Run1 hyperparameter grid parallelization
Efficiently executing grid search over massive parameter spaces using Python multiprocessing.From its SKILL.md
npx -y skills add cxcscmu/SkillLearnBench --skill run1_hyperparameter-grid-parallelizationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Parallel Execution
Use multiprocessing.Pool to distribute image-level processing. Since you are performing a grid search:
- Outer Loop: Define the product of hyperparameter combinations:
import itertools params = list(itertools.product(min_samples_range, epsilon_range, shape_weight_range)) - Mapping: Use
pool.starmapto pass a function that performs the loop overunique(file_rad)for each parameter set. - Memory Management: Ensure that individual worker processes clean up memory (DBSCAN structures) to prevent OOM errors, as the grid space involves hundreds of combinations.
What ships with it
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