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I found the following example on this site somewhere: 

  import multi import ctypes import numpy NP shared_array_base = multiprocessing.Array (ctypes.c_double, built 10 * 10) shared_array = np.ctypeslib.as_array (shared_array_base.get_obj ()) shared_array = shared_array.reshape (10, 10) as # not copy loud shared_array.base Was there. Based shared_array_base.get_obj () # parallel processing def My_func (I, Def_param = Shared_array): Shared_array [i] = i if __name__ == '__main__': pool = multiprocessing.Pool (procedures = 4) pool.map ( my_func, (10)) print shared_array   
 the above code works fine, but I want to add an array of shared array, some (like shared_array + = some_other_array up instead shared_array [i]; = i)  
 local variable 'shared_array' assignments referenced earlier  
 there is no reason to consider that I ACE Can not?   
 
 
 is that a variable is assigned anywhere in a function, it is regarded as a local variable Is  shared_array + = some_other_array  equals  shared_array = shared_array + some_other_array . Thus  shared_array  is considered as a local variable, which is not present at the time when you try to use it on the right side of the assignment.  
 If you want to use the global  shared_array  variable, you must explicitly mark it by entering a  global share_re  in your function.  
 The error with which you do not see  shared_array [i,:] = i  is that it does not specify the variable  shared_array . Instead, it changes the object that tells a piece of it. In Python, a bare name (for example,  shared_array = ... ) is different from another type of assignment (for example,  shared_array [...] = ... ), although they look similar.  
 Note, incidentally, there is nothing with multi-generator of error.

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