multiprocessing

multiprocessing — Process-based parallelism — Python 3.7.4 documentation

multiprocessing --- プロセスベースの並列処理 — Python 3.7.4 ドキュメント

Examples

`#41 - multiprocessing : Unidata Developer's Blog

plotting figures

code:python

import multiprocessing as mp

with mp.Pool(processes=4) as pool:

pool.close()

pool.join()

`#82 - Multiprocessing : Unidata Developer's Blog

code:pyhon

import multiprocessing as mp

for fname in data_path.glob("*.mts")

pool.apply_async(get_station_means,args=(fname,),callback=record_means)

pool.close()

pool.join()

writing data to a sigle file

`#127 - Multiprocessing and Managed Dictionaries : Unidata Developer's Blog

code:python

import multiprocessing as mp

from multiprocessing import Manager

def calculate_pw(d,station,timestamp):

def main():

manager = Manager()

results = manager.dict()

pool = mp.Pool(4)

jobs = []

t=datetime(2020,3,19)

for station in stations:

job=pool.apply_async(calculate_pw,(results,station,t))

jobs.append(job)

for job in jobs:

job.get()

pool.close()

pool.join()

if __name__ = '__main__':

main()

code:python

with mp.get_context("spawn").Pool(4) as p:

p.map(myplot, range(n))

雨の中、Cを書かずにPythonで並列計算をする人間がいてもいい。自由とはそういうものだ。 - Qiita

python - Easy parallelization of numpy.apply_along_axis()? - Stack Overflow

Tip

apply_async

The order of the results is not guaranteed to be the same as the order of the calls to Pool.apply_async.