50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
# Set Up Self-Hosted Runners for GitHub Action
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## Add a Runner
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### Step 1: Start a docker container.
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You can mount a folder for the shared huggingface model weights cache. The command below uses `/tmp/huggingface` as an example.
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```
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docker pull nvidia/cuda:12.1.1-devel-ubuntu22.04
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# Nvidia
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docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-devel-ubuntu22.04 /bin/bash
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# AMD
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docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.4.post3-rocm630 /bin/bash
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# AMD just the last 2 GPUs
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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:v0.4.4.post3-rocm630 /bin/bash
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```
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### Step 2: Configure the runner by `config.sh`
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Run these commands inside the container.
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```
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apt update && apt install -y curl python3-pip git
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export RUNNER_ALLOW_RUNASROOT=1
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```
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Then follow https://github.com/sgl-project/sglang/settings/actions/runners/new?arch=x64&os=linux to run `config.sh`
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**Notes**
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- Do not need to specify the runner group
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- Give it a name (e.g., `test-sgl-gpu-0`) and some labels (e.g., `1-gpu-runner`). The labels can be editted later in Github Settings.
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- Do not need to change the work folder.
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### Step 3: Run the runner by `run.sh`
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- Set up environment variables
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```
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export HF_HOME=/hf_home
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export SGLANG_IS_IN_CI=true
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export HF_TOKEN=hf_xxx
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export OPENAI_API_KEY=sk-xxx
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export CUDA_VISIBLE_DEVICES=0
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```
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- Run it forever
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```
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while true; do ./run.sh; echo "Restarting..."; sleep 2; done
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```
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