# Quickstart

## Loading 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", version=1)

# Create a 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)
```

This fetches version `1` of the kernel `kernels-community/activation`.
Kernels are versioned using a major version number. Using `version=1` will
get the latest kernel build from the `v1` branch.

Kernels within a version branch must never break the API or remove builds
for older PyTorch versions. This ensures that your code will continue to work.

Hub kernels must be loaded with either a `version` or an explicit `revision`.

## Checking Kernel Availability

You can check if a particular version of a kernel supports the environment
that the program is running on:

```python
from kernels import has_kernel

# Check if kernel is available for current environment
is_available = has_kernel("kernels-community/activation", version=1)
print(f"Kernel available: {is_available}")
```

## Inspecting Loaded Kernels

`get_loaded_kernels()` returns a snapshot of every kernel that has been loaded
into the current process. Each entry is a `LoadedKernel` namedtuple with the
imported `module`, the `package_name`, and `repo_infos` (repo id, resolved
revision, and the backend argument that was passed).

```python
from kernels import get_kernel, get_loaded_kernels

get_kernel("kernels-community/activation", version=1)

for loaded in get_loaded_kernels():
    print(loaded.package_name, loaded.repo_infos)
```

`repo_infos` is populated only for kernels loaded with `get_kernel`. Kernels
loaded from a local path (`get_local_kernel`) or via a lockfile
(`get_locked_kernel`, `load_kernel`) have `repo_infos=None`.

Browse through different kernels compatible with `kernels` from [here](https://huggingface.co/kernels).

A kernel can provide layers in addition to kernel functions. Refer to [Layers](../source/layers) to know more.