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llama.cpp/ci
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Eve bee378e098 ci: run the x64 and arm ci on the github machines instead (#16183) 
* run the x64 ci on regular machines

* set up the same thing for arm

fix test-quantize-perf just like #12306

* try to disable sve

* add another sve run
2025-09-25 08:06:06 +03:00
..
README-MUSA.md
ci : migrate ggml ci to self-hosted runners (#16116)
2025-09-21 16:50:45 +03:00
README.md
ci : migrate ggml ci to self-hosted runners (#16116)
2025-09-21 16:50:45 +03:00
run.sh
ci: run the x64 and arm ci on the github machines instead (#16183)
2025-09-25 08:06:06 +03:00

README.md

CI

This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to cover hardware configurations that are not available from Github-hosted runners and/or require more computational resource than normally available.

It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:

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

# with SYCL support
source /opt/intel/oneapi/setvars.sh
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# etc.

Adding self-hosted runners

  • Add a self-hosted ggml-ci workflow to .github/workflows/build.yml with an appropriate label
  • Request a runner token from ggml-org (for example, via a comment in the PR or email)
  • Set-up a machine using the received token (docs)
  • Optionally update ci/run.sh to build and run on the target platform by gating the implementation with a GG_BUILD_... env