From e70ff84ce41105c8031888615c30bcc97bdf8e5e Mon Sep 17 00:00:00 2001 From: jiqing-feng <107918818+jiqing-feng@users.noreply.github.com> Date: Thu, 12 Sep 2024 18:29:44 +0800 Subject: [PATCH] Add documentation IPEX section (#881) * ipex button * fix format * reorder sections to follow integration order * update documentation * add missing ponctuation --------- Co-authored-by: Ella Charlaix --- docs/source/index.mdx | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/source/index.mdx b/docs/source/index.mdx index c9ad66206..a23eb1dd2 100644 --- a/docs/source/index.mdx +++ b/docs/source/index.mdx @@ -19,21 +19,25 @@ limitations under the License. 🤗 Optimum Intel is the interface between the 🤗 Transformers and Diffusers libraries and the different tools and libraries provided by Intel to accelerate end-to-end pipelines on Intel architectures. -[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) (IPEX) is an open-source library which provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. - [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) 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. +[Intel Extension for PyTorch](https://intel.github.io/intel-extension-for-pytorch/#introduction) (IPEX) is an open-source library which provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. +
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Neural Compressor
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Learn how to apply compression techniques such as quantization, pruning and knowledge distillation to speed up inference with Intel Neural Compressor.

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Learn how to apply compression techniques such as quantization, pruning and knowledge distillation to speed up inference.
OpenVINO
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Learn how to run inference with OpenVINO Runtime and to apply quantization, pruning and knowledge distillation on your model to further speed up inference.

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Learn how to run inference with OpenVINO Runtime and to apply quantization to further speed up inference.

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IPEX
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Learn how to optimize your model with IPEX.
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