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Oliver Simons 7677678503 CUDA: Make CCCL configurable + pin it to 3.4.3 for CI jobs (#29792 )

Pinning to >= 3.4.3 is required to enable DeviceTopK, which was affected by
a race condition https://github.com/NVIDIA/cccl/pull/10627.

We will relax this for future CTK versions which will bundle CCCL >
3.4.X (CTK 13.5 will bundle CCCL 3.5.0 for example)

2026-10-01 13:05:29 +02:00

README-MUSA.md

musa: build the docker image and CI container from the MUSA SDK images (#29624 )

2026-09-29 10:42:20 +02:00

README.md

ci : migrate ggml ci to self-hosted runners (#16116 )

2025-09-21 16:50:45 +03:00

run.sh

CUDA: Make CCCL configurable + pin it to 3.4.3 for CI jobs (#29792 )

2026-10-01 13:05:29 +02:00

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