Python Pool.Starmap 未在打印时终止或输出

Question

我尝试了几种不同的方式来执行Pool.starmap。我尝试了各种不同的建议和答案，但无济于事。下面是我尝试运行的代码示例，但是它被捕获并且永远不会终止。我在这里做错了什么？

旁注：我使用的是python版本3.9.8

if __name__ == '__main__':
with get_context("spawn").Pool() as p:
    tasks = [(1,1),(2,2),(3,3)]
    print(p.starmap(add,tasks))
    p.close()
    p.join()

Answer 1

python 中的多处理具有一些您应该注意的复杂性，这使得它取决于您运行脚本的方式以及您使用的操作系统和 python 版本。

我经常看到的一个大问题是 Jupyter 和其他“笔记本”风格的 Python 环境并不总是能很好地支持多处理。从技术上讲，有一些方法可以解决这个问题，但我通常只是建议从更正常的系统终端执行代码。共同点是“交互式”解释器不能很好地工作，因为需要有一个“主”文件，而在交互模式下没有文件；它只是等待用户输入。

我无法确切知道您的问题是什么，因为您没有提供所有代码、您正在使用的操作系统以及您正在使用的 IDE，但我至少可以为您提供一个工作 (在我的设置上）示例。 （windows 10；python 3.9；带有运行设置的 Spyder IDE -> 在外部系统终端中执行）

import multiprocessing as mp

def add(a, b): #I'm assuming your "add" function looks a bit like this...
    return a+b

if __name__ == "__main__": 
    #this is critical when using "spawn" so code doesn't run when the file is imported
    #you should only define functions, classes, and static data outside this (constants)
    #most critically, it shouldn't be possible for a new child process to start outside this
    
    ctx = mp.get_context("spawn")
    #This is the only context available on windows, and the default for MacOS since python 3.8.
    #  Contexts are an important topic somewhat unique to python multiprocessing, and you should
    #  absolutely do some additional reading about "spawn" vs "fork". tldr; "spawn" starts a new
    #  process with no knowledge of the old one, and must `import` everything from __main__. 
    #  "fork" on the other hand copies the existing process and all its memory before branching. This is
    #  faster than re-starting the interpreter, and re-importing everything, but sometimes things
    #  get copied that shouldn't, and other things that should get copied don't.
    with ctx.Pool() as p: 
        #using `with` automatically shuts down the pool (forcibly) at the end of the block so you don't have to call `close` or `join`.
        #  It was also pointed out that due to the forcible shutdown, async calls like `map_async` may not finish unless you wait for the results
        #  before the end of the `with` block. `starmap` already waits for the results in this case however, so extra waiting is not needed.
        tasks = [(1,1),(2,2),(3,3)]
        print(p.starmap(add, tasks))