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.

🚀 Quick Start

Install the kernels package with pip (requires torch>=2.5 and CUDA):

pip install kernels

Here is how you would use the activation kernels from the Hugging Face Hub:

import torch

from kernels import get_kernel

# Download optimized kernels from the Hugging Face hub
activation = get_kernel("kernels-community/activation")

# Random tensor
x = torch.randn((10, 10), dtype=torch.float16, device="cuda")

# Run the kernel
y = torch.empty_like(x)
activation.gelu_fast(y, x)

print(y)

You can search for kernels on
the Hub.

kernels

🚀 Quick Start

📚 Documentation

Dependency	Optional
huggingface_hub	false
packaging	false
pyyaml	false
tomli	false
torch	true

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kernels

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