mirror of
https://github.com/ggerganov/llama.cpp.git
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492d7f1ff7
* musa: fix all warnings Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * musa: enable -DLLAMA_FATAL_WARNINGS=ON in run.sh Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * musa: update ci doc (install ccache) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * fix Windows build issue Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * Address review comments Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * Address review comments Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> --------- Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
69 lines
2.2 KiB
Markdown
69 lines
2.2 KiB
Markdown
# CI
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In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
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https://github.com/ggml-org/ci
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It monitors the `master` branch for new commits and runs the
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[ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
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to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
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to cover various hardware architectures, including GPU and Apple Silicon instances.
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Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
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Only the branches of this repo are monitored for this keyword.
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It is a good practice, before publishing changes to execute the full CI locally on your machine:
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```bash
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mkdir tmp
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# CPU-only build
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bash ./ci/run.sh ./tmp/results ./tmp/mnt
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# with CUDA support
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GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
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# with SYCL support
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source /opt/intel/oneapi/setvars.sh
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GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
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# with MUSA support
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GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
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```
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## Running MUSA CI in a Docker Container
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Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
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### 1. Create a local directory to store cached models, configuration files and venv:
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```bash
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mkdir -p $HOME/llama.cpp/ci-cache
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```
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### 2. Create a local directory to store CI run results:
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```bash
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mkdir -p $HOME/llama.cpp/ci-results
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```
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### 3. Start a Docker container and run the CI:
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```bash
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docker run --privileged -it \
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-v $HOME/llama.cpp/ci-cache:/ci-cache \
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-v $HOME/llama.cpp/ci-results:/ci-results \
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-v $PWD:/ws -w /ws \
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mthreads/musa:rc3.1.1-devel-ubuntu22.04
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```
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Inside the container, execute the following commands:
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```bash
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apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
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git config --global --add safe.directory /ws
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GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
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```
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This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.
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