---
title: "Set Up Self-Hosted Runners for GitHub Action"
metatags:
    description: "SGLang GitHub Actions self-hosted runner: Docker setup for NVIDIA/AMD GPUs, config.sh and run.sh."
---
## Add a Runner

### Step 1: Start a docker container.

**You can mount a folder for the shared huggingface model weights cache. **
The command below uses `/tmp/huggingface` as an example.

```
docker pull nvidia/cuda:12.9.1-devel-ubuntu22.04
# Nvidia
docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.9.1-devel-ubuntu22.04 /bin/bash
# AMD
docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.5.8-rocm700-mi30x /bin/bash
# AMD just the last 2 GPUs
docker run --rm --device=/dev/kfd --device=/dev/dri/renderD176 --device=/dev/dri/renderD184 --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.5.8-rocm700-mi30x /bin/bash
```

### Step 2: Configure the runner by `config.sh`

Run these commands inside the container.

```text Output
apt update && apt install -y curl python3-pip git
pip install --upgrade pip
export RUNNER_ALLOW_RUNASROOT=1
```

Then follow https://docs.github.com/en/actions/hosting-your-own-runners/managing-self-hosted-runners/adding-self-hosted-runners to run `config.sh`

**Notes**
- Do not need to specify the runner group
- Give it a name (e.g., `test-sgl-gpu-0`) and some labels (e.g., `1-gpu-h100`). The labels can be edited later in Github Settings.
- Do not need to change the work folder.

### Step 3: Run the runner by `run.sh`

- Set up environment variables
```text Output
export HF_HOME=/hf_home
export SGLANG_IS_IN_CI=true
export HF_TOKEN=hf_xxx
export OPENAI_API_KEY=sk-xxx
export CUDA_VISIBLE_DEVICES=0
```

- Run it forever
```text Output
while true; do ./run.sh; echo "Restarting..."; sleep 2; done
```
