Python 多处理终止进程

问题描述

我正在学习如何使用 Python 多处理库.然而，当我浏览一些示例时，我最终在我的后台运行了许多 python 进程.

I am learning how to use the Python multiprocessing library. However, while I am going through some of the examples, I ended up with many python processes running in my background.

其中一个示例如下所示:

from multiprocessing import Process, Lock

def f(l, i):
    l.acquire()
    print 'hello world', i
    l.release()

if __name__ == '__main__':
    lock = Lock()

    for num in range(10):   # I changed the number of iterations from 10 to 1000...
        Process(target=f, args=(lock, num)).start()

现在这是我的TOP"命令的屏幕截图:

Now here is a screen shot of my 'TOP' command:

88950  Python       0.0  00:00.00 1    0    9     91    1584K  5856K  2320K  1720K  2383M  82441 1     sleeping 1755113321 799
88949  Python       0.0  00:00.00 1    0    9     91    1584K  5856K  2320K  1720K  2383M  82441 1     sleeping 1755113321 798
88948  Python       0.0  00:00.00 1    0    9     91    1580K  5856K  2316K  1716K  2383M  82441 1     sleeping 1755113321 797
88947  Python       0.0  00:00.00 1    0    9     91    1580K  5856K  2316K  1716K  2383M  82441 1     sleeping 1755113321 796
88946  Python       0.0  00:00.00 1    0    9     91    1576K  5856K  2312K  1712K  2383M  82441 1     sleeping 1755113321 795
88945  Python       0.0  00:00.00 1    0    9     91    1576K  5856K  2312K  1712K  2383M  82441 1     sleeping 1755113321 794
88944  Python       0.0  00:00.00 1    0    9     91    1576K  5856K  2312K  1712K  2383M  82441 1     sleeping 1755113321 794
88943  Python       0.0  00:00.00 1    0    9     91    1572K  5856K  2308K  1708K  2383M  82441 1     sleeping 1755113321 792
88942  Python       0.0  00:00.00 1    0    9     91    1568K  5856K  2304K  1708K  2383M  82441 1     sleeping 1755113321 790
88941  Python       0.0  00:00.00 1    0    9     91    1564K  5856K  2300K  1704K  2383M  82441 1     sleeping 1755113321 789
88938  Python       0.0  00:00.00 1    0    9     91    1564K  5856K  2300K  1704K  2383M  82441 1     sleeping 1755113321 788
88936  Python       0.0  00:00.00 1    0    9     91    1576K  5856K  2296K  1716K  2383M  82441 1     sleeping 1755113321 787
88935  Python       0.0  00:00.00 1    0    9     91    1560K  5856K  2296K  1700K  2383M  82441 1     sleeping 1755113321 787
88934  Python       0.0  00:00.00 1    0    9     91    1560K  5856K  2296K  1700K  2383M  82441 1     sleeping 1755113321 786
88933  Python       0.0  00:00.00 1    0    9     91    1556K  5856K  2292K  1696K  2383M  82441 1     sleeping 1755113321 785
88932  Python       0.0  00:00.00 1    0    9     91    1556K  5856K  2292K  1696K  2383M  82441 1     sleeping 1755113321 784
88931  Python       0.0  00:00.00 1    0    9     91    1552K  5856K  2288K  1692K  2383M  82441 1     sleeping 1755113321 783
88930  Python       0.0  00:00.00 1    0    9     91    1612K  5856K  2288K  1752K  2383M  82441 1     sleeping 1755113321 783
88929  Python       0.0  00:00.00 1    0    9     91    1588K  5856K  2288K  1728K  2383M  82441 1     sleeping 1755113321 782
88927  Python       0.0  00:00.00 1    0    9     91    1608K  5856K  2284K  1748K  2383M  82441 1     sleeping 1755113321 781
88926  Python       0.0  00:00.00 1    0    9     91    1548K  5856K  2284K  1688K  2383M  82441 1     sleeping 1755113321 780
88924  Python       0.0  00:00.00 1    0    9     91    1556K  5856K  2276K  1700K  2383M  82441 1     sleeping 1755113321 778
88923  Python       0.0  00:00.00 1    0    9     91    1540K  5856K  2276K  1684K  2383M  82441 1     sleeping 1755113321 777
88922  Python       0.0  00:00.00 1    0    9     91    1540K  5856K  2276K  1684K  2383M  82441 1     sleeping 1755113321 776
88921  Python       0.0  00:00.00 1    0    9     91    1536K  5856K  2272K  1680K  2383M  82441 1     sleeping 1755113321 774
88920  Python       0.0  00:00.00 1    0    9     91    1528K  5856K  2264K  1672K  2383M  82441 1     sleeping 1755113321 771
88919  Python       0.0  00:00.00 1    0    9     91    1528K  5856K  2264K  1672K  2383M  82441 1     sleeping 1755113321 771
88918  Python       0.0  00:00.00 1    0    9     91    1528K  5856K  2264K  1672K  2383M  82441 1     sleeping 1755113321 770
....

1. 不知道怎么一口气干掉他们.

1. I don't know how to kill them in one go.

ps ... |grep python .... 杀死?

ps ... | grep python .... kill?

我需要添加什么样的python代码才能避免再次出现这种悲惨的情况.谢谢！

what kind of python code do I need to add to avoid this miserable situation again. Thanks!

推荐答案

你需要 .join() 在工作队列中的进程上，这会将它们锁定到调用应用程序，直到所有它们在父进程被杀死时成功或杀死，并以守护程序模式运行.

You need to .join() on your processes in a worker Queue, which will lock them to the calling application until all of them succeed or kill when the parent is killed, and run them in daemon mode.

http://forums.xkcd.com/viewtopic.php?f=11&t=94726

使用多处理模块结束守护进程

http://docs.python.org/2/library/multiprocessing.html#the-process-class

http://www.python.org/dev/peps/pep-3143/#correct-daemon-behaviour

这篇关于Python 多处理终止进程的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持html5模板网！