python多处理酸洗/管理器/ misc错误（来自PMOTW）

Question

我在通过Windows在Eclipse上运行以下代码时遇到了一些麻烦。该代码是从Doug Hellman：

import random 
import multiprocessing 
import time 


class ActivePool: 

    def __init__(self): 
     super(ActivePool, self).__init__() 
     self.mgr = multiprocessing.Manager() 
     self.active = self.mgr.list() 
     self.lock = multiprocessing.Lock() 

    def makeActive(self, name): 
     with self.lock: 
      self.active.append(name) 

    def makeInactive(self, name): 
     with self.lock: 
      self.active.remove(name) 

    def __str__(self): 
     with self.lock: 
      return str(self.active) 


def worker(s, pool): 
    name = multiprocessing.current_process().name 
    with s: 
     pool.makeActive(name) 
     print('Activating {} now running {}'.format(
      name, pool)) 
     time.sleep(random.random()) 
     pool.makeInactive(name) 


if __name__ == '__main__': 
    pool = ActivePool() 
    s = multiprocessing.Semaphore(3) 
    jobs = [ 
     multiprocessing.Process(
      target=worker, 
      name=str(i), 
      args=(s, pool), 
     ) 
     for i in range(10) 
    ] 

    for j in jobs: 
     j.start() 

    for j in jobs: 
     j.join() 
     print('Now running: %s' % str(pool)) 

我碰到下面的错误，我以为是由于某些酸洗问题与pool作为参数传递给Process。

Traceback (most recent call last): 
    File "E:\Eclipse_Workspace\CodeExamples\FromCodes\CodeTest.py", line 50, in <module> 
    j.start() 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\process.py", line 105, in start 
    self._popen = self._Popen(self) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\context.py", line 223, in _Popen 
    return _default_context.get_context().Process._Popen(process_obj) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\context.py", line 322, in _Popen 
    return Popen(process_obj) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__ 
    reduction.dump(process_obj, to_child) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\reduction.py", line 60, in dump 
    ForkingPickler(file, protocol).dump(obj) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\connection.py", line 939, in reduce_pipe_connection 
    dh = reduction.DupHandle(conn.fileno(), access) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\connection.py", line 170, in fileno 
    self._check_closed() 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\connection.py", line 136, in _check_closed 
    raise OSError("handle is closed") 
OSError: handle is closed 
Traceback (most recent call last): 
    File "<string>", line 1, in <module> 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\spawn.py", line 99, in spawn_main 
    new_handle = reduction.steal_handle(parent_pid, pipe_handle) 
    File "C:\Users\Bob\AppData\Local\Programs\Python\Python36-32\lib\multiprocessing\reduction.py", line 87, in steal_handle 
    _winapi.DUPLICATE_SAME_ACCESS | _winapi.DUPLICATE_CLOSE_SOURCE) 
PermissionError: [WinError 5] Access is denied 

类似的问题的answer似乎表明，我初始化pool与顶层的函数调用，但我不知道如何适用于这个例子。我是否在worker中初始化ActivePool？这似乎打败了赫尔曼的榜样精神。

另一个answer建议我用__getstate__，__setstate__，除去unpickleable对象和取储存时重建他们，但我不知道一个很好的方式与代理对象像经理要做到这一点，我居然不知道是什么不可取消的对象是。

有没有什么办法可以让这个例子在最小的变化下工作？我真的很想了解底下发生了什么。谢谢！

编辑 - 问题解决了：

酸洗问题是在事后很明显。 ActivePool的__init__包含一个Manager（）对象，看起来不可取。该代码运行正常按照赫尔曼的例子，如果我们去掉self.mgr，并在一行初始化列表ProxyObject：

def __init__(self): 
     super(ActivePool, self).__init__() 
     self.active = multiprocessing.Manager().list() 
     self.lock = multiprocessing.Lock() 

Answer 1

Comment: The 'join()' was in the Hellman example, but I forgot to add it into the code snippet. Any other ideas?

我运行Linux和它按预期工作时，Windows表现不同的阅读understanding-multiprocessing-shared-memory-management-locks-and-queues-in-pyt

要确定哪个参数args=(s, pool)引发错误，请删除一个并将其用作global。
变化：

def worker(s): 
    ... 

     args=(s,), 

---

Note: There is no need to enclose a multiprocessing.Manager().list() with a Lock() .
This is not the culprit of your error.

---

Question: Is there any way I can make this example work with minimal changes?

你__main__过程终止，因此，所有的启动的进程在执行不可预测的位置死亡。添加简单的一个.join()末让__main__等到所有进程完成：

for j in jobs: 
     j.join() 

    print('EXIT __main__') 

测试使用Python 3.4.2