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4dce5b71a0
Modernize the development installation:
```bash
# python setup.py develop
python -m pip install --no-build-isolation -e .
# python setup.py install
python -m pip install --no-build-isolation .
```
Now, the `python setup.py develop` is a wrapper around `python -m pip install -e .` since `setuptools>=80.0`:
- pypa/setuptools#4955
`python setup.py install` is deprecated and will emit a warning during run. The warning will become an error on October 31, 2025.
- 9c4d383631/setuptools/command/install.py (L58-L67)
> ```python
> SetuptoolsDeprecationWarning.emit(
> "setup.py install is deprecated.",
> """
> Please avoid running ``setup.py`` directly.
> Instead, use pypa/build, pypa/installer or other
> standards-based tools.
> """,
> see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
> due_date=(2025, 10, 31),
> )
> ```
- pypa/setuptools#3849
Additional Resource:
- [Why you shouldn't invoke setup.py directly](https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156027
Approved by: https://github.com/ezyang
This folder contains a number of scripts which are used as
part of the PyTorch build process. This directory also doubles
as a Python module hierarchy (thus the __init__.py).
Overview
Modern infrastructure:
- autograd - Code generation for autograd. This includes definitions of all our derivatives.
- jit - Code generation for JIT
- shared - Generic infrastructure that scripts in
tools may find useful.
- module_loader.py - Makes it easier to import arbitrary Python files in a script, without having to add them to the PYTHONPATH first.
Build system pieces:
- setup_helpers - Helper code for searching for third-party dependencies on the user system.
- build_pytorch_libs.py - cross-platform script that builds all of the constituent libraries of PyTorch, but not the PyTorch Python extension itself.
- build_libtorch.py - Script for building libtorch, a standalone C++ library without Python support. This build script is tested in CI.
Developer tools which you might find useful:
- git_add_generated_dirs.sh and git_reset_generated_dirs.sh - Use this to force add generated files to your Git index, so that you can conveniently run diffs on them when working on code-generation. (See also generated_dirs.txt which specifies the list of directories with generated files.)
Important if you want to run on AMD GPU:
- amd_build - HIPify scripts, for transpiling CUDA
into AMD HIP. Right now, PyTorch and Caffe2 share logic for how to
do this transpilation, but have separate entry-points for transpiling
either PyTorch or Caffe2 code.
- build_amd.py - Top-level entry point for HIPifying our codebase.
Tools which are only situationally useful:
- docker - Dockerfile for running (but not developing) PyTorch, using the official conda binary distribution. Context: https://github.com/pytorch/pytorch/issues/1619
- download_mnist.py - Download the MNIST dataset; this is necessary if you want to run the C++ API tests.