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huggingface/kernels
Build compute kernels and load them from the Hub.
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29 Nov 2024
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README
the first lines# kernels
The Kernel Hub allows Python libraries and applications to load compute
kernels directly from the Hub. To support this kind
of dynamic loading, Hub kernels differ from traditional Python kernel
packages in that they are made to be:
- Portable: a kernel can be loaded from paths outside `PYTHONPATH`.
- Unique: multiple versions of the same kernel can be loaded in the
same Python process.
- Compatible: kernels must support all recent versions of Python and
the different PyTorch build configurations (various CUDA versions
and C++ ABIs). Furthermore, older C library versions must be supported.
## Components
- You can load kernels from the Hub using the `kernels` Python package.
- If you are a kernel author, you can build your kernels with kernel-builder.
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Recent commits
default branch- 1hpython3Packages.torch_215: update to 2.15.0 (final) (#948) . Daniël de Kok
- 1hSet version to 0.19.0.dev0 (#947) . Daniël de Kok
- 1dfeat: trust kernels signed by kernels-community-rs (#942) . drbh
- 1d[docs] kernels: fix autotuning docs for triton. (#945) . Sayak Paul
- 2dkernels: add a test for get_file for RemoteKernel. (#944) . Sayak Paul
- 2ddocs: steer users towards using layer decoration as opposed to function decoration (#943) . Sayak Paul
- 2dkernels: do not cache device capability in `DeviceRepos` (#941) . Daniël de Kok
- 2dkernels: fix device name lookup in `collect_machine_info` (#940) . Daniël de Kok
- 3dkernels: implement kernels cache ls (#932) . Sayak Paul
- 3dkernels: fix malformed user-agent string when telemetry is disabled (#939) . Daniël de Kok
