This repository serves as a comprehensive toolset for building and indexing PyTorch-based packages with custom ops. It includes two primary GitHub workflows:

1. PyTorch Packages Builder Workflow:

   • Automates the building of PyTorch-based packages with custom ops on common architectures.
   • Publishes the built packages on GitHub releases.

2. PEP 503 Compliant Package Index Builder Workflow:

   • Creates a PEP 503 compliant package index.
   • Publishes the index using GitHub Pages for seamless integration with pip.

To utilize the complete package index from this repository, add the following to your pip install command:

pip install --extra-index-url https://miropsota.github.io/torch_packages_builder <your package list>

If you only need links for specific packages, add the following to your pip install command:

pip install --find-links https://miropsota.github.io/torch_packages_builder/<pep 503 normalized name> <your package list>

For example:

pip install --find-links https://miropsota.github.io/torch_packages_builder/detectron2/ <your package list>

You can also download a package and install it locally:

pip install <abs or rel path>
pip install --find-links <abs or rel dir path> <rel path of the package with respect to the directory>

Make sure to include the full version, including the local version identifier (part after +). The repository follows this version template:

<package_name>-<version>+<OPTIONAL_commit_hash>pt<PyTorch_version><compute_platform>

Where <compute_platform> is, as in PyTorch, one of cpu, cu<CUDA_version>, rocm<ROCM_version>.

detectron2==0.6+864913fpt1.11.0cpu
pytorch3d==0.7.6+pt2.2.1cu121

Tested with PyTorch 1.11.0 - 2.2.1 and their respective compute platforms and supported OSes, with an exception for cu102 on Windows (no VS 2017 on the GH windows-2019 runner), and the rocm platform. Only x86-64 architecture.

• No Support for Pip Cache: pip relies on http cache, and GitHub generates on-the-fly redirections for release links, so they are probably not playing nicely together.

A huge thanks to https://github.com/rusty1s/pytorch_cluster