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