Restructure your code so that the f() function is defined before you create instance of Pool. Otherwise the worker cannot see your function. #!/usr/bin/python # -*- coding: utf-8 -*- from multiprocessing import Pool def f(x): return x*x p = Pool(1) p.map(f, [1, 2, 3])
Paraphrasing wikipedia: Multiprogramming – A computer running more than one program at a time (like running Excel and Firefox simultaneously) http://en.wikipedia.org/wiki/Multiprogramming Multiprocessing – A computer using more than one CPU at a time http://en.wikipedia.org/wiki/Multiprocessing Multitasking – Tasks sharing a common resource (like 1 CPU) http://en.wikipedia.org/wiki/Computer_multitasking#Multithreading Thus, something like multithreading is an extension of multitasking.
You can use https://github.com/nalepae/pandarallel, as in the following example: from pandarallel import pandarallel from math import sin pandarallel.initialize() def func(x): return sin(x**2) df.parallel_apply(func, axis=1)
I was only able to really accomplish this by manually issuing a lock statement to a table. This does a complete table lock, so be careful with it! In my case it was useful for creating a queue that I didn’t want multiple processes touching at once. using (Entities entities = new Entities()) using (TransactionScope … Read more
Use a function object: class Copier(object): def __init__(self, tgtdir): self.target_dir = tgtdir def __call__(self, src): copy_file(src, self.target_dir) To run your Pool.map: p.map(Copier(target_dir), file_list)
Saravan Kumar, I understand the motivation behind your question. Before I started working on compilers, I also had a very similar wanting to know the internals of the Java Virtual Machine. First of all, I’m impressed by your question. There needs to be a couple of points of distinctions and understanding in order to solve … Read more
Manager proxy objects are unable to propagate changes made to (unmanaged) mutable objects inside a container. So in other words, if you have a manager.list() object, any changes to the managed list itself are propagated to all the other processes. But if you have a normal Python list inside that list, any changes to the … Read more
You can’t pass normal multiprocessing.Lock objects to Pool methods, because they can’t be pickled. There are two ways to get around this. One is to create Manager() and pass a Manager.Lock(): def main(): iterable = [1, 2, 3, 4, 5] pool = multiprocessing.Pool() m = multiprocessing.Manager() l = m.Lock() func = partial(target, l) pool.map(func, iterable) … Read more
The easiest way might be to just override sys.stdout. Slightly modifying an example from the multiprocessing manual: from multiprocessing import Process import os import sys def info(title): print title print ‘module name:’, __name__ print ‘parent process:’, os.getppid() print ‘process id:’, os.getpid() def f(name): sys.stdout = open(str(os.getpid()) + “.out”, “w”) info(‘function f’) print ‘hello’, name if … Read more
You should be able to safely combine asyncio and multiprocessing without too much trouble, though you shouldn’t be using multiprocessing directly. The cardinal sin of asyncio (and any other event-loop based asynchronous framework) is blocking the event loop. If you try to use multiprocessing directly, any time you block to wait for a child process, … Read more