagic/sparsezoo).
+These models were originally pruned on the COCO dataset achieving the following metrics:
+
+| Sparsification Type | Description                                                                       | COCO mAP@0.5 | Size on Disk | DeepSparse Performance** |
+|---------------------|-----------------------------------------------------------------------------------|--------------|--------------|--------------------------|
+| Baseline            | The baseline, pretrained model on the COCO dataset.                               | 0.642        | 194 MB       | 19.5 img/sec             |
+| Pruned              | A highly sparse, FP32 model that recovers close to the baseline model.            | 0.624        | 33.6 MB      | 34.0 img/sec             |
+| Pruned Quantized    | A highly sparse, INT8 model that recovers reasonably close to the baseline model. | 0.605        | 13.4 MB      | 90.4 img/sec             |
+** DeepSparse Performance measured on an AWS C5 instance with 24 cores, batch size 64, and 640x640 input with version 1.3 of the DeepSparse Engine.
+
+1) After deciding on which model meets your performance requirements for both speed and accuracy, the following code is used to download the PyTorch checkpoints for the desired model from the SparseZoo:
+    ```python
+    from sparsezoo import Model
+    
+    BASELINE_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none'
+    PRUNED_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97'
+    PRUNED_QUANT_STUB = 'zoo:cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94'
+    
+    stub = PRUNED_QUANT_STUB
+    model = Model(stub)
+    downloded_path = model.path
+    print(f'Model with stub {stub} downloaded to {downloded_path}.')
+    ```
+
+2) Once the desired checkpoint has downloaded, it must be reset for training again.
+    The `utility.py` script within YOLOv3 repository is used for this.
+    First change into the `yolov3` directory using `cd yolov3`.
+    Next run the following command to prepare the checkpoint for training:
+    ```bash
+    python utility.py strip /PATH/TO/DOWNLOADED/WEIGHTS.pt
+    ```
+   
+You are now ready to set up the data for training.
+
+## Setting Up the Data
+
+Note: If using your custom data, the Ultralytics repo contains a walk-through for [training custom data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data). 
+Otherwise, setup scripts for both [VOC](https://cs.stanford.edu/~roozbeh/pascal-context/) and [COCO](https://cocodataset.org/#home) can be found under the [yolov3/data/scripts path](https://github.com/ultralytics/yolov3/tree/master/data/scripts).
+
+1) For this tutorial, run the VOC setup script with the following command from the root of the `yolov3` repository:
+    
+    `bash data/scripts/get_voc.sh`
+2) Download and validation of the VOC dataset will begin and take around 10 minutes to finish. 
+    The script downloads the VOC dataset into a VOC folder under the parent directory. 
+    Notice that, once completed, the data is ready for training with the folder structure in the following state:
+    ```
+    |-- VOC
+    |   |-- images
+    |   |   |-- train
+    |   |   `-- val
+    |   `-- labels
+    |       |-- train
+    |       `-- val
+    |-- yolov3
+    |   |-- data
+    |   |-- models
+    |   |-- utils
+    |   |-- weights
+    |   |-- Dockerfile
+    |   |-- LICENSE
+    |   |-- README.md
+    |   |-- detect.py
+    |   |-- hubconf.py
+    |   |-- requirements.txt
+    |   |-- test.py
+    |   |-- train.py
+    |   `-- tutorial.ipynb
+    ```
+
+You are ready to transfer learn the model.
+
+## Transfer Learning the Model
+
+The training command will take a few hours to complete (~3 hours for a Titan RTX). 
+Afterward, you will have a sparse model transfer learned onto the VOC dataset.
+
+The command uses the `--recipe` argument to encode the proper hyperparams such that SparseML will enforce the sparsity for all layers.
+Without the proper recipes, the zeroed weights will slowly become dense as the model is trained further.
+The recipes are specific to the sparsification type, so the training command will differ based on if you are transfer learning the pruned quantized, pruned, or baseline model.
+
+1) Select the proper command to run based on the sparsification type of the model you downloaded earlier.
+   Change directory into the `yolov3` folder to use the `train.py` script.
+   - Pruned transfer learning, achieves 0.84 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned.md
+        ```
+   - Pruned-Quantized transfer learning, achieves 0.838 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml --recipe ../recipes/yolov3-spp.transfer_learn_pruned_quantized.md
+        ```
+     Note: Running quantization-aware training (QAT) will consume up to three times the memory as FP16 training. 
+     It is recommended to use this recipe with a batch size that fits in memory for FP16 as normal at the start, though.
+     This will keep the training times the same until the quantization portion.
+     Then it will fail with an out of memory exception, but you can use the `last.pt` checkpoint with the `--weights` argument to resume the run with a lower batch size for QAT.
+   - Baseline transfer learning, achieves 0.86 mAP@0.5 on the VOC dataset:
+        ```bash
+        python train.py --data voc.yaml --cfg ../models/yolov3-spp.lrelu.yaml --weights DOWNLOADED_PATH --hyp data/hyp.finetune.yaml
+        ```
+
+    Weights and Biases is very useful for comparing across the different runs; the epochs vs mAP@0.5 graph is supplied below:
+    
+    <img src="https://raw.githubusercontent.com/neuralmagic/sparseml/main/integrations/ultralytics-yolov3/tutorials/images/transfer-learning-wandb-chart.png" width="960px" style="border: 2px solid #000000;" />
+
+Afterward, you will have a sparse model trained on the VOC dataset almost ready for inference.
+The training command creates a `runs` directory under the `yolov3` repository directory. 
+This directory will contain the outputs from the training run, including experimental results along with the trained model:
+```
+|-- VOC
+|-- data
+|-- models
+|-- recipes
+|-- tutorials
+|-- yolov3
+|   |-- data
+|   |-- models
+|   |-- runs
+|   |   `-- train
+|   |       |-- exp
+|   |       |   |-- weights
+|   |       |   |   |-- best.pt
+|   |       |   |   `-- last.pt
+|   |       |   |-- F1_curve.png
+|   |       |   |-- PR_curve.png
+|   |       |   |-- P_curve.png
+|   |       |   |-- R_curve.png
+|   |       |   |-- confusion_matrix.png
+|   |       |   `-- ...
+|   |-- train.py
+|   `-- ...
+|-- README.md
+`-- setup_integration.sh
+```
+
+## Exporting for Inference
+
+This step loads a checkpoint file of the best weights measured on the validation set, and converts it into the more common inference formats. 
+Then, you can run the file through a compression algorithm to reduce its deployment size and run it in an inference engine such as DeepSparse.
+
+The `best.pt` file contains a checkpoint of the best weights measured on the validation set. 
+These weights can be loaded into the `train.py` and `test.py` scripts now. 
+However, other formats are generally more friendly for other inference deployment platforms, such as [ONNX](https://onnx.ai/).
+
+The [export.py script](https://github.com/neuralmagic/yolov3/blob/master/models/export.py) handles the logic behind loading the checkpoint and converting it into the more common inference formats, as described here.
+1) Enter the following command to load the PyTorch graph, convert to ONNX, and then correct any misformatted pieces for the pruned and quantized models.
+    ```bash
+    python models/export.py --weights PATH_TO_SPARSIFIED_WEIGHTS --img-size 512 512
+    ```
+    
+    The result is a new file added next to the sparsified checkpoint with a `.onnx` extension:
+    ```
+    |-- exp
+    |-- exp1
+    |-- weights
+    |   |-- best.pt
+    |   |-- best.onnx
+    |   `-- last.pt
+    `-- ...
+    ```
+2) Now you can run the `.onnx` file through a compression algorithm to reduce its deployment size and run it in ONNX-compatible inference engines such as [DeepSparse](https://github.com/neuralmagic/deepsparse).
+    The DeepSparse Engine is explicitly coded to support running sparsified models for significant improvements in inference performance. 
+    An example for benchmarking and deploying YOLOv3 models with DeepSparse can be found [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo).
+
+## Wrap-Up
+
+Neural Magic sparse models and recipes simplify the sparsification process by enabling sparse transfer learning to create highly accurate pruned and pruned-quantized YOLOv3 models. 
+In this tutorial, you downloaded a pre-sparsified model, applied a Neural Magic recipe for sparse transfer learning, and exported to ONNX to run through an inference engine.
+
+Now, refer [here](https://github.com/neuralmagic/deepsparse/tree/main/examples/ultralytics-yolo) for an example for benchmarking and deploying YOLOv3 models with DeepSparse.
+
+For Neural Magic Support, sign up or log in to our [**Deep Sparse Community Slack**](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ). Bugs, feature requests, or additional questions can also be posted to our [GitHub Issue Queue.](https://github.com/neuralmagic/sparseml/issues)

From a9d3534e430221bf46c9015826e4d5e1b4976b80 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 20:52:53 -0400
Subject: [PATCH 18/29] Update README.md

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index ff642c8d338..a6b48c88c29 100644
--- a/README.md
+++ b/README.md
@@ -53,7 +53,10 @@ limitations under the License.
 
 ## Overview
 
-SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. Once sparsified, the models can be deployed with [DeepSparse](https://github.com/neuralmagic/deepsparse) for GPU-class performance on CPUs.
+SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries to apply state-of-the-art sparsification algorithms such as pruning and quantization to your PyTorch, Hugging Face, and Ultralytics models. Once optimized, SparseML enabled you to export to the ONNX format for deployment with [DeepSparse](https://github.com/neuralmagic/deepsparse).
+
+<img alt="SparseML Flow" src="docs/images/sparseml-flow.png" width="960px" />
+
 
 
 General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

From 7250eb82911639dcf48e0a19bffc0ff06f5ea634 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 20:56:29 -0400
Subject: [PATCH 19/29] Update README.md

---
 README.md | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index a6b48c88c29..3f3a7b38f66 100644
--- a/README.md
+++ b/README.md
@@ -53,15 +53,16 @@ limitations under the License.
 
 ## Overview
 
-SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries to apply state-of-the-art sparsification algorithms such as pruning and quantization to your PyTorch, Hugging Face, and Ultralytics models. Once optimized, SparseML enabled you to export to the ONNX format for deployment with [DeepSparse](https://github.com/neuralmagic/deepsparse).
-
-<img alt="SparseML Flow" src="docs/images/sparseml-flow.png" width="960px" />
+SparseML enables you to create inference-optimized sparse models using state-of-the-art pruning and quantization algorithms. Models trained with SparseML can then be exported to ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
 
+SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries to apply state-of-the-art sparsification algorithms such as pruning and quantization to your PyTorch, Hugging Face, and Ultralytics models. Once optimized, SparseML enabled you to export to the ONNX format for deployment with [DeepSparse](https://github.com/neuralmagic/deepsparse).
 
+<p align="center">
+   <img alt="SparseML Flow" src="docs/images/sparseml-workflow.png" width="60%" />
+</p>
 
-General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.
+With SparseML, you can create inference-optimized sparse models that are smaller, faster, and equally accurate as their unoptimize counterpart.
 
-The [GitHub repository](https://github.com/neuralmagic/sparseml) contains integrations within the PyTorch, Hugging Face, and Ultralytics ecosystems, allowing for seamless model sparsification.
 
 <img alt="SparseML Flow" src="https://docs.neuralmagic.com/docs/source/infographics/sparseml.png" width="960px" />
 

From e2361ee46225919ae3f125381ea9505918f481f2 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:35:43 -0400
Subject: [PATCH 20/29] Update README.md

---
 integrations/ultralytics-yolov5/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/integrations/ultralytics-yolov5/README.md b/integrations/ultralytics-yolov5/README.md
index effabf7bb8d..b06e715d0e1 100644
--- a/integrations/ultralytics-yolov5/README.md
+++ b/integrations/ultralytics-yolov5/README.md
@@ -20,7 +20,6 @@ This directory explains how to use SparseML's `ultralytics/yolov5` integration t
 
 There are two main workflows enabled by SparseML:
 - **Sparse Transfer Learning** - fine-tune a pre-sparsified YOLOv5 checkpoint on your own dataset **[RECOMMENDED]**
-
 - **Sparsification from Scratch** - apply pruning and quantization to sparsify any YOLOv5 model from scratch
 
 Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse.

From 9cef525b6ea5d848112b6911e199eaa83aaac814 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:36:09 -0400
Subject: [PATCH 21/29] Update README.md

---
 integrations/huggingface-transformers/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/integrations/huggingface-transformers/README.md b/integrations/huggingface-transformers/README.md
index 137734fbe17..6b2c86c07a5 100644
--- a/integrations/huggingface-transformers/README.md
+++ b/integrations/huggingface-transformers/README.md
@@ -20,7 +20,6 @@ This directory explains how to use SparseML's `transformers` integration to trai
 
 There are two main workflows enabled by SparseML:
 - **Sparse Transfer Learning** - fine-tune a pre-sparsified checkpoint on your own dataset **[RECOMMENDED]**
-
 - **Sparsification from Scratch** - apply pruning and quantization to sparsify `transformer` models from scratch
 
 Once trained, SparseML enables you to export models to the ONNX format, such that they can be deployed with DeepSparse.

From 9b0e1c78598d3b690561848be0344ab0fafe9bfb Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:41:32 -0400
Subject: [PATCH 22/29] Update README.md

---
 README.md | 78 ++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 45 insertions(+), 33 deletions(-)

diff --git a/README.md b/README.md
index 3f3a7b38f66..6ff01773900 100644
--- a/README.md
+++ b/README.md
@@ -53,18 +53,18 @@ limitations under the License.
 
 ## Overview
 
-SparseML enables you to create inference-optimized sparse models using state-of-the-art pruning and quantization algorithms. Models trained with SparseML can then be exported to ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
-
-SparseML is a model optimization toolkit that includes APIs, CLIs, scripts and libraries to apply state-of-the-art sparsification algorithms such as pruning and quantization to your PyTorch, Hugging Face, and Ultralytics models. Once optimized, SparseML enabled you to export to the ONNX format for deployment with [DeepSparse](https://github.com/neuralmagic/deepsparse).
+SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using state-of-the-art pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
 
 <p align="center">
    <img alt="SparseML Flow" src="docs/images/sparseml-workflow.png" width="60%" />
 </p>
 
-With SparseML, you can create inference-optimized sparse models that are smaller, faster, and equally accurate as their unoptimize counterpart.
+## Workflows
 
+SparseML enables you to create a sparse model trained on your dataset in two ways:
+- **Sparse Transfer Learning** enables you to fine-tune a pre-sparsified model from [SparseZoo](https://sparsezoo.neuralmagic.com/) onto a downstream dataset, while maintaining sparsity. SparseZoo is an open-source repository of sparse models such as BERT, YOLOv5, and ResNet-50. Sparse Transfer is the easiest and preferred pathway and should be used for any model architectures available in the Zoo.
 
-<img alt="SparseML Flow" src="https://docs.neuralmagic.com/docs/source/infographics/sparseml.png" width="960px" />
+- **Sparsification from Scratch** - enables you to apply state-of-the-art pruning (like gradual magnitude pruning or OBS pruning) and quantization (like quantization aware training) algorithms to arbitrary PyTorch and Hugging Face models. This pathway requires more experimentation, but allows you to create a sparse version of any model. 
 
 ## Integrations
 
@@ -85,25 +85,22 @@ With SparseML, you can create inference-optimized sparse models that are smaller
 ### PyTorch
 - [Sparse Transfer Learning with the CLI](integrations/torchvision/tutorials/sparse-transfer-learning.md)
 - [Sparse Transfer Learning with the Python API](integrations/torchvision/tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
-- [Sparsify From Scatch with the CLI](integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb)
+- [Sparsify From Scratch with the Python API](integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb)
 
 ### Hugging Face Transformers
 - [Sparse Transfer Learning Overview with the Python API](integrations/huggingface-transformers/tutorials/sparse-transfer-learning-bert-python.md)
 - [Sparse Transfer Learning Overview with the CLI](integrations/huggingface-transformers/tutorials/sparse-transfer-learning-bert.md)
-- [Sparse Transfer Learning for Sentiment Analysis](integrations/huggingface-transformers/tutorials/sentiment-analysis/sentiment-analysis-cli.md)
-- [Sparse Transfer Learning for Text Classification](integrations/huggingface-transformers/tutorials/text-classification/text-classification-cli.md)
-- [Sparse Transfer Learning for Token Classification](integrations/huggingface-transformers/tutorials/token-classification/token-classification-cli.md)
-- [Sparse Transfer Learning for Question Answering](integrations/huggingface-transformers/tutorials/question-answering/question-answering-cli.md)
+- [Sparse Transfer Learning for Sentiment Analysis](integrations/huggingface-transformers/tutorials/sentiment-analysis/sentiment-analysis-cli.md), [for Text Classification](integrations/huggingface-transformers/tutorials/text-classification/text-classification-cli.md), [for Token Classification](integrations/huggingface-transformers/tutorials/token-classification/token-classification-cli.md), [for Question Answering](integrations/huggingface-transformers/tutorials/question-answering/question-answering-cli.md)
 
 ### Ultralytics YOLOv5
 
 - [Sparse Transfer Learning with the CLI](integrations/ultralytics-yolov5/tutorials/sparse-transfer-learning.md)
 - [Sparsify From Scatch with the CLI](integrations/ultralytics-yolov5/tutorials/sparsify-from-scratch.md)
 
-### Examples
+### Links to Additional Examples
 
 - [PyTorch](integrations/torchvision#tutorials)
-- [Hugging Face](integrations/huggingface-transformers#tutorials)
+- [Hugging Face Transformers](integrations/huggingface-transformers#tutorials)
 - [Ultralytics YOLOv5](integrations/ultralytics-yolov5#tutorials)
 
 ## Installation
@@ -122,44 +119,59 @@ More information on installation such as optional dependencies and requirements
 
 ## Quick Tour
 
-To enable flexibility, ease of use, and repeatability, sparsifying a model is done using a recipe.
-The recipes encode the instructions needed for modifying the model and/or training process as a list of modifiers.
-Example modifiers can be anything from setting the learning rate for the optimizer to gradual magnitude pruning.
-The files are written in [YAML](https://yaml.org/) and stored in YAML or [markdown](https://www.markdownguide.org/) files using [YAML front matter.](https://assemble.io/docs/YAML-front-matter.html) The rest of the SparseML system is coded to parse the recipes into a native format for the desired framework and apply the modifications to the model and training pipeline.
+### Recipes
+
+To enable flexibility, ease of use, and repeatability, SparseML uses a declarative interface called **recipes** for specifying the sparsity-related algorithms and hyperparamters that should be applied by SparseML.
+
+Recipes are YAML-files formatted as a list of `modifiers`, which encode the instructions for SparseML. Example `modifiers` can be anything from setting the learning rate to encoding the hyperparameters of the gradual magnitude pruning algorithm. The SparseML system parses the recipes into a native format for each framework and applies the modifications to the model and training pipeline.
+
+### Python API
 
-`ScheduledModifierManager` classes can be created from recipes in all supported ML frameworks.
-The manager classes handle overriding the training graphs to apply the modifiers as described in the desired recipe.
-Managers can apply recipes in one shot or training aware ways. 
-One shot is invoked by calling `.apply(...)` on the manager while training aware requires calls into `initialize(...)` (optional), `modify(...)`, and `finalize(...)`.
+Because of the declarative, recipe-based approach, you can add SparseML to your existing PyTorch traing pipelines. The `ScheduleModifierManager` class is responsible for parsing the YAML recipes and overriding standard PyTorch model and optimizer objects, encoding the logic of the sparsity algorithms from the recipe. Once you call `manager.modify`, you can then use the model and optimizer as usual, as SparseML abstracts away the complexity of the sparsification algorithms.
+
+The workflow looks like this:
 
-For the frameworks, this means only a few lines of code need to be added to begin supporting pruning, quantization, and other modifications to most training pipelines.
-For example, the following applies a recipe in a training aware manner:
 ```python
-model = Model()  # model definition
-optimizer = Optimizer()  # optimizer definition
-train_data = TrainData()  # train data definition
-batch_size = BATCH_SIZE  # training batch size
+model = Model()            # model definition
+optimizer = Optimizer()    # optimizer definition
+train_data = TrainData()   # train data definition
+batch_size = BATCH_SIZE    # training batch size
 steps_per_epoch = len(train_data) // batch_size
 
 from sparseml.pytorch.optim import ScheduledModifierManager
 manager = ScheduledModifierManager.from_yaml(PATH_TO_RECIPE)
 optimizer = manager.modify(model, optimizer, steps_per_epoch)
 
-# PyTorch training code
+# typical PyTorch training loop, using your model/optimizer as usual
 
 manager.finalize(model)
 ```
 
-Instead of training aware, the following example code shows how to execute a recipe in a one shot manner:
-```python
-model = Model()  # model definition
+- Check out the [PyTorch integration docs](integrations/torchvision) for full usage examples of the Python API.
+- Check out the [Hugging Face integration docs](integrations/huggingface-transformers) for details of using SparseML with the Hugging Face `Trainer`.
 
-from sparseml.pytorch.optim import ScheduledModifierManager
-manager = ScheduledModifierManager.from_yaml(PATH_TO_RECIPE)
-manager.apply(model)
+### SparseML CLI
+
+In addition to the code-level API, SparseML offers pre-made training pipelines for common NLP tasks via the CLI interface. The CLI enables you to kick-off training runs with various utilities like dataset loading and pre-processing, checkpoint saving, metric reporting, and logging handled for you. This makes it easy to get up and running in common training pathways.
+
+For instance, we can use the following to kick off a YOLOv5 sparse transfer learning run onto the VOC dataset (using SparseZoo stubs to pull down a sparse model checkpoint and transfer learning recipe):
+
+```bash
+sparseml.yolov5.train \
+  --weights zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned75_quant-none?recipe_type=transfer_learn \
+  --recipe zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned75_quant-none?recipe_type=transfer_learn \
+  --data VOC.yaml \
+  --hyp hyps/hyp.finetune.yaml --cfg yolov5s.yaml --patience 0
 ```
 
+- Check out the [YOLOv5 CLI example](ultralytics-yolov5/tutorials/sparse-transfer-learning.md) for more details on the YOLOv5 training pipeline
+- Check out the [Hugging Face CLI example](integrations/huggingface-transformers/tutorials/sparse-transfer-learning-bert.md) for more details on the available NLP training pipelines
+- Check out the [Torchvision CLI example](integrations/torchvision/tutorials/sparse-transfer-learning.md) for more details on the image classification training pipelines
+
+### Additional Resources 
+
 More information on the codebase and contained processes can be found in the SparseML docs:
+- [Examples and Tutorials](integrations)
 - [Sparsification Code](https://docs.neuralmagic.com/get-started/sparsify-a-model)
 - [Sparsification Recipes](https://docs.neuralmagic.com/user-guides/recipes)
 - [Exporting to ONNX](https://docs.neuralmagic.com/user-guides/onnx-export)

From 73852683249e51283b24cec9fd9fb1c3039e2588 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:47:06 -0400
Subject: [PATCH 23/29] Update README.md

---
 README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 6ff01773900..6c76ccb3d85 100644
--- a/README.md
+++ b/README.md
@@ -53,7 +53,7 @@ limitations under the License.
 
 ## Overview
 
-SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using state-of-the-art pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
+SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with [DeepSparse](https://github.com/neuralmagic/deepsparse/) for GPU-class performance on CPU hardware.
 
 <p align="center">
    <img alt="SparseML Flow" src="docs/images/sparseml-workflow.png" width="60%" />
@@ -62,9 +62,9 @@ SparseML is an open-source model optimization toolkit that enables you to create
 ## Workflows
 
 SparseML enables you to create a sparse model trained on your dataset in two ways:
-- **Sparse Transfer Learning** enables you to fine-tune a pre-sparsified model from [SparseZoo](https://sparsezoo.neuralmagic.com/) onto a downstream dataset, while maintaining sparsity. SparseZoo is an open-source repository of sparse models such as BERT, YOLOv5, and ResNet-50. Sparse Transfer is the easiest and preferred pathway and should be used for any model architectures available in the Zoo.
+- **Sparse Transfer Learning** enables you to fine-tune a pre-sparsified model from [SparseZoo, an open-source repository of sparse models such as BERT, YOLOv5, and ResNet-50](https://sparsezoo.neuralmagic.com/) onto your dataset, while maintaining sparsity. This pathway works just like typical fine-tuning you are used to in training CV and NLP models, and is strongly preferred for if your model architecture is availble in SparseZoo.
 
-- **Sparsification from Scratch** - enables you to apply state-of-the-art pruning (like gradual magnitude pruning or OBS pruning) and quantization (like quantization aware training) algorithms to arbitrary PyTorch and Hugging Face models. This pathway requires more experimentation, but allows you to create a sparse version of any model. 
+- **Sparsification from Scratch** enables you to apply state-of-the-art pruning (like gradual magnitude pruning or OBS pruning) and quantization (like quantization aware training) algorithms to arbitrary PyTorch and Hugging Face models. This pathway requires more experimentation, but allows you to create a sparse version of any model. 
 
 ## Integrations
 
@@ -93,7 +93,6 @@ SparseML enables you to create a sparse model trained on your dataset in two way
 - [Sparse Transfer Learning for Sentiment Analysis](integrations/huggingface-transformers/tutorials/sentiment-analysis/sentiment-analysis-cli.md), [for Text Classification](integrations/huggingface-transformers/tutorials/text-classification/text-classification-cli.md), [for Token Classification](integrations/huggingface-transformers/tutorials/token-classification/token-classification-cli.md), [for Question Answering](integrations/huggingface-transformers/tutorials/question-answering/question-answering-cli.md)
 
 ### Ultralytics YOLOv5
-
 - [Sparse Transfer Learning with the CLI](integrations/ultralytics-yolov5/tutorials/sparse-transfer-learning.md)
 - [Sparsify From Scatch with the CLI](integrations/ultralytics-yolov5/tutorials/sparsify-from-scratch.md)
 
@@ -106,6 +105,7 @@ SparseML enables you to create a sparse model trained on your dataset in two way
 ## Installation
 
 This repository is tested on Python 3.7-3.10, and Linux/Debian systems.
+
 It is recommended to install in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep your system in order.
 Currently supported ML Frameworks are the following: `torch>=1.1.0,<=1.12.1`, `tensorflow>=1.8.0,<2.0.0`, `tensorflow.keras >= 2.2.0`.
 
@@ -121,13 +121,13 @@ More information on installation such as optional dependencies and requirements
 
 ### Recipes
 
-To enable flexibility, ease of use, and repeatability, SparseML uses a declarative interface called **recipes** for specifying the sparsity-related algorithms and hyperparamters that should be applied by SparseML.
+To enable flexibility, ease of use, and repeatability, SparseML uses a declarative interface called `recipes` for specifying the sparsity-related algorithms and hyperparamters that should be applied by SparseML.
 
-Recipes are YAML-files formatted as a list of `modifiers`, which encode the instructions for SparseML. Example `modifiers` can be anything from setting the learning rate to encoding the hyperparameters of the gradual magnitude pruning algorithm. The SparseML system parses the recipes into a native format for each framework and applies the modifications to the model and training pipeline.
+`Recipes` are YAML-files formatted as a list of `modifiers`, which encode the instructions for SparseML. Example `modifiers` can be anything from setting the learning rate to encoding the hyperparameters of the gradual magnitude pruning algorithm. The SparseML system parses the `recipes` into a native format for each framework and applies the modifications to the model and training pipeline.
 
 ### Python API
 
-Because of the declarative, recipe-based approach, you can add SparseML to your existing PyTorch traing pipelines. The `ScheduleModifierManager` class is responsible for parsing the YAML recipes and overriding standard PyTorch model and optimizer objects, encoding the logic of the sparsity algorithms from the recipe. Once you call `manager.modify`, you can then use the model and optimizer as usual, as SparseML abstracts away the complexity of the sparsification algorithms.
+Because of the declarative, recipe-based approach, you can add SparseML to your existing PyTorch traing pipelines. The `ScheduleModifierManager` class is responsible for parsing the YAML `recipes` and overriding standard PyTorch model and optimizer objects, encoding the logic of the sparsity algorithms from the recipe. Once you call `manager.modify`, you can then use the model and optimizer as usual, as SparseML abstracts away the complexity of the sparsification algorithms.
 
 The workflow looks like this:
 
@@ -152,7 +152,7 @@ manager.finalize(model)
 
 ### SparseML CLI
 
-In addition to the code-level API, SparseML offers pre-made training pipelines for common NLP tasks via the CLI interface. The CLI enables you to kick-off training runs with various utilities like dataset loading and pre-processing, checkpoint saving, metric reporting, and logging handled for you. This makes it easy to get up and running in common training pathways.
+In addition to the code-level API, SparseML offers pre-made training pipelines for common NLP and CV tasks via the CLI interface. The CLI enables you to kick-off training runs with various utilities like dataset loading and pre-processing, checkpoint saving, metric reporting, and logging handled for you. This makes it easy to get up and running in common training pathways.
 
 For instance, we can use the following to kick off a YOLOv5 sparse transfer learning run onto the VOC dataset (using SparseZoo stubs to pull down a sparse model checkpoint and transfer learning recipe):
 

From ae49287c39bc03edc40cd9617bc79cd33fdfc021 Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:49:30 -0400
Subject: [PATCH 24/29] Update README.md

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 6c76ccb3d85..e5f27e8533b 100644
--- a/README.md
+++ b/README.md
@@ -62,20 +62,20 @@ SparseML is an open-source model optimization toolkit that enables you to create
 ## Workflows
 
 SparseML enables you to create a sparse model trained on your dataset in two ways:
-- **Sparse Transfer Learning** enables you to fine-tune a pre-sparsified model from [SparseZoo, an open-source repository of sparse models such as BERT, YOLOv5, and ResNet-50](https://sparsezoo.neuralmagic.com/) onto your dataset, while maintaining sparsity. This pathway works just like typical fine-tuning you are used to in training CV and NLP models, and is strongly preferred for if your model architecture is availble in SparseZoo.
+- **Sparse Transfer Learning** enables you to fine-tune a pre-sparsified model from [SparseZoo](https://sparsezoo.neuralmagic.com/) (an open-source repository of sparse models such as BERT, YOLOv5, and ResNet-50) onto your dataset, while maintaining sparsity. This pathway works just like typical fine-tuning you are used to in training CV and NLP models, and is strongly preferred for if your model architecture is availble in SparseZoo.
 
 - **Sparsification from Scratch** enables you to apply state-of-the-art pruning (like gradual magnitude pruning or OBS pruning) and quantization (like quantization aware training) algorithms to arbitrary PyTorch and Hugging Face models. This pathway requires more experimentation, but allows you to create a sparse version of any model. 
 
 ## Integrations
 
 <p>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/torchvision">
+    <a href="integrations/torchvision">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/pytorch-torchvision.png" width="136px" />
     </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/ultralytics-yolov5">
+    <a href="integrations/ultralytics-yolov5">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/ultralytics-yolov5.png" width="136px" />
     </a>
-    <a href="https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers">
+    <a href="integrations/huggingface-transformers">
         <img src="https://docs.neuralmagic.com/docs/source/highlights/sparseml/huggingface-transformers.png" width="136px" />
     </a>
 </p>

From 401862f37896b142b9b0aa95c8dc4549ac44aa1a Mon Sep 17 00:00:00 2001
From: Robert Shaw <rshaw@neuralmagic.com>
Date: Wed, 22 Mar 2023 20:51:47 -0400
Subject: [PATCH 25/29] stashing updated ipynb notebooks

---
 ...cratch-densenet-flowers-pruned-quant.ipynb | 2457 -----------------
 .../densenet-flowers-dense-recipe.yaml        |   16 -
 .../densenet-flowers-pruning-recipe.yaml      |   56 -
 .../mobilenetv2-beans-pruning-recipe.yaml     |   60 +-
 .../recipes/quantization-recipe.yaml          |   29 -
 .../resnet50-beans-pruning-recipe.yaml        |   64 +-
 6 files changed, 80 insertions(+), 2602 deletions(-)
 delete mode 100644 integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
 delete mode 100644 integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
 delete mode 100644 integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
 delete mode 100644 integrations/torchvision/tutorials/recipes/quantization-recipe.yaml

diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
deleted file mode 100644
index 725441a825c..00000000000
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb
+++ /dev/null
@@ -1,2457 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "00e081b7",
-   "metadata": {},
-   "source": [
-    "# Sparsifying DenseNet121 from Scratch (Flower102)\n",
-    "\n",
-    "In this example, we will demonstrate how to sparsify an image classification model from scratch using SparseML's PyTorch integration. We train and prune [DenseNet121](https://pytorch.org/vision/main/models/generated/torchvision.models.densenet121.html) on the downstream [Oxford Flower 102 dataset](https://pytorch.org/vision/main/generated/torchvision.datasets.Flowers102.html#:~:text=Oxford%20102%20Flower%20is%20an,scale%2C%20pose%20and%20light%20variations) using the Global Magnitude Pruning algorithm. \n",
-    "\n",
-    "## Agenda\n",
-    "\n",
-    "There are a few steps:\n",
-    "\n",
-    " 1. Setup the dataset\n",
-    " 2. Setup the PyTorch training loop\n",
-    " 3. Train a dense version of DenseNet121\n",
-    " 4. Run the GMP pruning algorithm on the dense model\n",
-    " \n",
-    "## Installation\n",
-    "\n",
-    "Install SparseML with `pip`:\n",
-    "\n",
-    "```\n",
-    "pip install sparseml[torchvision]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1ad80edf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import sparseml\n",
-    "import torchvision\n",
-    "from sparseml.pytorch.optim import ScheduledModifierManager\n",
-    "from sparseml.pytorch.utils import TensorBoardLogger, ModuleExporter, get_prunable_layers, tensor_sparsity\n",
-    "\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torch.nn import CrossEntropyLoss\n",
-    "from torch.optim import Adam\n",
-    "\n",
-    "from torchvision import transforms\n",
-    "from tqdm.auto import tqdm\n",
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a128dcc",
-   "metadata": {},
-   "source": [
-    "## **Step 1: Setup Dataset**\n",
-    "\n",
-    "Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images. The images have large scale, pose and light variations. In addition, there are categories that have large variations within the category, and several very similar categories.\n",
-    "\n",
-    "We use the standard PyTorch `datasets` and `dataloaders` to manage the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7d180bbc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NUM_LABELS = 102\n",
-    "BATCH_SIZE = 16\n",
-    "\n",
-    "# imagenet transforms\n",
-    "imagenet_transform = transforms.Compose([\n",
-    "   transforms.Resize(size=256, interpolation=transforms.InterpolationMode.BILINEAR, max_size=None, antialias=None),\n",
-    "   transforms.CenterCrop(size=(224, 224)),\n",
-    "   transforms.ToTensor(),\n",
-    "   transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
-    "])\n",
-    "\n",
-    "# datasets\n",
-    "train_dataset = torchvision.datasets.Flowers102(\n",
-    "    root=\"./data\",\n",
-    "    split=\"train\",\n",
-    "    transform=imagenet_transform,\n",
-    "    download=True\n",
-    ")\n",
-    "\n",
-    "val_dataset = torchvision.datasets.Flowers102(\n",
-    "    root=\"./data\",\n",
-    "    split=\"val\",\n",
-    "    transform=imagenet_transform,\n",
-    "    download=True\n",
-    ")\n",
-    "\n",
-    "# dataloaders\n",
-    "train_loader = DataLoader(train_dataset, BATCH_SIZE, shuffle=True, pin_memory=True, num_workers=16)\n",
-    "val_loader = DataLoader(val_dataset, BATCH_SIZE, shuffle=False, pin_memory=True, num_workers=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "890cca4a",
-   "metadata": {},
-   "source": [
-    "## Step 2: Setup PyTorch Training Loop\n",
-    "\n",
-    "We will use this training loop below. This is standard PyTorch functionality."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5f1b878a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cuda\n"
-     ]
-    }
-   ],
-   "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "print(device)\n",
-    "\n",
-    "def run_model_one_epoch(model, data_loader, criterion, device, train=False, optimizer=None):\n",
-    "    if train:\n",
-    "        model.train()\n",
-    "    else:\n",
-    "        model.eval()\n",
-    "\n",
-    "    running_loss = 0.0\n",
-    "    total_correct = 0\n",
-    "    total_predictions = 0\n",
-    "\n",
-    "    # loop through batches\n",
-    "    for step, (inputs, labels) in tqdm(enumerate(data_loader), total=len(data_loader)):\n",
-    "        inputs = inputs.to(device)\n",
-    "        labels = labels.to(device)\n",
-    "\n",
-    "        if train:\n",
-    "            optimizer.zero_grad()\n",
-    "\n",
-    "        # compute loss, run backpropogation\n",
-    "        outputs = model(inputs)  # model returns logits\n",
-    "        loss = criterion(outputs, labels)\n",
-    "        if train:\n",
-    "            loss.backward()\n",
-    "            optimizer.step()\n",
-    "\n",
-    "        running_loss += loss.item()\n",
-    "\n",
-    "        # run evaluation\n",
-    "        predictions = outputs.argmax(dim=1)\n",
-    "        total_correct += torch.sum(predictions == labels).item()\n",
-    "        total_predictions += inputs.size(0)\n",
-    "\n",
-    "    # return loss and evaluation metric\n",
-    "    loss = running_loss / (step + 1.0)\n",
-    "    accuracy = total_correct / total_predictions\n",
-    "    return loss, accuracy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b385497a",
-   "metadata": {},
-   "source": [
-    "## **Step 3: Train DenseNet121 on Flowers102**\n",
-    "\n",
-    "First, we will train a dense version of DenseNet121 on the Flowers dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4d554578",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# download pre-trained model, setup classification head\n",
-    "model = torchvision.models.densenet121(weights=torchvision.models.DenseNet121_Weights.DEFAULT)\n",
-    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
-    "model.to(device)\n",
-    "\n",
-    "# setup loss function and optimizer\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3) # lr will be override by sparseml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eb400dc3",
-   "metadata": {
-    "scrolled": true
-   },
-   "source": [
-    "Next, we will use SparseML's recipes to set the hyperparameters of training loop. In this case, we will use the following recipe:\n",
-    "\n",
-    "```yaml\n",
-    "# Epoch and Learning-Rate variables\n",
-    "num_epochs: 10.0\n",
-    "init_lr: 0.0005\n",
-    "\n",
-    "training_modifiers:\n",
-    "  - !EpochRangeModifier\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "  - !LearningRateFunctionModifier\n",
-    "    final_lr: 0.0\n",
-    "    init_lr: eval(init_lr)\n",
-    "    lr_func: cosine\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "```\n",
-    "\n",
-    "As you can see, the recipe includes an `!EpochRangeModifier` and a `!LearningRateFunctionModifier`. These modifiers simply set the number of epochs to train for and the learning rate schedule. As a result, the final model will be dense."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "5f6a53f1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dense_recipe_path = \"./recipes/densenet-flowers-dense-recipe.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8653c9bc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!cat ./recipes/densenet-flowers-dense-recipe.yaml"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c31aed1b",
-   "metadata": {},
-   "source": [
-    "Next, we use SparseML's `ScheduledModifierManager` to parse and apply the recipe. The `manager.modify` function modifies and wraps the `model` and `optimizer` with the instructions from the recipe. You can use the `model` and `optimizer` just like standard PyTorch objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1749e00d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(dense_recipe_path)\n",
-    "optimizer = manager.modify(model, optimizer, steps_per_epoch=len(train_loader))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee67c917",
-   "metadata": {},
-   "source": [
-    "Kick off the transfer learning loop. Our run reached ~91.8% validation accuracy after 10 epochs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d00d175b",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Training Epoch 1/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3bff056f89743c3a030bf38186113dd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 1/10\n",
-      "Training Loss: 3.705597158521414\n",
-      "Top 1 Acc: 0.25392156862745097\n",
-      "\n",
-      "Running Validation Epoch 1/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6979449794948349a2ccc8599f9958d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 1/10\n",
-      "Val Loss: 2.1234971778467298\n",
-      "Top 1 Acc: 0.5637254901960784\n",
-      "\n",
-      "Running Training Epoch 2/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6a116d25fa6a45d193d8203737d4ecfb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 2/10\n",
-      "Training Loss: 1.32985198777169\n",
-      "Top 1 Acc: 0.8382352941176471\n",
-      "\n",
-      "Running Validation Epoch 2/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1181f510948745b69dbd75ff476135c8",
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-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 2/10\n",
-      "Val Loss: 0.956481144297868\n",
-      "Top 1 Acc: 0.8303921568627451\n",
-      "\n",
-      "Running Training Epoch 3/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c130a002f8944a3ea6a3a4c1544c3c7a",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 3/10\n",
-      "Training Loss: 0.4278648835606873\n",
-      "Top 1 Acc: 0.9803921568627451\n",
-      "\n",
-      "Running Validation Epoch 3/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a5cc3447dac84178b3800c1a30325e23",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 3/10\n",
-      "Val Loss: 0.6528619016171433\n",
-      "Top 1 Acc: 0.8578431372549019\n",
-      "\n",
-      "Running Training Epoch 4/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d68ce2162a92413d9a74ae4b8c200a95",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 4/10\n",
-      "Training Loss: 0.15628680447116494\n",
-      "Top 1 Acc: 0.9970588235294118\n",
-      "\n",
-      "Running Validation Epoch 4/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f527bc8cfa6c4222b5703eeeaa2c07e9",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 4/10\n",
-      "Val Loss: 0.5102186386066023\n",
-      "Top 1 Acc: 0.9049019607843137\n",
-      "\n",
-      "Running Training Epoch 5/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d60459fa3c46401baa940ff07489ee04",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 5/10\n",
-      "Training Loss: 0.060788551229052246\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 5/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2b5ab2844704170b08ffe5857d3bf0d",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 5/10\n",
-      "Val Loss: 0.42239774897461757\n",
-      "Top 1 Acc: 0.9107843137254902\n",
-      "\n",
-      "Running Training Epoch 6/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "65c3b9cd9e844ee096d6152cc07146ae",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 6/10\n",
-      "Training Loss: 0.0473741386376787\n",
-      "Top 1 Acc: 0.9990196078431373\n",
-      "\n",
-      "Running Validation Epoch 6/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ee91c900ebb419c8da0edb371d73dda",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 6/10\n",
-      "Val Loss: 0.40489213640103117\n",
-      "Top 1 Acc: 0.9098039215686274\n",
-      "\n",
-      "Running Training Epoch 7/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c29b7b84bbaa42629bd39eb7b233f111",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 7/10\n",
-      "Training Loss: 0.03090776341559831\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 7/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8f37fdd27b647f0a8b00a987906e355",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 7/10\n",
-      "Val Loss: 0.39284458926704247\n",
-      "Top 1 Acc: 0.9166666666666666\n",
-      "\n",
-      "Running Training Epoch 8/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f09644d377b843d6969b52284d3b80f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 8/10\n",
-      "Training Loss: 0.027710435475455597\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 8/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1f0d5025929b4cc2837a4e4b27e969ad",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 8/10\n",
-      "Val Loss: 0.3832304854586255\n",
-      "Top 1 Acc: 0.9176470588235294\n",
-      "\n",
-      "Running Training Epoch 9/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "81aee6700e444b8cb6cb99d31c69980a",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 9/10\n",
-      "Training Loss: 0.026527045309194364\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 9/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c0e66e476ac049f487204671bb3c41ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 9/10\n",
-      "Val Loss: 0.36842548519780394\n",
-      "Top 1 Acc: 0.9186274509803921\n",
-      "\n",
-      "Running Training Epoch 10/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "756a007a3b02480b8872b65fb88277ab",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 10/10\n",
-      "Training Loss: 0.025542080067680217\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 10/10\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "affa32f0824e4f77a830bf1392c8b3cb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 10/10\n",
-      "Val Loss: 0.3675568748876685\n",
-      "Top 1 Acc: 0.9186274509803921\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "epoch = 0\n",
-    "for epoch in range(manager.max_epochs):\n",
-    "    # run training loop\n",
-    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
-    "    \n",
-    "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
-    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
-    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
-    "\n",
-    "# clean up\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1eb3d14e",
-   "metadata": {},
-   "source": [
-    "Export the model in case we want to reload in the future, so we do not have to rerun."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11fa4cf2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_dir = \"densenet-models\"\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"dense-model.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "4532442e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8a7d6309",
-   "metadata": {},
-   "source": [
-    "## Step 4: Prune The Model\n",
-    "\n",
-    "With a model trained on Flowers, we are now ready to apply the GMP algorithm to prune the model. The GMP algorithm is an interative pruning algorithm. At the end of each epoch, we identify the lowest magnitude weights (those closest to 0) and remove them from the network starting from an initial level of sparsity until a final level of sparsity. The remaining nonzero weights are then fine-tuned onto training dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8447d074",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# first, load the trained model from Part 3\n",
-    "checkpoint = torch.load(\"./densenet-models/training/dense-model.pth\")\n",
-    "model = torchvision.models.densenet121()\n",
-    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
-    "model.load_state_dict(checkpoint['state_dict'])\n",
-    "model.to(device)\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7a05d884",
-   "metadata": {},
-   "source": [
-    "Next, we need to create a SparseML recipe which includes the GMP algorithm. The `!GlobalMagnitudePruningModifier` modifier instructs SparseML to apply the GMP algorithm at a global level (pruning the lowest magnitude weights across all layers).\n",
-    "\n",
-    "Firstly, we need to decide identify which parameters of the model to apply the GMP algorithm to. We can use the `get_prunable_layers` function to inspect:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "d367905a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "features.conv0\n",
-      "features.denseblock1.denselayer1.conv1\n",
-      "features.denseblock1.denselayer1.conv2\n",
-      "features.denseblock1.denselayer2.conv1\n",
-      "features.denseblock1.denselayer2.conv2\n",
-      "features.denseblock1.denselayer3.conv1\n",
-      "features.denseblock1.denselayer3.conv2\n",
-      "features.denseblock1.denselayer4.conv1\n",
-      "features.denseblock1.denselayer4.conv2\n",
-      "features.denseblock1.denselayer5.conv1\n",
-      "features.denseblock1.denselayer5.conv2\n",
-      "features.denseblock1.denselayer6.conv1\n",
-      "features.denseblock1.denselayer6.conv2\n",
-      "features.transition1.conv\n",
-      "features.denseblock2.denselayer1.conv1\n",
-      "features.denseblock2.denselayer1.conv2\n",
-      "features.denseblock2.denselayer2.conv1\n",
-      "features.denseblock2.denselayer2.conv2\n",
-      "features.denseblock2.denselayer3.conv1\n",
-      "features.denseblock2.denselayer3.conv2\n",
-      "features.denseblock2.denselayer4.conv1\n",
-      "features.denseblock2.denselayer4.conv2\n",
-      "features.denseblock2.denselayer5.conv1\n",
-      "features.denseblock2.denselayer5.conv2\n",
-      "features.denseblock2.denselayer6.conv1\n",
-      "features.denseblock2.denselayer6.conv2\n",
-      "features.denseblock2.denselayer7.conv1\n",
-      "features.denseblock2.denselayer7.conv2\n",
-      "features.denseblock2.denselayer8.conv1\n",
-      "features.denseblock2.denselayer8.conv2\n",
-      "features.denseblock2.denselayer9.conv1\n",
-      "features.denseblock2.denselayer9.conv2\n",
-      "features.denseblock2.denselayer10.conv1\n",
-      "features.denseblock2.denselayer10.conv2\n",
-      "features.denseblock2.denselayer11.conv1\n",
-      "features.denseblock2.denselayer11.conv2\n",
-      "features.denseblock2.denselayer12.conv1\n",
-      "features.denseblock2.denselayer12.conv2\n",
-      "features.transition2.conv\n",
-      "features.denseblock3.denselayer1.conv1\n",
-      "features.denseblock3.denselayer1.conv2\n",
-      "features.denseblock3.denselayer2.conv1\n",
-      "features.denseblock3.denselayer2.conv2\n",
-      "features.denseblock3.denselayer3.conv1\n",
-      "features.denseblock3.denselayer3.conv2\n",
-      "features.denseblock3.denselayer4.conv1\n",
-      "features.denseblock3.denselayer4.conv2\n",
-      "features.denseblock3.denselayer5.conv1\n",
-      "features.denseblock3.denselayer5.conv2\n",
-      "features.denseblock3.denselayer6.conv1\n",
-      "features.denseblock3.denselayer6.conv2\n",
-      "features.denseblock3.denselayer7.conv1\n",
-      "features.denseblock3.denselayer7.conv2\n",
-      "features.denseblock3.denselayer8.conv1\n",
-      "features.denseblock3.denselayer8.conv2\n",
-      "features.denseblock3.denselayer9.conv1\n",
-      "features.denseblock3.denselayer9.conv2\n",
-      "features.denseblock3.denselayer10.conv1\n",
-      "features.denseblock3.denselayer10.conv2\n",
-      "features.denseblock3.denselayer11.conv1\n",
-      "features.denseblock3.denselayer11.conv2\n",
-      "features.denseblock3.denselayer12.conv1\n",
-      "features.denseblock3.denselayer12.conv2\n",
-      "features.denseblock3.denselayer13.conv1\n",
-      "features.denseblock3.denselayer13.conv2\n",
-      "features.denseblock3.denselayer14.conv1\n",
-      "features.denseblock3.denselayer14.conv2\n",
-      "features.denseblock3.denselayer15.conv1\n",
-      "features.denseblock3.denselayer15.conv2\n",
-      "features.denseblock3.denselayer16.conv1\n",
-      "features.denseblock3.denselayer16.conv2\n",
-      "features.denseblock3.denselayer17.conv1\n",
-      "features.denseblock3.denselayer17.conv2\n",
-      "features.denseblock3.denselayer18.conv1\n",
-      "features.denseblock3.denselayer18.conv2\n",
-      "features.denseblock3.denselayer19.conv1\n",
-      "features.denseblock3.denselayer19.conv2\n",
-      "features.denseblock3.denselayer20.conv1\n",
-      "features.denseblock3.denselayer20.conv2\n",
-      "features.denseblock3.denselayer21.conv1\n",
-      "features.denseblock3.denselayer21.conv2\n",
-      "features.denseblock3.denselayer22.conv1\n",
-      "features.denseblock3.denselayer22.conv2\n",
-      "features.denseblock3.denselayer23.conv1\n",
-      "features.denseblock3.denselayer23.conv2\n",
-      "features.denseblock3.denselayer24.conv1\n",
-      "features.denseblock3.denselayer24.conv2\n",
-      "features.transition3.conv\n",
-      "features.denseblock4.denselayer1.conv1\n",
-      "features.denseblock4.denselayer1.conv2\n",
-      "features.denseblock4.denselayer2.conv1\n",
-      "features.denseblock4.denselayer2.conv2\n",
-      "features.denseblock4.denselayer3.conv1\n",
-      "features.denseblock4.denselayer3.conv2\n",
-      "features.denseblock4.denselayer4.conv1\n",
-      "features.denseblock4.denselayer4.conv2\n",
-      "features.denseblock4.denselayer5.conv1\n",
-      "features.denseblock4.denselayer5.conv2\n",
-      "features.denseblock4.denselayer6.conv1\n",
-      "features.denseblock4.denselayer6.conv2\n",
-      "features.denseblock4.denselayer7.conv1\n",
-      "features.denseblock4.denselayer7.conv2\n",
-      "features.denseblock4.denselayer8.conv1\n",
-      "features.denseblock4.denselayer8.conv2\n",
-      "features.denseblock4.denselayer9.conv1\n",
-      "features.denseblock4.denselayer9.conv2\n",
-      "features.denseblock4.denselayer10.conv1\n",
-      "features.denseblock4.denselayer10.conv2\n",
-      "features.denseblock4.denselayer11.conv1\n",
-      "features.denseblock4.denselayer11.conv2\n",
-      "features.denseblock4.denselayer12.conv1\n",
-      "features.denseblock4.denselayer12.conv2\n",
-      "features.denseblock4.denselayer13.conv1\n",
-      "features.denseblock4.denselayer13.conv2\n",
-      "features.denseblock4.denselayer14.conv1\n",
-      "features.denseblock4.denselayer14.conv2\n",
-      "features.denseblock4.denselayer15.conv1\n",
-      "features.denseblock4.denselayer15.conv2\n",
-      "features.denseblock4.denselayer16.conv1\n",
-      "features.denseblock4.denselayer16.conv2\n",
-      "classifier\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print parameters\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "322fa136",
-   "metadata": {},
-   "source": [
-    "We will apply pruning to each of the `convs` and exclude the `classifier` (which is the final projection head). Fortunately, SparseML allows us to pass regexes to identify layers in the network.\n",
-    "\n",
-    "Here is what the recipe looks like:\n",
-    "\n",
-    "```yaml\n",
-    "# Epoch and Learning-Rate variables\n",
-    "num_epochs: 13.0\n",
-    "pruning_epochs: 10.0\n",
-    "init_lr: 0.0005\n",
-    "final_lr: 0.0001\n",
-    "inter_func: cubic\n",
-    "mask_type: unstructured\n",
-    "\n",
-    "training_modifiers:\n",
-    "  - !EpochRangeModifier\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "  - !LearningRateFunctionModifier\n",
-    "    final_lr: eval(final_lr)\n",
-    "    init_lr: eval(init_lr)\n",
-    "    lr_func: cosine\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(pruning_epochs)\n",
-    "    \n",
-    "  - !LearningRateFunctionModifier\n",
-    "    final_lr: eval(final_lr)\n",
-    "    init_lr: eval(init_lr)\n",
-    "    lr_func: cosine\n",
-    "    start_epoch: eval(pruning_epochs)\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "# Pruning\n",
-    "pruning_modifiers:\n",
-    "  - !GlobalMagnitudePruningModifier\n",
-    "    init_sparsity: 0.05\n",
-    "    final_sparsity: 0.9\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(pruning_epochs)\n",
-    "    update_frequency: 0.5\n",
-    "    params: \n",
-    "        - 'features.conv0.weight'\n",
-    "        - 're:features.denseblock1.*.conv1.weight'\n",
-    "        - 're:features.denseblock1.*.conv2.weight'\n",
-    "        - 're:features.transition1.conv.weight'\n",
-    "        - 're:features.denseblock2.*.conv1.weight'\n",
-    "        - 're:features.denseblock2.*.conv2.weight'\n",
-    "        - 're:features.transition2.conv.weight'\n",
-    "        - 're:features.denseblock3.*.conv1.weight'\n",
-    "        - 're:features.denseblock3.*.conv2.weight'\n",
-    "        - 're:features.transition3.conv.weight'\n",
-    "        - 're:features.denseblock4.*.conv1.weight'\n",
-    "        - 're:features.denseblock4.*.conv2.weight'\n",
-    "    leave_enabled: True\n",
-    "    inter_func: eval(inter_func)\n",
-    "    mask_type: eval(mask_type)\n",
-    "```\n",
-    "\n",
-    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an `init_sparsity` level of 5% and gradually increase sparsity to a `final_sparsity` level of 90% following a `cubic` curve across each of the `convs` in the network. The pruning is applied in an `unstructured` manner, meaning that any weight can be pruned in any pattern.\n",
-    "\n",
-    "Over the final 3 epochs, we fine-tune the 90% pruned model further. Since we set `leave_enabled=True` the sparsity level will be maintained as the fine-tuning occurs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "b8518b39",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pruning_recipe_path = \"./recipes/densenet-flowers-pruning-recipe.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9b17fc31",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "!cat ./recipes/densenet-flowers-pruning-recipe.yaml"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "68646bc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(pruning_recipe_path)\n",
-    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs/densenet/pruning-run\")\n",
-    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da0a5909",
-   "metadata": {},
-   "source": [
-    "Next, kick off the GMP training loop. \n",
-    "\n",
-    "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to use the `optimizer` and `model` as usual, with all of the pruning-related logic handled by SparseML.\n",
-    "\n",
-    "Our 90% pruned model reaches ~91.8% validation accuracy (vs ~91.9% for the dense model)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "601c8c21",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Training Epoch 1/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5790f1d7b4ae4329af773cc7a7537fe2",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 1/13\n",
-      "Training Loss: 0.22910855180816725\n",
-      "Top 1 Acc: 0.9735294117647059\n",
-      "\n",
-      "Running Validation Epoch 1/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb1be77737da4057a7553cc3546c5092",
-       "version_major": 2,
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 1/13\n",
-      "Val Loss: 0.9752587381517515\n",
-      "Top 1 Acc: 0.7333333333333333\n",
-      "\n",
-      "Running Training Epoch 2/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b7c7a74efde14715a4a7148cbc748a67",
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-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 2/13\n",
-      "Training Loss: 0.1926842465181835\n",
-      "Top 1 Acc: 0.9676470588235294\n",
-      "\n",
-      "Running Validation Epoch 2/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b841d8f660e415781a589f4c2537c6c",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 2/13\n",
-      "Val Loss: 0.5537524847895838\n",
-      "Top 1 Acc: 0.85\n",
-      "\n",
-      "Running Training Epoch 3/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b04f316fb1b480bb9e53ba6745aca99",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 3/13\n",
-      "Training Loss: 0.0884120583650656\n",
-      "Top 1 Acc: 0.9882352941176471\n",
-      "\n",
-      "Running Validation Epoch 3/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4db527bfb864464a818217a04dc6b9c3",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 3/13\n",
-      "Val Loss: 0.458682878066611\n",
-      "Top 1 Acc: 0.8764705882352941\n",
-      "\n",
-      "Running Training Epoch 4/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9715e9c734da4f598febbe6f6115f3f4",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 4/13\n",
-      "Training Loss: 0.07008300906454679\n",
-      "Top 1 Acc: 0.9941176470588236\n",
-      "\n",
-      "Running Validation Epoch 4/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "10fca0f38b4d486eb37fbe85151eae00",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 4/13\n",
-      "Val Loss: 0.4294259559828788\n",
-      "Top 1 Acc: 0.8872549019607843\n",
-      "\n",
-      "Running Training Epoch 5/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50143e51efae42bdb8e27f2a103f187c",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 5/13\n",
-      "Training Loss: 0.058840728845098056\n",
-      "Top 1 Acc: 0.9941176470588236\n",
-      "\n",
-      "Running Validation Epoch 5/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "612376311bfd4f47805fd0a943d5da8e",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 5/13\n",
-      "Val Loss: 0.4169776111084502\n",
-      "Top 1 Acc: 0.8970588235294118\n",
-      "\n",
-      "Running Training Epoch 6/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c2b54036b759447a9f11b1d38b85be1d",
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 6/13\n",
-      "Training Loss: 0.06827072701707948\n",
-      "Top 1 Acc: 0.9931372549019608\n",
-      "\n",
-      "Running Validation Epoch 6/13\n"
-     ]
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-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b944f8a61c241df9c9b81cd97fda4e3",
-       "version_major": 2,
-       "version_minor": 0
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 6/13\n",
-      "Val Loss: 0.4382545033295173\n",
-      "Top 1 Acc: 0.8921568627450981\n",
-      "\n",
-      "Running Training Epoch 7/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "637f8d0ce14943cabdab66efe3755b21",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 7/13\n",
-      "Training Loss: 0.07268102490343153\n",
-      "Top 1 Acc: 0.9921568627450981\n",
-      "\n",
-      "Running Validation Epoch 7/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e80a8bd36ef14a20983353949775e5a4",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 7/13\n",
-      "Val Loss: 0.4186342091416009\n",
-      "Top 1 Acc: 0.8941176470588236\n",
-      "\n",
-      "Running Training Epoch 8/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef52b8ed50b746409cfe942bf1043cdd",
-       "version_major": 2,
-       "version_minor": 0
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 8/13\n",
-      "Training Loss: 0.062260200167656876\n",
-      "Top 1 Acc: 0.9950980392156863\n",
-      "\n",
-      "Running Validation Epoch 8/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3998fd77d6ae4769b9e829f6b7303a75",
-       "version_major": 2,
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 8/13\n",
-      "Val Loss: 0.4408616948639974\n",
-      "Top 1 Acc: 0.8872549019607843\n",
-      "\n",
-      "Running Training Epoch 9/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec575690c5074b558e9ca9cc9b00b91c",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 9/13\n",
-      "Training Loss: 0.0411255351791624\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 9/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "91c6e3618a1c4d3fa6c19768dd6d2bdb",
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 9/13\n",
-      "Val Loss: 0.4180755140114343\n",
-      "Top 1 Acc: 0.9019607843137255\n",
-      "\n",
-      "Running Training Epoch 10/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "781584fe5b2d4d5ea951292b374846e5",
-       "version_major": 2,
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 10/13\n",
-      "Training Loss: 0.027908188028959557\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 10/13\n"
-     ]
-    },
-    {
-     "data": {
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 10/13\n",
-      "Val Loss: 0.40239793025830295\n",
-      "Top 1 Acc: 0.8980392156862745\n",
-      "\n",
-      "Running Training Epoch 11/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "caf1167eb6d04a2e87614e712cee2815",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 11/13\n",
-      "Training Loss: 0.02035851358959917\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 11/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0b54ca171d254220b64003390124cde4",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 11/13\n",
-      "Val Loss: 0.3589820931883878\n",
-      "Top 1 Acc: 0.9088235294117647\n",
-      "\n",
-      "Running Training Epoch 12/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f60ba49298345a48864fa001c26b8ce",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 12/13\n",
-      "Training Loss: 0.012050284800352529\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 12/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b98b4757b144440b63af638152e34c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 12/13\n",
-      "Val Loss: 0.35020815586904064\n",
-      "Top 1 Acc: 0.9127450980392157\n",
-      "\n",
-      "Running Training Epoch 13/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e242702241c045308936b241588e3fd4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 13/13\n",
-      "Training Loss: 0.007597094325319631\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 13/13\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80fa6f95af8a43b8bdbeffaa502061e1",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 13/13\n",
-      "Val Loss: 0.3375227072065172\n",
-      "Top 1 Acc: 0.9186274509803921\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# run GMP algorithm\n",
-    "epoch = 0\n",
-    "for epoch in range(manager.max_epochs):\n",
-    "    # run training loop\n",
-    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
-    "    \n",
-    "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
-    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
-    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
-    "    \n",
-    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22c96232",
-   "metadata": {},
-   "source": [
-    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
-    "\n",
-    "![tensorboard output](./images/densenet-flowers-tensorboard-output.png)\n",
-    "\n",
-    "The resulting model is is 90% sparse and achieves validation accuracy of ~91.8% (vs the unoptimized dense model at ~92.5%) without much hyperparameter search. Key hyperparameter experiments you may want to run include:\n",
-    "- Learning rate\n",
-    "- Learning rate schedule\n",
-    "- Sparsity level\n",
-    "- Number of pruning epochs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "df5afa94",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sparsity By Layer:\n",
-      "features.conv0.weight: 0.4737\n",
-      "features.denseblock1.denselayer1.conv1.weight: 0.7423\n",
-      "features.denseblock1.denselayer1.conv2.weight: 0.8321\n",
-      "features.denseblock1.denselayer2.conv1.weight: 0.7501\n",
-      "features.denseblock1.denselayer2.conv2.weight: 0.8680\n",
-      "features.denseblock1.denselayer3.conv1.weight: 0.7543\n",
-      "features.denseblock1.denselayer3.conv2.weight: 0.8259\n",
-      "features.denseblock1.denselayer4.conv1.weight: 0.8161\n",
-      "features.denseblock1.denselayer4.conv2.weight: 0.8302\n",
-      "features.denseblock1.denselayer5.conv1.weight: 0.8723\n",
-      "features.denseblock1.denselayer5.conv2.weight: 0.8858\n",
-      "features.denseblock1.denselayer6.conv1.weight: 0.8428\n",
-      "features.denseblock1.denselayer6.conv2.weight: 0.8443\n",
-      "features.transition1.conv.weight: 0.7129\n",
-      "features.denseblock2.denselayer1.conv1.weight: 0.9314\n",
-      "features.denseblock2.denselayer1.conv2.weight: 0.8886\n",
-      "features.denseblock2.denselayer2.conv1.weight: 0.8830\n",
-      "features.denseblock2.denselayer2.conv2.weight: 0.8611\n",
-      "features.denseblock2.denselayer3.conv1.weight: 0.8865\n",
-      "features.denseblock2.denselayer3.conv2.weight: 0.8476\n",
-      "features.denseblock2.denselayer4.conv1.weight: 0.8632\n",
-      "features.denseblock2.denselayer4.conv2.weight: 0.8669\n",
-      "features.denseblock2.denselayer5.conv1.weight: 0.8920\n",
-      "features.denseblock2.denselayer5.conv2.weight: 0.8509\n",
-      "features.denseblock2.denselayer6.conv1.weight: 0.9111\n",
-      "features.denseblock2.denselayer6.conv2.weight: 0.8686\n",
-      "features.denseblock2.denselayer7.conv1.weight: 0.8802\n",
-      "features.denseblock2.denselayer7.conv2.weight: 0.8758\n",
-      "features.denseblock2.denselayer8.conv1.weight: 0.8886\n",
-      "features.denseblock2.denselayer8.conv2.weight: 0.8620\n",
-      "features.denseblock2.denselayer9.conv1.weight: 0.9019\n",
-      "features.denseblock2.denselayer9.conv2.weight: 0.8794\n",
-      "features.denseblock2.denselayer10.conv1.weight: 0.8742\n",
-      "features.denseblock2.denselayer10.conv2.weight: 0.8851\n",
-      "features.denseblock2.denselayer11.conv1.weight: 0.8860\n",
-      "features.denseblock2.denselayer11.conv2.weight: 0.8894\n",
-      "features.denseblock2.denselayer12.conv1.weight: 0.8729\n",
-      "features.denseblock2.denselayer12.conv2.weight: 0.9024\n",
-      "features.transition2.conv.weight: 0.7744\n",
-      "features.denseblock3.denselayer1.conv1.weight: 0.9163\n",
-      "features.denseblock3.denselayer1.conv2.weight: 0.8984\n",
-      "features.denseblock3.denselayer2.conv1.weight: 0.9366\n",
-      "features.denseblock3.denselayer2.conv2.weight: 0.8999\n",
-      "features.denseblock3.denselayer3.conv1.weight: 0.9175\n",
-      "features.denseblock3.denselayer3.conv2.weight: 0.9029\n",
-      "features.denseblock3.denselayer4.conv1.weight: 0.9195\n",
-      "features.denseblock3.denselayer4.conv2.weight: 0.8846\n",
-      "features.denseblock3.denselayer5.conv1.weight: 0.9337\n",
-      "features.denseblock3.denselayer5.conv2.weight: 0.8808\n",
-      "features.denseblock3.denselayer6.conv1.weight: 0.9185\n",
-      "features.denseblock3.denselayer6.conv2.weight: 0.8808\n",
-      "features.denseblock3.denselayer7.conv1.weight: 0.9392\n",
-      "features.denseblock3.denselayer7.conv2.weight: 0.8726\n",
-      "features.denseblock3.denselayer8.conv1.weight: 0.9046\n",
-      "features.denseblock3.denselayer8.conv2.weight: 0.8859\n",
-      "features.denseblock3.denselayer9.conv1.weight: 0.9096\n",
-      "features.denseblock3.denselayer9.conv2.weight: 0.8791\n",
-      "features.denseblock3.denselayer10.conv1.weight: 0.9393\n",
-      "features.denseblock3.denselayer10.conv2.weight: 0.8824\n",
-      "features.denseblock3.denselayer11.conv1.weight: 0.9334\n",
-      "features.denseblock3.denselayer11.conv2.weight: 0.8691\n",
-      "features.denseblock3.denselayer12.conv1.weight: 0.9233\n",
-      "features.denseblock3.denselayer12.conv2.weight: 0.8895\n",
-      "features.denseblock3.denselayer13.conv1.weight: 0.9379\n",
-      "features.denseblock3.denselayer13.conv2.weight: 0.8606\n",
-      "features.denseblock3.denselayer14.conv1.weight: 0.9445\n",
-      "features.denseblock3.denselayer14.conv2.weight: 0.8798\n",
-      "features.denseblock3.denselayer15.conv1.weight: 0.9243\n",
-      "features.denseblock3.denselayer15.conv2.weight: 0.8870\n",
-      "features.denseblock3.denselayer16.conv1.weight: 0.9368\n",
-      "features.denseblock3.denselayer16.conv2.weight: 0.9131\n",
-      "features.denseblock3.denselayer17.conv1.weight: 0.9187\n",
-      "features.denseblock3.denselayer17.conv2.weight: 0.9029\n",
-      "features.denseblock3.denselayer18.conv1.weight: 0.9293\n",
-      "features.denseblock3.denselayer18.conv2.weight: 0.8975\n",
-      "features.denseblock3.denselayer19.conv1.weight: 0.9232\n",
-      "features.denseblock3.denselayer19.conv2.weight: 0.8956\n",
-      "features.denseblock3.denselayer20.conv1.weight: 0.9250\n",
-      "features.denseblock3.denselayer20.conv2.weight: 0.9041\n",
-      "features.denseblock3.denselayer21.conv1.weight: 0.9216\n",
-      "features.denseblock3.denselayer21.conv2.weight: 0.8955\n",
-      "features.denseblock3.denselayer22.conv1.weight: 0.9245\n",
-      "features.denseblock3.denselayer22.conv2.weight: 0.8802\n",
-      "features.denseblock3.denselayer23.conv1.weight: 0.9230\n",
-      "features.denseblock3.denselayer23.conv2.weight: 0.9111\n",
-      "features.denseblock3.denselayer24.conv1.weight: 0.9264\n",
-      "features.denseblock3.denselayer24.conv2.weight: 0.9092\n",
-      "features.transition3.conv.weight: 0.8496\n",
-      "features.denseblock4.denselayer1.conv1.weight: 0.8750\n",
-      "features.denseblock4.denselayer1.conv2.weight: 0.9133\n",
-      "features.denseblock4.denselayer2.conv1.weight: 0.8727\n",
-      "features.denseblock4.denselayer2.conv2.weight: 0.9468\n",
-      "features.denseblock4.denselayer3.conv1.weight: 0.8816\n",
-      "features.denseblock4.denselayer3.conv2.weight: 0.9228\n",
-      "features.denseblock4.denselayer4.conv1.weight: 0.9007\n",
-      "features.denseblock4.denselayer4.conv2.weight: 0.9406\n",
-      "features.denseblock4.denselayer5.conv1.weight: 0.9005\n",
-      "features.denseblock4.denselayer5.conv2.weight: 0.9582\n",
-      "features.denseblock4.denselayer6.conv1.weight: 0.8966\n",
-      "features.denseblock4.denselayer6.conv2.weight: 0.9577\n",
-      "features.denseblock4.denselayer7.conv1.weight: 0.9111\n",
-      "features.denseblock4.denselayer7.conv2.weight: 0.9601\n",
-      "features.denseblock4.denselayer8.conv1.weight: 0.9141\n",
-      "features.denseblock4.denselayer8.conv2.weight: 0.9624\n",
-      "features.denseblock4.denselayer9.conv1.weight: 0.9165\n",
-      "features.denseblock4.denselayer9.conv2.weight: 0.9656\n",
-      "features.denseblock4.denselayer10.conv1.weight: 0.9182\n",
-      "features.denseblock4.denselayer10.conv2.weight: 0.9647\n",
-      "features.denseblock4.denselayer11.conv1.weight: 0.9191\n",
-      "features.denseblock4.denselayer11.conv2.weight: 0.9645\n",
-      "features.denseblock4.denselayer12.conv1.weight: 0.9226\n",
-      "features.denseblock4.denselayer12.conv2.weight: 0.9655\n",
-      "features.denseblock4.denselayer13.conv1.weight: 0.9245\n",
-      "features.denseblock4.denselayer13.conv2.weight: 0.9638\n",
-      "features.denseblock4.denselayer14.conv1.weight: 0.9280\n",
-      "features.denseblock4.denselayer14.conv2.weight: 0.9642\n",
-      "features.denseblock4.denselayer15.conv1.weight: 0.9290\n",
-      "features.denseblock4.denselayer15.conv2.weight: 0.9638\n",
-      "features.denseblock4.denselayer16.conv1.weight: 0.9251\n",
-      "features.denseblock4.denselayer16.conv2.weight: 0.9610\n",
-      "classifier.weight: 0.0000\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Sparsity By Layer:\")\n",
-    "for (name, layer) in get_prunable_layers(model):\n",
-    "    print(f\"{name}.weight: {tensor_sparsity(layer.weight).item():.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18fed85b",
-   "metadata": {},
-   "source": [
-    "Finally, export your model to ONNX."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "417ac105",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_dir = \"densenet-models\"\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"densenet-pruned-fp32.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"pruned-fp32-model.onnx\", convert_qat=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c379d623",
-   "metadata": {},
-   "source": [
-    "## Step 5: Quantize the Model\n",
-    "\n",
-    "With a sparse model trained on Flowers, we are now ready to apply the QAT algorithm to quantize the model. Generally, we can run QAT for 5-6 epochs. Quantizing the model together with pruning allows us to gain a speedup when running with DeepSparse."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "11f97312",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "fda0599c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# first, load the trained model from Step 4\n",
-    "checkpoint = torch.load(\"./densenet-models/training/densenet-pruned-fp32.pth\")\n",
-    "model = torchvision.models.densenet121()\n",
-    "model.classifier = torch.nn.Linear(model.classifier.in_features, NUM_LABELS)\n",
-    "model.load_state_dict(checkpoint['state_dict'])\n",
-    "model.to(device)\n",
-    "\n",
-    "# setup loss function and optimizer, LR will be overriden by sparseml\n",
-    "criterion = CrossEntropyLoss()\n",
-    "optimizer = Adam(model.parameters(), lr=8e-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0473fcbd",
-   "metadata": {},
-   "source": [
-    "Next, we will create a Quantization recipe. We will us the `QuantizationModifier` (which instructs SparseML to run QAT) along with the `ConstantPruningModifier` (which instructs SparseML to maintain sparsity).\n",
-    "\n",
-    "Here is what the recipe looks like:\n",
-    "\n",
-    "```yaml\n",
-    "# Epoch and Learning-Rate variables\n",
-    "num_epochs: 6.0\n",
-    "init_lr: 0.0004\n",
-    "\n",
-    "# quantization variables\n",
-    "quantization_epochs: 6.0\n",
-    "\n",
-    "training_modifiers:\n",
-    "  - !EpochRangeModifier\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "  - !LearningRateFunctionModifier\n",
-    "    final_lr: 0.0\n",
-    "    init_lr: eval(init_lr)\n",
-    "    lr_func: cosine\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "# Phase 1 Sparse Transfer Learning / Recovery\n",
-    "sparse_transfer_learning_modifiers:\n",
-    "  - !ConstantPruningModifier\n",
-    "    start_epoch: 0.0\n",
-    "    params: __ALL_PRUNABLE__\n",
-    "\n",
-    "# Phase 2 Apply quantization\n",
-    "sparse_quantized_transfer_learning_modifiers:\n",
-    "  - !QuantizationModifier\n",
-    "    start_epoch: eval(num_epochs - quantization_epochs)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "6d2c1be3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "quantization_recipe_path = \"./recipes/quantization-recipe.yaml\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "6b8a4324",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# create ScheduledModifierManager and Optimizer wrapper\n",
-    "manager = ScheduledModifierManager.from_yaml(quantization_recipe_path)\n",
-    "logger = TensorBoardLogger(log_path=\"./tensorboard_outputs/densenet/quantization-run\")\n",
-    "optimizer = manager.modify(model, optimizer, loggers=[logger], steps_per_epoch=len(train_loader))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "208fc85c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running Training Epoch 1/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ed2d6e3d1ac42369f8a9ea020909d50",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 1/5\n",
-      "Training Loss: 0.020167092072369996\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 1/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8426880f7f1c4c2aa5aa022b0909288d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 1/5\n",
-      "Val Loss: 0.3898960210317455\n",
-      "Top 1 Acc: 0.8980392156862745\n",
-      "\n",
-      "Running Training Epoch 2/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c368eb0488549a0b50aca44c594bbf5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 2/5\n",
-      "Training Loss: 0.011033405537091312\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 2/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03148f10352a4213a5b49f7db26e2b4f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 2/5\n",
-      "Val Loss: 0.381910876960319\n",
-      "Top 1 Acc: 0.8970588235294118\n",
-      "\n",
-      "Running Training Epoch 3/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "73988d5735404cc08795116a2d27e19e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 3/5\n",
-      "Training Loss: 0.006457825023971964\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 3/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "61e8db2842564befb05c6c18582093ff",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 3/5\n",
-      "Val Loss: 0.33566539320781885\n",
-      "Top 1 Acc: 0.9137254901960784\n",
-      "\n",
-      "Running Training Epoch 4/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d548a06770834517ad9db8fdb99a1ae3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 4/5\n",
-      "Training Loss: 0.003771477568989212\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 4/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13afa51fa11b44aaa237067823ae9137",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 4/5\n",
-      "Val Loss: 0.34420355190604823\n",
-      "Top 1 Acc: 0.907843137254902\n",
-      "\n",
-      "Running Training Epoch 5/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "857e1593d5ca457ba73918723f04c008",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Epoch: 5/5\n",
-      "Training Loss: 0.002937028719316004\n",
-      "Top 1 Acc: 1.0\n",
-      "\n",
-      "Running Validation Epoch 5/5\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3be1fd3d62ed487fb2d6bacecebc4160",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/64 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Validation Epoch: 5/5\n",
-      "Val Loss: 0.3459806305090751\n",
-      "Top 1 Acc: 0.9088235294117647\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# run QAT algorithm\n",
-    "epoch = 0\n",
-    "for epoch in range(manager.max_epochs):\n",
-    "    # run training loop\n",
-    "    epoch_name = f\"{epoch + 1}/{manager.max_epochs}\"\n",
-    "    \n",
-    "    print(f\"Running Training Epoch {epoch_name}\")\n",
-    "    train_loss, train_acc = run_model_one_epoch(model, train_loader, criterion, device, train=True, optimizer=optimizer)\n",
-    "    print(f\"Training Epoch: {epoch_name}\\nTraining Loss: {train_loss}\\nTop 1 Acc: {train_acc}\\n\")\n",
-    "\n",
-    "    # run validation loop\n",
-    "    print(f\"Running Validation Epoch {epoch_name}\")\n",
-    "    val_loss, val_acc = run_model_one_epoch(model, val_loader, criterion, device)\n",
-    "    print(f\"Validation Epoch: {epoch_name}\\nVal Loss: {val_loss}\\nTop 1 Acc: {val_acc}\\n\")\n",
-    "    \n",
-    "    logger.log_scalar(\"Metrics/Loss (Train)\", train_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Train)\", train_acc, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Loss (Validation)\", val_loss, epoch)\n",
-    "    logger.log_scalar(\"Metrics/Accuracy (Validation)\", val_acc, epoch)\n",
-    "\n",
-    "manager.finalize(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52ac51e0",
-   "metadata": {},
-   "source": [
-    "The resulting model is is 90% sparse and INT8 quantized and achieves validation accuracy of ~90.9% (vs the unoptimized dense model at ~91.9%) without much hyperparameter search. Key hyperparameter experiments you may want to run for QAT are:\n",
-    "- Learning rate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eec331b7",
-   "metadata": {},
-   "source": [
-    "Finally, export to ONNX:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "87f21bc7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-03-14 23:27:05 sparseml.pytorch.sparsification.quantization.quantize_qat_export INFO     Converted 59 quantizable Conv ops with weight and bias to ConvInteger and Add\n",
-      "2023-03-14 23:27:05 sparseml.pytorch.sparsification.quantization.quantize_qat_export INFO     Converted 1 quantizable Gemm ops with weight and bias to MatMulInteger and Add\n"
-     ]
-    }
-   ],
-   "source": [
-    "save_dir = \"densenet-models\"\n",
-    "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"densenet-pruned-int8.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"pruned-int8-model.onnx\", convert_qat=True)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
deleted file mode 100644
index e5afb1d4499..00000000000
--- a/integrations/torchvision/tutorials/recipes/densenet-flowers-dense-recipe.yaml
+++ /dev/null
@@ -1,16 +0,0 @@
-
-# Epoch and Learning-Rate variables
-num_epochs: 10.0
-init_lr: 0.0005
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-  - !LearningRateFunctionModifier
-    final_lr: 0.0
-    init_lr: eval(init_lr)
-    lr_func: cosine
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
diff --git a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
deleted file mode 100644
index b23986e4d24..00000000000
--- a/integrations/torchvision/tutorials/recipes/densenet-flowers-pruning-recipe.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-# Epoch and Learning-Rate variables
-num_epochs: 13.0
-pruning_epochs: 10.0
-init_lr: 0.0005
-final_lr: 0.0001
-inter_func: cubic
-mask_type: unstructured
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-  - !LearningRateFunctionModifier
-    final_lr: eval(final_lr)
-    init_lr: eval(init_lr)
-    lr_func: cosine
-    start_epoch: 0.0
-    end_epoch: eval(pruning_epochs)
-    
-  - !LearningRateFunctionModifier
-    final_lr: eval(final_lr)
-    init_lr: eval(init_lr)
-    lr_func: cosine
-    start_epoch: eval(pruning_epochs)
-    end_epoch: eval(num_epochs)
-
-# Pruning
-pruning_modifiers:
-  - !GlobalMagnitudePruningModifier
-    init_sparsity: 0.05
-    final_sparsity: 0.9
-    start_epoch: 0.0
-    end_epoch: eval(pruning_epochs)
-    update_frequency: 0.5
-    params: 
-        - 'features.conv0.weight'
-        - 're:features.denseblock1.*.conv1.weight'
-        - 're:features.denseblock1.*.conv2.weight'
-        - 're:features.transition1.conv.weight'
-        - 're:features.denseblock2.*.conv1.weight'
-        - 're:features.denseblock2.*.conv2.weight'
-        - 're:features.transition2.conv.weight'
-        - 're:features.denseblock3.*.conv1.weight'
-        - 're:features.denseblock3.*.conv2.weight'
-        - 're:features.transition3.conv.weight'
-        - 're:features.denseblock4.*.conv1.weight'
-        - 're:features.denseblock4.*.conv2.weight'
-    leave_enabled: True
-    inter_func: eval(inter_func)
-    mask_type: eval(mask_type)
-
-finetuning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: eval(pruning_epochs)
-    params: __ALL_PRUNABLE__
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
index f995f3194af..6c4b9c1f412 100644
--- a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
@@ -1,35 +1,53 @@
-# Epoch and Learning-Rate variables
-num_epochs: 13.0
-pruning_epochs: 10.0
-init_lr: 0.0005
-inter_func: cubic
-mask_type: unstructured
+# Epoch hyperparams
+stabilization_epochs: 1.0
+pruning_epochs: 9.0
+finetuning_epochs: 5.0
 
+# Learning rate hyperparams
+init_lr: 0.0001
+final_lr: 0.00005
+
+# Pruning hyperparams
+init_sparsity: 0.05
+final_sparsity: 0.9
+
+# Stabalization Stage
 training_modifiers:
   - !EpochRangeModifier
     start_epoch: 0.0
-    end_epoch: eval(num_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)
+  
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: eval(init_lr)
 
+# Pruning Stage
+pruning_modifiers:
   - !LearningRateFunctionModifier
-    final_lr: 0.0
     init_lr: eval(init_lr)
+    final_lr: eval(final_lr)
     lr_func: cosine
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-# Pruning
-pruning_modifiers:
+    start_epoch: eval(stabilization_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs)
+    
   - !GlobalMagnitudePruningModifier
-    init_sparsity: 0.05
-    final_sparsity: 0.90
-    start_epoch: 0.0
-    end_epoch: eval(pruning_epochs)
-    update_frequency: 1.0
-    params: 
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(final_sparsity)
+    start_epoch: eval(stabilization_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs)
+    update_frequency: 0.5
+    params:        
         - 'features.0.0.weight'
         - 'features.18.0.weight'
         - 're:features.*.conv.*.weight'
         - 're:features.*.conv.*.*.weight'
     leave_enabled: True
-    inter_func: eval(inter_func)
-    mask_type: eval(mask_type)
\ No newline at end of file
+
+# Finetuning Stage
+finetuning_modifiers:
+  - !LearningRateFunctionModifier
+    init_lr: eval(init_lr)
+    final_lr: eval(final_lr)
+    lr_func: cosine
+    start_epoch: eval(stabilization_epochs + pruning_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml b/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml
deleted file mode 100644
index 6df4f5ff921..00000000000
--- a/integrations/torchvision/tutorials/recipes/quantization-recipe.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-# Epoch and Learning-Rate variables
-num_epochs: 5.0
-init_lr: 0.0004
-
-# quantization variables
-quantization_epochs: 6.0
-
-training_modifiers:
-  - !EpochRangeModifier
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-  - !LearningRateFunctionModifier
-    final_lr: 0.0
-    init_lr: eval(init_lr)
-    lr_func: cosine
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-# Phase 1 Sparse Transfer Learning / Recovery
-sparse_transfer_learning_modifiers:
-  - !ConstantPruningModifier
-    start_epoch: 0.0
-    params: __ALL_PRUNABLE__
-
-# Phase 2 Apply quantization
-sparse_quantized_transfer_learning_modifiers:
-  - !QuantizationModifier
-    start_epoch: eval(num_epochs - quantization_epochs)
\ No newline at end of file
diff --git a/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
index 1bc94036805..bf63be0fd89 100644
--- a/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/resnet50-beans-pruning-recipe.yaml
@@ -1,31 +1,42 @@
-# Epoch and Learning-Rate variables
-num_epochs: 13.0
-pruning_epochs: 10.0
-init_lr: 0.00025
-inter_func: cubic
-mask_type: unstructured
+# Epoch hyperparams
+stabilization_epochs: 1.0
+pruning_epochs: 9.0
+finetuning_epochs: 5.0
 
+# Learning rate hyperparams
+init_lr: 0.0001
+final_lr: 0.00005
+
+# Pruning hyperparams
+init_sparsity: 0.05
+final_sparsity: 0.9
+
+# Stabalization Stage
 training_modifiers:
   - !EpochRangeModifier
     start_epoch: 0.0
-    end_epoch: eval(num_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)
+  
+  - !SetLearningRateModifier
+    start_epoch: 0.0
+    learning_rate: eval(init_lr)
 
+# Pruning Stage
+pruning_modifiers:
   - !LearningRateFunctionModifier
-    final_lr: 0.0
     init_lr: eval(init_lr)
+    final_lr: eval(final_lr)
     lr_func: cosine
-    start_epoch: 0.0
-    end_epoch: eval(num_epochs)
-
-# Pruning
-pruning_modifiers:
-   - !GlobalMagnitudePruningModifier
-     init_sparsity: 0.05
-     final_sparsity: 0.90
-     start_epoch: 0.0
-     end_epoch: eval(pruning_epochs)
-     update_frequency: 1.0
-     params: 
+    start_epoch: eval(stabilization_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs)
+    
+  - !GlobalMagnitudePruningModifier
+    init_sparsity: eval(init_sparsity)
+    final_sparsity: eval(final_sparsity)
+    start_epoch: eval(stabilization_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs)
+    update_frequency: 0.5
+    params:        
         - 'conv1.weight'
         - 're:layer1.*.conv1.weight'
         - 're:layer1.*.conv2.weight'
@@ -43,6 +54,13 @@ pruning_modifiers:
         - 're:layer4.*.conv2.weight'
         - 're:layer4.*.conv3.weight'
         - 're:layer4.0.downsample.0.weight'
-     leave_enabled: True
-     inter_func: eval(inter_func)
-     mask_type: eval(mask_type)
\ No newline at end of file
+    leave_enabled: True
+
+# Finetuning Stage
+finetuning_modifiers:
+  - !LearningRateFunctionModifier
+    init_lr: eval(init_lr)
+    final_lr: eval(final_lr)
+    lr_func: cosine
+    start_epoch: eval(stabilization_epochs + pruning_epochs)
+    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)     
\ No newline at end of file

From 01cb4773aeb1b3bdf0b6ae6e4c11505bd2693c5c Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Thu, 23 Mar 2023 01:04:30 +0000
Subject: [PATCH 26/29] updated rn-50 prune from scratch

---
 ...sparsify-from-scratch-resnet50-beans.ipynb | 581 +++++++++++-------
 .../densenet-flowers-tensorboard-output.png   | Bin 700598 -> 0 bytes
 .../mobilenetv2-beans-tensorboard-output.png  | Bin 161928 -> 0 bytes
 .../resnet-beans-tensorboard-output.png       | Bin 102164 -> 0 bytes
 4 files changed, 348 insertions(+), 233 deletions(-)
 delete mode 100644 integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png
 delete mode 100644 integrations/torchvision/tutorials/images/mobilenetv2-beans-tensorboard-output.png
 delete mode 100644 integrations/torchvision/tutorials/images/resnet-beans-tensorboard-output.png

diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb
index f3726bb5a39..ab133807bec 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb
@@ -20,16 +20,17 @@
     " \n",
     "## Installation\n",
     "\n",
-    "Install SparseML with `pip`:\n",
+    "Install SparseML and `datasets` with `pip`:\n",
     "\n",
     "```\n",
     "pip install sparseml[torchvision]\n",
+    "pip install datasets\n",
     "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "1ad80edf",
    "metadata": {},
    "outputs": [],
@@ -74,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "4115c9d8",
    "metadata": {},
    "outputs": [
@@ -82,8 +83,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train/bean_rust/bean_rust_train.84.jpg\n",
-      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/bean_rust/bean_rust_val.36.jpg\n"
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/7ad2d437b751e134577576a32849c44a9ade89297680ad5f6a64051e2108810b/train/angular_leaf_spot/angular_leaf_spot_train.0.jpg\n",
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/1141f86479bc0bb56c75616d153591cc8299d1ea4edc53bb1ab65edd2c65b240/validation/angular_leaf_spot/angular_leaf_spot_val.0.jpg\n"
      ]
     }
    ],
@@ -94,18 +95,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "00aaf463",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train\"\n",
-    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation\""
+    "train_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/7ad2d437b751e134577576a32849c44a9ade89297680ad5f6a64051e2108810b/train\"\n",
+    "val_path = \"/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/1141f86479bc0bb56c75616d153591cc8299d1ea4edc53bb1ab65edd2c65b240/validation\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "7d180bbc",
    "metadata": {},
    "outputs": [],
@@ -150,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "5f1b878a",
    "metadata": {},
    "outputs": [
@@ -216,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "4d554578",
    "metadata": {},
    "outputs": [],
@@ -271,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "id": "1b19a8d8",
    "metadata": {},
    "outputs": [],
@@ -289,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "id": "1749e00d",
    "metadata": {},
    "outputs": [],
@@ -309,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "id": "d00d175b",
    "metadata": {
     "scrolled": false
@@ -325,7 +326,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "975dfb9440f748c296156a532b0e4cb2",
+       "model_id": "d5b96520239d4d728d23a4f099eb8fd3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -341,8 +342,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 0.4227903384496184\n",
-      "Top 1 Acc: 0.8355899419729207\n",
+      "Training Loss: 0.45206236138063316\n",
+      "Top 1 Acc: 0.8085106382978723\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -350,7 +351,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ac44c8fd5ab41e4945a053976c1ce4f",
+       "model_id": "287feebeb9514f33b58e0cb6a825f672",
        "version_major": 2,
        "version_minor": 0
       },
@@ -366,8 +367,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 0.166859846872588\n",
-      "Top 1 Acc: 0.9398496240601504\n",
+      "Val Loss: 0.8609340712428093\n",
+      "Top 1 Acc: 0.849624060150376\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -375,7 +376,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ac4ab50f4b74673a929620a215c5265",
+       "model_id": "76336e81f4c24749b17ca07b2ce5bd69",
        "version_major": 2,
        "version_minor": 0
       },
@@ -391,8 +392,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 0.06358965676661361\n",
-      "Top 1 Acc: 0.9816247582205029\n",
+      "Training Loss: 0.1085617478717776\n",
+      "Top 1 Acc: 0.9584139264990329\n",
       "\n",
       "Running Validation Epoch 2/10\n"
      ]
@@ -400,7 +401,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fac7b5df3ae64f94bb6ed1b19a795c19",
+       "model_id": "c34d773bae5c48ce96743bd2b807a6f7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -416,8 +417,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 0.6292845177813433\n",
-      "Top 1 Acc: 0.9548872180451128\n",
+      "Val Loss: 0.1460044514387846\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -425,7 +426,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "673576da2a604abdacb88963abddb880",
+       "model_id": "aa655dafb1514e9684fd3d949effd615",
        "version_major": 2,
        "version_minor": 0
       },
@@ -441,8 +442,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.032505514428896064\n",
-      "Top 1 Acc: 0.9893617021276596\n",
+      "Training Loss: 0.07007143213687574\n",
+      "Top 1 Acc: 0.97678916827853\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -450,7 +451,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d988e7a3c164f0a9c8ef194b7100d51",
+       "model_id": "d4c8ea7aa9d04edea382c7f14a03a065",
        "version_major": 2,
        "version_minor": 0
       },
@@ -466,7 +467,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.047614920418709517\n",
+      "Val Loss: 0.027990046694564324\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 4/10\n"
@@ -475,7 +476,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "475850d6ad78458184acaa2a21f5b9db",
+       "model_id": "34da8fe5ac4a48d584517b9861c6965b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -491,8 +492,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.03107739576850744\n",
-      "Top 1 Acc: 0.9903288201160542\n",
+      "Training Loss: 0.017229604699155864\n",
+      "Top 1 Acc: 0.9970986460348162\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -500,7 +501,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec810e59cf784d58956449df7a77779d",
+       "model_id": "1f06331bc3a3451f8220574fa627d139",
        "version_major": 2,
        "version_minor": 0
       },
@@ -516,8 +517,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.038283314109624676\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.024875935167074203\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Training Epoch 5/10\n"
      ]
@@ -525,7 +526,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "60680c7c2e9b4ed281ed4b444ce475a8",
+       "model_id": "ecbbd1253c8c474d9822ca9aa3f32358",
        "version_major": 2,
        "version_minor": 0
       },
@@ -541,8 +542,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.11940328791217111\n",
-      "Top 1 Acc: 0.9941972920696325\n",
+      "Training Loss: 0.0032389334005796734\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 5/10\n"
      ]
@@ -550,7 +551,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b4444fa067714efda9fc5f6eb7354599",
+       "model_id": "9a3cd1a358764830b5b657cc77f2c57c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -566,8 +567,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.04354451159209324\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.020098081634690363\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 6/10\n"
      ]
@@ -575,7 +576,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c49c726e3ce141ea8a4236cc1d1e3eb2",
+       "model_id": "e454370d9ac948c9ac2015297a578ff6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -591,8 +592,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.05448708479183123\n",
-      "Top 1 Acc: 0.9893617021276596\n",
+      "Training Loss: 0.0009401099643368713\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 6/10\n"
      ]
@@ -600,7 +601,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f84aaa08b92b4486ada7d57d2e96338e",
+       "model_id": "e881bba812114c39a09b3341efc5a4ae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -616,8 +617,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.05759213243921598\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Val Loss: 0.013677676247122387\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 7/10\n"
      ]
@@ -625,7 +626,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49a0611edd8740aea4ebe3861df90e64",
+       "model_id": "1bdc58cad336471580d42b9c869f8379",
        "version_major": 2,
        "version_minor": 0
       },
@@ -641,8 +642,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.01337281458408517\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Loss: 0.0007901448304491008\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 7/10\n"
      ]
@@ -650,7 +651,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b13f9fccf7e4bac95d533bff9d40c21",
+       "model_id": "071da6885f4644f4abb9e3c68850ea07",
        "version_major": 2,
        "version_minor": 0
       },
@@ -666,8 +667,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.026064214219028752\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Val Loss: 0.011986067402176559\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -675,7 +676,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5516a804e8714ed2a0a326593304fc31",
+       "model_id": "94495ac2721a4112b6b53b42ac16787c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -691,7 +692,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.0028814816066776126\n",
+      "Training Loss: 0.001196134924505125\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 8/10\n"
@@ -700,7 +701,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36497f568ff9499d823b742bfb627d07",
+       "model_id": "c2a2315f371443c89e8492cadb9b98ae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -716,8 +717,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.026710195777316887\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Val Loss: 0.008087300811894238\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -725,7 +726,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "faaf8be44fc44587b5cb96fa338c8dba",
+       "model_id": "e4cbaef71d224dcab1918a8e68738567",
        "version_major": 2,
        "version_minor": 0
       },
@@ -741,7 +742,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.0022678900106043063\n",
+      "Training Loss: 0.0012429110587725196\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
@@ -750,7 +751,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6e4f22651d6e41cb9d1e98e8cc536314",
+       "model_id": "458f09dd3c004d65b683786268d2bce1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -766,8 +767,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.024938953380721312\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Val Loss: 0.01152486567540715\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -775,7 +776,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f0844eedab5d436e890e55eb5401fe96",
+       "model_id": "bd1541379cdd496b8fd93b8ced597067",
        "version_major": 2,
        "version_minor": 0
       },
@@ -791,8 +792,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.0028374234782805774\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.017825346116082715\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 10/10\n"
      ]
@@ -800,7 +801,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d244e2fc3059410a8f6a01cda2e77417",
+       "model_id": "c952c716ba6d46d9919d29b05fd5b2df",
        "version_major": 2,
        "version_minor": 0
       },
@@ -816,8 +817,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.021825105145884056\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Val Loss: 0.01295137139580523\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n"
      ]
     }
@@ -842,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "11fa4cf2",
    "metadata": {},
    "outputs": [],
@@ -851,13 +852,12 @@
     "\n",
     "save_dir = \"dense_model\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"resnet-50-dense-beans.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
+    "exporter.export_pytorch(name=\"resnet-50-dense-beans.pth\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "1617f476",
    "metadata": {},
    "outputs": [],
@@ -877,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
@@ -907,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "8950185f",
    "metadata": {
     "scrolled": false
@@ -1008,34 +1008,45 @@
     "Here is what the recipe looks like:\n",
     "\n",
     "```yaml\n",
-    "# Epoch and Learning-Rate variables\n",
-    "num_epochs: 13.0\n",
-    "pruning_epochs: 10.0\n",
-    "init_lr: 0.00025\n",
-    "inter_func: cubic\n",
-    "mask_type: unstructured\n",
+    "# Epoch hyperparams\n",
+    "stabilization_epochs: 1.0\n",
+    "pruning_epochs: 9.0\n",
+    "finetuning_epochs: 5.0\n",
     "\n",
+    "# Learning rate hyperparams\n",
+    "init_lr: 0.0001\n",
+    "final_lr: 0.00005\n",
+    "\n",
+    "# Pruning hyperparams\n",
+    "init_sparsity: 0.05\n",
+    "final_sparsity: 0.9\n",
+    "\n",
+    "# Stabalization Stage\n",
     "training_modifiers:\n",
     "  - !EpochRangeModifier\n",
     "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)\n",
+    "  \n",
+    "  - !SetLearningRateModifier\n",
+    "    start_epoch: 0.0\n",
+    "    learning_rate: eval(init_lr)\n",
     "\n",
+    "# Pruning Stage\n",
+    "pruning_modifiers:\n",
     "  - !LearningRateFunctionModifier\n",
-    "    final_lr: 0.0\n",
     "    init_lr: eval(init_lr)\n",
+    "    final_lr: eval(final_lr)\n",
     "    lr_func: cosine\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "# Pruning\n",
-    "pruning_modifiers:\n",
-    "   - !GlobalMagnitudePruningModifier\n",
-    "     init_sparsity: 0.05\n",
-    "     final_sparsity: 0.90\n",
-    "     start_epoch: 0.0\n",
-    "     end_epoch: eval(pruning_epochs)\n",
-    "     update_frequency: 1.0\n",
-    "     params: \n",
+    "    start_epoch: eval(stabilization_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    \n",
+    "  - !GlobalMagnitudePruningModifier\n",
+    "    init_sparsity: eval(init_sparsity)\n",
+    "    final_sparsity: eval(final_sparsity)\n",
+    "    start_epoch: eval(stabilization_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    update_frequency: 0.5\n",
+    "    params:        \n",
     "        - 'conv1.weight'\n",
     "        - 're:layer1.*.conv1.weight'\n",
     "        - 're:layer1.*.conv2.weight'\n",
@@ -1053,14 +1064,21 @@
     "        - 're:layer4.*.conv2.weight'\n",
     "        - 're:layer4.*.conv3.weight'\n",
     "        - 're:layer4.0.downsample.0.weight'\n",
-    "     leave_enabled: True\n",
-    "     inter_func: eval(inter_func)\n",
-    "     mask_type: eval(mask_type)\n",
+    "    leave_enabled: True\n",
+    "\n",
+    "# Finetuning Stage\n",
+    "finetuning_modifiers:\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    init_lr: eval(init_lr)\n",
+    "    final_lr: eval(final_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs) \n",
     "```\n",
     "\n",
-    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
+    "This recipe specifies that we will run the GMP algorithm for 9 epochs after running one warmup epoch. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
     "\n",
-    "Over the final 3 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
+    "Over the final 5 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
    ]
   },
   {
@@ -1075,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "9cc0e8ab",
    "metadata": {},
    "outputs": [],
@@ -1085,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "68646bc7",
    "metadata": {},
    "outputs": [],
@@ -1110,7 +1128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "601c8c21",
    "metadata": {
     "scrolled": false
@@ -1120,13 +1138,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running Training Epoch 1/13\n"
+      "Running Training Epoch 1/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a687385d2d3244c189ffac1e58c63f4b",
+       "model_id": "1c7ce2fc1cfd45979ada8fa0b6fcf3e6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1141,17 +1159,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 1/13\n",
-      "Training Loss: 0.016460627817329678\n",
-      "Top 1 Acc: 0.995164410058027\n",
+      "Training Epoch: 1/15\n",
+      "Training Loss: 0.00033969324495581747\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 1/13\n"
+      "Running Validation Epoch 1/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f75f7df5e4dd444c990312a33b5e55dd",
+       "model_id": "e3aef9e2789941b0ae700fde6e266376",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1166,17 +1184,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 1/13\n",
-      "Val Loss: 0.025849940720945597\n",
+      "Validation Epoch: 1/15\n",
+      "Val Loss: 0.013618851000501309\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
-      "Running Training Epoch 2/13\n"
+      "Running Training Epoch 2/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b9800318d914258896ddf34e4a34c54",
+       "model_id": "fffd99128b11403f9b57011320732c16",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1191,17 +1209,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 2/13\n",
-      "Training Loss: 0.009291439395918347\n",
-      "Top 1 Acc: 0.9970986460348162\n",
+      "Training Epoch: 2/15\n",
+      "Training Loss: 9.199756892913309e-05\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 2/13\n"
+      "Running Validation Epoch 2/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "895ac43d40ba4fd0a4f52caaa6e97dcb",
+       "model_id": "e328d77edeb4431e90201713fdb617dd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1216,17 +1234,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 2/13\n",
-      "Val Loss: 0.02041603586015602\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 2/15\n",
+      "Val Loss: 0.025973695794164087\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
-      "Running Training Epoch 3/13\n"
+      "Running Training Epoch 3/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1ac0de20101d4e94888bc5c25dc36203",
+       "model_id": "2e68e294bf494959be7e44222cfca3ae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1241,17 +1259,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 3/13\n",
-      "Training Loss: 0.012328761325026041\n",
-      "Top 1 Acc: 0.9941972920696325\n",
+      "Training Epoch: 3/15\n",
+      "Training Loss: 0.0006384848421235159\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 3/13\n"
+      "Running Validation Epoch 3/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28fd64b3fe6343beb2d66230be57f937",
+       "model_id": "13a6a67a5ccf49468cc98169c5b425a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1266,17 +1284,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 3/13\n",
-      "Val Loss: 0.010778552386909723\n",
+      "Validation Epoch: 3/15\n",
+      "Val Loss: 0.0031044411274100034\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 4/13\n"
+      "Running Training Epoch 4/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c3382a6ee094132b0dce1a8be81d09c",
+       "model_id": "db7f16560b2044a5b7bf4b99684ccaba",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1291,17 +1309,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 4/13\n",
-      "Training Loss: 0.006804226791513536\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Epoch: 4/15\n",
+      "Training Loss: 0.0006065369069641319\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 4/13\n"
+      "Running Validation Epoch 4/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6787b4d7046f437a917554555849a56e",
+       "model_id": "a663816b9381480eb5f924efd32c8fd3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1316,17 +1334,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 4/13\n",
-      "Val Loss: 0.006039845136304696\n",
+      "Validation Epoch: 4/15\n",
+      "Val Loss: 0.002252104241051711\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 5/13\n"
+      "Running Training Epoch 5/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb378f0699fa4a09b849c195d4575e65",
+       "model_id": "18df31a72edb49e4a1f1c747589bdac3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1341,17 +1359,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 5/13\n",
-      "Training Loss: 0.012682446326567409\n",
-      "Top 1 Acc: 0.9980657640232108\n",
+      "Training Epoch: 5/15\n",
+      "Training Loss: 0.031195201322963627\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 5/13\n"
+      "Running Validation Epoch 5/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0bf4e5a11b234dcaacb8b08a570b9e17",
+       "model_id": "8bc095e5df214848a718235df4717e74",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1366,17 +1384,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 5/13\n",
-      "Val Loss: 0.00994978565722704\n",
+      "Validation Epoch: 5/15\n",
+      "Val Loss: 0.01123013386192421\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 6/13\n"
+      "Running Training Epoch 6/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc4c1ed11e5a4f4f80904b210ce2fce6",
+       "model_id": "1d08639063644773b61b000c5b1f2efe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1391,17 +1409,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 6/13\n",
-      "Training Loss: 0.0117055864265079\n",
+      "Training Epoch: 6/15\n",
+      "Training Loss: 0.010756760572001119\n",
       "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 6/13\n"
+      "Running Validation Epoch 6/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a8867c5c5759450bbe91369534e73e1d",
+       "model_id": "68c1be15dc7d4af68ec6c19153f692f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1416,17 +1434,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 6/13\n",
-      "Val Loss: 0.01689507276751101\n",
+      "Validation Epoch: 6/15\n",
+      "Val Loss: 0.07213001201550166\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "Running Training Epoch 7/13\n"
+      "Running Training Epoch 7/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "728e29c381be4ffe95fa4e2607aaead2",
+       "model_id": "1e97320b9902466ea5c3c4eae6bdf9a2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1441,17 +1459,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 7/13\n",
-      "Training Loss: 0.012449477751301053\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Epoch: 7/15\n",
+      "Training Loss: 0.026524252669118783\n",
+      "Top 1 Acc: 0.9970986460348162\n",
       "\n",
-      "Running Validation Epoch 7/13\n"
+      "Running Validation Epoch 7/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f089807ef4704eac91a29faa131fc1d4",
+       "model_id": "3ce2e377d4dc4cc79f0f486ba85cee39",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1466,17 +1484,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 7/13\n",
-      "Val Loss: 0.02214170355970661\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 7/15\n",
+      "Val Loss: 0.177118182182312\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 8/13\n"
+      "Running Training Epoch 8/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d09f08b9aa7e4a459f0b5ecc68415fa0",
+       "model_id": "a49d19cebfea407eac41f8f48f1026ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1491,17 +1509,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 8/13\n",
-      "Training Loss: 0.01196294802460162\n",
+      "Training Epoch: 8/15\n",
+      "Training Loss: 0.02232004324083819\n",
       "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 8/13\n"
+      "Running Validation Epoch 8/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b784f2a60e147d9b93808a1f64398c8",
+       "model_id": "7a76f59cc2944c69b531baf0f07bd609",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1516,17 +1534,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 8/13\n",
-      "Val Loss: 0.02145360441257556\n",
-      "Top 1 Acc: 1.0\n",
+      "Validation Epoch: 8/15\n",
+      "Val Loss: 0.10358777642250061\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
-      "Running Training Epoch 9/13\n"
+      "Running Training Epoch 9/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f81d0020ba0413185f316c800afb0ee",
+       "model_id": "f5ebb79fa89345a7ae603be67cbe179b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1541,17 +1559,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 9/13\n",
-      "Training Loss: 0.01053273505257333\n",
+      "Training Epoch: 9/15\n",
+      "Training Loss: 0.029385707779403997\n",
       "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 9/13\n"
+      "Running Validation Epoch 9/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96bace92d85e4cef9f7d65846f6a8756",
+       "model_id": "33b7f4317a3d42d0b7baa010ebc2bdcb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1566,17 +1584,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 9/13\n",
-      "Val Loss: 0.02385075483471155\n",
+      "Validation Epoch: 9/15\n",
+      "Val Loss: 0.042455013220508896\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 10/13\n"
+      "Running Training Epoch 10/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bd969b4487d34464944cd720f734fc9d",
+       "model_id": "dbdb5527e3684766bc7015ff58bdce55",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1591,17 +1609,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 10/13\n",
-      "Training Loss: 0.004325077581383726\n",
+      "Training Epoch: 10/15\n",
+      "Training Loss: 0.007427054580629748\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 10/13\n"
+      "Running Validation Epoch 10/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c8549b27bfb54e208303eddf59acf0b1",
+       "model_id": "b73a0d01704c495e90d46ce5b418ae3b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1616,17 +1634,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 10/13\n",
-      "Val Loss: 0.017839766883601744\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 10/15\n",
+      "Val Loss: 0.020314963534474373\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 11/13\n"
+      "Running Training Epoch 11/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "33b3406e7b4f44ffab8765971a5eba20",
+       "model_id": "d48f532ce1fe443986390a95d10a214a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1641,17 +1659,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 11/13\n",
-      "Training Loss: 0.003297735289807486\n",
+      "Training Epoch: 11/15\n",
+      "Training Loss: 0.005854974952800309\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 11/13\n"
+      "Running Validation Epoch 11/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93e5a79272034e3da9fe9f7b2ce79d5a",
+       "model_id": "66decd3b06e14086a29442a5dc339bce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1666,17 +1684,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 11/13\n",
-      "Val Loss: 0.012569367730369171\n",
+      "Validation Epoch: 11/15\n",
+      "Val Loss: 0.013980435362706581\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 12/13\n"
+      "Running Training Epoch 12/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d1a07c0564a4be8a55c882e95c58de0",
+       "model_id": "761dd586eea2462d8059642a6fa01861",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1691,17 +1709,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 12/13\n",
-      "Training Loss: 0.004005543901311124\n",
+      "Training Epoch: 12/15\n",
+      "Training Loss: 0.002781623138991349\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 12/13\n"
+      "Running Validation Epoch 12/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5003c9fb24924629ae11f10bda705979",
+       "model_id": "ef312ea80a14430ebfc5367acbb70d52",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1716,17 +1734,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 12/13\n",
-      "Val Loss: 0.013252861564978957\n",
+      "Validation Epoch: 12/15\n",
+      "Val Loss: 0.011425975399712721\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Training Epoch 13/13\n"
+      "Running Training Epoch 13/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce3f08d614364ae0bc2a09f80dee38a3",
+       "model_id": "322dec36498c44a3ae953f2ea2364734",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1741,17 +1759,117 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 13/13\n",
-      "Training Loss: 0.005194885152912534\n",
+      "Training Epoch: 13/15\n",
+      "Training Loss: 0.004056999764596934\n",
+      "Top 1 Acc: 0.9990328820116054\n",
+      "\n",
+      "Running Validation Epoch 13/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a431075ae9345cf894e5aa41a086257",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 13/15\n",
+      "Val Loss: 0.013013879070058465\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Training Epoch 14/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67fa6ab896e945b2a29e27f361e6075a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 14/15\n",
+      "Training Loss: 0.002845817831043592\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 14/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7e16741614424bb3b4ae46bc60ca99ea",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 14/15\n",
+      "Val Loss: 0.010720587490747372\n",
       "Top 1 Acc: 1.0\n",
       "\n",
-      "Running Validation Epoch 13/13\n"
+      "Running Training Epoch 15/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb350d3d25914b7cbc88a2c642abbd3d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/17 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 15/15\n",
+      "Training Loss: 0.009813380410538657\n",
+      "Top 1 Acc: 0.9980657640232108\n",
+      "\n",
+      "Running Validation Epoch 15/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9362c25ec45a43f1992d5a501bfd3df8",
+       "model_id": "bc2e3959fb7945ef821b9c58291bdff6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1766,8 +1884,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 13/13\n",
-      "Val Loss: 0.012128440197557211\n",
+      "Validation Epoch: 15/15\n",
+      "Val Loss: 0.011411373193065325\n",
       "Top 1 Acc: 1.0\n",
       "\n"
      ]
@@ -1796,18 +1914,6 @@
     "manager.finalize(model)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "7aa91abe",
-   "metadata": {},
-   "source": [
-    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
-    "\n",
-    "![tensorboard output](./images/resnet-beans-tensorboard-output.png)\n",
-    "\n",
-    "We can print layer-by-layer sparsity as well."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -1893,7 +1999,16 @@
    "execution_count": 23,
    "id": "417ac105",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/rshaw/sparseml/env/lib/python3.10/site-packages/torch/onnx/utils.py:439: UserWarning: It is recommended that constant folding be turned off ('do_constant_folding=False') when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some learnable model parameters may not translate correctly in the exported ONNX model because constant folding mutates model parameters. Please consider turning off constant folding or setting the training=TrainingMode.EVAL.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "save_dir = \"experiment-0\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
@@ -1936,7 +2051,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png b/integrations/torchvision/tutorials/images/densenet-flowers-tensorboard-output.png
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From 00ad840ee79f6b5adcd0cd830f1fd0e9ee8ce250 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Thu, 23 Mar 2023 01:17:53 +0000
Subject: [PATCH 27/29] updaed mobilenetv2 example

---
 ...rsify-from-scratch-mobilenetv2-beans.ipynb | 788 +++++++++++-------
 .../mobilenetv2-beans-pruning-recipe.yaml     |   4 +-
 2 files changed, 501 insertions(+), 291 deletions(-)

diff --git a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
index 8e1fec22d4b..c01b31342a6 100644
--- a/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
+++ b/integrations/torchvision/tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb
@@ -64,10 +64,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "f8eb3642",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset beans (/home/ubuntu/.cache/huggingface/datasets/beans/default/0.0.0/90c755fb6db1c0ccdad02e897a37969dbf070bed3755d4391e269ff70642d791)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7a630bb60c4a4413a0d43039cd9ac5d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "beans_dataset = datasets.load_dataset(\"beans\")"
    ]
@@ -82,8 +104,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/eeb026374cf5ecfd5f40131a3159be9b9055ac21a3da11690e7eb4d117c99eee/train/bean_rust/bean_rust_train.84.jpg\n",
-      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/f287261265d2f9a3e8f87a5526a54d1847b17f7c3ec5714e5719432f2b3e4a73/validation/bean_rust/bean_rust_val.36.jpg\n"
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/7ad2d437b751e134577576a32849c44a9ade89297680ad5f6a64051e2108810b/train/angular_leaf_spot/angular_leaf_spot_train.0.jpg\n",
+      "/home/ubuntu/.cache/huggingface/datasets/downloads/extracted/1141f86479bc0bb56c75616d153591cc8299d1ea4edc53bb1ab65edd2c65b240/validation/angular_leaf_spot/angular_leaf_spot_val.0.jpg\n"
      ]
     }
    ],
@@ -215,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "id": "4d554578",
    "metadata": {
     "scrolled": true
@@ -262,17 +284,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "2723a40f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r\n",
+      "# Epoch and Learning-Rate variables\r\n",
+      "num_epochs: 10.0\r\n",
+      "init_lr: 0.0005\r\n",
+      "\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)\r\n",
+      "\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    final_lr: 0.0\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(num_epochs)"
+     ]
+    }
+   ],
    "source": [
     "!cat ./recipes/mobilenetv2-beans-dense-recipe.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "id": "cec41d57",
    "metadata": {},
    "outputs": [],
@@ -290,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "id": "1749e00d",
    "metadata": {},
    "outputs": [],
@@ -310,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "id": "d00d175b",
    "metadata": {
     "scrolled": false
@@ -326,7 +371,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aaa6edfc1be94d68a8eee93e1691e3aa",
+       "model_id": "5cc895557d43463fa06855d8cb6a04b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -342,8 +387,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 1/10\n",
-      "Training Loss: 0.47380749520027277\n",
-      "Top 1 Acc: 0.8152804642166345\n",
+      "Training Loss: 0.5145365252639308\n",
+      "Top 1 Acc: 0.8268858800773694\n",
       "\n",
       "Running Validation Epoch 1/10\n"
      ]
@@ -351,7 +396,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b703e41c6e804bbeacf25aace9f67748",
+       "model_id": "d7e8b1b81291424d88d0bde70b3b21ee",
        "version_major": 2,
        "version_minor": 0
       },
@@ -367,8 +412,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 1/10\n",
-      "Val Loss: 0.12894474416971208\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.13084974102675914\n",
+      "Top 1 Acc: 0.9398496240601504\n",
       "\n",
       "Running Training Epoch 2/10\n"
      ]
@@ -376,7 +421,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e03a4c8f3706490ab3642e81b149c890",
+       "model_id": "c1fe04fab0584f509ceb2cba7a20e70f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -392,8 +437,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 2/10\n",
-      "Training Loss: 0.09328031384696563\n",
-      "Top 1 Acc: 0.9690522243713733\n",
+      "Training Loss: 0.1433423740619963\n",
+      "Top 1 Acc: 0.9497098646034816\n",
       "\n",
       "Running Validation Epoch 2/10\n"
      ]
@@ -401,7 +446,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4a00338db9b54e1ebf5be2154d026bd4",
+       "model_id": "daf68b9458b84bb49a33f78bf0c5a68d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -417,8 +462,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 2/10\n",
-      "Val Loss: 0.1064663665369153\n",
-      "Top 1 Acc: 0.9548872180451128\n",
+      "Val Loss: 0.06366700865328312\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 3/10\n"
      ]
@@ -426,7 +471,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "87566c12df134f38ad2d9f831998f01c",
+       "model_id": "3151d6a608db42a792372a3aa4467e38",
        "version_major": 2,
        "version_minor": 0
       },
@@ -442,8 +487,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 3/10\n",
-      "Training Loss: 0.04011029944839803\n",
-      "Top 1 Acc: 0.9903288201160542\n",
+      "Training Loss: 0.04070456024033554\n",
+      "Top 1 Acc: 0.9922630560928434\n",
       "\n",
       "Running Validation Epoch 3/10\n"
      ]
@@ -451,7 +496,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f66ea6ca87944d63ab5d8a2a3fb07969",
+       "model_id": "2ee08ae3a7144f75831f9e1cd3d7b454",
        "version_major": 2,
        "version_minor": 0
       },
@@ -467,8 +512,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 3/10\n",
-      "Val Loss: 0.06654092976823449\n",
-      "Top 1 Acc: 0.9699248120300752\n",
+      "Val Loss: 0.06802571751177311\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 4/10\n"
      ]
@@ -476,7 +521,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c0ab06fdf344eb4a98aa54b02f6e4b0",
+       "model_id": "dc12cd7f00284ea58433ed0a5c826791",
        "version_major": 2,
        "version_minor": 0
       },
@@ -492,8 +537,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 4/10\n",
-      "Training Loss: 0.013448385923931544\n",
-      "Top 1 Acc: 0.9990328820116054\n",
+      "Training Loss: 0.03408790949844953\n",
+      "Top 1 Acc: 0.9903288201160542\n",
       "\n",
       "Running Validation Epoch 4/10\n"
      ]
@@ -501,7 +546,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f2d999410c2f42fe8ef5c695c51c908d",
+       "model_id": "facab31d1719407d95198e46bd9444b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -517,8 +562,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 4/10\n",
-      "Val Loss: 0.04685080051422119\n",
-      "Top 1 Acc: 0.9699248120300752\n",
+      "Val Loss: 0.0302915308624506\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 5/10\n"
      ]
@@ -526,7 +571,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "178d61945ca94dffa214bdb40d7d6bfd",
+       "model_id": "59c280ab06744fbeb0234b55667c9fd4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -542,8 +587,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 5/10\n",
-      "Training Loss: 0.011129878745343762\n",
-      "Top 1 Acc: 0.9970986460348162\n",
+      "Training Loss: 0.014188278910957954\n",
+      "Top 1 Acc: 0.9980657640232108\n",
       "\n",
       "Running Validation Epoch 5/10\n"
      ]
@@ -551,7 +596,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "37456b087b96435dadb09b871e32694b",
+       "model_id": "79553b09130d408f90e9825fcb16c1e5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -567,7 +612,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 5/10\n",
-      "Val Loss: 0.04289984308416024\n",
+      "Val Loss: 0.036852234788239\n",
       "Top 1 Acc: 0.9774436090225563\n",
       "\n",
       "Running Training Epoch 6/10\n"
@@ -576,7 +621,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4b8d82516d1d40659e6b2ae7aabf59fb",
+       "model_id": "00eeaceb759d4bedade5095b8bccfc14",
        "version_major": 2,
        "version_minor": 0
       },
@@ -592,7 +637,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 6/10\n",
-      "Training Loss: 0.007474540554650241\n",
+      "Training Loss: 0.008212485654053815\n",
       "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 6/10\n"
@@ -601,7 +646,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5bc30bbadd84ddebcc25510617ae2ce",
+       "model_id": "17012e571c1742d394678adda75e0a27",
        "version_major": 2,
        "version_minor": 0
       },
@@ -617,7 +662,7 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 6/10\n",
-      "Val Loss: 0.038813505135476586\n",
+      "Val Loss: 0.023899264284409583\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 7/10\n"
@@ -626,7 +671,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3df44744907d4833b6e925f8ed7ef4be",
+       "model_id": "8423061cd4b3428d81985783be66ae24",
        "version_major": 2,
        "version_minor": 0
       },
@@ -642,8 +687,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 7/10\n",
-      "Training Loss: 0.022992545157621586\n",
-      "Top 1 Acc: 0.9961315280464217\n",
+      "Training Loss: 0.003027547925188573\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 7/10\n"
      ]
@@ -651,7 +696,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9b42a80e927a41559038ed25a5ba85d9",
+       "model_id": "f2eeb131d20f474585d960b1756fc6a8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -667,8 +712,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 7/10\n",
-      "Val Loss: 0.038337701372802256\n",
-      "Top 1 Acc: 0.9699248120300752\n",
+      "Val Loss: 0.02565102996304631\n",
+      "Top 1 Acc: 0.9924812030075187\n",
       "\n",
       "Running Training Epoch 8/10\n"
      ]
@@ -676,7 +721,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cfb91384c3ee4dc0aa18605ca65295b3",
+       "model_id": "94b20d80ad3b4c1d8536928b380a6306",
        "version_major": 2,
        "version_minor": 0
       },
@@ -692,8 +737,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 8/10\n",
-      "Training Loss: 0.01563219685693074\n",
-      "Top 1 Acc: 0.9941972920696325\n",
+      "Training Loss: 0.003363958947981397\n",
+      "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 8/10\n"
      ]
@@ -701,7 +746,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce3a2318d0d14f12b79e6d011afecaaa",
+       "model_id": "28472058de974ef48a845de629c800f9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -717,8 +762,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 8/10\n",
-      "Val Loss: 0.052491285931319\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Val Loss: 0.0232504392741248\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 9/10\n"
      ]
@@ -726,7 +771,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15077760bb514451a4fc7fbc45762a6d",
+       "model_id": "a7481a29e458478f9fd4113a025003dc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -742,7 +787,7 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 9/10\n",
-      "Training Loss: 0.004495378677154694\n",
+      "Training Loss: 0.004655612831949397\n",
       "Top 1 Acc: 1.0\n",
       "\n",
       "Running Validation Epoch 9/10\n"
@@ -751,7 +796,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "770b9912502a4f5eaf820fda80fb0b5c",
+       "model_id": "ff7d10297b3d4658a227df552c3a622d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -767,8 +812,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 9/10\n",
-      "Val Loss: 0.03483513812534511\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Val Loss: 0.027894998691044746\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n",
       "Running Training Epoch 10/10\n"
      ]
@@ -776,7 +821,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec9f06d2c1b44fcb8273d49bd106a542",
+       "model_id": "ad177de3d1384cb4a32a97a3fd5f5c46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -792,8 +837,8 @@
      "output_type": "stream",
      "text": [
       "Training Epoch: 10/10\n",
-      "Training Loss: 0.003678643000911865\n",
-      "Top 1 Acc: 1.0\n",
+      "Training Loss: 0.01938260115937076\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
       "Running Validation Epoch 10/10\n"
      ]
@@ -801,7 +846,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57a78c45e414444593c0dbcc0e08761e",
+       "model_id": "8637fb0cea6e45a5b47dcea94cf7cc71",
        "version_major": 2,
        "version_minor": 0
       },
@@ -817,8 +862,8 @@
      "output_type": "stream",
      "text": [
       "Validation Epoch: 10/10\n",
-      "Val Loss: 0.04160818513482809\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Val Loss: 0.027051440824288876\n",
+      "Top 1 Acc: 0.9849624060150376\n",
       "\n"
      ]
     }
@@ -843,7 +888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "11fa4cf2",
    "metadata": {},
    "outputs": [],
@@ -852,8 +897,7 @@
     "\n",
     "save_dir = \"dense_model\"\n",
     "exporter = ModuleExporter(model, output_dir=save_dir)\n",
-    "exporter.export_pytorch(name=\"mobilenet-v2-dense-beans.pth\")\n",
-    "exporter.export_onnx(torch.randn(1, 3, 224, 224), name=\"dense-model.onnx\", convert_qat=True)"
+    "exporter.export_pytorch(name=\"mobilenet-v2-dense-beans.pth\")"
    ]
   },
   {
@@ -868,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "id": "8447d074",
    "metadata": {},
    "outputs": [],
@@ -896,7 +940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 14,
    "id": "44578399",
    "metadata": {
     "scrolled": true
@@ -983,61 +1027,139 @@
     "Here is what the recipe looks like:\n",
     "\n",
     "```yaml\n",
-    "# Epoch and Learning-Rate variables\n",
-    "num_epochs: 13.0\n",
-    "pruning_epochs: 10.0\n",
+    "# Epoch hyperparams\n",
+    "stabilization_epochs: 1.0\n",
+    "pruning_epochs: 9.0\n",
+    "finetuning_epochs: 5.0\n",
+    "\n",
+    "# Learning rate hyperparams\n",
     "init_lr: 0.0005\n",
-    "inter_func: cubic\n",
-    "mask_type: unstructured\n",
+    "final_lr: 0.00025\n",
+    "\n",
+    "# Pruning hyperparams\n",
+    "init_sparsity: 0.05\n",
+    "final_sparsity: 0.9\n",
     "\n",
+    "# Stabalization Stage\n",
     "training_modifiers:\n",
     "  - !EpochRangeModifier\n",
     "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)\n",
+    "  \n",
+    "  - !SetLearningRateModifier\n",
+    "    start_epoch: 0.0\n",
+    "    learning_rate: eval(init_lr)\n",
     "\n",
+    "# Pruning Stage\n",
+    "pruning_modifiers:\n",
     "  - !LearningRateFunctionModifier\n",
-    "    final_lr: 0.0\n",
     "    init_lr: eval(init_lr)\n",
+    "    final_lr: eval(final_lr)\n",
     "    lr_func: cosine\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(num_epochs)\n",
-    "\n",
-    "# Pruning\n",
-    "pruning_modifiers:\n",
+    "    start_epoch: eval(stabilization_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    \n",
     "  - !GlobalMagnitudePruningModifier\n",
-    "    init_sparsity: 0.05\n",
-    "    final_sparsity: 0.90\n",
-    "    start_epoch: 0.0\n",
-    "    end_epoch: eval(pruning_epochs)\n",
-    "    update_frequency: 1.0\n",
-    "    params: \n",
+    "    init_sparsity: eval(init_sparsity)\n",
+    "    final_sparsity: eval(final_sparsity)\n",
+    "    start_epoch: eval(stabilization_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    update_frequency: 0.5\n",
+    "    params:        \n",
     "        - 'features.0.0.weight'\n",
     "        - 'features.18.0.weight'\n",
     "        - 're:features.*.conv.*.weight'\n",
     "        - 're:features.*.conv.*.*.weight'\n",
     "    leave_enabled: True\n",
-    "    inter_func: eval(inter_func)\n",
-    "    mask_type: eval(mask_type)\n",
+    "\n",
+    "# Finetuning Stage\n",
+    "finetuning_modifiers:\n",
+    "  - !LearningRateFunctionModifier\n",
+    "    init_lr: eval(init_lr)\n",
+    "    final_lr: eval(final_lr)\n",
+    "    lr_func: cosine\n",
+    "    start_epoch: eval(stabilization_epochs + pruning_epochs)\n",
+    "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)\n",
     "```\n",
     "\n",
-    "This recipe specifies that we will run the GMP algorithm for the first 10 epochs. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
+    "This recipe specifies that we will run the GMP algorithm for 9 epochs after 1 warmup epoch. We start at an init_sparsity level of 5% and gradually increase sparsity to a final_sparsity level of 90% following a cubic curve. The pruning is applied in an unstructured manner, meaning that any weight can be pruned.\n",
     "\n",
-    "Over the final 3 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
+    "Over the final 5 epochs, we will fine-tune the 90% pruned model further. Since we set leave_enabled=True the sparsity level will be maintained as the fine-tuning occurs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "ca5c28b2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# Epoch hyperparams\r\n",
+      "stabilization_epochs: 1.0\r\n",
+      "pruning_epochs: 9.0\r\n",
+      "finetuning_epochs: 5.0\r\n",
+      "\r\n",
+      "# Learning rate hyperparams\r\n",
+      "init_lr: 0.0005\r\n",
+      "final_lr: 0.00025\r\n",
+      "\r\n",
+      "# Pruning hyperparams\r\n",
+      "init_sparsity: 0.05\r\n",
+      "final_sparsity: 0.9\r\n",
+      "\r\n",
+      "# Stabalization Stage\r\n",
+      "training_modifiers:\r\n",
+      "  - !EpochRangeModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)\r\n",
+      "  \r\n",
+      "  - !SetLearningRateModifier\r\n",
+      "    start_epoch: 0.0\r\n",
+      "    learning_rate: eval(init_lr)\r\n",
+      "\r\n",
+      "# Pruning Stage\r\n",
+      "pruning_modifiers:\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    final_lr: eval(final_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: eval(stabilization_epochs)\r\n",
+      "    end_epoch: eval(stabilization_epochs + pruning_epochs)\r\n",
+      "    \r\n",
+      "  - !GlobalMagnitudePruningModifier\r\n",
+      "    init_sparsity: eval(init_sparsity)\r\n",
+      "    final_sparsity: eval(final_sparsity)\r\n",
+      "    start_epoch: eval(stabilization_epochs)\r\n",
+      "    end_epoch: eval(stabilization_epochs + pruning_epochs)\r\n",
+      "    update_frequency: 0.5\r\n",
+      "    params:        \r\n",
+      "        - 'features.0.0.weight'\r\n",
+      "        - 'features.18.0.weight'\r\n",
+      "        - 're:features.*.conv.*.weight'\r\n",
+      "        - 're:features.*.conv.*.*.weight'\r\n",
+      "    leave_enabled: True\r\n",
+      "\r\n",
+      "# Finetuning Stage\r\n",
+      "finetuning_modifiers:\r\n",
+      "  - !LearningRateFunctionModifier\r\n",
+      "    init_lr: eval(init_lr)\r\n",
+      "    final_lr: eval(final_lr)\r\n",
+      "    lr_func: cosine\r\n",
+      "    start_epoch: eval(stabilization_epochs + pruning_epochs)\r\n",
+      "    end_epoch: eval(stabilization_epochs + pruning_epochs + finetuning_epochs)"
+     ]
+    }
+   ],
    "source": [
     "!cat ./recipes/mobilenetv2-beans-pruning-recipe.yaml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "id": "b55a7c74",
    "metadata": {},
    "outputs": [],
@@ -1047,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 17,
    "id": "68646bc7",
    "metadata": {},
    "outputs": [],
@@ -1067,12 +1189,12 @@
     "\n",
     "As you can see, we use the wrapped `optimizer` and `model` in the same way as above. SparseML parsed the recipe and updated the `optimizer` with the logic of GMP algorithm from the recipe. This allows you to just the `optimizer` and `model` as usual, with all of the pruning-related logic specified by the declarative recipe interface.\n",
     "\n",
-    "Our 90% pruned model reaches ~99% validation accuracy (vs ~99% for the dense model)."
+    "Our 90% pruned model reaches ~99% validation accuracy (vs ~98% for the dense model)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 18,
    "id": "601c8c21",
    "metadata": {
     "scrolled": false
@@ -1082,13 +1204,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running Training Epoch 1/13\n"
+      "Running Training Epoch 1/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1532a057991c4f92ae19103ed357b47d",
+       "model_id": "3b037b49d42c41abadc2bbab546e5760",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1103,17 +1225,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 1/13\n",
-      "Training Loss: 0.030314430911940606\n",
-      "Top 1 Acc: 0.9893617021276596\n",
+      "Training Epoch: 1/15\n",
+      "Training Loss: 0.04981424145058334\n",
+      "Top 1 Acc: 0.988394584139265\n",
       "\n",
-      "Running Validation Epoch 1/13\n"
+      "Running Validation Epoch 1/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f8229930b27d4f22a4b9071e2047f0cd",
+       "model_id": "aa75f114f7754ea4a4b7509826d769e3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1128,17 +1250,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 1/13\n",
-      "Val Loss: 0.10016061989590526\n",
-      "Top 1 Acc: 0.9699248120300752\n",
+      "Validation Epoch: 1/15\n",
+      "Val Loss: 0.06650052629411221\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 2/13\n"
+      "Running Training Epoch 2/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b044a0e5d8414ea68ee41547c4a27210",
+       "model_id": "50b86debcf3148a687d17c36c601c99d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1153,17 +1275,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 2/13\n",
-      "Training Loss: 0.03320332106721418\n",
-      "Top 1 Acc: 0.9903288201160542\n",
+      "Training Epoch: 2/15\n",
+      "Training Loss: 0.08335415836226082\n",
+      "Top 1 Acc: 0.9777562862669246\n",
       "\n",
-      "Running Validation Epoch 2/13\n"
+      "Running Validation Epoch 2/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e5dd99cf59ab499db75f0cebe474a798",
+       "model_id": "e741553cb741416b98bfa577d529d575",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1178,17 +1300,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 2/13\n",
-      "Val Loss: 0.05205255227629095\n",
+      "Validation Epoch: 2/15\n",
+      "Val Loss: 0.06197752933949232\n",
       "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 3/13\n"
+      "Running Training Epoch 3/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ff9d00403a5c4a4a951d7c4c7a280da0",
+       "model_id": "0b86b60f094e4d1e9338aaa664cdf901",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1203,17 +1325,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 3/13\n",
-      "Training Loss: 0.02863485404558367\n",
-      "Top 1 Acc: 0.9922630560928434\n",
+      "Training Epoch: 3/15\n",
+      "Training Loss: 0.040097551427386476\n",
+      "Top 1 Acc: 0.9854932301740812\n",
       "\n",
-      "Running Validation Epoch 3/13\n"
+      "Running Validation Epoch 3/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e239549148104b41a2c845856c105288",
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        "version_major": 2,
        "version_minor": 0
       },
@@ -1228,17 +1350,67 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 3/13\n",
-      "Val Loss: 0.04446154618635774\n",
+      "Validation Epoch: 3/15\n",
+      "Val Loss: 0.05052837722469121\n",
       "Top 1 Acc: 0.9849624060150376\n",
       "\n",
-      "Running Training Epoch 4/13\n"
+      "Running Training Epoch 4/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6c7cc173588c4ade9a5315a16ac9eec8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 4/15\n",
+      "Training Loss: 0.03676384532203277\n",
+      "Top 1 Acc: 0.9893617021276596\n",
+      "\n",
+      "Running Validation Epoch 4/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0394c6e5c0d749daae91ac40c14d9577",
+       "model_id": "99aefcf3d0b2421a896fa2b9d03e4fc1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 4/15\n",
+      "Val Loss: 0.08736732564866542\n",
+      "Top 1 Acc: 0.9624060150375939\n",
+      "\n",
+      "Running Training Epoch 5/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37d08c11516b44fbaf3cdb52b46b5144",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1253,17 +1425,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 4/13\n",
-      "Training Loss: 0.028403193393552847\n",
+      "Training Epoch: 5/15\n",
+      "Training Loss: 0.03125915414244501\n",
       "Top 1 Acc: 0.9932301740812379\n",
       "\n",
-      "Running Validation Epoch 4/13\n"
+      "Running Validation Epoch 5/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7210b33d08c14e4191c0d843ff49bf2c",
+       "model_id": "e2ff32388c1746588cc01452aa64e7bd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1278,17 +1450,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 4/13\n",
-      "Val Loss: 0.021256184950470925\n",
+      "Validation Epoch: 5/15\n",
+      "Val Loss: 0.09458159506320954\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "Running Training Epoch 5/13\n"
+      "Running Training Epoch 6/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "afb5a222c0314969b10b4f8bf123c607",
+       "model_id": "8ff4dcbc05de41d5b22850148996c74c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1303,17 +1475,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 5/13\n",
-      "Training Loss: 0.10820412065720919\n",
-      "Top 1 Acc: 0.9671179883945842\n",
+      "Training Epoch: 6/15\n",
+      "Training Loss: 0.0748459067421429\n",
+      "Top 1 Acc: 0.9796905222437138\n",
       "\n",
-      "Running Validation Epoch 5/13\n"
+      "Running Validation Epoch 6/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed07d03c2d4e4dfc8fb1d5b39d67a789",
+       "model_id": "5d12d2c66f06462aa9cc276d9ee2ea38",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1328,17 +1500,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 5/13\n",
-      "Val Loss: 0.07088811788707972\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Validation Epoch: 6/15\n",
+      "Val Loss: 0.20114150643348694\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
-      "Running Training Epoch 6/13\n"
+      "Running Training Epoch 7/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e94599ac85bd46b0ac5ca2200745ce42",
+       "model_id": "4fab3bd349f44218a93177ca9d73481f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1353,17 +1525,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 6/13\n",
-      "Training Loss: 0.21380004890714632\n",
-      "Top 1 Acc: 0.9216634429400387\n",
+      "Training Epoch: 7/15\n",
+      "Training Loss: 0.10897209077621951\n",
+      "Top 1 Acc: 0.965183752417795\n",
       "\n",
-      "Running Validation Epoch 6/13\n"
+      "Running Validation Epoch 7/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75a137efb0df4a78879678d686263c69",
+       "model_id": "4d54279b4f8d456eb485c6a749248603",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1378,17 +1550,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 6/13\n",
-      "Val Loss: 0.07981351003982126\n",
-      "Top 1 Acc: 0.9774436090225563\n",
+      "Validation Epoch: 7/15\n",
+      "Val Loss: 0.3452451154589653\n",
+      "Top 1 Acc: 0.8421052631578947\n",
       "\n",
-      "Running Training Epoch 7/13\n"
+      "Running Training Epoch 8/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "18bf542d5d09438d93969342de4fab9e",
+       "model_id": "39115311d08a4bc6a94d8e266991cce7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1403,17 +1575,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 7/13\n",
-      "Training Loss: 0.19059311598539352\n",
-      "Top 1 Acc: 0.9332688588007737\n",
+      "Training Epoch: 8/15\n",
+      "Training Loss: 0.13403249407807985\n",
+      "Top 1 Acc: 0.9506769825918762\n",
       "\n",
-      "Running Validation Epoch 7/13\n"
+      "Running Validation Epoch 8/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "177ed7c1ade646f092ba59bafb462b5e",
+       "model_id": "a0cb864970ad4f4d928f59f34eaadeb4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1428,17 +1600,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 7/13\n",
-      "Val Loss: 0.07885787561535836\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 8/15\n",
+      "Val Loss: 0.20368995219469072\n",
+      "Top 1 Acc: 0.9323308270676691\n",
       "\n",
-      "Running Training Epoch 8/13\n"
+      "Running Training Epoch 9/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "74e04f218de44f8ea10e4b261f9473d0",
+       "model_id": "655cc448f70746e7bc20bd6e834ae19f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1453,17 +1625,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 8/13\n",
-      "Training Loss: 0.14277072397596907\n",
-      "Top 1 Acc: 0.965183752417795\n",
+      "Training Epoch: 9/15\n",
+      "Training Loss: 0.12121003064693826\n",
+      "Top 1 Acc: 0.9671179883945842\n",
       "\n",
-      "Running Validation Epoch 8/13\n"
+      "Running Validation Epoch 9/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be70a6fffa6f419f82f87212155b98fe",
+       "model_id": "2d55e9450fae497e96b86211ca33812e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1478,17 +1650,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 8/13\n",
-      "Val Loss: 0.08072680607438087\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 9/15\n",
+      "Val Loss: 0.11063596755266189\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 9/13\n"
+      "Running Training Epoch 10/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "246b3bf84568417f97c4bae88f83b4fb",
+       "model_id": "2744a665b1094c96a80cbe44a87207cb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1503,17 +1675,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 9/13\n",
-      "Training Loss: 0.11926381854396878\n",
-      "Top 1 Acc: 0.9680851063829787\n",
+      "Training Epoch: 10/15\n",
+      "Training Loss: 0.09124855725376894\n",
+      "Top 1 Acc: 0.9642166344294004\n",
       "\n",
-      "Running Validation Epoch 9/13\n"
+      "Running Validation Epoch 10/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b41b1ccbde8c4857897e7b16758260dc",
+       "model_id": "6779935332bb4ebdb701baedf3866911",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1528,17 +1700,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 9/13\n",
-      "Val Loss: 0.08937856331467628\n",
-      "Top 1 Acc: 0.9849624060150376\n",
+      "Validation Epoch: 10/15\n",
+      "Val Loss: 0.0751135416328907\n",
+      "Top 1 Acc: 0.9699248120300752\n",
       "\n",
-      "Running Training Epoch 10/13\n"
+      "Running Training Epoch 11/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eacdaf91b1e9436890b621b5095bfe05",
+       "model_id": "4b84d196120844b9ba1254eda5a61c06",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1553,17 +1725,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 10/13\n",
-      "Training Loss: 0.08645325391130014\n",
-      "Top 1 Acc: 0.9854932301740812\n",
+      "Training Epoch: 11/15\n",
+      "Training Loss: 0.04337389501884128\n",
+      "Top 1 Acc: 0.9912959381044487\n",
       "\n",
-      "Running Validation Epoch 10/13\n"
+      "Running Validation Epoch 11/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0d39663296c0495f9a9578847a5678f3",
+       "model_id": "88d8406bbfcc4886a9415ace34de12a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1578,17 +1750,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 10/13\n",
-      "Val Loss: 0.06663795448839664\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 11/15\n",
+      "Val Loss: 0.034493994899094105\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 11/13\n"
+      "Running Training Epoch 12/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e738fd26e239454387c7b528b6b0e33c",
+       "model_id": "afec4141ee4d4e4080fb460168a09534",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1603,17 +1775,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 11/13\n",
-      "Training Loss: 0.07962510655775215\n",
-      "Top 1 Acc: 0.9816247582205029\n",
+      "Training Epoch: 12/15\n",
+      "Training Loss: 0.018291185736994852\n",
+      "Top 1 Acc: 0.9980657640232108\n",
       "\n",
-      "Running Validation Epoch 11/13\n"
+      "Running Validation Epoch 12/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fcfbbe2836864d618f6dea028dd7c37f",
+       "model_id": "f21726e565b14415975169049d1c5931",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1628,17 +1800,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 11/13\n",
-      "Val Loss: 0.0655035775154829\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 12/15\n",
+      "Val Loss: 0.04321025339886546\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n",
-      "Running Training Epoch 12/13\n"
+      "Running Training Epoch 13/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0ba18ba8e68a46169c9faef52291598a",
+       "model_id": "0eb6ef213d5f41758f2c89f31fb6aa01",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1653,17 +1825,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 12/13\n",
-      "Training Loss: 0.08157054532432195\n",
-      "Top 1 Acc: 0.9825918762088974\n",
+      "Training Epoch: 13/15\n",
+      "Training Loss: 0.0146882157899778\n",
+      "Top 1 Acc: 0.9970986460348162\n",
       "\n",
-      "Running Validation Epoch 12/13\n"
+      "Running Validation Epoch 13/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cc213871f6bf4851817a59c8a282b98d",
+       "model_id": "9aeef2ccf0dd4fb8941bafd967316189",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1678,17 +1850,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 12/13\n",
-      "Val Loss: 0.061416388303041455\n",
+      "Validation Epoch: 13/15\n",
+      "Val Loss: 0.03916811663657427\n",
       "Top 1 Acc: 0.9924812030075187\n",
       "\n",
-      "Running Training Epoch 13/13\n"
+      "Running Training Epoch 14/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac56ff40a6e348ae88f943de31efce6d",
+       "model_id": "32f885475f8049b2ab560ffbc4c9390f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1703,17 +1875,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Training Epoch: 13/13\n",
-      "Training Loss: 0.06805486869857166\n",
-      "Top 1 Acc: 0.9874274661508704\n",
+      "Training Epoch: 14/15\n",
+      "Training Loss: 0.014786866712242816\n",
+      "Top 1 Acc: 0.9990328820116054\n",
       "\n",
-      "Running Validation Epoch 13/13\n"
+      "Running Validation Epoch 14/15\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9cf25c82c70c449ebbdd8b6bf4493075",
+       "model_id": "2a55281ea41c4263874838cd59440ef9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1728,9 +1900,59 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Validation Epoch: 13/13\n",
-      "Val Loss: 0.06589164286851883\n",
-      "Top 1 Acc: 0.9924812030075187\n",
+      "Validation Epoch: 14/15\n",
+      "Val Loss: 0.040105257043614985\n",
+      "Top 1 Acc: 0.9849624060150376\n",
+      "\n",
+      "Running Training Epoch 15/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7bf416c8c5f4ee5893e170b01e1adcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/33 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Epoch: 15/15\n",
+      "Training Loss: 0.007484704270168688\n",
+      "Top 1 Acc: 1.0\n",
+      "\n",
+      "Running Validation Epoch 15/15\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2e9f4636ad844a869db5541b37f9732a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validation Epoch: 15/15\n",
+      "Val Loss: 0.02973868220578879\n",
+      "Top 1 Acc: 0.9774436090225563\n",
       "\n"
      ]
     }
@@ -1758,21 +1980,9 @@
     "manager.finalize(model)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "64aabe37",
-   "metadata": {},
-   "source": [
-    "Here is a sample of the TensorBoard output, showing the validation accuracy, a particular layer's sparsity level, and the learning rate over time.\n",
-    "\n",
-    "![tensorboard output](./images/mobilenetv2-beans-tensorboard-output.png)\n",
-    "\n",
-    "We can print layer-by-layer sparsity as well."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 19,
    "id": "2bf48ed2",
    "metadata": {},
    "outputs": [
@@ -1780,58 +1990,58 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "features.0.0.weight: 0.4861\n",
-      "features.1.conv.0.0.weight: 0.4688\n",
-      "features.1.conv.1.weight: 0.5156\n",
-      "features.2.conv.0.0.weight: 0.5547\n",
-      "features.2.conv.1.0.weight: 0.2905\n",
-      "features.2.conv.2.weight: 0.4865\n",
-      "features.3.conv.0.0.weight: 0.6102\n",
-      "features.3.conv.1.0.weight: 0.4961\n",
-      "features.3.conv.2.weight: 0.6111\n",
-      "features.4.conv.0.0.weight: 0.5229\n",
-      "features.4.conv.1.0.weight: 0.2731\n",
-      "features.4.conv.2.weight: 0.5187\n",
-      "features.5.conv.0.0.weight: 0.6901\n",
-      "features.5.conv.1.0.weight: 0.5880\n",
-      "features.5.conv.2.weight: 0.7126\n",
-      "features.6.conv.0.0.weight: 0.6934\n",
-      "features.6.conv.1.0.weight: 0.6111\n",
-      "features.6.conv.2.weight: 0.7402\n",
-      "features.7.conv.0.0.weight: 0.5495\n",
-      "features.7.conv.1.0.weight: 0.3634\n",
+      "features.0.0.weight: 0.4896\n",
+      "features.1.conv.0.0.weight: 0.4722\n",
+      "features.1.conv.1.weight: 0.5234\n",
+      "features.2.conv.0.0.weight: 0.5501\n",
+      "features.2.conv.1.0.weight: 0.2859\n",
+      "features.2.conv.2.weight: 0.4813\n",
+      "features.3.conv.0.0.weight: 0.6137\n",
+      "features.3.conv.1.0.weight: 0.4977\n",
+      "features.3.conv.2.weight: 0.6131\n",
+      "features.4.conv.0.0.weight: 0.5214\n",
+      "features.4.conv.1.0.weight: 0.2785\n",
+      "features.4.conv.2.weight: 0.5184\n",
+      "features.5.conv.0.0.weight: 0.6903\n",
+      "features.5.conv.1.0.weight: 0.5804\n",
+      "features.5.conv.2.weight: 0.7134\n",
+      "features.6.conv.0.0.weight: 0.6929\n",
+      "features.6.conv.1.0.weight: 0.6117\n",
+      "features.6.conv.2.weight: 0.7383\n",
+      "features.7.conv.0.0.weight: 0.5493\n",
+      "features.7.conv.1.0.weight: 0.3588\n",
       "features.7.conv.2.weight: 0.6127\n",
-      "features.8.conv.0.0.weight: 0.8370\n",
-      "features.8.conv.1.0.weight: 0.7405\n",
-      "features.8.conv.2.weight: 0.8673\n",
-      "features.9.conv.0.0.weight: 0.8272\n",
-      "features.9.conv.1.0.weight: 0.7384\n",
-      "features.9.conv.2.weight: 0.8634\n",
-      "features.10.conv.0.0.weight: 0.8271\n",
-      "features.10.conv.1.0.weight: 0.7870\n",
-      "features.10.conv.2.weight: 0.8725\n",
-      "features.11.conv.0.0.weight: 0.7251\n",
-      "features.11.conv.1.0.weight: 0.7980\n",
-      "features.11.conv.2.weight: 0.7725\n",
-      "features.12.conv.0.0.weight: 0.8820\n",
-      "features.12.conv.1.0.weight: 0.7932\n",
-      "features.12.conv.2.weight: 0.9047\n",
-      "features.13.conv.0.0.weight: 0.8708\n",
-      "features.13.conv.1.0.weight: 0.8169\n",
-      "features.13.conv.2.weight: 0.9118\n",
-      "features.14.conv.0.0.weight: 0.7866\n",
-      "features.14.conv.1.0.weight: 0.6883\n",
-      "features.14.conv.2.weight: 0.8266\n",
-      "features.15.conv.0.0.weight: 0.9452\n",
-      "features.15.conv.1.0.weight: 0.7866\n",
-      "features.15.conv.2.weight: 0.9565\n",
+      "features.8.conv.0.0.weight: 0.8385\n",
+      "features.8.conv.1.0.weight: 0.7396\n",
+      "features.8.conv.2.weight: 0.8687\n",
+      "features.9.conv.0.0.weight: 0.8278\n",
+      "features.9.conv.1.0.weight: 0.7376\n",
+      "features.9.conv.2.weight: 0.8643\n",
+      "features.10.conv.0.0.weight: 0.8293\n",
+      "features.10.conv.1.0.weight: 0.7876\n",
+      "features.10.conv.2.weight: 0.8721\n",
+      "features.11.conv.0.0.weight: 0.7277\n",
+      "features.11.conv.1.0.weight: 0.7995\n",
+      "features.11.conv.2.weight: 0.7729\n",
+      "features.12.conv.0.0.weight: 0.8823\n",
+      "features.12.conv.1.0.weight: 0.7951\n",
+      "features.12.conv.2.weight: 0.9053\n",
+      "features.13.conv.0.0.weight: 0.8716\n",
+      "features.13.conv.1.0.weight: 0.8135\n",
+      "features.13.conv.2.weight: 0.9115\n",
+      "features.14.conv.0.0.weight: 0.7872\n",
+      "features.14.conv.1.0.weight: 0.6908\n",
+      "features.14.conv.2.weight: 0.8274\n",
+      "features.15.conv.0.0.weight: 0.9449\n",
+      "features.15.conv.1.0.weight: 0.7853\n",
+      "features.15.conv.2.weight: 0.9563\n",
       "features.16.conv.0.0.weight: 0.9238\n",
-      "features.16.conv.1.0.weight: 0.7926\n",
-      "features.16.conv.2.weight: 0.9577\n",
-      "features.17.conv.0.0.weight: 0.8770\n",
-      "features.17.conv.1.0.weight: 0.9112\n",
-      "features.17.conv.2.weight: 0.9516\n",
-      "features.18.0.weight: 0.9784\n",
+      "features.16.conv.1.0.weight: 0.7931\n",
+      "features.16.conv.2.weight: 0.9572\n",
+      "features.17.conv.0.0.weight: 0.8772\n",
+      "features.17.conv.1.0.weight: 0.9105\n",
+      "features.17.conv.2.weight: 0.9512\n",
+      "features.18.0.weight: 0.9781\n",
       "classifier.1.weight: 0.0000\n"
      ]
     }
@@ -1851,7 +2061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 21,
    "id": "48fb7c0e",
    "metadata": {},
    "outputs": [],
@@ -1897,7 +2107,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
index 6c4b9c1f412..68260e3c701 100644
--- a/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
+++ b/integrations/torchvision/tutorials/recipes/mobilenetv2-beans-pruning-recipe.yaml
@@ -4,8 +4,8 @@ pruning_epochs: 9.0
 finetuning_epochs: 5.0
 
 # Learning rate hyperparams
-init_lr: 0.0001
-final_lr: 0.00005
+init_lr: 0.0005
+final_lr: 0.00025
 
 # Pruning hyperparams
 init_sparsity: 0.05

From 21564c5750bb03302b46d7ca26aa77fd20278dc9 Mon Sep 17 00:00:00 2001
From: rsnm2 <rshaw@neuralmagic.com>
Date: Thu, 23 Mar 2023 01:22:52 +0000
Subject: [PATCH 28/29] fixed README

---
 integrations/torchvision/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/integrations/torchvision/README.md b/integrations/torchvision/README.md
index ee9ab8e90bc..241a7a8ff03 100644
--- a/integrations/torchvision/README.md
+++ b/integrations/torchvision/README.md
@@ -39,7 +39,6 @@ pip install sparseml[torchvision]
 - [Sparse Transfer Learning with the Python API - ResNet-50](tutorials/docs-torchvision-python-transfer-imagenette.ipynb)
 - [Sparsification from Scratch with the Python API - ResNet-50](tutorials/docs-torchvision-sparsify-from-scratch-resnet50-beans.ipynb)
 - [Sparsification from Scratch with the Python API - MobileNetv2](tutorials/docs-torchvision-sparsify-from-scratch-mobilenetv2-beans.ipynb)
-- [Sparsification from Scratch with the Python API - DenseNet-121](tutorials/docs-torchvision-sparsify-from-scratch-densenet-flowers-pruned-quant.ipynb)
 
 ## Quick Tour
 

From 9824abdd06272da99e4f1416d3ebe5c99458ab2c Mon Sep 17 00:00:00 2001
From: Robert Shaw <114415538+rsnm2@users.noreply.github.com>
Date: Thu, 23 Mar 2023 08:35:22 -0400
Subject: [PATCH 29/29] Update sparse-transfer-learning.md

---
 .../tutorials/sparse-transfer-learning.md        | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/integrations/torchvision/tutorials/sparse-transfer-learning.md b/integrations/torchvision/tutorials/sparse-transfer-learning.md
index 92e4c82947c..bc3e1ca7d22 100644
--- a/integrations/torchvision/tutorials/sparse-transfer-learning.md
+++ b/integrations/torchvision/tutorials/sparse-transfer-learning.md
@@ -1,3 +1,19 @@
+<!--
+Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 # Sparsifying Image Classification Models with the CLI
 
 This page explains how to fine-tune a pre-sparsified Torchvision image classification models like ResNet-50 with SparseML's CLI.