在多处理中，不同工人的输出相同 [英] Same output in different workers in multiprocessing

问题描述

我有非常简单的情况，可以将要完成的工作分解并分配给工人.我从此处尝试了一个非常简单的多处理示例:

I have very simple cases where the work to be done can be broken up and distributed among workers. I tried a very simple multiprocessing example from here:

import multiprocessing
import numpy as np
import time

def do_calculation(data):
    rand=np.random.randint(10)
    print data, rand
    time.sleep(rand)
    return data * 2

if __name__ == '__main__':
    pool_size = multiprocessing.cpu_count() * 2
    pool = multiprocessing.Pool(processes=pool_size)

    inputs = list(range(10))
    print 'Input   :', inputs

    pool_outputs = pool.map(do_calculation, inputs)
    print 'Pool    :', pool_outputs

上面的程序产生以下输出:

The above program produces the following output :

Input   : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
0 7
1 7
2 7
5 7
3 7
4 7
6 7
7 7
8 6
9 6
Pool    : [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

为什么打印相同的随机数? (我的机器上有4个CPU).这是最好/最简单的方法吗?

Why is the same random number getting printed? (I have 4 cpus in my machine). Is this the best/simplest way to go ahead?

推荐答案

我认为您需要使用

I think you'll need to re-seed the random number generator using numpy.random.seed in your do_calculation function.

我的猜测是，当您导入模块时，随机数生成器(RNG)会被植入种子.然后，当您使用多处理时，您将使用已播种的RNG来分叉当前进程-因此，您的所有进程都为RNG共享相同的种子值，因此它们将生成相同的数字序列.

My guess is that the random number generator (RNG) gets seeded when you import the module. Then, when you use multiprocessing, you fork the current process with the RNG already seeded -- Thus, all your processes are sharing the same seed value for the RNG and so they'll generate the same sequences of numbers.

例如:

def do_calculation(data):
    np.random.seed()
    rand=np.random.randint(10)
    print data, rand
    return data * 2

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