如何访问在多处理过程中调用的函数的结果？答案

【问题标题】：How to access the result of a function called in a multiprocessing process?如何访问在多处理过程中调用的函数的结果？
【发布时间】：2015-01-04 15:43:11
【问题描述】：

我正在运行这段代码：

import random
import multiprocessing
import time

def list_append(count, id):
    out_list = []
    for i in range(count):
        out_list.append(random.random())
    return out_list


if __name__ == "__main__":
    t0 = time.clock()
    size = 10000000   # Number of random numbers to add
    procs = 2   # Number of processes to create

    jobs = []
    for i in range(0, procs):
        process = multiprocessing.Process(target=list_append,args=(size, i))                                        
        jobs.append(process)

# Start the processes (i.e. calculate the random number lists)  
    res=[]  
    for j in jobs:
        r= j.start()
        res.append(r)

# Ensure all of the processes have finished
    for j in jobs:
        j.join()

    print "List processing complete."
    print time.clock()-t0,"seconds"

不幸的是，在它的最后，res = [None,None] 虽然我希望它被我在函数list_append 中填写的列表填充。

【问题讨论】：

它对您通过multiprocesssing.Pool 获得结果有用吗？就像在这个example 上一样？我阅读了Process class 的文档，发现没有办法得到结果，只有退出代码。

标签： python list function multiprocessing

【解决方案1】：

你需要使用data structures that can be shared between processes:

def list_append(count, id, res):
    #                      ^^^
    out_list = []
    for i in range(count):
        out_list.append(random.random())
    res[id] = out_list  # <------

if __name__ == "__main__":
    size = 10000000
    procs = 2   
    manager = multiprocessing.Manager()  # <---
    res = manager.dict()                 # <---
    jobs = []
    for i in range(0, procs):
        process = multiprocessing.Process(target=list_append,args=(size, i, res))
        #                                                                   ^^^^
        jobs.append(process)

    for j in jobs:
        r = j.start()

    for j in jobs:
        j.join()

    print "List processing complete."
    # now `res` will contain results

正如avenet所说，使用multiprocessing.Pool会更简单：

def list_append(args):
    count, id = args
    out_list = []
    for i in range(count):
        out_list.append(random.random())
    return out_list

if __name__ == "__main__":
    size = 10000000
    procs = 2

    pool = multiprocessing.Pool(procs)
    res = pool.map(list_append, [(size, i) for i in range(procs)])
    pool.close()
    pool.join()

    print "List processing complete."
    # print res

【讨论】：