parallel.py 文件源码

def run_raw_multiprocess(func, tasks, *args, **kwargs):
    pool = multiprocessing.Pool(processes=multiprocessing.cpu_count(), maxtasksperchild=1)
    futures = []
    for task in tasks:
        futures.append(pool.apply_async(func, tuple(chain([task], args)), kwargs))
    pool.close()
    results = list(map(lambda x: x.get(), futures))
    return results