High Memory Usage Using Python Multiprocessing

I did a lot of research, and couldn’t find a solution to fix the problem per se. But there is a decent work around that prevents the memory blowout for a small cost, worth especially on server side long running code.

The solution essentially was to restart individual worker processes after a fixed number of tasks. The Pool class in python takes maxtasksperchild as an argument. You can specify maxtasksperchild=1000 thus limiting 1000 tasks to be run on each child process. After reaching the maxtasksperchild number, the pool refreshes its child processes. Using a prudent number for maximum tasks, one can balance the max memory that is consumed, with the start up cost associated with restarting back-end process. The Pool construction is done as :

pool = mp.Pool(processes=2,maxtasksperchild=1000)

I am putting my full solution here so it can be of use to others!

import multiprocessing as mp
import time

def calculate(num):
    l = [num*num for num in range(num)]
    s = sum(l)
    del l       # delete lists as an  option
    return s

if __name__ == "__main__":

    # fix is in the following line #
    pool = mp.Pool(processes=2,maxtasksperchild=1000)

    print "launching calculation"
    num_tasks = 1000
    tasks =  [pool.apply_async(calculate,(i,)) for i in range(num_tasks)]
    for f in tasks:    
        print f.get(5)
    print "calculation finished"
    print "closing  pool"
    print "closed pool"
    print "joining pool"
    print "joined pool"

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