parallel-grid-search SKILL.md skillA
parallel-grid-search from cxcscmu/skilllearnbench is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cxcscmu/skilllearnbench/parallel-grid-search@git:20260424.d50cf5aPins git:20260424.d50cf5a in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cxcscmu/skilllearnbench
- Path
skills/b1-one-shot-claude-sonnet-4-6/dbscan-parameter-tuning/parallel-grid-search/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260424.d50cf5a, committed Apr 24, 2026
- Size
- 5,151 bytes
- Repository stars
- 91
- License
- MIT
Parallelize hyperparameter grid searches using joblib for CPU-bound tasks like DBSCAN clustering evaluation loops.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Parallel Grid Search with joblib
## Overview
`joblib` provides easy parallelism via `Parallel` and `delayed`. Ideal for embarrassingly parallel grid searches where each parameter combination is independent.
## Installation
```bash
pip install joblib
```
## Basic Pattern
```python
from joblib import Parallel, delayed
import itertools
def evaluate_params(param_combo, data):
"""Evaluate one parameter combination. Must be picklable (no lambdas)."""
epsilon, min_samples, shape_weight = param_combo
# ... run DBSCAN, compute metrics ...
return {'epsilon': epsilon, 'min_samples': min_samples,
'shape_weight': shape_weight, 'F1': f1, 'delta': delta}
# Define search space
epsilons = range(4, 25, 2) # 4,6,8,...,24
min_samples_range = range(3, 10) # 3,4,...,9
shape_weights = [round(0.9 + 0.1*i, 1) for i in range(11)] # 0.9..1.9
all_combos = list(itertools.product(epsilons, min_samples_range, shape_weights))
# Run in parallel (n_jobs=-1 uses all available cores)
results = Parallel(n_jobs=-1, verbose=1)(
delayed(evaluate_params)(combo, data)
for combo in all_combos
)
import pandas as pd
results_df = pd.DataFrame(results)
```
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How to install
mdr add cxcscmu/skilllearnbench/parallel-grid-search@sha256:785cf68e2d9439b8The line at the top of this page pins the label git:20260424.d50cf5a, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_nmcmsec2df5dncgg)
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Versions
The registry has recorded 1 version of this file, committed on Apr 24, 2026.
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Source
cxcscmu/skilllearnbench · 91 stars · license MIT · pushed 2026-10-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_nmcmsec2df5dncgg GET https://markdownregistry.com/api/v1/resolve?ref=cxcscmu/skilllearnbench/parallel-grid-search GET https://markdownregistry.com/api/v1/blob/785cf68e2d9439b84818bfe6a1c05d66bf23ca878349888b9655d9106210ed30
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