我是Python的新手，我想知道如何在语法上有效地实现以下问题。
我有函数f1，f2 ... fN这些函数是生成新进程的包装器（带有目标_f1，_f2，.. _ fN），
将其参数（arg1，arg2，...）传递给子进程并接收返回值
使用这样的代码，我希望模块功能在不同的进程中执行，然后调用者（模块的用户）进程
函数f1，f2，... fN（分别为_f1，f2，... _fN）可能有不同的原型。
in a module
def _f1(arg1, arg2, ... argn, connection):
...
connection.send(return_value)
connection.close()
def f1(arg1, arg2, ... argn):
parent_conn, child_conn = Pipe()
p = Process(target=_f1, args=(arg1, arg2, ... argn, child_conn))
p.start()
p.join()
return parent_conn.recv()
def _f2(arg1, arg2, ... argm, connection):
...
connection.send(return_value)
connection.close()
def f2(arg1, arg2, ... argn):
parent_conn, child_conn = Pipe()
p = Process(target=_f2, args=(arg1, arg2, ... argm, child_conn))
p.start()
p.join()
return parent_conn.recv()
...
def _fn(arg1, arg2, ... argk, connection):
...
connection.send(return_value)
connection.close()
def fN(arg1, arg2, ... argn):
parent_conn, child_conn = Pipe()
p = Process(target=_fN, args=(arg1, arg2, ... argk, child_conn))
p.start()
p.join()
return parent_conn.recv()
很明显，包装函数f1，f2，fN大致相同。我可以将它们实现为单个包装函数吗？ 我希望执行不会阻塞。例如，模块的用户应该能够同时执行f1和f2。
我希望我已经设法解释了我的问题。
这里有两个函数sum（）和sin（）的具体例子：
def _sum(a, b, connection):
return_value=a+b
connection.send(return_value)
connection.close()
def sum(a, b):
parent_conn, child_conn = Pipe()
p = Process(target=_sum, args=(a, b, child_conn))
p.start()
p.join()
return parent_conn.recv()
def _sin(x, connection):
return_value=sin(x)
connection.send(return_value)
connection.close()
def sin(x):
parent_conn, child_conn = Pipe()
p = Process(target=_sin, args=(x, child_conn))
p.start()
p.join()
return parent_conn.recv()
采取srj关于使用装饰的想法我来到下面发布的解决方案。 我试图进一步扩展它以装饰connection.send（return_value）和connection.close（），但它对我不起作用。代码下方。通过评论，我指定了什么是有效的，以及（我认为）不起作用的等价物。有什么帮助吗？
from multiprocessing import Process, Pipe
def process_wrapper1(func):
def wrapper(*args):
parent_conn, child_conn = Pipe()
f_args = args + (child_conn,)
p = Process(target=func, args=f_args)
p.start()
p.join()
return parent_conn.recv()
return wrapper
def process_wrapper2(func):
def wrapper(*args):
res=func(*args[0:len(args)-1])
args[-1].send(res)
args[-1].close()
return wrapper
#def _sum(a, b, connection): #Working
# return_value=a+b
# connection.send(return_value)
# connection.close()
def __sum(a, b): #Doesn't work, see the error bellow
return(a+b)
_sum=process_wrapper2(__sum)
sum=process_wrapper1(_sum)
Pyzo ipython shell中的上述代码生成以下结果：
In [3]: import test1
In [4]: test1.sum(2,3)
---------------------------------------------------------------------------
PicklingError Traceback (most recent call last)
<ipython-input-4-8c542dc5e11a> in <module>()
----> 1 test1.sum(2,3)
C:\projects\PYnGUInLib\test1.py in wrapper(*args)
11 f_args = (child_conn,) + args
12 p = Process(target=func, args=f_args)
---> 13 p.start()
14 p.join()
15 return parent_conn.recv()
C:\pyzo2014a_64b\lib\multiprocessing\process.py in start(self)
103 'daemonic processes are not allowed to have children'
104 _cleanup()
--> 105 self._popen = self._Popen(self)
106 self._sentinel = self._popen.sentinel
107 _children.add(self)
C:\pyzo2014a_64b\lib\multiprocessing\context.py in _Popen(process_obj)
210 @staticmethod
211 def _Popen(process_obj):
--> 212 return _default_context.get_context().Process._Popen(process_obj)
213
214 class DefaultContext(BaseContext):
C:\pyzo2014a_64b\lib\multiprocessing\context.py in _Popen(process_obj)
311 def _Popen(process_obj):
312 from .popen_spawn_win32 import Popen
--> 313 return Popen(process_obj)
314
315 class SpawnContext(BaseContext):
C:\pyzo2014a_64b\lib\multiprocessing\popen_spawn_win32.py in __init__(self, process_obj)
64 try:
65 reduction.dump(prep_data, to_child)
---> 66 reduction.dump(process_obj, to_child)
67 finally:
68 context.set_spawning_popen(None)
C:\pyzo2014a_64b\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
57 def dump(obj, file, protocol=None):
58 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 59 ForkingPickler(file, protocol).dump(obj)
60
61 #
PicklingError: Can't pickle <function process_wrapper2.<locals>.wrapper at 0x0000000005541048>: attribute lookup wrapper on test1 failed
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\pyzo2014a_64b\lib\multiprocessing\spawn.py", line 106, in spawn_main
exitcode = _main(fd)
File "C:\pyzo2014a_64b\lib\multiprocessing\spawn.py", line 116, in _main
self = pickle.load(from_parent)
EOFError: Ran out of input
In [5]:
答案 0 :(得分：1)
您可以使用decorator将函数包装为创建流程并执行它的样板。
def process_wrapper(func):
def wrapper(*args):
parent_conn, child_conn = Pipe()
#attach the connection to the arguments
f_args = args + (child_conn,)
p = Process(target=func, args=f_args)
p.start()
p.join()
return parent_conn.recv()
return wrapper
并将函数定义为
@process_wrapper
def _f2(arg1, arg2, ... argm, connection):
...
connection.send(return_value)
connection.close()
说明 process_wrapper函数接受一个具有N个位置参数的函数，其中最后一个始终是管道连接。它返回一个带有N-1个参数的函数，并在其中预填充连接。
如果你的具体功能，
@process_wrapper
def sin(x, connection):
return_value=sin(x)
connection.send(return_value)
connection.close()
@process_wrapper
def sum(a, b, connection):
return_value=a+b
connection.send(return_value)
connection.close()
您可以将该功能称为
sum(a,b)
对python装饰器的更多引用 http://www.jeffknupp.com/blog/2013/11/29/improve-your-python-decorators-explained/
答案 1 :(得分：0)
您应该使用multiprocessing.Pool。这是一个例子：
def f1(*args):
rv = do_calculations()
return rv
def f2(*args):
...
...
def fN(*args):
...
def worker(args):
fn = args[0]
return fn(*args[1:])
inputs = [
[f1, f1_args],
[f2, f2_args],
...
[fN, fN_args]
]
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
results = pool.map(worker, inputs)