尝试使用多重处理在python中填充数组 [英] Trying to use multiprocessing to fill an array in python

问题描述

我有这样的代码

x = 3;
y = 3;
z = 10;
ar = np.zeros((x,y,z))

from multiprocessing import Process, Pool

para = []
process = []
def local_func(section):
    print "section %s" % str(section)
    ar[2,2,section] = 255
    print "value set %d", ar[2,2,section]

pool = Pool(1)

run_list = range(0,10)
list_of_results = pool.map(local_func, run_list)

print ar

ar中的值未通过多线程更改，这可能是什么问题?

The value in ar was not changed with multithreading, what might be wrong?

谢谢

推荐答案

您在这里使用的是多个进程，而不是多个线程.因此，每个local_func实例都获得其自己的ar副本.您可以使用自定义Manager 来创建共享的numpy数组，您可以将其传递给每个子进程并获得期望的结果:

You're using multiple processes here, not multiple threads. Because of that, each instance of local_func gets its own separate copy of ar. You can use a custom Manager to create a shared numpy array, which you can pass to each child process and get the results you expect:

import numpy as np
from functools import partial
from multiprocessing import Process, Pool
import multiprocessing.managers

x = 3;
y = 3;
z = 10; 

class MyManager(multiprocessing.managers.BaseManager):
    pass
MyManager.register('np_zeros', np.zeros, multiprocessing.managers.ArrayProxy)


para = []
process = []
def local_func(ar, section):
    print "section %s" % str(section)
    ar[2,2,section] = 255 
    print "value set %d", ar[2,2,section]

if __name__ == "__main__":
    m = MyManager()
    m.start()
    ar = m.np_zeros((x,y,z))

    pool = Pool(1)

    run_list = range(0,10)
    func = partial(local_func, ar)
    list_of_results = pool.map(func, run_list)

    print ar

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