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Add meta tag to torch_export_aoti_python (#3036)
* Add meta tag to torch_export_aoti_python * Feature on the landing page
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conf.py

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#
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# needs_sphinx = '1.0'
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html_meta = {
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'description': 'Master PyTorch with our step-by-step tutorials for all skill levels. Start your journey to becoming a PyTorch expert today!',
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'keywords': 'PyTorch, tutorials, Getting Started, deep learning, AI',
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'author': 'PyTorch Contributors'
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}
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# Add any Sphinx extension module names here, as strings. They can be
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.

index.rst

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**What's new in PyTorch tutorials?**
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* `torch.export AOTInductor Tutorial for Python runtime (Beta) <https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html>`__
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* `A guide on good usage of non_blocking and pin_memory() in PyTorch <https://pytorch.org/tutorials/intermediate/pinmem_nonblock.html>`__
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* `Introduction to Distributed Pipeline Parallelism <https://pytorch.org/tutorials/intermediate/pipelining_tutorial.html>`__
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* `Introduction to Libuv TCPStore Backend <https://pytorch.org/tutorials/intermediate/TCPStore_libuv_backend.html>`__

recipes_source/torch_export_aoti_python.py

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# -*- coding: utf-8 -*-
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"""
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(Beta) ``torch.export`` AOTInductor Tutorial for Python runtime
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.. meta::
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:description: An end-to-end example of how to use AOTInductor for Python runtime.
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:keywords: torch.export, AOTInductor, torch._inductor.aot_compile, torch._export.aot_load
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``torch.export`` AOTInductor Tutorial for Python runtime (Beta)
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===============================================================
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**Author:** Ankith Gunapal, Bin Bao, Angela Yi
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"""
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# a shared library that can be run in a non-Python environment.
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#
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#
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# In this tutorial, you will learn an end-to-end example of how to use AOTInductor for python runtime.
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# In this tutorial, you will learn an end-to-end example of how to use AOTInductor for Python runtime.
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# We will look at how to use :func:`torch._inductor.aot_compile` along with :func:`torch.export.export` to generate a
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# shared library. Additionally, we will examine how to execute the shared library in Python runtime using :func:`torch._export.aot_load`.
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# You will learn about the speed up seen in the first inference time using AOTInductor, especially when using

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