parallelize python method

parallelize.py

import multiprocessing, concurrent.futures
from functools import partial

WORKER_THREAD_COUNT = multiprocessing.cpu_count()

def parallelize(partials):
    results = []

    with concurrent.futures.ProcessPoolExecutor(max_workers=WORKER_THREAD_COUNT) as executor:
        jobs = [ executor.submit(p) for p in partials ]
        for future in concurrent.futures.as_completed(jobs):
            results.append(future.result())

    return results

# example
def work(x): print(x)
items = [1,2,3]
partials = map(lambda item: partial(work, item), items)
results = parallelize(partials)

👍

Refactored using partials (curried functions) so that parallelize doesn't need to care about function arguments.