python-multiprocessing

I have a simple python multiprocessing script that sets up a pool of workers that attempt to append work-output to a Manager list. The script has 3 call stacks: - main calls f1 that spawns several worker processes that call another function g1. When one attempts to debug the script (incidentally on Windows 7/64 bit/VS 2010/PyTools) the script runs into a nested process creation loop, spawning an endless number of processes. Can anyone determine why? I'm sure I am missing something very simple. Here's the problematic code: - import multiprocessing import logging manager = multiprocessing

I found how to execute tornado with multi-process. server = HTTPServer(app) server.bind(8888) server.start(0) #Forks multiple sub-processes IOLoop.current().start() In this situation is there any way to share resource over processes? and It seems using the same port over processes. Does tornado balance the load itself for each process? If so, how does it do? In general, when using multi-process mode the processes only communicate via external services: databases, cache servers, message queues, etc. There are some additional options available for processes that are running on the same machine

I get the following error: multiprocessing.pool.MaybeEncodingError: Error sending result: '<multiprocessing.pool.ExceptionWithTraceback object at 0x7f758760d6a0>'. Reason: 'TypeError("cannot serialize '_io.BufferedReader' object",)' When running this code: from operator import itemgetter from multiprocessing import Pool import wget def f(args): print(args[1]) wget.download(args[1], "tests/" + target + '/' + str(args[0]), bar=None) if __name__ == "__main__": a = Pool(2) a.map(f, list(enumerate(urls))) #urls is a list of urls. What does the error mean and how can I fix it? First couple of

I used this script (see code at the end) to assess whether a global object is shared or copied when the parent process is forked. Briefly, the script creates a global data object, and the child processes iterate over data . The script also monitors the memory usage to assess whether the object was copied in the child processes. Here are the results: data = np.ones((N,N)) . Operation in the child process: data.sum() . Result: data is shared (no copy) data = list(range(pow(10, 8))) . Operation in the child process: sum(data) . Result: data is copied . data = list(range(pow(10, 8))) . Operation

Here is a complete simple working example import multiprocessing as mp import time import random class Foo: def __init__(self): # some expensive set up function in the real code self.x = 2 print('initializing') def run(self, y): time.sleep(random.random() / 10.) return self.x + y def f(y): foo = Foo() return foo.run(y) def main(): pool = mp.Pool(4) for result in pool.map(f, range(10)): print(result) pool.close() pool.join() if __name__ == '__main__': main() How can I modify it so Foo is only initialized once by each worker, not every task? Basically I want the init called 4 times, not 10. I am

I'm struggling again to improve the execution time of this piece of code. Since the calculations are really time-consuming I think that the best solution would be to parallelize the code. I was first working with maps as explained in this question, but then I tried a more simple approach thinking that I could find a better solution. However I couldn't come up with anything yet, so since it's a different problem I decided to post it as a new question. I am working on a Windows platform, using Python 3.4. Here's the code: similarity_matrix = [[0 for x in range(word_count)] for x in range(word

I am learning python multiprocessing, and I am trying to use this feature to populate a list with all the files present in an os. However, the code that I wrote is executing sequentially only. #!/usr/bin/python import os import multiprocessing tld = [os.path.join("/", f) for f in os.walk("/").next()[1]] #Gets a top level directory names inside "/" manager = multiprocessing.Manager() files = manager.list() def get_files(x): for root, dir, file in os.walk(x): for name in file: files.append(os.path.join(root, name)) mp = [multiprocessing.Process(target=get_files, args=(tld[x],)) for x in range