[docs] Move jupyter notebooks to DJL Demo

.github/workflows/docs.yml

@@ -3,7 +3,6 @@ name: Docs
 on:
   pull_request:
     paths:
-      - "**.ipynb"
       - "docs/mkdocs.yml"
   # Publish docs weekly
   schedule:
@@ -38,10 +37,6 @@ jobs:
       - name: add mybinder link
         run: |
           python3 tools/scripts/add_online_runner.py
-      - name: run Notebooks
-        run: |
-          cd jupyter
-          bash test_notebook.sh
       - name: clone demos
         run: |
           cd docs
@@ -50,6 +45,10 @@ jobs:
         run: |
           cd docs
           git clone https://github.com/deepjavalibrary/djl-serving.git serving
+      - name: run Notebooks
+        run: |
+          cd docs/demos/jupyter
+          bash test_notebook.sh
       - name: build docs
         run: |
           cd docs

api/src/main/java/ai/djl/inference/Predictor.java

@@ -60,14 +60,13 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/03_image_classification_with_your_model.ipynb">Inference
+ *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html">Inference
  *       with a custom trained model</a>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/object_detection_with_model_zoo.ipynb">Inference
+ *       href="http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html">Inference
  *       with a model zoo model</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
- *       with an MXNet model</a>
+ *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
+ *       MXNet model</a>
  * </ul>
  *
  * @param <I> the input type

api/src/main/java/ai/djl/nn/Block.java

@@ -105,7 +105,7 @@
  * further refine these elements, use {@link Block#freezeParameters(boolean)} to unfreeze them.
  *
  * @see <a
- *     href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/01_create_your_first_network.ipynb">this
+ *     href="http://docs.djl.ai/docs/demos/jupyter/tutorial/01_create_your_first_network.html">this
  *     tutorial on creating your first network</a>
  * @see <a href="https://d2l.djl.ai/chapter_deep-learning-computation/model-construction.html">The
  *     D2L chapter on blocks</a> and <a

api/src/main/java/ai/djl/training/Trainer.java

@@ -52,14 +52,12 @@
  *
  * <ul>
  *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/tutorial/02_train_your_first_model.ipynb">Training
+ *       href="http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html">Training
  *       your first model</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/transfer_learning_on_cifar10.ipynb">Training
+ *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html">Training
  *       using transfer learning</a>
- *   <li><a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/load_mxnet_model.ipynb">Inference
- *       with an MXNet model</a>
+ *   <li><a href="http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html">Inference with an
+ *       MXNet model</a>
  * </ul>
  *
  * @see <a href="http://docs.djl.ai/docs/development/memory_management.html">The guide on memory

docs/README.md

@@ -20,14 +20,14 @@ Note: when searching in JavaDoc, if your access is denied, please try removing t
 - [Troubleshooting](development/troubleshooting.md)
 - [Inference Optimization](development/inference_performance_optimization.md)
 
-## [Jupyter notebook tutorials](../jupyter/README.md)
-
-- **[Beginner Jupyter Tutorial](../jupyter/tutorial/README.md)**
-- [Run object detection with model zoo](../jupyter/object_detection_with_model_zoo.ipynb)
-- [Load pre-trained PyTorch model](../jupyter/load_pytorch_model.ipynb)
-- [Load pre-trained Apache MXNet model](../jupyter/load_mxnet_model.ipynb)
-- [Transfer learning example](../jupyter/transfer_learning_on_cifar10.ipynb)
-- [Question answering example](../jupyter/BERTQA.ipynb)
+## [Jupyter notebook tutorials](http://docs.djl.ai/docs/demos/jupyter/index.html)
+
+- **[Beginner Jupyter Tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html)**
+- [Run object detection with model zoo](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html)
+- [Load pre-trained PyTorch model](http://docs.djl.ai/docs/demos/jupyter/load_pytorch_model.html)
+- [Load pre-trained Apache MXNet model](http://docs.djl.ai/docs/demos/jupyter/load_mxnet_model.html)
+- [Transfer learning example](http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html)
+- [Question answering example](http://docs.djl.ai/docs/demos/jupyter/BERTQA.html)
 
 ## [API Examples](../examples/README.md)
 

docs/interactive_tool.md

@@ -63,17 +63,17 @@ After that, click `run` and you should see the following result:
 
 Finally, you can get the running project setup by clicking `Get Template`. This will bring you a gradle project that can be used in your local machine.
 
-## [Java Jupyter Notebook](../jupyter/README.md)
+## [Java Jupyter Notebook](http://docs.djl.ai/docs/demos/jupyter/index.html)
 
 Wait a second, are we talking about hosting Jupyter Notebook in python? 
 No, it’s Java 11, only.
 
 ![jupyter](https://djl-ai.s3.amazonaws.com/web-data/images/jupyter.gif)
 
 Inspired by Spencer Park’s [IJava project](https://github.com/SpencerPark/IJava), we integrated DJL with Jupyter Notebooks. 
-For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](../jupyter/README.md#setup).
+For more information on the simple setup, follow the instructions in [DJL Jupyter notebooks](http://docs.djl.ai/docs/demos/jupyter/index.html#setup).
 After that, use the Jupyter Notebook freely in your hosted server. You can do all kinds of work, like block building and plotting a graph.
-There are [tutorials and instructions](../jupyter/README.md#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
+There are [tutorials and instructions](http://docs.djl.ai/docs/demos/jupyter/index.html#djl---jupyter-notebooks) to guide you how you can run training and/or inference with Java.
 
 ## About Future Lab
 

docs/mkdocs.yml

@@ -61,15 +61,15 @@ nav:
       - 'docs/faq.md'
   - Tutorials:
       - Beginner Tutorial:
-          - 'jupyter/tutorial/01_create_your_first_network.ipynb'
-          - 'jupyter/tutorial/02_train_your_first_model.ipynb'
-          - 'jupyter/tutorial/03_image_classification_with_your_model.ipynb'
+          - 'docs/demos/jupyter/tutorial/01_create_your_first_network.ipynb'
+          - 'docs/demos/jupyter/tutorial/02_train_your_first_model.ipynb'
+          - 'docs/demos/jupyter/tutorial/03_image_classification_with_your_model.ipynb'
       - 'docs/d2l.md'
-      - 'jupyter/rank_classification_using_BERT_on_Amazon_Review.ipynb'
-      - 'jupyter/transfer_learning_on_cifar10.ipynb'
+      - 'docs/demos/jupyter/rank_classification_using_BERT_on_Amazon_Review.ipynb'
+      - 'docs/demos/jupyter/transfer_learning_on_cifar10.ipynb'
       - Load your own BERT:
-          - BERT with MXNet: 'jupyter/mxnet/load_your_own_mxnet_bert.ipynb'
-          - BERT with PyTorch: 'jupyter/pytorch/load_your_own_pytorch_bert.ipynb'
+          - BERT with MXNet: 'docs/demos/jupyter/mxnet/load_your_own_mxnet_bert.ipynb'
+          - BERT with PyTorch: 'docs/demos/jupyter/pytorch/load_your_own_pytorch_bert.ipynb'
   - Guides:
       - Models:
           - 'docs/load_model.md'
@@ -97,25 +97,25 @@ nav:
           - PyTorch NDArray Operators: 'docs/pytorch/pytorch-djl-ndarray-cheatsheet.md'
           - PyTorch Model Zoo: 'engines/pytorch/pytorch-model-zoo/README.md'
           - Import PyTorch Model: 'docs/pytorch/how_to_convert_your_model_to_torchscript.md'
-          - Load a PyTorch Model: 'jupyter/load_pytorch_model.ipynb'
+          - Load a PyTorch Model: 'docs/demos/jupyter/load_pytorch_model.ipynb'
       - TensorFlow:
           - Overview: 'engines/tensorflow/README.md'
           - TensorFlow Engine: 'engines/tensorflow/tensorflow-engine/README.md'
           - TensorFlow Model Zoo: 'engines/tensorflow/tensorflow-model-zoo/README.md'
           - Import TensorFlow Model: 'docs/tensorflow/how_to_import_tensorflow_models_in_DJL.md'
-          - Load a TensorFlow Model: 'jupyter/tensorflow/pneumonia_detection.ipynb'
+          - Load a TensorFlow Model: 'docs/demos/jupyter/tensorflow/pneumonia_detection.ipynb'
       - Apache MXNet:
           - Overview: 'engines/mxnet/README.md'
           - MXNet Engine: 'engines/mxnet/mxnet-engine/README.md'
           - MXNet Model Zoo: 'engines/mxnet/mxnet-model-zoo/README.md'
           - Import Gluon Model: 'docs/mxnet/how_to_convert_your_model_to_symbol.md'
-          - Load a MXNet Model: 'jupyter/load_mxnet_model.ipynb'
+          - Load a MXNet Model: 'docs/demos/jupyter/load_mxnet_model.ipynb'
           - Backend Optimizer for MXNet: 'docs/mxnet/mxnet_backend_optimizer.md'
       - Hybrid engines:
           - Hybrid engine overview: 'docs/hybrid_engine.md'
           - ONNX Runtime:
               - Overview: 'engines/onnxruntime/onnxruntime-engine/README.md'
-              - Load a ONNX Model: 'jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb'
+              - Load a ONNX Model: 'docs/demos/jupyter/onnxruntime/machine_learning_with_ONNXRuntime.ipynb'
           - PaddlePaddle:
               - Overview: 'engines/paddlepaddle/README.md'
               - PaddlePaddle Engine: 'engines/paddlepaddle/paddlepaddle-engine/README.md'
@@ -124,11 +124,11 @@ nav:
                   - English: 'docs/paddlepaddle/how_to_create_paddlepaddle_model.md'
                   - 中文: 'docs/paddlepaddle/how_to_create_paddlepaddle_model_zh.md'
               - Facemask detection using PaddlePaddle:
-                  - English: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb'
-                  - 中文: 'jupyter/paddlepaddle/face_mask_detection_paddlepaddle_zh.ipynb'
+                  - English: 'docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb'
+                  - 中文: 'docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle_zh.ipynb'
               - PaddleOCR example:
-                  - English: 'jupyter/paddlepaddle/paddle_ocr_java.ipynb'
-                  - 中文: 'jupyter/paddlepaddle/paddle_ocr_java_zh.ipynb'
+                  - English: 'docs/demos/jupyter/paddlepaddle/paddle_ocr_java.ipynb'
+                  - 中文: 'docs/demos/jupyter/paddlepaddle/paddle_ocr_java_zh.ipynb'
           - XGBoost: 'engines/ml/xgboost/README.md'
           - LightGBM: 'engines/ml/lightgbm/README.md'
           - TensorRT: 'engines/tensorrt/README.md'

docs/paddlepaddle/how_to_create_paddlepaddle_model.md

@@ -157,5 +157,5 @@ predictor.predict(list);
 
 As mentioned, you need to find out what is the input for the model, like images usually interpret as NCHW (batch_size, channel, height, width).
 
-However, usage like this is really basic, you can write a `Translator` in DJL for it. You can find some code examples [here](../../jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb).
+However, usage like this is really basic, you can write a `Translator` in DJL for it. You can find some code examples [here](http://docs.djl.ai/docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.html).
 

docs/paddlepaddle/how_to_create_paddlepaddle_model_zh.md

@@ -156,4 +156,4 @@ predictor.predict(list);
 
 在这里，你需要知道模型的输入输出格式, 比如图片经常表达成 NCHW (批大小, RGB通道, 高度, 宽度)的多维矩阵。
 
-虽然这样可以让模型跑起来, 但是最好还是结合 DJL 的 `Translator` class 使用。你可以在 [这里](../../jupyter/paddlepaddle/face_mask_detection_paddlepaddle.ipynb) 找到一些示例代码。
+虽然这样可以让模型跑起来, 但是最好还是结合 DJL 的 `Translator` class 使用。你可以在 [这里](http://docs.djl.ai/docs/demos/jupyter/paddlepaddle/face_mask_detection_paddlepaddle.html) 找到一些示例代码。

docs/quick_start.md

@@ -1,7 +1,7 @@
 # Quick start
 
 Deep Java Library (DJL) is designed to be easy to get started with and simple to use.
-The easiest way to learn DJL is to read the [beginner tutorial](../jupyter/tutorial/README.md) or
+The easiest way to learn DJL is to read the [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/README.md) or
 our [examples](../examples/README.md).
 
 You can also view our 1.5 hour long (in 8 x ~10 minute segments) DJL 101 tutorial video series:
@@ -22,7 +22,7 @@ See [DJL Future Labs](interactive_tool.md)
 
 ## Beginner tutorial
 
-To get started, we recommend that you follow our short [beginner tutorial](../jupyter/tutorial/README.md). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
+To get started, we recommend that you follow our short [beginner tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/index.html). It takes you through some of the basics of deep learning to create a model, train your model, and run inference using your trained model.
 
 ## Run examples
 
@@ -33,7 +33,7 @@ All of our examples are executed by a simple command. For detailed command line
 - [Train your first model](../examples/docs/train_mnist_mlp.md)
 - [Single-shot Object Detection inference example](../examples/docs/object_detection.md)
 - [More examples](https://github.com/deepjavalibrary/djl/tree/master/examples)
-- [Jupyter examples](../jupyter/README.md)
+- [Jupyter examples](http://docs.djl.ai/docs/demos/jupyter/index.html)
 
 ## Other resources
 

examples/docs/image_classification.md

@@ -6,7 +6,7 @@ In this example, you learn how to implement inference code with Deep Java Librar
 
 The image classification example code can be found at [ImageClassification.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ImageClassification.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/tutorial/03_image_classification_with_your_model.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/03_image_classification_with_your_model.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup Guide

examples/docs/object_detection.md

@@ -7,7 +7,7 @@ In this example, you learn how to implement inference code with a [ModelZoo mode
 
 The source code can be found at [ObjectDetection.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/inference/ObjectDetection.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/object_detection_with_model_zoo.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/object_detection_with_model_zoo.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

examples/docs/train_cifar10_resnet.md

@@ -5,7 +5,7 @@ In this example, you learn how to train the [CIFAR-10](https://www.cs.toronto.ed
 
 You can find the example source code in: [TrainResnetWithCifar10.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/transferlearning/TrainResnetWithCifar10.java).
 
-You can also find the Jupyter notebook tutorial [here](../../jupyter/transfer_learning_on_cifar10.ipynb).
+You can also find the Jupyter notebook tutorial [here](http://docs.djl.ai/docs/demos/jupyter/transfer_learning_on_cifar10.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

examples/docs/train_mnist_mlp.md

@@ -6,7 +6,7 @@ In this example, you learn how to train the MNIST dataset with Deep Java Library
 
 The source code for this example can be found at [TrainMnist.java](https://github.com/deepjavalibrary/djl/blob/master/examples/src/main/java/ai/djl/examples/training/TrainMnist.java).
 
-You can also use the [Jupyter notebook tutorial](../../jupyter/tutorial/02_train_your_first_model.ipynb).
+You can also use the [Jupyter notebook tutorial](http://docs.djl.ai/docs/demos/jupyter/tutorial/02_train_your_first_model.html).
 The Jupyter notebook explains the key concepts in detail.
 
 ## Setup guide

examples/src/main/java/ai/djl/examples/inference/BertQaInference.java

@@ -34,9 +34,8 @@
  * <p>See:
  *
  * <ul>
- *   <li>the <a
- *       href="https://github.com/deepjavalibrary/djl/blob/master/jupyter/BERTQA.ipynb">jupyter
- *       demo</a> with more information about BERT.
+ *   <li>the <a href="http://docs.djl.ai/docs/demos/jupyter/BERTQA.html">jupyter demo</a> with more
+ *       information about BERT.
  *   <li>the <a
  *       href="https://github.com/deepjavalibrary/djl/blob/master/examples/docs/BERT_question_and_answer.md">docs</a>
  *       for information about running this example.

index1.0.html

@@ -59,7 +59,7 @@
                     <li><a href="website/javadoc.html">JavaDoc</a></li>
                     <li><a href="website/demo.html">Demos</a></li>
                     <li><a href="website/blog.html">Blogs</a></li>
-                    <li><a href="https://docs.djl.ai/jupyter/tutorial/index.html">Tutorial</a></li>
+                    <li><a href="https://docs.djl.ai/docs/demos/jupyter/tutorial/index.html">Tutorial</a></li>
                     <li><a href="https://github.com/deepjavalibrary/djl/tree/master/examples">Examples</a></li>
                     <li><a
                             href="https://deepjavalibrary.slack.com/join/shared_invite/zt-ar91gjkz-qbXhr1l~LFGEIEeGBibT7w#/">Slack</a>
@@ -73,7 +73,7 @@
                     <li><a href="website/javadoc.html">JavaDoc</a></li>
                     <li><a href="website/demo.html">Demos</a></li>
                     <li><a href="website/blog.html">Blogs</a></li>
-                    <li><a href="https://docs.djl.ai/jupyter/tutorial/index.html">Tutorial</a></li>
+                    <li><a href="https://docs.djl.ai/docs/demos/jupyter/tutorial/index.html">Tutorial</a></li>
                     <li><a href="https://github.com/deepjavalibrary/djl/tree/master/examples">Examples</a></li>
                     <li><a
                             href="https://deepjavalibrary.slack.com/join/shared_invite/zt-ar91gjkz-qbXhr1l~LFGEIEeGBibT7w#/">Slack</a>