Python Multiprocessing with shared data source and multiple class instances -

my program needs spawn multiple instances of class, each processing data coming streaming data source.

for example:

parameters = [1, 2, 3]  class fakestreamingapi:     def __init__(self):         pass      def data(self):         return 42     pass  class dostuff:     def __init__(self, parameter):         self.parameter = parameter      def run(self):         data = streaming_api.data()         output = self.parameter ** 2 + data # cpu intensive task         print output  streaming_api = fakestreamingapi()  # here's how work no multiprocessing instance_1 = dostuff(parameters[0]) instance_1.run() 

once instances running don't need interact each other, have data comes in. (and print error messages, etc)

i totally @ loss how make work multiprocessing, since first have create new instance of class dostuff, , have run.

this not way it:

# let's try multiprocessing import multiprocessing  parameter in parameters:     processes = [ multiprocessing.process(target = dostuff, args = (parameter)) ]  # hmm, doesn't work... 

we try defining function spawn classes, seems ugly:

import multiprocessing  def spawn_classes(parameter):     instance = dostuff(parameter)     instance.run()  parameter in parameters:         processes = [ multiprocessing.process(target = spawn_classes, args = (parameter,)) ]  # can't tell if works -- no output on screen? 

plus, don't want have 3 different copies of api interface class running, want data shared between processes... , far can tell, multiprocessing creates copies of each new process.

ideas?

edit: think may have got it... there wrong this?

import multiprocessing  parameters = [1, 2, 3]  class fakestreamingapi:     def __init__(self):         pass      def data(self):         return 42     pass  class worker(multiprocessing.process):     def __init__(self, parameter):         super(worker, self).__init__()         self.parameter = parameter      def run(self):         data = streaming_api.data()         output = self.parameter ** 2 + data # cpu intensive task         print output  streaming_api = fakestreamingapi()  if __name__ == '__main__':     jobs = []     parameter in parameters:         p = worker(parameter)         jobs.append(p)         p.start()     j in jobs:         j.join()