带有全局数据的python并行映射(multiprocessing.Pool.map) [英] python parallel map (multiprocessing.Pool.map) with global data

问题描述

我正在尝试在多个进程上调用一个函数.显而易见的解决方案是python的multiprocessing模块.问题在于该功能有副作用.它创建一个临时文件，并使用atexit.register和全局列表注册要在退出时删除的文件.下面应该演示该问题(在不同的上下文中).

I'm trying to call a function on multiple processes. The obvious solution is python's multiprocessing module. The problem is that the function has side effects. It creates a temporary file and registers that file to be deleted on exit using the atexit.register and a global list. The following should demonstrate the problem (in a different context).

import multiprocessing as multi

glob_data=[]
def func(a):
    glob_data.append(a)

map(func,range(10))
print glob_data  #[0,1,2,3,4 ... , 9]  Good.

p=multi.Pool(processes=8)
p.map(func,range(80))

print glob_data  #[0,1,2,3,4, ... , 9] Bad, glob_data wasn't updated.

有什么办法可以更新全局数据?

Is there any way to have the global data updated?

请注意，如果您尝试上述脚本，则可能不应该从交互式解释器中尝试它，因为multiprocessing要求模块__main__可由子进程导入.

Note that if you try out the above script, you probably shouldn't try it from the interactive interpreter since multiprocessing requires the module __main__ to be importable by child processes.

更新

在func中添加global关键字无济于事-例如:

Added the global keyword in func doesn't help -- e.g.:

def func(a):  #Still doesn't work.
    global glob_data
    glob_data.append(a)

推荐答案

您需要列表glob_data由共享内存支持，Multiprocessing的Manager仅提供以下内容:

You need the list glob_data to be backed by shared memory, Multiprocessing's Manager gives you just that:

import multiprocessing as multi
from multiprocessing import Manager

manager = Manager()

glob_data = manager.list([])

def func(a):
    glob_data.append(a)

map(func,range(10))
print glob_data  # [0,1,2,3,4 ... , 9] Good.

p = multi.Pool(processes=8)
p.map(func,range(80))

print glob_data # Super Good.

对于某些背景:

https://docs.python.org/3/library/multiprocessing .html#managers

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