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copied!<blockquote> <p>(1) How could I modify the decorator so that if the f object it takes is an instance method of a class, then the executor it returns is also an instance method of that class object (so that this business about not being able to pickle does not happen, since I can pickle those instance methods)? </p> </blockquote> <pre><code>>>> myfoo.parSquare <bound method Foo.executor of <__main__.Foo object at 0x101332510>> </code></pre> <p>As you can see parSquare is actually executor which has become an instance method, this is no surprise, since decorators are sort of function wrappers ...</p> <p><a href="https://stackoverflow.com/questions/739654/understanding-python-decorators">How to make a chain of function decorators?</a> probably has the best description of decorators.</p> <blockquote> <p>(2) Is it better to create addiitional _pickle_function and _unpickle_function methods?</p> </blockquote> <p>you don't need to python already support them, as a matter of fact this <code>copy_reg.pickle(types.FunctionType, _pickle_method, _unpickle_method)</code> seems a bit strange since you are using the same algorithm to pickle both types.</p> <p>Now the bigger questions is why are we getting <code>PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed</code> the error itself seems some what vague but it looks like it failed to lookup something, our function?<br/> I think whats going on is that the decorator is overriding the function with one that was defined internally in your case <code>parSquare</code> becomes <code>executor</code> but <code>executor</code> is an internal function to <code>parallel</code> as such it isn't importable hence the lookup seems to be failing, this is just a hunch.<br></p> <p>lets try a simpler example.</p> <pre><code>>>> def parallel(function): ... def apply(values): ... from multiprocessing import Pool ... pool = Pool(4) ... result = pool.map(function, values) ... pool.close() ... pool.join() ... return result ... return apply ... >>> @parallel ... def square(value): ... return value**2 ... >>> >>> square([1,2,3,4]) Exception in thread Thread-1: Traceback (most recent call last): File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/threading.py", line 522, in __bootstrap_inner self.run() File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/threading.py", line 477, in run self.__target(*self.__args, **self.__kwargs) File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/multiprocessing/pool.py", line 225, in _handle_tasks put(task) PicklingError: Can't pickle <type 'function'>: attribute lookup __builtin__.function failed </code></pre> <p>pretty much the same error we were getting.<br/> note that the above code is equivalent to:</p> <pre><code>def parallel(function): def apply(values): from multiprocessing import Pool pool = Pool(4) result = pool.map(function, values) pool.close() pool.join() return result return apply def square(value): return value**2 square = parallel(square) </code></pre> <p>which produces the same error, also note that if we don't rename our functions.</p> <pre><code>>>> def parallel(function): ... def apply(values): ... from multiprocessing import Pool ... pool = Pool(4) ... result = pool.map(function, values) ... pool.close() ... pool.join() ... return result ... return apply ... >>> def _square(value): ... return value**2 ... >>> square = parallel(_square) >>> square([1,2,3,4]) [1, 4, 9, 16] >>> </code></pre> <p>it works just fine, I've being looking for a way to control the way decorators work with names, but to no avail, I still want to use them with multiprocessing, so I came up with a somewhat ugly work around:</p> <pre><code>>>> def parallel(function): ... def temp(_): ... def apply(values): ... from multiprocessing import Pool ... pool = Pool(4) ... result = pool.map(function, values) ... pool.close() ... pool.join() ... return result ... return apply ... return temp ... >>> def _square(value): ... return value*value ... >>> @parallel(_square) ... def square(values): ... pass ... >>> square([1,2,3,4]) [1, 4, 9, 16] >>> </code></pre> <p>so basically I passed the real function to the decorator then I used a second function to deal with the values, as you can see it works just fine.</p> <p>I've slightly modify your initial code to better handle the decorator, though it isn't perfect.</p> <pre><code>import types, copy_reg, multiprocessing as mp def parallel(f): def executor(*args): _pool = mp.Pool(2) func = getattr(args[0], f.__name__) # This will get the actual method function so we can use our own pickling procedure _result = _pool.map(func, args[1]) _pool.close() _pool.join() return _result return executor def _pickle_method(method): func_name = method.im_func.__name__ obj = method.im_self cls = method.im_class cls_name = '' if func_name.startswith('__') and not func_name.endswith('__'): cls_name = cls.__name__.lstrip('_') if cls_name: func_name = '_' + cls_name + func_name return _unpickle_method, (func_name, obj, cls) def _unpickle_method(func_name, obj, cls): func = None for cls in cls.mro(): if func_name in cls.__dict__: func = cls.__dict__[func_name] # This will fail with the decorator, since parSquare is being wrapped around as executor break else: for attr in dir(cls): prop = getattr(cls, attr) if hasattr(prop, '__call__') and prop.__name__ == func_name: func = cls.__dict__[attr] break if func == None: raise KeyError("Couldn't find function %s withing %s" % (str(func_name), str(cls))) return func.__get__(obj, cls) copy_reg.pickle(types.MethodType, _pickle_method, _unpickle_method) class Foo(object): def __init__(self, args): self.my_args = args def squareArg(self, arg): return arg**2 def par_squareArg(self): p = mp.Pool(2) # Replace 2 with the number of processors. q = p.map(self.squareArg, self.my_args) p.close() p.join() return q @parallel def parSquare(self, num): return self.squareArg(num) if __name__ == "__main__": myfoo = Foo([1,2,3,4]) print myfoo.par_squareArg() print myfoo.parSquare(myfoo.my_args) </code></pre> <p>fundamentally this still fails, giving us <code>AssertionError: daemonic processes are not allowed to have children</code> since the subprocess tries to call the function, keep in mind that subprocess don't really copy the code simply the names ...</p> <p>one work around is similar to the one I mentioned previously:</p> <pre><code>import types, copy_reg, multiprocessing as mp def parallel(f): def temp(_): def executor(*args): _pool = mp.Pool(2) func = getattr(args[0], f.__name__) # This will get the actual method function so we can use our own pickling procedure _result = _pool.map(func, args[1]) _pool.close() _pool.join() return _result return executor return temp def _pickle_method(method): func_name = method.im_func.__name__ obj = method.im_self cls = method.im_class cls_name = '' if func_name.startswith('__') and not func_name.endswith('__'): cls_name = cls.__name__.lstrip('_') if cls_name: func_name = '_' + cls_name + func_name return _unpickle_method, (func_name, obj, cls) def _unpickle_method(func_name, obj, cls): func = None for cls in cls.mro(): if func_name in cls.__dict__: func = cls.__dict__[func_name] # This will fail with the decorator, since parSquare is being wrapped around as executor break else: for attr in dir(cls): prop = getattr(cls, attr) if hasattr(prop, '__call__') and prop.__name__ == func_name: func = cls.__dict__[attr] break if func == None: raise KeyError("Couldn't find function %s withing %s" % (str(func_name), str(cls))) return func.__get__(obj, cls) copy_reg.pickle(types.MethodType, _pickle_method, _unpickle_method) class Foo(object): def __init__(self, args): self.my_args = args def squareArg(self, arg): return arg**2 def par_squareArg(self): p = mp.Pool(2) # Replace 2 with the number of processors. q = p.map(self.squareArg, self.my_args) p.close() p.join() return q def _parSquare(self, num): return self.squareArg(num) @parallel(_parSquare) def parSquare(self, num): pass if __name__ == "__main__": myfoo = Foo([1,2,3,4]) print myfoo.par_squareArg() print myfoo.parSquare(myfoo.my_args) [1, 4, 9, 16] [1, 4, 9, 16] </code></pre> <p>One last thing, be very careful when multithreading, depending on how you segment your data you can actually have a slower time multithreaded than single threaded, mainly due to the overhead of copying values back and forth as well creating and destroying subprocess. <br/></p> <p>Always benchmark single/multithreaded and properly segment your data when possible.</p> <p>case in point:</p> <pre><code>import numpy import time from multiprocessing import Pool def square(value): return value*value if __name__ == '__main__': pool = Pool(5) values = range(1000000) start = time.time() _ = pool.map(square, values) pool.close() pool.join() end = time.time() print "multithreaded time %f" % (end - start) start = time.time() _ = map(square, values) end = time.time() print "single threaded time %f" % (end - start) start = time.time() _ = numpy.asarray(values)**2 end = time.time() print "numpy time %f" % (end - start) v = numpy.asarray(values) start = time.time() _ = v**2 end = time.time() print "numpy without pre-initialization %f" % (end - start) </code></pre> <p>gives us:</p> <pre><code>multithreaded time 0.484441 single threaded time 0.196421 numpy time 0.184163 numpy without pre-initialization 0.004490 </code></pre>

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