Buckets:
| # Installation | |
| > [!WARNING] | |
| > `kernels` has not reached `1.0` yet. Until then, minor releases may contain | |
| > breaking changes. If you depend on `kernels` in a library or application, we | |
| > **strongly recommend pinning a version range** rather than an unbounded | |
| > dependency. For example, in `pyproject.toml`: | |
| > | |
| > ```toml | |
| > dependencies = [ | |
| > "kernels>=0.15,<0.16", | |
| > ] | |
| > ``` | |
| > | |
| > or equivalently `kernels~=0.15` (compatible release). This protects your | |
| > project from unexpected breakage when a new `kernels` version is released. | |
| Install the `kernels` package with `pip` (requires `torch>=2.5` and CUDA): | |
| ```bash | |
| pip install kernels | |
| ``` | |
| or with `uv` | |
| ```bash | |
| uv pip install kernels | |
| ``` | |
| or if you want the latest version from the `main` branch: | |
| ```bash | |
| pip install "kernels[benchmark] @ git+https://github.com/huggingface/kernels#subdirectory=kernels" | |
| ``` | |
| > [!IMPORTANT] | |
| > On Windows, we recommend using the Linux version of Torch through | |
| > [WSL 2](https://learn.microsoft.com/en-us/windows/wsl/install), since | |
| > many more kernels support Linux. If you want to use GPU acceleration, | |
| > check out the [CUDA on WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#getting-started-with-cuda-on-wsl-2) | |
| > and [PyTorch with DirectML on WSL 2](https://learn.microsoft.com/en-us/windows/ai/directml/pytorch-wsl) | |
| > guides. | |
| > [!IMPORTANT] | |
| > We strongly recommend not using a free-threaded Python build yet. | |
| > These builds are not only experimental, but do not support the stable ABI | |
| > on Python versions before 3.15. Kernels are compiled with the stable ABI | |
| > to support a wide range of Python versions. | |
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