2021年的原生Python异步调用方式，Python 3.9也适用于Jupyter / Ipython内核

Camabeh的答案是从Python 3.3开始的。

异步def display_date(循环): End_time = loop.time() + 5.0 而真正的: print (datetime.datetime.now ()) If (loop.time() + 1.0) >= end_time: 打破 等待asyncio.sleep (1) Loop = asyncio.get_event_loop() #阻塞调用，当display_date()协程完成时返回 loop.run_until_complete (display_date(循环) loop.close ()

这将在Jupyter笔记本/ Jupyter实验室工作，但抛出一个错误:

RuntimeError: This event loop is already running

由于Ipython使用事件循环，我们需要一些嵌套异步循环，这在Python中还没有实现。幸运的是，有nest_asyncio来处理这个问题。你所需要做的就是:

!pip install nest_asyncio # use ! within Jupyter Notebook, else pip install in shell
import nest_asyncio
nest_asyncio.apply()

(基于此线程)

只有在调用loop.close()时，它才会抛出另一个错误，因为它可能指向Ipython的主循环。

RuntimeError: Cannot close a running event loop

一旦有人回答了这个github问题，我就会更新这个答案。

2021年的原生Python异步调用方式，Python 3.9也适用于Jupyter / Ipython内核

Camabeh的答案是从Python 3.3开始的。

异步def display_date(循环): End_time = loop.time() + 5.0 而真正的: print (datetime.datetime.now ()) If (loop.time() + 1.0) >= end_time: 打破 等待asyncio.sleep (1) Loop = asyncio.get_event_loop() #阻塞调用，当display_date()协程完成时返回 loop.run_until_complete (display_date(循环) loop.close ()

这将在Jupyter笔记本/ Jupyter实验室工作，但抛出一个错误:

RuntimeError: This event loop is already running

由于Ipython使用事件循环，我们需要一些嵌套异步循环，这在Python中还没有实现。幸运的是，有nest_asyncio来处理这个问题。你所需要做的就是:

!pip install nest_asyncio # use ! within Jupyter Notebook, else pip install in shell
import nest_asyncio
nest_asyncio.apply()

(基于此线程)

只有在调用loop.close()时，它才会抛出另一个错误，因为它可能指向Ipython的主循环。

RuntimeError: Cannot close a running event loop

一旦有人回答了这个github问题，我就会更新这个答案。

Just

import threading, time

def f():
    print "f started"
    time.sleep(3)
    print "f finished"

threading.Thread(target=f).start()

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()

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。

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