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本文介紹了multiprocessing.Pool 似乎可以在 Windows 中工作，但不能在 ubuntu 中工作?的處理方法，對大家解決問題具有一定的參考價值，需要的朋友們下面隨著小編來一起學習吧！

問題描述

限時送ChatGPT賬號..

已解決:問題出在 Wingware Python IDE 上.我想現在很自然的問題是這怎么可能以及如何解決.

SOLVED: The problem was Wingware Python IDE. I guess the natural question now is how it is possible and how this could be fixed.

我昨天問了一個問題(Python 中 multiprocessing.Pool 的問題) 這個問題幾乎是一樣的，但我發現它似乎可以在 Windows 計算機上運行，??而不是在我的 ubuntu 中運行.在這篇文章的最后，我將發布一個略有不同的代碼版本.

I asked a question yesterday ( Problem with multiprocessing.Pool in Python ) and this question is almost the same but I have figured out that it seems to work on a Windows computer and not in my ubuntu. At the end of this post I will post a slightly different version of the code that does the same thing.

我的問題的簡短摘要:在 Python 中使用 multiprocessing.Pool 時，我并不總是能夠獲得我所要求的工人數量.發生這種情況時，程序就會停止.

Short summary of my problem: When using multiprocessing.Pool in Python I am not always able to get the amount of workers that I am asking for. When this happens, the program just stalls.

我整天都在尋找解決方案，然后我開始思考 Noahs 對我之前的問題的評論.他說它可以在他的機器上運行，所以我把代碼提供給了我的同事，他運行的是一臺帶有 Enthoughts 64 位 Python 2.7.1 發行版的 Windows 機器.我與我在 ubuntu 上運行的巨大差異相同.我還提到我們都有 Wingware Python IDE，但我懷疑這是否重要?

I have been working for a solution all day, and then I came to think about Noahs' comment on my previous question. He said that it worked on his machine so I gave the code to my colleague who runs a Windows machine with Enthoughts 64-bit Python 2.7.1 distribution. I have the same with the big difference that mine runs on ubuntu. I also mention that we both have Wingware Python IDE, but I doubt that this is of any importance?

當我的同事在他的機器上運行代碼時，我的代碼不會出現兩個問題.

There are two problems with my code that don't arise when my colleague runs the code on his machine.

我并不總是能夠得到我要求的四個工人(盡管我的機器有 12 個工人).發生這種情況時，該過程會停止并且不會繼續.沒有引發異常或錯誤.

I am not always able to get the four workers I am asking for (Although my machine has 12 workers). When this happens, the process just stalls and does not continue. No exception or Error is raised.

當我能夠得到我要求的四名工人時(大約 5 次左右出現 1 次)，所產生的數字(普通隨機數)對于所有四張圖片都是完全相同的.我的同事不是這樣.

When I am able to get the four workers I ask for (which happens approximately 1 out 5 times or so), the figures that are produced (plain random numbers) are EXACTLY the same for all four pictures. This is not the case for my colleague.

有些事情很可疑，我非常感謝你們提供的任何幫助.

Something is very fishy and I am very thankful for any kind of help you guys can offer.

代碼:

import multiprocessing as mp
import scipy as sp
import scipy.stats as spstat
import pylab

def testfunc(x0, N):
    print 'working with x0 = %s' % x0
    x = [x0]
    for i in xrange(1,N):
        x.append(spstat.norm.rvs(size = 1)) # stupid appending to make it slower
        if i % 10000 == 0:
            print 'x0 = %s, i = %s' % (x0, i)
    return sp.array(x)

def testfuncParallel(fargs):
    return testfunc(*fargs)


# Define Number of tasks.
nTasks = 4
N = 100000

if __name__ == '__main__':

    """
    Try number 1. Using multiprocessing.Pool together with Pool.map_async
    """
    pool = mp.Pool(processes = nTasks) # I have 12 threads (six cores) available so I am suprised that it does not get access to nTasks = 4 amount of workers

    # Define tasks:
    tasks = [(x, n) for x, n in enumerate(nTasks*[N])] # nTasks different tasks

    # Compute parallel: async - asynchronically, i.e. not necessary in order.
    result = pool.map_async(testfuncParallel, tasks)

    pool.close() # These are needed if map_async is used
    pool.join()

    # Get results:
    sim = sp.zeros((N, nTasks)) 

    for nn, res in enumerate(result.get()):    
        sim[:, nn] = res

    pylab.figure()
    for i in xrange(nTasks):
        pylab.subplot(nTasks,1, i + 1)
        pylab.plot(sim[:, i])

    pylab.show()

提前致謝.

真誠地，馬蒂亞斯

multiprocessing.Pool 似乎可以在 Windows 中工作，但不

問題描述

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