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Update OpenVINO documentation links in README.md (huggingface#587)
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* Update OpenVINO documentation links in README.md

The links are now aligned with OpenVINO 2024.0 documentation, and include permalinks instead of direct links, when possible.

* Update inference.mdx

* Update index.mdx

* Update installation.mdx

* Update README.md
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@kblaszczak-intel @PenghuiCheng
kblaszczak-intel authored and PenghuiCheng committed Mar 13, 2024
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8 changes: 4 additions & 4 deletions README.md
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Intel [Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) is an open-source library enabling the usage of the most popular compression techniques such as quantization, pruning and knowledge distillation. It supports automatic accuracy-driven tuning strategies in order for users to easily generate quantized model. The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. It also supports different weight pruning techniques enabling the creation of pruned model giving a predefined sparsity target.

[OpenVINO](https://docs.openvino.ai/latest/index.html) is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators ([see](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.
[OpenVINO](https://docs.openvino.ai) is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators ([see](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html) the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.


## Installation
Expand All @@ -20,7 +20,7 @@ To install the latest release of 🤗 Optimum Intel with the corresponding requi
| Accelerator | Installation |
|:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|
| [Intel Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) | `pip install --upgrade-strategy eager "optimum[neural-compressor]"` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `pip install --upgrade-strategy eager "optimum[openvino]"` |
| [OpenVINO](https://docs.openvino.ai) | `pip install --upgrade-strategy eager "optimum[openvino]"` |
| [Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) | `pip install --upgrade-strategy eager "optimum[ipex]"` |

The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.
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## OpenVINO

Below are the examples of how to use OpenVINO and its [NNCF](https://docs.openvino.ai/latest/tmo_introduction.html) framework to accelerate inference.
Below are examples of how to use OpenVINO and its [NNCF](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/compressing-models-during-training.html) framework to accelerate inference.

#### Export:

It is possible to export your model to the [OpenVINO](https://docs.openvino.ai/2023.1/openvino_ir.html) IR format with the CLI :
It is possible to export your model to the [OpenVINO IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) format with the CLI :

```plain
optimum-cli export openvino --model gpt2 ov_model
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4 changes: 2 additions & 2 deletions docs/source/index.mdx
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Expand Up @@ -21,7 +21,7 @@ limitations under the License.

[Intel Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) is an open-source library enabling the usage of the most popular compression techniques such as quantization, pruning and knowledge distillation. It supports automatic accuracy-driven tuning strategies in order for users to easily generate quantized model. The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. It also supports different weight pruning techniques enabling the creation of pruned model giving a predefined sparsity target.

[OpenVINO](https://docs.openvino.ai/latest/index.html) is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators ([see](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.
[OpenVINO](https://docs.openvino.ai) is an open-source toolkit that enables high performance inference capabilities for Intel CPUs, GPUs, and special DL inference accelerators ([see](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html) the full list of supported devices). It is supplied with a set of tools to optimize your models with compression techniques such as quantization, pruning and knowledge distillation. Optimum Intel provides a simple interface to optimize your Transformers and Diffusers models, convert them to the OpenVINO Intermediate Representation (IR) format and run inference using OpenVINO Runtime.

<div class="mt-10">
<div class="w-full flex flex-col space-x-4 md:grid md:grid-cols-2 md:gap-x-5">
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<p class="text-gray-700">Learn how to run inference with OpenVINO Runtime and to apply quantization, pruning and knowledge distillation on your model to further speed up inference.</p>
</a>
</div>
</div>
</div>
7 changes: 4 additions & 3 deletions docs/source/inference.mdx
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Expand Up @@ -13,7 +13,8 @@ Optimum Intel can be used to load optimized models from the [Hugging Face Hub](h

## Transformers models

You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors ([see](https://docs.openvino.ai/latest/openvino_docs_OV_UG_supported_plugins_Supported_Devices.html) the full list of supported devices).
You can now easily perform inference with OpenVINO Runtime on a variety of Intel processors
([see](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html) the full list of supported devices).
For that, just replace the `AutoModelForXxx` class with the corresponding `OVModelForXxx` class.

As shown in the table below, each task is associated with a class enabling to automatically load your model.
Expand All @@ -33,7 +34,7 @@ As shown in the table below, each task is associated with a class enabling to au

### Export

It is possible to export your model to the [OpenVINO](https://docs.openvino.ai/2023.1/openvino_ir.html) IR format with the CLI :
It is possible to export your model to the [OpenVINO IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) format with the CLI :

```bash
optimum-cli export openvino --model gpt2 ov_model
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model.compile()
```

To run inference on Intel integrated or discrete GPU, use `.to("gpu")`. On GPU, models run in FP16 precision by default. (See [OpenVINO documentation](https://docs.openvino.ai/nightly/openvino_docs_install_guides_configurations_for_intel_gpu.html) about installing drivers for GPU inference).
To run inference on Intel integrated or discrete GPU, use `.to("gpu")`. On GPU, models run in FP16 precision by default. (See [OpenVINO documentation](https://docs.openvino.ai/2024/get-started/configurations/configurations-intel-gpu.html) about installing drivers for GPU inference).

```python
# Static shapes speed up inference
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4 changes: 2 additions & 2 deletions docs/source/installation.mdx
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Expand Up @@ -21,7 +21,7 @@ To install the latest release of 🤗 Optimum Intel with the corresponding requi
| Accelerator | Installation |
|:-----------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
| [Intel Neural Compressor (INC)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) | `pip install --upgrade-strategy eager "optimum[neural-compressor]"`|
| [Intel OpenVINO](https://docs.openvino.ai/latest/index.html) | `pip install --upgrade-strategy eager "optimum[openvino]"` |
| [Intel OpenVINO](https://docs.openvino.ai ) | `pip install --upgrade-strategy eager "optimum[openvino]"` |

The `--upgrade-strategy eager` option is needed to ensure `optimum-intel` is upgraded to the latest version.

Expand All @@ -42,4 +42,4 @@ or to install from source including dependencies:
python -m pip install "optimum-intel[extras]"@git+https://github.com/huggingface/optimum-intel.git
```

where `extras` can be one or more of `neural-compressor`, `openvino`, `nncf`.
where `extras` can be one or more of `neural-compressor`, `openvino`, `nncf`.

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