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My problem seems to be a simple one, but so far I haven't found a satisfactory answer. The code I am running is very time consuming, and I need to run it many times (ideally 100 times or more) and average the results from each trial. I have been told to try multiprocessing, and I have made some progress (in JupyterLab). 

#my_code.py
def Run_Code():
    <code>
    return result

import multiprocessing as mp
import numpy as np
import my_code as mc

trial_amount = 2
if __name__ == '__main__':
    pool = mp.Pool(2)
    result = pool.map(mc.Run_code, np.arange(trial_amount))
    print(result)

I was guided by this introduction (https://sebastianraschka.com/Articles/2014_multiprocessing.html#sections). The ultimate goal is to just run each trial simultaneously (or as many as possible at once and once it finished start another trial, and so on) and put the results in a list that will then be averaged. I tried this and it continued running for hours, much longer than a single trial, and never finished.

roshoka
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1 Answers1

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Try mpi4py - https://stackoverflow.com/a/15717768/1021819 gives an example of how it goes.

There is a great and simple tutorial here:

https://mpi4py.readthedocs.io/en/stable/tutorial.html

It really is just a few lines. The following also has enough to get you going. It splits a loop of work over cores then aggregates the results on master:

https://stackoverflow.com/a/51318100/1021819

jtlz2
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