[python](windows)分布式进程问题：pickle模块不能序列化lambda函数

 1 #!/usr/bin/env python3
 2 # -*- coding: utf-8 -*-
 3 
 4 import random, time, queue
 5 from multiprocessing.managers import BaseManager
 6 
 7 # 发送任务的队列:
 8 task_queue = queue.Queue()
 9 # 接收结果的队列:
10 result_queue = queue.Queue()
11 
12 # 从BaseManager继承的QueueManager:
13 class QueueManager(BaseManager):
14     pass
15 
16 # 把两个Queue都注册到网络上, callable参数关联了Queue对象:
17 QueueManager.register(‘get_task_queue‘, callable=lambda: task_queue)
18 QueueManager.register(‘get_result_queue‘, callable=lambda: result_queue)
19 
20 # 绑定端口5000, 设置验证码‘abc‘:
21 manager = QueueManager(address=(‘‘, 5000), authkey=b‘abc‘)
22 
23 # 启动Queue:
24 manager.start()
25 
26 # 获得通过网络访问的Queue对象:
27 task = manager.get_task_queue()
28 result = manager.get_result_queue()
29 
30 # 放几个任务进去:
31 for i in range(10):
32     n = random.randint(0, 10000)
33     print(‘Put task %d...‘ % n)
34     task.put(n)
35 
36 # 从result队列读取结果:
37 print(‘Try get results...‘)
38 for i in range(10):
39     r = result.get(timeout=10)
40     print(‘Result: %s‘ % r)
41 
42 # 关闭:
43 manager.shutdown()
44 print(‘master exit.‘)

 1 Traceback (most recent call last):
 2   File "task_master.py", line 22, in <module>
 3     manager.start()
 4   File "E:\Anaconda\Anaconda3\lib\multiprocessing\managers.py", line 513, in start
 5     self._process.start()
 6   File "E:\Anaconda\Anaconda3\lib\multiprocessing\process.py", line 105, in start
 7     self._popen = self._Popen(self)
 8   File "E:\Anaconda\Anaconda3\lib\multiprocessing\context.py", line 322, in _Popen
 9     return Popen(process_obj)
10   File "E:\Anaconda\Anaconda3\lib\multiprocessing\popen_spawn_win32.py", line 65, in __init__
11     reduction.dump(process_obj, to_child)
12   File "E:\Anaconda\Anaconda3\lib\multiprocessing\reduction.py", line 60, in dump
13     ForkingPickler(file, protocol).dump(obj)
14 _pickle.PicklingError: Can‘t pickle <function <lambda> at 0x000002BAAEF12F28>: attribute lookup <lambda> on __main__ failed

 1 #!/usr/bin/env python3
 2 # -*- coding: utf-8 -*-
 3 
 4 import random, time, queue
 5 from multiprocessing.managers import BaseManager
 6 
 7 # 发送任务的队列:
 8 task_queue = queue.Queue()
 9 # 接收结果的队列:
10 result_queue = queue.Queue()
11 
12 # 自定义函数re_task_queue
13 def re_task_queue():  
14     global task_queue  
15     return task_queue  
16 
17 # 自定义函数re_result_queue
18 def re_result_queue():  
19     global result_queue  
20     return result_queue
21 
22 # 从BaseManager继承的QueueManager:
23 class QueueManager(BaseManager):
24     pass
25 
26 if __name__ == ‘__main__‘:
27 
28     # 把两个Queue都注册到网络上, callable参数关联了Queue对象:
29     QueueManager.register(‘get_task_queue‘, callable=re_task_queue)
30     QueueManager.register(‘get_result_queue‘, callable=re_result_queue)
31 
32     # 绑定端口5000, 设置验证码‘abc‘:
33     manager = QueueManager(address=(‘127.0.0.1‘, 5000), authkey=b‘abc‘)
34 
35     # 启动Queue:
36     manager.start()
37 
38     # 获得通过网络访问的Queue对象:
39     task = manager.get_task_queue()
40     result = manager.get_result_queue()
41 
42     # 放几个任务进去:
43     for i in range(10):
44         n = random.randint(0, 10000)
45         print(‘Put task %d...‘ % n)
46         task.put(n)
47 
48     # 从result队列读取结果:
49     print(‘Try get results...‘)
50     for i in range(10):
51         r = result.get(timeout=10)
52         print(‘Result: %s‘ % r)
53         
54     # 关闭:
55     manager.shutdown()
56     print(‘master exit.‘)

C:\Users\Lucky丶M\python>python task_master.py
Put task 4962...
Put task 3460...
Put task 4774...
Put task 4301...
Put task 9120...
Put task 7183...
Put task 4915...
Put task 3173...
Put task 9138...
Put task 5798...
Try get results...