# kernels
The Kernel Hub allows Python libraries and applications to load compute
kernels directly from the [Hub](https://hf.co/). 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):
```bash
pip install kernels
```
Here is how you would use the [activation](https://huggingface.co/kernels-community/activation) kernels from the Hugging Face Hub:
```python
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](https://huggingface.co/models?other=kernel) on
the Hub.
## 📚 Documentation
- [Introduction](docs/source/index.md)
- [Installation](docs/source/installation.md)
- [Basic usage](docs/source/basic-usage.md)
- [Using layers](docs/source/layers.md)
- [Locking kernel/layer versions](docs/source/locking.md)
- [Environment variables](docs/source/env.md)
- [Kernel requirements](docs/source/kernel-requirements.md)
- [Frequently Asked Questions](docs/source/faq.md)
- [Writing kernels](https://github.com/huggingface/kernel-builder/blob/main/docs/writing-kernels.md) using [kernel-builder](https://github.com/huggingface/kernel-builder/)