Question

我有一个模块A，它通过获取数据并将其发送到模块B，C，D等进行分析然后将它们的结果连接在一起来执行基本map / reduce。

但似乎模块B，C，D等本身不能创建多处理池，否则我得到

AssertionError: daemonic processes are not allowed to have children

是否有可能以其他方式并行化这些工作？

为了清楚起见，这里是一个（通常是坏的）婴儿的例子。（我通常会尝试/捕捉，但你得到了要点。）

A.py:

  import B
  from multiprocessing import Pool

  def main():
    p = Pool()
    results = p.map(B.foo,range(10))
    p.close()
    p.join()
    return results


B.py:

  from multiprocessing import Pool

  def foo(x):
    p = Pool()
    results = p.map(str,x)
    p.close()
    p.join()
    return results

Answer 1

是否可以在游泳池内设置游泳池？

是的，除非你想提出an army of zombies，否则它可能不是一个好主意。来自Python Process Pool non-daemonic?：

import multiprocessing.pool
from contextlib import closing
from functools import partial

class NoDaemonProcess(multiprocessing.Process):
    # make 'daemon' attribute always return False
    def _get_daemon(self):
        return False
    def _set_daemon(self, value):
        pass
    daemon = property(_get_daemon, _set_daemon)

# We sub-class multiprocessing.pool.Pool instead of multiprocessing.Pool
# because the latter is only a wrapper function, not a proper class.
class Pool(multiprocessing.pool.Pool):
    Process = NoDaemonProcess

def foo(x, depth=0):
    if depth == 0:
        return x
    else:
        with closing(Pool()) as p:
            return p.map(partial(foo, depth=depth-1), range(x + 1))

if __name__ == "__main__":
    from pprint import pprint
    pprint(foo(10, depth=2))

输出

[[0],
 [0, 1],
 [0, 1, 2],
 [0, 1, 2, 3],
 [0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4, 5],
 [0, 1, 2, 3, 4, 5, 6],
 [0, 1, 2, 3, 4, 5, 6, 7],
 [0, 1, 2, 3, 4, 5, 6, 7, 8],
 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]

concurrent.futures默认支持它：

# $ pip install futures # on Python 2
from concurrent.futures import ProcessPoolExecutor as Pool
from functools import partial

def foo(x, depth=0):
    if depth == 0:
        return x
    else:
        with Pool() as p:
            return list(p.map(partial(foo, depth=depth-1), range(x + 1)))

if __name__ == "__main__":
    from pprint import pprint
    pprint(foo(10, depth=2))

它产生相同的输出。

是否有可能以其他方式并行化这些工作？

是。例如，查看celery allows to create a complex workflow。

的方式

Python多处理：是否可以在池中设置池？

1 个答案:

输出