为什么多进程在同一进程中运行的东西？

本文介绍了为什么多进程在同一进程中运行的东西？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 我从运行以下解决方案如何恢复传递给multiprocessing.Process的函数的返回值？： import multiprocessingfrom os import getpiddef worker(procnum): print('I am number %d in process %d' % (procnum, getpid())) return getpid()if __name__ == '__main__': pool = multiprocessing.Pool(processes = 3) print(pool.map(worker, range(5)))它应该输出如下：I am number 0 in process 19139I am number 1 in process 19138I am number 2 in process 19140I am number 3 in process 19139I am number 4 in process 19140[19139, 19138, 19140, 19139, 19140]但我只能获得[4212, 4212, 4212, 4212, 4212]如果我给pool.map提供了一个范围为1,000,000的范围，使用超过10个进程，我最多看到两个不同的pids。If I feed pool.map a range of 1,000,000 using more than 10 processes I see at most two different pids.为什么是推荐答案 TL; DR ：任务不是以任何方式分配的，也许您的任务太短，他们都在其他进程开始之前完成。TL;DR: tasks are not specifically distributed in any way, perhaps your tasks are so short they are all completed before the other processes get started. multiprocessing 的来源，似乎任务被简单地放在队列中，工作进程从中读取 worker 从 Pool._inqueue 中读取。没有计算的分布，工人只是尽可能努力地工作。From looking at the source of multiprocessing, it appears that tasks are simply put in a Queue, which the worker processes read from (function worker reads from Pool._inqueue). There's no calculated distribution going on, the workers just race to work as hard as possible.最有可能的赌注，将是，因为任务是非常短，所以一个过程完成所有的过程之前，其他人有机会看或甚至开始。您可以通过向任务添加两秒钟睡眠来轻松检查是否是这种情况。The most likely bet then, would be that as the tasks are simply very short, so one process finishes all of them before the others have a chance to look or even get started. You can easily check if this is the case this by adding a two-second sleep to the task.将注意到在我的机器上，任务都很均匀地分布在进程上（同样对于#processes> #cores）。所以似乎有一些系统依赖，即使所有进程在工作排队之前应该有 .start（） ed。I'll note that on my machine, the tasks all get spread over the processes pretty homogeneously (also for #processes > #cores). So there seems to be some system-dependence, even though all processes should have .start()ed before work is queued.这里是来自 worker 的修剪源代码，它显示每个进程只从队列中读取任务，所以以伪随机顺序：Here's some trimmed source from worker, which shows that the tasks are just read from the queue by each process, so in pseudo-random order:def worker(inqueue, outqueue, ...): ... get = inqueue.get ... while maxtasks is None or (maxtasks and completed < maxtasks): try: task = get() ... SimpleQueue 使用 Pipe ，从 SimpleQueue 构造函数：self._reader, self._writer = Pipe(duplex=False) $ b b EDIT ：可能关于进程开始太慢的部分是false，所以我删除它。在任何工作排队之前，所有进程都是 .start（） ed（可能 platform -dependent）。我找不到进程是否准备好了 .start（）返回。EDIT: possibly the part about processes starting too slow is false, so I removed it. All processes are .start()ed before any work is queued (which may be platform-dependent). I can't find whether the process is ready at the moment .start() returns. 这篇关于为什么多进程在同一进程中运行的东西？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！