Python多处理帮助有条件退出

Question

我在Python内的多处理方面breaking之以鼻，但我没有运气将话题缠住。基本上，我有一个过程很耗时。我需要将其运行在1到100的范围内，但是一旦满足我要寻找的条件，我想中止所有进程。条件是返回值==
90。

这是一个非多进程代码块。谁能给我一个例子，说明如何将其转换为多进程函数，一旦满足“ 90”的条件，代码将退出所有进程？

def Addsomething(i):
    SumOfSomething = i + 1    
    return SumOfSomething

def RunMyProcess():
    for i in range(100):
        Something = Addsomething(i)
        print Something
    return

if __name__ == "__main__":
    RunMyProcess()

编辑：

测试第3版时出现此错误。知道是什么原因造成的吗？

Exception in thread Thread-3:
Traceback (most recent call last):
  File "C:\Python27\lib\threading.py", line 554, in __bootstrap_inner
    self.run()
  File "C:\Python27\lib\threading.py", line 507, in run
    self.__target(*self.__args, **self.__kwargs)
  File "C:\Python27\lib\multiprocessing\pool.py", line 379, in _handle_results
    cache[job]._set(i, obj)
  File "C:\Python27\lib\multiprocessing\pool.py", line 527, in _set
    self._callback(self._value)
  File "N:\PV\_Proposals\2013\ESS - Clear Sky\01-CODE\MultiTest3.py", line 20, in check_result
    pool.terminate()
  File "C:\Python27\lib\multiprocessing\pool.py", line 423, in terminate
    self._terminate()
  File "C:\Python27\lib\multiprocessing\util.py", line 200, in __call__
    res = self._callback(*self._args, **self._kwargs)
  File "C:\Python27\lib\multiprocessing\pool.py", line 476, in _terminate_pool
    result_handler.join(1e100)
  File "C:\Python27\lib\threading.py", line 657, in join
    raise RuntimeError("cannot join current thread")
RuntimeError: cannot join current thread

Answer 1

也许您正在寻找这样的东西？请记住，我正在为Python 3编写。上面的打印语句是Python 2，在这种情况下，应注意的是使用xrange而不是range。

from argparse import ArgumentParser
from random import random
from subprocess import Popen
from sys import exit
from time import sleep

def add_something(i):

    # Sleep to simulate the long calculation
    sleep(random() * 30)
    return i + 1

def run_my_process():

    # Start up all of the processes, pass i as command line argument
    # since you have your function in the same file, we'll have to handle that
    # inside 'main' below
    processes = []
    for i in range(100):
        processes.append(Popen(['python', 'thisfile.py', str(i)]))

    # Wait for your desired process result
    # Might want to add a short sleep to the loop
    done = False
    while not done:
       for proc in processes:
            returncode = proc.poll()
            if returncode == 90:
                done = True
                break

    # Kill any process that are still running
    for proc in processes:

        if proc.returncode is None:

            # Might run into a race condition here,
            # so might want to wrap with try block
            proc.kill()

if __name__ == '__main__':

    # Look for optional i argument here
    parser = ArgumentParser()
    parser.add_argument('i', type=int, nargs='?')
    i = parser.parse_args().i

    # If there isn't an i, then run the whole thing
    if i is None:
        run_my_process()

    else:
        # Otherwise, run your expensive calculation and return the result
        returncode = add_something(i)
        print(returncode)
        exit(returncode)

编辑：

这是一个使用多处理模块而不是子进程的更干净的版本：

from random import random
from multiprocessing import Process
from sys import exit
from time import sleep

def add_something(i):

    # Sleep to simulate the long calculation
    sleep(random() * 30)

    exitcode = i + 1
    print(exitcode)
    exit(exitcode)

def run_my_process():

    # Start up all of the processes
    processes = []
    for i in range(100):
        proc = Process(target=add_something, args=[i])
        processes.append(proc)
        proc.start()

    # Wait for the desired process result
    done = False
    while not done:
        for proc in processes:
            if proc.exitcode == 90:
                done = True
                break

    # Kill any processes that are still running
    for proc in processes:
        if proc.is_alive():
            proc.terminate()

if __name__ == '__main__':
    run_my_process()

编辑2：

这是最后一个版本，我认为它比其他两个版本要好得多：

from random import random
from multiprocessing import Pool
from time import sleep

def add_something(i):

    # Sleep to simulate the long calculation
    sleep(random() * 30)
    return i + 1

def run_my_process():

    # Create a process pool
    pool = Pool(100)

    # Callback function that checks results and kills the pool
    def check_result(result):
        print(result)
        if result == 90:
            pool.terminate()

    # Start up all of the processes
    for i in range(100):
        pool.apply_async(add_something, args=[i], callback=check_result)

    pool.close()
    pool.join()

if __name__ == '__main__':
    run_my_process()