Question

我想在for循环中填充2D-numpy数组，并使用多处理来固定计算。

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4个子进程并占用4个CPU核心但是执行没有完成且数组没有打印。如果我尝试将数组写入磁盘，则没有任何反应。

谁能告诉我为什么？

Answer 1

以下作品。首先，保护主块内部代码的主要部分是个好主意，以避免出现奇怪的副作用。 poo.map()的结果是一个列表，其中包含迭代器list_start_vals中每个值的计算结果，这样您就不必在之前创建array_2D。

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

也许你会在使用pool.close()时遇到麻烦，从@hpaulj的评论中你可以删除这一行，以防你遇到问题......

Answer 2

如果您仍想使用数组填充，则可以使用pool.apply_async代替pool.map。在Saullo的回答中工作：

import numpy as np
from multiprocessing import Pool

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

if __name__=='__main__':
    pool = Pool(processes=4)
    list_start_vals = range(40, 60)
    array_2D = np.zeros((20,10))
    for line, val in enumerate(list_start_vals):
        result = pool.apply_async(fill_array, [val])
        array_2D[line,:] = result.get()
    pool.close()
    print array_2D

这比map慢一点。但它不会像我对地图版本的测试那样产生运行时错误：Exception RuntimeError: RuntimeError('cannot join current thread',) in <Finalize object, dead> ignored

Answer 3

问题是由于在for循环中运行pool.map，map（）方法的结果在功能上等同于内置map（），除了单个任务是并行运行的。所以在你的情况下，pool.map（fill_array，list_start_vals）将被调用20次，并开始为for循环的每次迭代并行运行，下面的代码应该可以工作

<强>代码：

#!/usr/bin/python

import numpy
from multiprocessing import Pool

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

if __name__ == "__main__":
    array_2D = numpy.zeros((20,10))
    pool = Pool(processes = 4)    
    list_start_vals = range(40,60)

    # running the pool.map in a for loop is wrong
    #for line in xrange(20):
    #    array_2D[line,:] = pool.map(fill_array,list_start_vals)

    # get the result of pool.map (list of values returned by fill_array)
    # in a pool_result list 
    pool_result = pool.map(fill_array,list_start_vals)

    # the pool is processing its inputs in parallel, close() and join() 
    #can be used to synchronize the main process 
    #with the task processes to ensure proper cleanup.
    pool.close()
    pool.join()

    # Now assign the pool_result to your numpy
    for line,result in enumerate(pool_result):
        array_2D[line,:] = result

    print array_2D

如何使用Python多处理Pool.map在for循环中填充numpy数组

3 个答案: