mirror of
https://github.com/CHN-beta/nixpkgs.git
synced 2026-01-12 02:40:31 +08:00
02a696bccd
Reason: keep ci directory in sync - https://github.com/NixOS/nixpkgs/pull/431450#issuecomment-3209546418 This requires that we have a modules directory, in which case the easy and robust solution is to only port the addition parts of the refactor. It's about as easy as a .keep file, but more useful. This means that some duplication is created, but we avoid backporting the changes to the documentation generation, which is a somewhat complex component I'd rather not touch until these changes have been proven out on unstable.
Nixpkgs CI evaluation
The code in this directory is used by the eval.yml GitHub Actions workflow to evaluate the majority of Nixpkgs for all PRs, effectively making sure that when the development branches are processed by Hydra, no evaluation failures are encountered.
Furthermore it also allows local evaluation using
nix-build ci -A eval.full \
--max-jobs 4 \
--cores 2 \
--arg chunkSize 10000 \
--arg evalSystems '["x86_64-linux" "aarch64-darwin"]'
--max-jobs: The maximum number of derivations to run at the same time. Only each supported system gets a separate derivation, so it doesn't make sense to set this higher than that number.--cores: The number of cores to use for each job. Recommended to set this to the amount of cores on your system divided by--max-jobs.chunkSize: The number of attributes that are evaluated simultaneously on a single core. Lowering this decreases memory usage at the cost of increased evaluation time. If this is too high, there won't be enough chunks to process them in parallel, and will also increase evaluation time.evalSystems: The set of systems for whichnixpkgsshould be evaluated. Defaults to the four official platforms (x86_64-linux,aarch64-linux,x86_64-darwinandaarch64-darwin).
A good default is to set chunkSize to 10000, which leads to about 3.6GB max memory usage per core, so suitable for fully utilising machines with 4 cores and 16GB memory, 8 cores and 32GB memory or 16 cores and 64GB memory.
Note that 16GB memory is the recommended minimum, while with less than 8GB memory evaluation time suffers greatly.