name: parallel-processing description: Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.

Parallel Processing with joblib

Speed up computationally intensive tasks by distributing work across multiple CPU cores.

Basic Usage

from joblib import Parallel, delayed

def process_item(x):
    """Process a single item."""
    return x ** 2

# Sequential
results = [process_item(x) for x in range(100)]

# Parallel (uses all available cores)
results = Parallel(n_jobs=-1)(
    delayed(process_item)(x) for x in range(100)
)

Key Parameters

• n_jobs: -1 for all cores, 1 for sequential, or specific number
• verbose: 0 (silent), 10 (progress), 50 (detailed)
• backend: 'loky' (CPU-bound, default) or 'threading' (I/O-bound)

Grid Search Example

from joblib import Parallel, delayed
from itertools import product

def evaluate_params(param_a, param_b):
    """Evaluate one parameter combination."""
    score = expensive_computation(param_a, param_b)
    return {'param_a': param_a, 'param_b': param_b, 'score': score}

# Define parameter grid
params = list(product([0.1, 0.5, 1.0], [10, 20, 30]))

# Parallel grid search
results = Parallel(n_jobs=-1, verbose=10)(
    delayed(evaluate_params)(a, b) for a, b in params
)

# Filter results
results = [r for r in results if r is not None]
best = max(results, key=lambda x: x['score'])

Pre-computing Shared Data

When all tasks need the same data, pre-compute it once:

# Pre-compute once
shared_data = load_data()

def process_with_shared(params, data):
    return compute(params, data)

# Pass shared data to each task
results = Parallel(n_jobs=-1)(
    delayed(process_with_shared)(p, shared_data)
    for p in param_list
)

Performance Tips

• Only worth it for tasks taking >0.1s per item (overhead cost)
• Watch memory usage - each worker gets a copy of data
• Use verbose=10 to monitor progress