---
name: parallel-grid-search
description: Efficiently perform hyperparameter grid search using parallel processing.
---

# Parallel Grid Search

Parallelizing grid searches can significantly reduce computation time, especially for independent trials.

## Using joblib
`joblib` is a popular library for parallelizing Python loops.

```python
from joblib import Parallel, delayed
import itertools

def run_experiment(params):
    min_samples, epsilon, shape_weight = params
    # ... logic to run DBSCAN and evaluate ...
    return results

# Define search space
min_samples_range = range(3, 10)
epsilon_range = range(4, 26, 2)
shape_weight_range = [round(x * 0.1, 1) for x in range(9, 20)]

param_combinations = list(itertools.product(min_samples_range, epsilon_range, shape_weight_range))

# Run in parallel
results = Parallel(n_jobs=-1)(delayed(run_experiment)(p) for p in param_combinations)
```

## Considerations
- **Data Sharing**: Pass only necessary data to workers to minimize overhead.
- **Progress Tracking**: Use `tqdm` if a progress bar is needed.
- **Resource Management**: Be mindful of memory usage when running many parallel processes.