fix format issue in installation guide (#2141)

jingxu10 · web-flow · commit 7585497e7cf3 · 2023-01-05T16:03:11.000+08:00
* fix format issue in installation guide
diff --git a/docs/tutorials/blogs_publications.md b/docs/tutorials/blogs_publications.md
@@ -1,10 +1,24 @@
 Blogs & Publications
 ====================
 
-* [What is new in Intel Extension for PyTorch (PyTorch Conference 2022 Breakout Session)](https://www.youtube.com/watch?v=SE56wFXdvP4&t=1s)
-* [Accelerating PyTorch with Intel® Extension for PyTorch\*](https://medium.com/pytorch/accelerating-pytorch-with-intel-extension-for-pytorch-3aef51ea3722)
-* [Intel and Facebook Accelerate PyTorch Performance with 3rd Gen Intel® Xeon® Processors and Intel® Deep Learning Boost’s new BFloat16 capability](https://www.intel.com/content/www/us/en/artificial-intelligence/posts/intel-facebook-boost-bfloat16.html)
-* [Accelerate PyTorch with the extension and oneDNN using Intel BF16 Technology](https://medium.com/pytorch/accelerate-pytorch-with-ipex-and-onednn-using-intel-bf16-technology-dca5b8e6b58f)
-  * *Note*: APIs mentioned in it are deprecated.
-* [Scaling up BERT-like model Inference on modern CPU - Part 1 by the launcher of the extension](https://huggingface.co/blog/bert-cpu-scaling-part-1)
-* [KT Optimizes Performance for Personalized Text-to-Speech](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/KT-Optimizes-Performance-for-Personalized-Text-to-Speech/post/1337757)
+* [What is New in Intel Extension for PyTorch, PyTorch Conference, Dec 2022](https://www.youtube.com/watch?v=SE56wFXdvP4&t=1s)
+* [Accelerating PyG on Intel CPUs, Dec 2022](https://www.pyg.org/ns-newsarticle-accelerating-pyg-on-intel-cpus)
+* [PyTorch Stable Diffusion Using Hugging Face and Intel Arc, Nov 2022](https://towardsdatascience.com/pytorch-stable-diffusion-using-hugging-face-and-intel-arc-77010e9eead6)
+* [Empowering PyTorch on Intel® Xeon® Scalable processors with Bfloat16, Aug 2022](https://pytorch.org/blog/empowering-pytorch-on-intel-xeon-scalable-processors-with-bfloat16/)
+* [Accelerating PyTorch Vision Models with Channels Last on CPU, Aug 2022](https://pytorch.org/blog/accelerating-pytorch-vision-models-with-channels-last-on-cpu/)
+* [Accelerating PyTorch with Intel® Extension for PyTorch, May 2022](https://medium.com/pytorch/accelerating-pytorch-with-intel-extension-for-pytorch-3aef51ea3722)
+* [Grokking PyTorch Intel CPU performance from first principles, Apr 2022](https://pytorch.org/tutorials/intermediate/torchserve_with_ipex.html)
+* [Grokking PyTorch Intel CPU performance from first principles, Apr 2022](https://medium.com/pytorch/grokking-pytorch-intel-cpu-performance-from-first-principles-7e39694412db)
+* [KT Optimizes Performance for Personalized Text-to-Speech, Nov 2021](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/KT-Optimizes-Performance-for-Personalized-Text-to-Speech/post/1337757)
+* [Scaling up BERT-like model Inference on modern CPU - Part 1, Apr 2021](https://huggingface.co/blog/bert-cpu-scaling-part-1)
+* [Accelerating PyTorch distributed fine-tuning with Intel technologies, Nov 2021](https://huggingface.co/blog/accelerating-pytorch)
+* [Intel® Extensions for PyTorch, Feb 2021](https://pytorch.org/tutorials/recipes/recipes/intel_extension_for_pytorch.html)
+* [Optimizing DLRM by using PyTorch with oneCCL Backend, Feb 2021](https://pytorch.medium.com/optimizing-dlrm-by-using-pytorch-with-oneccl-backend-9f85b8ef6929)
+* [Accelerate PyTorch with IPEX and oneDNN using Intel BF16 Technology, Feb 2021](https://medium.com/pytorch/accelerate-pytorch-with-ipex-and-onednn-using-intel-bf16-technology-dca5b8e6b58f)
+  *Note*: APIs mentioned in it are deprecated.
+* [Scaling up BERT-like model Inference on modern CPU - Part 1](https://huggingface.co/blog/bert-cpu-scaling-part-1)
+* [Intel and Facebook Accelerate PyTorch Performance with 3rd Gen Intel® Xeon® Processors and Intel® Deep Learning Boost’s new BFloat16 capability, Jun 2020](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Intel-and-Facebook-Accelerate-PyTorch-Performance-with-3rd-Gen/post/1335659)
+* [OneAPI Dev Summit 2022](https://www.oneapi.io/event-sessions/accelerating-pytorch-deep-learning-models-on-intel-xpus-2-ai-hpc-2022/)
+* [Scaling Inference on CPUs with TorchServe, PyTorch Conference 2022](https://www.youtube.com/watch?v=066_Jd6cwZg)
+* [Grokking PyTorch Intel CPU Performance From First Principles, PyTorch Blog](https://pytorch.org/tutorials/intermediate/torchserve_with_ipex.html?highlight=grokking)
+* [Grokking PyTorch Intel CPU Performance From First Principles (Part 2), PyTorch Blog](https://pytorch.org/tutorials/intermediate/torchserve_with_ipex_2.html?highlight=grokking%20pytorch%20intel%20cpu%20performance%20from%20first%20principles%20part)
diff --git a/docs/tutorials/examples.md b/docs/tutorials/examples.md
@@ -652,7 +652,7 @@ modelJit = convert_jit(modelJit, True)
 modelJit(data)
 ```
 
-### `torch.xpu.optimize`
+### torch.xpu.optimize
 
 `torch.xpu.optimize` is an alternative of `ipex.optimize` in Intel® Extension for PyTorch*, to provide identical usage for XPU device only. The motivation of adding this alias is to unify the coding style in user scripts base on torch.xpu modular. Refer to below example for usage.
 
diff --git a/docs/tutorials/features.rst b/docs/tutorials/features.rst
@@ -53,7 +53,7 @@ Intel® Extension for PyTorch* provides built-in quantization recipes to deliver
 
 Check more detailed information for `INT8 Quantization [CPU] <features/int8_overview.md>`_ and `INT8 recipe tuning API guide (Experimental, *NEW feature in 1.13.0* on CPU) <features/int8_recipe_tuning_api.md>`_ on CPU side.
 
-On Intel® GPUs, quantization usages follows PyTorch default quantization APIs.
+On Intel® GPUs, quantization usages follow PyTorch default quantization APIs. Check sample codes at `Examples <./examples.html#int8>`_ page.
 
 .. toctree::
    :hidden:
diff --git a/docs/tutorials/installation.md b/docs/tutorials/installation.md
@@ -8,36 +8,31 @@ Installation Guide
 Verified Hardware Platforms:
  - Intel® Data Center GPU Flex Series 170
  - Intel® Data Center GPU Max Series
- - Intel® Arc™ series GPUs (Experimental support)
+ - Intel® Arc™ A-Series GPUs (Experimental support)
 
 ### Software Requirements
 
-- Ubuntu 22.04 (64-bit)
-- Intel GPU Drivers 
-  - Intel® Data Center GPU Flex Series [Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)
-  - Intel® Data Center GPU Max Series [Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)
-  - Intel® Arc™ A-Series Graphics [Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)
+- OS & Intel GPU Drivers
+
+|Hardware|OS|Driver|
+|-|-|-|
+|Intel® Data Center GPU Flex Series|Ubuntu 22.04, Red Hat 8.6|[Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)|
+|Intel® Data Center GPU Max Series|Red Hat 8.6, Sles 15sp3/sp4|[Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)|
+|Intel® Arc™ A-Series Graphics|Ubuntu 22.04|[Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html)|
+|Intel® Arc™ A-Series Graphics|Windows 11 or Windows 10 21H2 (via WSL2)|[for Windows 11 or Windows 10 21H2](https://www.intel.com/content/www/us/en/download/726609/intel-arc-graphics-windows-dch-driver.html)|
+
 - Intel® oneAPI Base Toolkit 2023.0
 - Python 3.7-3.10
 - Verified with GNU GCC 11
 
-## PyTorch-Intel® Extension for PyTorch\* Version Mapping
-
-Intel® Extension for PyTorch\* has to work with a corresponding version of PyTorch. Here are the PyTorch versions that we support and the mapping relationship:
-
-|PyTorch Version|Extension Version|
-|--|--|
-|[v1.13.\*](https://github.com/pytorch/pytorch/tree/v1.13.0) (patches needed)|[v1.13.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.10+xpu)|
-|[v1.10.\*](https://github.com/pytorch/pytorch/tree/v1.10.0) (patches needed)|[v1.10.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.10.200+gpu)|
-
 ## Preparations
 
 ### Install Intel GPU Driver
 
 |OS|Instructions for installing Intel GPU Driver|
-|-|-|-|
-|Ubuntu 22.04|Refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-jammy-arc.html) for the latest driver installation. When installing the verified [Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html) driver, use a specific version for component package names, such as `sudo apt-get install intel-opencl-icd=22.43.24595.35`|
-|WSL2 Ubuntu 20.04 on Windows 11 or Windows 10 21H2|Please download drivers for Intel® Arc™ series [for Windows 11 or Windows 10 21H2](https://www.intel.com/content/www/us/en/download/726609/intel-arc-graphics-windows-dch-driver.html). Please note that you would have to follow the rest of the steps in WSL2, but the drivers should be installed on Windows|
+|-|-|
+|Linux\*|Refer to the [Installation Guides](https://dgpu-docs.intel.com/installation-guides/index.html) for the latest driver installation for individual Linux\* distributions. When installing the verified [Stable 540](https://dgpu-docs.intel.com/releases/stable_540_20221205.html) driver, use a specific version for component package names, such as `sudo apt-get install intel-opencl-icd=22.43.24595.35`|
+|Windows 11 or Windows 10 21H2 (via WSL2)|Please download drivers for Intel® Arc™ A-Series [for Windows 11 or Windows 10 21H2](https://www.intel.com/content/www/us/en/download/726609/intel-arc-graphics-windows-dch-driver.html). Please note that you would have to follow the rest of the steps in WSL2, but the drivers should be installed on Windows|
 
 ### Install oneAPI Base Toolkit
 
@@ -55,6 +50,15 @@ Default installation location *{ONEAPI_ROOT}* is `/opt/intel/oneapi` for root ac
 source {ONEAPI_ROOT}/setvars.sh
 ```
 
+## PyTorch-Intel® Extension for PyTorch\* Version Mapping
+
+Intel® Extension for PyTorch\* has to work with a corresponding version of PyTorch. Here are the PyTorch versions that we support and the mapping relationship:
+
+|PyTorch Version|Extension Version|
+|--|--|
+|[v1.13.\*](https://github.com/pytorch/pytorch/tree/v1.13.0) (patches needed)|[v1.13.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.13.10+xpu)|
+|[v1.10.\*](https://github.com/pytorch/pytorch/tree/v1.10.0) (patches needed)|[v1.10.\*](https://github.com/intel/intel-extension-for-pytorch/tree/v1.10.200+gpu)|
+
 ## Install via wheel files
 
 Prebuilt wheel files availability matrix for Python versions:
@@ -64,18 +68,24 @@ Prebuilt wheel files availability matrix for Python versions:
 | 1.13.10+xpu |  | ✔️ | ✔️ | ✔️ | ✔️ |
 | 1.10.200+gpu | ✔️ | ✔️ | ✔️ | ✔️ |  |
 
-### Install PyTorch and TorchVision for stock Python
+**Note:** Wheel files for Intel® Distribution for Python\* only supports Python 3.9.
+
+**Note:** Wheel files supporting Intel® Distribution for Python\* starts from 1.13.
+
+### Repositories for prebuilt wheel files
 
-```bash
-python -m pip install torch==1.13.0a0 torchvision==0.14.1a0 -f https://developer.intel.com/ipex-whl-stable-xpu
 ```
+# Stock PyTorch
+REPO_URL: https://developer.intel.com/ipex-whl-stable-xpu
 
-**Note:** Installation of TorchVision is optional.
+# Intel® Distribution for Python*
+REPO_URL: https://developer.intel.com/ipex-whl-stable-xpu-idp
+```
 
-### Install PyTorch and TorchVision for Intel® Distribution for Python\*
+### Install PyTorch and TorchVision
 
 ```bash
-python -m pip install torch==1.13.0a0 torchvision==0.14.1a0 -f https://developer.intel.com/ipex-whl-stable-xpu-idp
+python -m pip install torch==1.13.0a0 torchvision==0.14.1a0 -f <REPO_URL>
 ```
 
 **Note:** Installation of TorchVision is optional.
@@ -89,7 +99,7 @@ Intel® Extension for PyTorch\* doesn't depend on torchaudio. If you need TorchA
 ### Install Intel® Extension for PyTorch\*
 
 ```bash
-python -m pip install intel_extension_for_pytorch==1.13.10+xpu -f https://developer.intel.com/ipex-whl-stable-xpu
+python -m pip install intel_extension_for_pytorch==1.13.10+xpu -f <REPO_URL>
 ```
 
 ## Install via compiling from source
@@ -140,5 +150,5 @@ $ pip install dist/*.whl
 
 |Issue|Explanation|
 |-|-|
-|Building from source for Intel® Arc™ series GPUs failed on WSL2 without any error thrown|Your system probably does not have enough RAM, so Linux kernel's Out-of-memory killer got invoked. You can verify it by running `dmesg` on bash (WSL2 terminal). If the OOM killer had indeed killed the build process, then you can try increasing the swap-size of WSL2, and/or decreasing the number of parallel build jobs with the environment variable `MAX_JOBS` (by default, it's equal to the number of logical CPU cores. So, setting `MAX_JOBS` to 1 is a very conservative approach, which would slow things down a lot).|
+|Building from source for Intel® Arc™ A-Series GPUs failed on WSL2 without any error thrown|Your system probably does not have enough RAM, so Linux kernel's Out-of-memory killer got invoked. You can verify it by running `dmesg` on bash (WSL2 terminal). If the OOM killer had indeed killed the build process, then you can try increasing the swap-size of WSL2, and/or decreasing the number of parallel build jobs with the environment variable `MAX_JOBS` (by default, it's equal to the number of logical CPU cores. So, setting `MAX_JOBS` to 1 is a very conservative approach, which would slow things down a lot).|
 |On WSL2, some workloads terminate with an error `CL_DEVICE_NOT_FOUND` after some time | This is due to the [TDR feature](https://learn.microsoft.com/en-us/windows-hardware/drivers/display/tdr-registry-keys#tdrdelay) in Windows. You can try increasing TDRDelay in your Windows Registry to a large value, such as 20 (it is 2 seconds, by default), and reboot.|
