80e011146b
Various improvements such as: 1. Avoiding deduplications when there can't be any duplicates 2. Avoiding O(n^2) deduplications 3. Using builtins.any to avoid list allocations 4. Using builtins.concatMap instead of lib.flatten when it's known that there's only one level of nesting 5. Using builtins.groupBy instead of folding with an accumulator In particular 5. should fix CI exceeding the stack size on staging: https://github.com/NixOS/nixpkgs/actions/runs/12989244871/job/36240781244?pr=377253 While 2. in particular should make CI a lot faster.
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.