mirror of
https://github.com/ggml-org/llama.cpp.git
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28baac9c9f
* ci : migrate ggml ci to a self-hosted runners * ci : add T4 runner * ci : add instructions for adding self-hosted runners * ci : disable test-backend-ops from debug builds due to slowness * ci : add AMD V710 runner (vulkan) * cont : add ROCM workflow * ci : switch to qwen3 0.6b model * cont : fix the context size
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Running MUSA CI in a Docker Container
Assuming $PWD is the root of the llama.cpp repository, follow these steps to set up and run MUSA CI in a Docker container:
1. Create a local directory to store cached models, configuration files and venv:
mkdir -p $HOME/llama.cpp/ci-cache
2. Create a local directory to store CI run results:
mkdir -p $HOME/llama.cpp/ci-results
3. Start a Docker container and run the CI:
docker run --privileged -it \
-v $HOME/llama.cpp/ci-cache:/ci-cache \
-v $HOME/llama.cpp/ci-results:/ci-results \
-v $PWD:/ws -w /ws \
mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
Inside the container, execute the following commands:
apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
git config --global --add safe.directory /ws
GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.