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Remove graphcore from documentation quickstart #1201

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Jul 18, 2023
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Remove graphcore from documentation quickstart #1201

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1 change: 0 additions & 1 deletion docs/source/installation.mdx
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,6 @@ If you'd like to use the accelerator-specific features of 🤗 Optimum, you can
| [ONNX runtime](https://onnxruntime.ai/docs/) | `python -m pip install optimum[onnxruntime]` |
| [Intel Neural Compressor (INC)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) | `python -m pip install optimum[neural-compressor]`|
| [Intel OpenVINO](https://docs.openvino.ai/latest/index.html) | `python -m pip install optimum[openvino,nncf]` |
| [Graphcore IPU](https://www.graphcore.ai/products/ipu) | `python -m pip install optimum[graphcore]` |
| [Habana Gaudi Processor (HPU)](https://habana.ai/training/) | `python -m pip install optimum[habana]` |


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87 changes: 24 additions & 63 deletions docs/source/quicktour.mdx
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Expand Up @@ -16,6 +16,30 @@ This quick tour is intended for developers who are ready to dive into the code a

## Accelerated inference

#### OpenVINO

To load a model and run inference with OpenVINO Runtime, you can just replace your `AutoModelForXxx` class with the corresponding `OVModelForXxx` class.
If you want to load a PyTorch checkpoint, set `export=True` to convert your model to the OpenVINO IR (Intermediate Representation).

```diff
- from transformers import AutoModelForSequenceClassification
+ from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

# Download a tokenizer and model from the Hub and convert to OpenVINO format
tokenizer = AutoTokenizer.from_pretrained(model_id)
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
- model = AutoModelForSequenceClassification.from_pretrained(model_id)
+ model = OVModelForSequenceClassification.from_pretrained(model_id, export=True)

# Run inference!
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
results = classifier("He's a dreadful magician.")
```

You can find more examples in the [documentation](https://huggingface.co/docs/optimum/intel/inference) and in the [examples](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino).


#### ONNX Runtime

To accelerate inference with ONNX Runtime, 🤗 Optimum uses _configuration objects_ to define parameters for graph optimization and quantization. These objects are then used to instantiate dedicated _optimizers_ and _quantizers_.
Expand Down Expand Up @@ -67,30 +91,6 @@ In this example, we've quantized a model from the Hugging Face Hub, in the same
You can find more examples in the [documentation](https://huggingface.co/docs/optimum/onnxruntime/quickstart) and in the [examples](https://github.com/huggingface/optimum/tree/main/examples/onnxruntime).


#### Intel

To load a model and run inference with OpenVINO Runtime, you can just replace your `AutoModelForXxx` class with the corresponding `OVModelForXxx` class.
If you want to load a PyTorch checkpoint, set `export=True` to convert your model to the OpenVINO IR (Intermediate Representation).

```diff
- from transformers import AutoModelForSequenceClassification
+ from optimum.intel.openvino import OVModelForSequenceClassification
from transformers import AutoTokenizer, pipeline

# Download a tokenizer and model from the Hub and convert to OpenVINO format
tokenizer = AutoTokenizer.from_pretrained(model_id)
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
- model = AutoModelForSequenceClassification.from_pretrained(model_id)
+ model = OVModelForSequenceClassification.from_pretrained(model_id, export=True)

# Run inference!
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
results = classifier("He's a dreadful magician.")
```

You can find more examples in the [documentation](https://huggingface.co/docs/optimum/intel/inference) and in the [examples](https://github.com/huggingface/optimum-intel/tree/main/examples/openvino).


## Accelerated training

#### Habana
Expand Down Expand Up @@ -130,45 +130,6 @@ To train transformers on Habana's Gaudi processors, 🤗 Optimum provides a `Gau
You can find more examples in the [documentation](https://huggingface.co/docs/optimum/habana/quickstart) and in the [examples](https://github.com/huggingface/optimum-habana/tree/main/examples).


#### Graphcore

To train transformers on Graphcore's IPUs, 🤗 Optimum provides a `IPUTrainer` that is very similar to the 🤗 Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer). Here is a simple example:

```diff
- from transformers import Trainer, TrainingArguments
+ from optimum.graphcore import IPUConfig, IPUTrainer, IPUTrainingArguments

# Download a pretrained model from the Hub
model = AutoModelForXxx.from_pretrained("bert-base-uncased")

# Define the training arguments
- training_args = TrainingArguments(
+ training_args = IPUTrainingArguments(
output_dir="path/to/save/folder/",
+ ipu_config_name="Graphcore/bert-base-ipu", # Any IPUConfig on the Hub or stored locally
...
)

# Define the configuration to compile and put the model on the IPU
+ ipu_config = IPUConfig.from_pretrained(training_args.ipu_config_name)

# Initialize the trainer
- trainer = Trainer(
+ trainer = IPUTrainer(
model=model,
+ ipu_config=ipu_config
args=training_args,
train_dataset=train_dataset
...
)

# Use Graphcore IPU for training!
trainer.train()
```

You can find more examples in the [documentation](https://huggingface.co/docs/optimum/graphcore/quickstart) and in the [examples](https://github.com/huggingface/optimum-graphcore/tree/main/examples).


#### ONNX Runtime

To train transformers with ONNX Runtime's acceleration features, 🤗 Optimum provides a `ORTTrainer` that is very similar to the 🤗 Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer). Here is a simple example:
Expand Down
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