GPU-acceleration has become a fundamental prerequisite to machine learning work and scientific computing. Without support for GPU-acceleration, the Nixpkgs ecosystem will remain largely invisible to practitioners.
Nixpkgs and NixOS give us the tools we need to build reproducible and robust packages and environments. However, the state of scientific computing in Nixpkgs does not reflect this.
Goals and Responsibilities
The singular focus of the CUDA-maintainers team is to make Nixpkgs the best repository of packages leveraging CUDA, especially in machine learning and scientific computing. Towards that end, we seek to:
- improve the quantity and quality of CUDA-enabled package in Nixpkgs
- document CUDA packaging best practices, guidelines, and patterns
- support community efforts involving packaging or distributing CUDA-enabled software
Call for Sponsors
The CUDA-maintainers team is seeking sponsors to help fund or supply technical infrastructure related to our mission. Therefore, if you or your organization:
- are impacted by these issues,
- have related issues with Nixpkgs CUDA support,
- would like to prioritize or accelerate certain work,
please consider supporting this effort directly or through the NixOS Foundation. Reach out via GitHub @connorbaker, or email firstname.lastname@example.org to get involved.
Contact and Information
Discussions are held on Matrix, Discourse, and on GitHub issues and pull requests.
Nixpkgs manual's CUDA section Discourse Matrix GitHub Project board GitHub Team page