Jul 15, 2016 · # For python 2/3 compatibility, define pool context manager # to support the 'with' statement in Python 2 if sys.version_info[0] == 2: from contextlib import contextmanager @contextmanager def multiprocessing_context(*args, **kwargs): pool = multiprocessing.Pool(*args, **kwargs) yield pool pool.terminate() else: multiprocessing_context ...

Jul 30, 2015 · Now, you also have Pool.apply_async and Pool.map_async, which return the result as soon as the process has finished, which is essentially similar to the Process class above. The advantage may be that they provide you with the convenient apply and map functionality that you know from Python's in-built apply and map

Dec 16, 2011 · The multiprocessing.Pool modules tries to provide a similar interface. Pool.apply is like Python apply, except that the function call is performed in a separate process. Pool.apply blocks until the function is completed. Pool.apply_async is also like Python's built-in apply, except

Sep 24, 2013 · Global keyword works on the same file only. Another way is to set value dynamically in pool process initialiser, somefile.py can just be an empty file: import importlib def pool_process_init(): m = importlib.import_module("somefile.py") m.my_global_var = "some value" pool = Pool(4, initializer=pool_process_init) How to use the var in task:

Dec 11, 2010 · I'm trying to learn how to use multiprocessing, and found the following example. I want to sum values as follows: from multiprocessing import Pool from time import time N = 10 K = 50 w = 0 def

I am trying to use a worker Pool in python using Process objects. Each worker (a Process) does some initialization (takes a non-trivial amount of time), gets passed a series of jobs (ideally using map() ), and returns something.

# For python 2/3 compatibility, define pool context manager # to support the 'with' statement in Python 2 if sys.version_info[0] == 2: from contextlib import contextmanager @contextmanager def multiprocessing_context(*args, **kwargs): pool = multiprocessing.Pool(*args, **kwargs) yield pool pool.terminate() else: multiprocessing_context ...

Jun 12, 2017 · if __name__ == "__main__": pool = multiprocessing.Pool(processes=2) # lets use just 2 workers count = get_counter(30) # get our counter iterator set to iterate from 0-29 count_iterator = iterator_slice(count, 7) # we'll process them in chunks of 7 queue = [] # a queue for our current worker async results, a deque would be faster while count ...

However when python exits, it will kill this new instance of python and leave the application running. The solution is to do it the hard way, by finding the pid of the python process that is created, getting the children of that pid, and killing them.

Jan 29, 2017 · To make my code more "pythonic" and faster, I use multiprocessing and a map function to send it a) the function and b) the range of iterations. The implanted solution (i.e., calling tqdm