Over the past few months, we have worked towards this goal. In the process, we also almost completely redesigned the project. This post will summarize the major updates we have shipped and what’s coming.

We have introduced a new repository type on the Hub called "kernel". This enables us to cater to users with compute-related specificities. For example, a user can get a sense of which accelerators, operating systems, and backend versions are supported for a given kernel:

One can browse all available kernels on the Hub here: https://huggingface.co/kernels.

Making these kernels first-class citizens of the Hub also benefits the AI ecosystem. Users can now see trends across kernels, models, and the applications that use them. The kernels become more discoverable to users.

Kernels run native code with the same privileges as the Python process that loads them, so a malicious kernel can do real harm. Therefore, security has always been of utmost importance to the Kernels project.

This is why we focused early on reproducibility: you should be able to recompile a kernel yourself and verify that it matches the publicly available source. We use Nix to make this possible, since it keeps builds pure through hermetic evaluation of the build recipe and a strongly isolated sandbox. We further improve provenance by embedding the source Git SHA1 into the kernel itself.

In recent months, we have added additional layers of defense: trusted kernel publishers and code signing.

With the new repo type, we also introduced “trusted publishers”.

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