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Audit and update the pip package metadata (#3265)
Fill out the recommended `project` keys, most of which will affect the web page that PyPI will render for the `executorch` package. See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#about-your-project for the latest guidance. Use https://github.com/pytorch/pytorch/blob/a21327e0b03cc18850a0608be2d9c5bd38fd4646/setup.py#L1394 as a guide for the actual values. Add a README-wheel.md file that will be included in the wheel, and will become the main page contents on PyPI. Test Plan: * Installed the package with `./install_requirements.sh` * Looked at the files under ~/miniconda3/envs/executorch/lib/python3.10/site-packages/executorch-0.2.0a0+1a499e0.dist-info. METADATA and LICENSE both contain the new metadata.
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README-wheel.md

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**ExecuTorch** is a [PyTorch](https://pytorch.org/) platform that provides
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infrastructure to run PyTorch programs everywhere from AR/VR wearables to
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standard on-device iOS and Android mobile deployments. One of the main goals for
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ExecuTorch is to enable wider customization and deployment capabilities of the
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PyTorch programs.
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Please visit the [ExecuTorch website](https://pytorch.org/executorch/) for
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tutorials and documentation. Here are some starting points:
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* [Getting
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Started](https://pytorch.org/executorch/stable/getting-started-setup.html)
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* Set up the ExecuTorch environment and run PyTorch models locally.
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* [Working with
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local LLMs](https://pytorch.org/executorch/stable/llm/getting-started.html)
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* Learn how to use ExecuTorch to export and accelerate a large-language model
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from scratch.
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* [Exporting to
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ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial.html)
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* Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and
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optimizing its performance using quantization and hardware delegation.
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* Running LLaMA on
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[iOS](https://pytorch.org/executorch/stable/llm/llama-demo-ios.html) and
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[Android](https://pytorch.org/executorch/stable/llm/llama-demo-android.html)
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devices.
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* Build and run LLaMA in a demo mobile app, and learn how to integrate models
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with your own apps.

pyproject.toml

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# setup.py will set the version.
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'version',
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]
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# Python dependencies required for development
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description = "On-device AI across mobile, embedded and edge for PyTorch"
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readme = "README-wheel.md"
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authors = [
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{name="PyTorch Team", email="[email protected]"},
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]
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license = {file = "LICENSE"}
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keywords = ["pytorch", "machine learning"]
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# PyPI package information.
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classifiers = [
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# How mature is this project? Common values are
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# 3 - Alpha
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# 4 - Beta
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# 5 - Production/Stable
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"Intended Audience :: Education",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: BSD License",
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"Topic :: Scientific/Engineering",
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"Topic :: Scientific/Engineering :: Mathematics",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Software Development",
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"Topic :: Software Development :: Libraries",
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"Topic :: Software Development :: Libraries :: Python Modules",
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"Programming Language :: C++",
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"Programming Language :: Python :: 3",
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# Update this as we support more versions of python.
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"Programming Language :: Python :: 3.10",
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]
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# Python dependencies required for use.
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requires-python = ">=3.10"
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dependencies=[
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"expecttest",
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"flatbuffers",
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"tabulate",
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]
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[project.urls]
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# The keys are arbitrary but will be visible on PyPI.
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Homepage = "https://pytorch.org/executorch/"
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Repository = "https://github.com/pytorch/executorch"
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Issues = "https://github.com/pytorch/executorch/issues"
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Changelog = "https://github.com/pytorch/executorch/releases"
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# Tell setuptools to generate commandline wrappers for tools that we install
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# under data/bin in the pip package. This will put these commands on the user's
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# path.

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