mirror of
https://github.com/ggerganov/llama.cpp.git
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413e7b0559
* scripts : add lib.sh and lib_test.sh * scripts : stub out new ci-run.sh script * scripts : switch to PascalCase for functions This looks a little odd at first, but I find it very useful as a convention to know if a command is part of our code vs a builtin. * scripts : add some fancy conversion from snake_case to PascalCase * Add venv to ci/run.sh * Revert scripts work * scripts : add wrapper script for local use of ci/run.sh * Simplify .gitignore for tests, clang-tidy fixes * Label all ctest tests * ci : ctest uses -L main * Attempt at writing ctest_with_model * Update test-model-load-cancel * ci : add ctest_with_model for debug and release ggml-ci * Fix gg_get_model function ggml-ci * got stuck on CMake * Add get_model.cpp to tests/CMakeLists.txt ggml-ci * Fix README.md output for ctest_with_model ggml-ci * workflows : use `-L main` for all ctest ggml-ci * Fixes * GG_RUN_CTEST_MODELFILE => LLAMACPP_TESTMODELFILE * Always show warning rather than failing if model file variable is not set * scripts : update usage text for ci-run.sh |
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CI
In addition to Github Actions llama.cpp uses a custom CI framework:
https://github.com/ggml-org/ci
It monitors the master branch for new commits and runs the
ci/run.sh script on dedicated cloud instances. This allows us
to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
to cover various hardware architectures, including GPU and Apple Silicon instances.
Collaborators can optionally trigger the CI run by adding the ggml-ci keyword to their commit message.
Only the branches of this repo are monitored for this keyword.
It is a good practice, before publishing changes to execute the full CI locally on your machine:
mkdir tmp
# CPU-only build
bash ./ci/run.sh ./tmp/results ./tmp/mnt
# with CUDA support
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt