Memory usage of optimize_mp increases over time

[Jhsmit]

When running a large batch of optimizations in parrallel, the memory usage gradually increases over time.
(Ubuntu, 6.7k cells, i7-8700K, 12 processes)

context manager with close() does not seem to help.

https://stackoverflow.com/questions/18414020/memory-usage-keep-growing-with-pythons-multiprocessing-pool
https://chase-seibert.github.io/blog/2013/08/03/diagnosing-memory-leaks-python.html

[Jhsmit]

https://stackoverflow.com/questions/49429368/how-to-solve-memory-issues-problems-while-multiprocessing-using-pool-map

https://github.com/DonaldWhyte/high-performance-data-processing-in-python/blob/master/code/find_outliers_parallel_nocopy.py

[Jhsmit]

https://stackoverflow.com/questions/24717468/context-managers-and-multiprocessing-pools

from multiprocessing import Process
from multiprocessing.pool import Pool

class SafeProcess(Process):
    """ Process that will cleanup before exit """
    def run(self, *args, **kw):
        result = super().run(*args, **kw)
        # cleanup however you want here
        return result


class SafePool(Pool):
    Process = SafeProcess

pool = SafePool(4) # use it as standard Pool