b1f3a6e5db
* llama: automatically fit args to free memory llama-fit-params tool * fix CI * hints for bug reports, ensure no reallocation * fix segfault with Vulkan * add llama-fit-params to CI * fix CI * fix CI * fix CI * minor adjustments * fix assignment of 1 dense layer * fix logger not being reset on model load failure * remove --n-gpu-layer hint on model load failure * fix llama-fit-params verbosity * fix edge case * fix typo [no ci] |
||
|---|---|---|
| .. | ||
| README-MUSA.md | ||
| README.md | ||
| run.sh | ||
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-ciworkflow 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