[英]Python functions in a separate process. Can i unify the wrapper functions
我是Python的新手,我想知道如何在语法上更有效地实现以下问题。 我有函数f1,f2 ... fN这些函数是包装器,它们产生新的进程(目标为_f1,_f2,.. _fN),并将其参数(arg1,arg2,...)传递给子进程并接收返回值值
使用这样的代码,我希望模块功能在与调用方(模块的用户)过程不同的过程中执行。
函数f1,f2,...,fN(分别为_f1,f2,...,_ fN)可能具有不同的原型。
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器功能吗? 我希望执行不被阻塞。 例如,模块的用户应该能够同时执行f1和f2。
我希望我能解释我的问题。
这是带有两个函数sum()和sin()的具体示例:
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()
带着关于使用装饰的高级想法,我来到了下面发布的解决方案。 我试图进一步扩展它来装饰connection.send(return_value)和connection.close(),但是它对我不起作用。 下面的代码。 我用评论指出了什么是有效的,什么等效物(我认为)是无效的。 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中的以上代码生成以下结果:
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]:
您可以使用装饰器将函数包装为创建过程并执行过程的样板。
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
函数采用具有N个位置参数的函数,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,并在其中预填充了连接。
如果您有具体的功能,
@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()
您可以将函数调用为
sum(a,b)
有关python装饰器的更多参考http://www.jeffknupp.com/blog/2013/11/29/improve-your-python-decorators-explained/
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)
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