Python多处理Linux Windows的区别 [英] Python multiprocessing linux windows difference

问题描述

此代码在linux上执行但抛出AttributeError:类型对象'T'在Windows上没有属性'val'，为什么?

This code executes on linux but throws an AttributeError: type object 'T' has no attribute 'val' on windows, why?

from multiprocessing import Process
import sys

class T():
    @classmethod
    def init(cls, val):
        cls.val = val

def f():
    print(T.val)

if __name__ == '__main__':
    T.init(5)

    f()

    p = Process(target=f, args=())
    p.start()

推荐答案

Windows缺少 系统调用，它会复制当前进程.这具有许多含义，包括 Windows多处理文档页面上列出的含义. .更具体地说:

Windows lacks a fork() system call, which duplicates current process. This has many implications, including those listed on the windows multiprocessing documentation page. More specifically:

请记住，如果在子进程中运行的代码尝试访问全局变量，则它看到的值(如果有)可能与Process.start时父进程中的值不同.叫.

Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that Process.start was called.

在内部，python通过从头开始一个新进程并告诉它再次加载所有模块，从而在Windows上创建了一个新进程.因此，您在当前流程中所做的任何更改都将不会显示.

In internals, python creates a new process on windows by starting a new process from scratch, and telling it to load all modules again. So any change you have done in current process will not be seen.

在您的示例中，这意味着将在子进程中加载​​您的模块，但不会运行if __name__ == '__main__'部分.因此T.init将不会被调用，并且T.val将不存在，因此会看到错误.

In your example, this means that in the child process, your module will be loaded, but the if __name__ == '__main__' section will not be run. So T.init will not be called, and T.val won't exist, thus the error you see.

另一方面，在POSIX系统(包括Linux)上，进程创建使用fork，所有全局状态保持不变.该子项带有所有内容的副本，因此不必重新加载任何内容，并且将看到其T副本及其val副本.

On the other hand, on POSIX systems (that includes Linux), process creation uses fork, and all global state is left untouched. The child runs with a copy of everything, so it does not have to reload anything and will see its copy of T with its copy of val.

这也意味着在POSIX系统上，进程的创建要快得多，资源占用也要少得多，特别是因为复制"使用写时复制来避免实际复制数据的开销.

This also means that Process creation is much faster and much lighter on resources on POSIX systems, especially as the "duplication" uses copy-on-write to avoid the overhead of actually copying the data.

使用多重处理时还有其他怪癖，请参见

There are other quirks when using multiprocessing, all of which are detailed in the python multiprocessing guidelines.

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