Refactor the docs (#9031)

docs/developer_guide/setup_github_runner.md

@@ -0,0 +1,49 @@
+# Set Up Self-Hosted Runners for GitHub Action
+
+## 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:12.1.1-devel-ubuntu22.04
+# Nvidia
+docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-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:v0.5.0rc0-rocm630 /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:v0.5.0rc0-rocm630 /bin/bash
+```
+
+### Step 2: Configure the runner by `config.sh`
+
+Run these commands inside the container.
+
+```
+apt update && apt install -y curl python3-pip git
+export RUNNER_ALLOW_RUNASROOT=1
+```
+
+Then follow https://github.com/sgl-project/sglang/settings/actions/runners/new?arch=x64&os=linux 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-runner`). 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
+```
+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
+```
+while true; do ./run.sh; echo "Restarting..."; sleep 2; done
+```