Question

我想在Python中编写一个类，它可以在给定的时间段内运行某个算法，然后停止并返回它在超时之前找到的最新uptodate值。

作为一个例子，我写了一个简单的类来查找在向量中找到最大值：

import time, multiprocessing

class AnytimeAlgorithm:
    def __init__(self, vector):
        self.vector = vector
        self.result = 0

    def update_forever(self):
        while True:
            i = random.randint(0, len(self.vector) - 1)
            if self.vector[i] > self.result:
                self.result = self.vector[i]
                print("self", self, "result", self.result)

    def result_after(self, seconds):
        p = multiprocessing.Process(target=self.update_forever, name="update_forever", args=())
        p.start()
        p.join(seconds)
        if p.is_alive():
            p.terminate()
        p.join()
        print("self", self, "final result", self.result)
        return self.result


if __name__ == "__main__":
    import random, numpy as np
    vector = np.random.rand(10000000)
    maximizer = AnytimeAlgorithm(vector)
    print(maximizer.result_after(0.01))

当我运行这个类时，它表明，正如预期的那样，结果会随着时间的推移而增加。但是，返回值始终为0！这是典型的输出：

self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.420804014071
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.444555804935
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.852844624467
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.915336332491
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.964438367823
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.975029317702
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.975906346116
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.987784181209
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.996998726143
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.999480015562
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> result 0.999798469992
self <__main__.AnytimeAlgorithm object at 0x7f8e94de1cf8> final result 0

我的错误是什么？

Answer 1

当你在Python中使用multiprocessing时，它会创建一个新的独立Python进程并运行你要求的任何内容。简化API以使其看起来像<{3}}这样的事实不应该让您感到困惑。在主过程中，您将创建一个AnytimeAlgorithm对象。然后，创建一个运行函数的Process;这会创建一个新进程并复制解释器的状态，因此您在新进程中也可以使用AnytimeAlgorithm的副本。但是，这两个对象并不相同，它们甚至不会存在于同一个进程中，因此它们无法（直接）共享任何信息。您在新流程中对对象所做的更改只会影响该流程中的对象副本，而不会影响原始流程中的对象副本。

您可以查看有关如何在主流程和衍生流程之间共享信息的文档，例如使用multithreading或pipes, queues，这可能是一个不错的选择：

import multiprocessing import random import numpy as np class AnytimeAlgorithm: def __init__(self, vector): self.vector = vector self.result = multiprocessing.Value('d', 0.0) def update_forever(self): while True: i = random.randint(0, len(self.vector) - 1) if self.vector[i] > self.result.value: self.result.value = self.vector[i] print("self", self, "result", self.result.value) def result_after(self, seconds): p = multiprocessing.Process(target=self.update_forever, name="update_forever", args=()) p.start() p.join(seconds) if p.is_alive(): p.terminate() p.join() print("self", self, "final result", self.result.value) return self.result.value if __name__ == "__main__": import random, numpy as np vector = np.random.rand(10000000) maximizer = AnytimeAlgorithm(vector) print(maximizer.result_after(0.1))

输出：

self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.01491873361800522 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.060776471658675835 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.7476611733129928 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.9468162088782311 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.9531978645650057 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.9992671080742871 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.999293465561661 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.9996894825552965 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.9998511378366163 self <__mp_main__.AnytimeAlgorithm object at 0x0000017D26AB7898> result 0.999933119926922 self <__main__.AnytimeAlgorithm object at 0x00000195FBDC7908> final result 0.999933119926922 0.999933119926922

请注意，由于进程间同步访问，使用shared memory会产生额外的开销。阅读文档以了解锁定如何适用于此类，并考虑以最小化访问共享资源的方式编写算法（例如，使用在每次计算结束时编写的时态局部变量）。

任何时候使用python多处理的算法

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