如何使用 Python 多处理 Pool.map 在 for 循环中填充 numpy 数组 [英] How to use Python multiprocessing Pool.map to fill numpy array in a for loop

问题描述

我想在 for 循环中填充一个 2D-numpy 数组并通过使用多处理来加快计算.

I want to fill a 2D-numpy array within a for loop and fasten the calculation by using multiprocessing.

import numpy
from multiprocessing import Pool


array_2D = numpy.zeros((20,10))
pool = Pool(processes = 4)

def fill_array(start_val):
    return range(start_val,start_val+10)

list_start_vals = range(40,60)
for line in xrange(20):
    array_2D[line,:] = pool.map(fill_array,list_start_vals)
pool.close()

print array_2D

执行它的效果是 Python 运行了 4 个子进程并占用了 4 个 CPU 内核，但执行未完成且未打印数组.如果我尝试将数组写入磁盘，则没有任何反应.

The effect of executing it is that Python runs 4 subprocesses and occupies 4 CPU cores BUT the execution doesn´t finish and the array is not printed. If I try to write the array to the disk, nothing happens.

谁能告诉我为什么?

推荐答案

以下有效.首先，最好在主块内保护代码的主要部分，以避免奇怪的副作用.poo.map() 的结果是一个包含迭代器 list_start_vals 中每个值的评估的列表，这样您就不必创建 array_2D 之前.

The following works. First it is a good idea to protect the main part of your code inside a main block in order to avoid weird side effects. The result of poo.map() is a list containing the evaluations for each value in the iterator list_start_vals, such that you don't have to create array_2D before.

import numpy as np
from multiprocessing import Pool

def fill_array(start_val):
    return list(range(start_val, start_val+10))

if __name__=='__main__':
    pool = Pool(processes=4)
    list_start_vals = range(40, 60)
    array_2D = np.array(pool.map(fill_array, list_start_vals))
    pool.close() # ATTENTION HERE
    print array_2D

也许你在使用 pool.close() 时会遇到问题，从@hpaulj 的评论中你可以删除这一行，以防你遇到问题......

perhaps you will have trouble using pool.close(), from the comments of @hpaulj you can just remove this line in case you have problems...

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