feat(helm): cap numerical backend threads via defaults.threadpoolLimit - #31
feat(helm): cap numerical backend threads via defaults.threadpoolLimit#31lewisjared wants to merge 2 commits into
Conversation
Renders OMP_NUM_THREADS and the five sibling backend variables into every worker Secret, per the upstream memory use guide. Without a cap, numpy and scipy size their thread pools off the host core count, so parallel diagnostics oversubscribe the CPUs on large nodes. The name follows threadpoolctl, which manages the same libraries. Tunable per provider, with an explicit env key winning over the cap.
|
Warning Review limit reached
Next review available in: 58 minutes Enable usage-based reviews in Billing to review now. Otherwise, wait until the next included review is available. How can I continue?After more reviews become available, a review can be triggered using the To avoid repeated limits, reduce automatic review volume by pausing incremental auto-reviews earlier, using label-based review opt-in, excluding WIP or generated PR titles, or requesting reviews manually when the PR is ready. If your team needs uninterrupted high-volume reviews, an organization admin can enable usage-based reviews. How do review limits work?CodeRabbit enforces per-developer PR review limits for each organization. Most developers receive the normal plan review availability. For paid Pro and Pro+ PR reviews, CodeRabbit uses adaptive limits for sustained high-volume activity. When a developer's recent PR review activity reaches the 95th percentile or higher among CodeRabbit users, additional reviews become available more gradually as earlier reviews age out of the rolling window. Please refer docs for additional details. Review details⚙️ Run configurationConfiguration used: Organization UI Review profile: CHILL Plan: Pro Plus Run ID: 📒 Files selected for processing (5)
✨ Finishing Touches🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
1 participant
Split out of #29 so the worker tuning feature and the esmvaltool config fix can be reviewed separately.
defaults.threadpoolLimit(default 4), rendered asOMP_NUM_THREADS,OPENBLAS_NUM_THREADS,MKL_NUM_THREADS,NUMEXPR_NUM_THREADS,VECLIB_MAXIMUM_THREADSandBLIS_NUM_THREADSin every worker Secret, per the memory use guide. Without a cap, numpy/scipy size their thread pools off the host core count, so parallel diagnostics oversubscribe the CPUs on large nodes.providers.<name>.threadpoolLimit. An individual variable set inenvwins over the cap, andnullleaves the backends unbounded.threadpool_limitsetting if this moves upstream into climate-ref itself.envoverride, which reaches the template as nil rather than an empty map.This may be better placed upstream (climate-ref could set these in the executor, e.g. via threadpoolctl), in which case this chart value becomes a passthrough or goes away.