python多处理脚本不会退出

时间:2016-12-22 13:45:57

标签: python queue multiprocessing exit multiple-processes

我试图通过python2.7多处理模块获得更多的安慰。所以我编写了一个小脚本,它将文件名和所需数量的进程作为输入,然后启动多个进程以将函数应用于队列中的每个文件名。它看起来像这样:

import multiprocessing, argparse, sys
from argparse import RawTextHelpFormatter

def parse_arguments():
    descr='%r\n\nTest different functions of multiprocessing module\n%r' % ('_'*80, '_'*80)
    parser=argparse.ArgumentParser(description=descr.replace("'", ""), formatter_class=RawTextHelpFormatter)
    parser.add_argument('-f', '--files', help='list of filenames', required=True, nargs='+')
    parser.add_argument('-p', '--processes', help='number of processes for script', default=1, type=int)
    args=parser.parse_args()
    return args 

def print_names(name):
    print name


###MAIN###

if __name__=='__main__':
    args=parse_arguments()
    q=multiprocessing.Queue()
    procs=args.processes
    proc_num=0
    for name in args.files:
        q.put(name)
    while q.qsize()!=0:
        for x in xrange(procs):
            proc_num+=1
            file_name=q.get()
            print 'Starting process %d' % proc_num
            p=multiprocessing.Process(target=print_names, args=(file_name,))
            p.start()
            p.join()
            print 'Process %d finished' % proc_num

脚本运行正常并且每次旧进程完成时都会启动一个新进程(我认为它是如何工作的?),直到队列中的所有对象都用完为止。但是,脚本在完成队列后不会退出,但是处于空闲状态,我必须使用Ctrl+C将其终止。这里有什么问题?

感谢您的回答!

1 个答案:

答案 0 :(得分:1)

似乎你在那里混合了一些东西。 您生成一个进程,让它完成它的工作,并在下一次迭代中开始一个新进程之前等待它退出。使用这种方法,您将陷入顺序处理,这里没有实际的多处理。

也许您想以此为出发点:

import sys
import os
import time
import multiprocessing as mp

def work_work(q):
    # Draw work from the queue
    item = q.get()
    while item:
        # Print own process id and the item drawn from the queue
        print(os.getpid(), item)
        # Sleep is only for demonstration here. Usually, you 
        # do not want to use this! In this case, it gives the processes
        # the chance to "work" in parallel, otherwise one process
        # would have finished the entire queue before a second one
        # could be spawned, because this work is quickly done.
        time.sleep(0.1)
        # Draw new work
        item = q.get()

if __name__=='__main__':
    nproc = 2  # Number of processes to be used
    procs = [] # List to keep track of all processes

    work = [chr(i + 65) for i in range(5)]
    q = mp.Queue() # Create a queue...
    for w in work:
        q.put(w) # ...and fill it with some work.

    for _ in range(nproc):
        # Spawn new processes and pass each of them a reference
        # to the queue where they can pull their work from.
        procs.append(mp.Process(target=work_work, args=(q,)))
        # Start the process just created.
        procs[-1].start()

    for p in procs:
        # Wait for all processes to finish their work. They only
        # exit once the queue is empty.
        p.join()
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