Easy-to-use Python module for executing functions in parallel with joblib

multiProcess.py

from joblib import Parallel, delayed
import multiprocessing
import warnings

DEDICATED_CORE_NUMBER = max(multiprocessing.cpu_count() - 2, 1)

def multiProcess(f, argumentsList, coreNumber=DEDICATED_CORE_NUMBER, prt=1):
    """Takes a function, a list of parameter sets for the function, and a number of cores to use as parameters.
    Executes the function in parallel across all parameter sets (using 'coreNumber' cores)
    Returns the list of function returns.

    EXAMPLE:

    def f(a, n=10):
        r = 0
        for i in range(n):
            r += a
        return (r, str(a) + str(n))

    results = mp.multiProcess(f, [(3, 5), (3, 10), (3, 15)])

    results will be [(15, '35'), (30, '310'), (45, '315')]
    """
    if len(argumentsList) > DEDICATED_CORE_NUMBER:
        txt = "Warning: "
        txt += "The program can only use " + str(DEDICATED_CORE_NUMBER) + " CPU cores,"
        txt += "\n however, it has been requested to execute the function " + str(
            len(argumentsList)) + " times simultaneously."
        warnings.warn(txt)

    if prt == 1:
        print("Using " + str(len(argumentsList)) + " cores out of " + str(
            multiprocessing.cpu_count()) + " on the processor.")

    resultsList = Parallel(n_jobs=coreNumber)(delayed(f)(*arguments) for arguments in argumentsList)
    return (resultsList)

# EXAMPLE OF USE

def f(a, n=10):
    r = 0
    for i in range(n):
        r += a
    return (r, str(a) + str(n))


results = multiProcess(f, [(3, 5), (3, 10), (3, 15)])

print(results)