What is Python pool.map for multiple arguments?

General Discussions
,
#1

How can I use python pool map with multiple arguments in the multiprocessing library?

In Python’s multiprocessing library, is there a variant of pool.map that supports multiple arguments? Here’s an example where I am trying to use it:

import multiprocessing

text = “test”

def harvester(text, case): X = case[0] text + str(X)

if name == ‘main’: pool = multiprocessing.Pool(processes=6) case = RAW_DATASET pool.map(harvester(text, case), case, 1) pool.close() pool.join()

How can I correctly implement python pool map to handle multiple arguments?

#2

Hey All!

Using functools.partial" Have you ever faced the challenge of passing multiple arguments to a multiprocessing function? The functools.partial method is a lifesaver here. It allows you to “freeze” some arguments, making it easier to handle the rest via pool.map.

import multiprocessing
from functools import partial

text = "test"

def harvester(text, case):
    X = case[0]
    return text + str(X)

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=6)
    case = RAW_DATASET
    # Use partial to fix the `text` argument and pass `case` to map
    pool.map(partial(harvester, text), case)
    pool.close()
    pool.join()

Here, partial(harvester, text) binds the text argument, letting pool.map handle the dynamic case. Elegant and straightforward, right?

#3

Using zip to Pair Arguments" Adding to @emma-crepeau solution, here’s another approach when dealing with separate lists of arguments. You can use zip to pair arguments into tuples and pass them to pool.map.

import multiprocessing

text = "test"

def harvester(text, case):
    X = case[0]
    return text + str(X)

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=6)
    case = RAW_DATASET
    # Pair arguments using zip
    pool.map(harvester, zip([text] * len(case), case))
    pool.close()
    pool.join()

Using zip([text] * len(case), case) ensures that each tuple contains the necessary arguments for harvester. It’s a handy trick for cleaner argument handling.

#4

“Using starmap” @vindhya.rddy idea is great, but what if we want an even more specialized method? Enter pool.starmap, a method designed specifically for unpacking tuples into multiple arguments.

import multiprocessing

text = "test"

def harvester(text, case):
    X = case[0]
    return text + str(X)

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=6)
    case = RAW_DATASET
    # Use starmap to unpack multiple arguments
    pool.starmap(harvester, [(text, item) for item in case])
    pool.close()
    pool.join()

starmap takes care of unpacking tuples and passing them as arguments to the function, simplifying the process even further.