![]() ![]() Furthermore the child should close the parent's end of the pipe to prevent a deadlock. The solution is to read the child's data before join()ing the child. The solution by mrule is correct but has a bug: if the child sends back a large amount of data, it can fill the pipe's buffer, blocking on the child's nd(), while the parent is waiting for the child to exit on pipe.join(). ![]() Why do anything else that's more fragile and specific to a single case? > from pathos.multiprocessing import ProcessingPool as PoolĪnd, just to show off a little more of what it can do: > from pathos.multiprocessing import ProcessingPool as Pool PYTHON3 CLASS METHOD MAP CODEIt even handles the code you wrote initially, without modification, and from the interpreter. What can multiprocessing and dill do together? Pathos.multiprocessing also provides an asynchronous map function… and it can map functions with multiple arguments (e.g. This is because dill is used instead of pickle or cPickle, and dill can serialize almost anything in python. If you use a fork of multiprocessing called pathos.multiprocesssing, you can directly use classes and class methods in multiprocessing's map functions. Multiprocessing and pickling is broken and limited unless you jump outside the standard library. Proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) import multiprocessingĭef parmap(f, X, nprocs=multiprocessing.cpu_count()): I also enabled the "daemon" mode for the workers s.t. it spawns a predefined amount of workers and only iterates through the input list if there exists an idle worker. I could not use the codes posted so far because the codes using "multiprocessing.Pool" do not work with lambda expressions and the codes not using "multiprocessing.Pool" spawn as many processes as there are work items. ![]()
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