你可以使用过程。如果你想永远运行它，在你的函数中使用while(比如networking):

from multiprocessing import Process
def foo():
    while 1:
        # Do something

p = Process(target = foo)
p.start()

如果你只想运行一次，可以这样做:

from multiprocessing import Process
def foo():
    # Do something

p = Process(target = foo)
p.start()
p.join()

你可以使用并发。期货(在Python 3.2中添加)。

import time
from concurrent.futures import ThreadPoolExecutor


def long_computation(duration):
    for x in range(0, duration):
        print(x)
        time.sleep(1)
    return duration * 2


print('Use polling')
with ThreadPoolExecutor(max_workers=1) as executor:
    future = executor.submit(long_computation, 5)
    while not future.done():
        print('waiting...')
        time.sleep(0.5)

    print(future.result())

print('Use callback')
executor = ThreadPoolExecutor(max_workers=1)
future = executor.submit(long_computation, 5)
future.add_done_callback(lambda f: print(f.result()))

print('waiting for callback')

executor.shutdown(False)  # non-blocking

print('shutdown invoked')

我的解决方案是:

import threading

class TimeoutError(RuntimeError):
    pass

class AsyncCall(object):
    def __init__(self, fnc, callback = None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        self.Thread = threading.Thread(target = self.run, name = self.Callable.__name__, args = args, kwargs = kwargs)
        self.Thread.start()
        return self

    def wait(self, timeout = None):
        self.Thread.join(timeout)
        if self.Thread.isAlive():
            raise TimeoutError()
        else:
            return self.Result

    def run(self, *args, **kwargs):
        self.Result = self.Callable(*args, **kwargs)
        if self.Callback:
            self.Callback(self.Result)

class AsyncMethod(object):
    def __init__(self, fnc, callback=None):
        self.Callable = fnc
        self.Callback = callback

    def __call__(self, *args, **kwargs):
        return AsyncCall(self.Callable, self.Callback)(*args, **kwargs)

def Async(fnc = None, callback = None):
    if fnc == None:
        def AddAsyncCallback(fnc):
            return AsyncMethod(fnc, callback)
        return AddAsyncCallback
    else:
        return AsyncMethod(fnc, callback)

并完全按要求工作:

@Async
def fnc():
    pass

有什么理由不使用线程吗?您可以使用线程类。 使用isAlive()函数代替finished()函数。result()函数可以join()线程并检索结果。并且，如果可以的话，重写run()和__init__函数来调用构造函数中指定的函数，并将值保存到类实例的某个地方。

您可以实现一个装饰器来使您的函数异步，尽管这有点棘手。多处理模块充满了小怪癖和看似任意的限制——尽管如此，更有理由将它封装在友好的界面后面。

from inspect import getmodule
from multiprocessing import Pool


def async(decorated):
    r'''Wraps a top-level function around an asynchronous dispatcher.

        when the decorated function is called, a task is submitted to a
        process pool, and a future object is returned, providing access to an
        eventual return value.

        The future object has a blocking get() method to access the task
        result: it will return immediately if the job is already done, or block
        until it completes.

        This decorator won't work on methods, due to limitations in Python's
        pickling machinery (in principle methods could be made pickleable, but
        good luck on that).
    '''
    # Keeps the original function visible from the module global namespace,
    # under a name consistent to its __name__ attribute. This is necessary for
    # the multiprocessing pickling machinery to work properly.
    module = getmodule(decorated)
    decorated.__name__ += '_original'
    setattr(module, decorated.__name__, decorated)

    def send(*args, **opts):
        return async.pool.apply_async(decorated, args, opts)

    return send

下面的代码说明了装饰器的用法:

@async
def printsum(uid, values):
    summed = 0
    for value in values:
        summed += value

    print("Worker %i: sum value is %i" % (uid, summed))

    return (uid, summed)


if __name__ == '__main__':
    from random import sample

    # The process pool must be created inside __main__.
    async.pool = Pool(4)

    p = range(0, 1000)
    results = []
    for i in range(4):
        result = printsum(i, sample(p, 100))
        results.append(result)

    for result in results:
        print("Worker %i: sum value is %i" % result.get())

在实际的情况下，我将详细介绍装饰器，提供一些方法来关闭它以进行调试(同时保持未来的接口在适当的位置)，或者可能是处理异常的工具;但我认为这充分说明了原理。

从Python 3.5开始，可以对异步函数使用增强的生成器。

import asyncio
import datetime

增强的生成器语法:

@asyncio.coroutine
def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        yield from asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()

新的async/await语法:

async def display_date(loop):
    end_time = loop.time() + 5.0
    while True:
        print(datetime.datetime.now())
        if (loop.time() + 1.0) >= end_time:
            break
        await asyncio.sleep(1)


loop = asyncio.get_event_loop()
# Blocking call which returns when the display_date() coroutine is done
loop.run_until_complete(display_date(loop))
loop.close()