---
library_name: kernels
license: mit
---

This is the repository card of kernels-community/deep-ep that has been pushed on the Hub. It was built to be used with the [`kernels` library](https://github.com/huggingface/kernels). This card was automatically generated.

## How to use

Usage example not available.

## Available functions

Function list not available.

## Requirements

This is [DeepEP](https://github.com/deepseek-ai/DeepEP) 2.5.0 (`def8651`), with
[DeepJIT](https://github.com/deepseek-ai/DeepJIT) `cba802d`. Only DeepEP's host
extension is prebuilt; its communication kernels are compiled at runtime. On
top of the Hub kernel's own requirements, using it needs:

- NVIDIA Hopper or newer GPUs, connected with NVLink (RDMA for multi-node).
- NCCL >= 2.32.3, loaded by PyTorch. The kernel does not ship NCCL: it uses the
  NCCL library that PyTorch loads, and compiles its kernels against that
  library's headers. PyTorch 2.14 and 2.15 ship NCCL 2.30.7; upgrade it with
  `pip install "nvidia-nccl-cu13>=2.32.3"` (or use a PyTorch build that ships
  2.32.3 or newer).
- A CUDA toolkit >= 13.1 (`nvcc`) and a C++20 host compiler at runtime, found
  through `CUDA_HOME`, `CUDA_PATH`, `nvcc` on `PATH` or `/usr/local/cuda`.

These are checked when the first buffer is created, with an error explaining
what is missing. Compiled kernels are cached in `$HOME/.dj` (`EP_JIT_CACHE_DIR`).

## Benchmarks

No benchmark available yet.

## Source code

Source code of this kernel originally comes from https://github.com/deepseek-ai/DeepEP and it was repurposed for compatibility with `kernels`.