multiprocessing --- Process-based parallelism — Process Pools
synopsis: Create pools of processes. One can create a pool of processes which will carry out tasks submitted to it with the Pool class. A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has
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synopsis: Create pools of processes.
One can create a pool of processes which will carry out tasks submitted to it with the Pool class.
A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.
processes is the number of worker processes to use. If processes is None then the number returned by os.process_cpu_count is used.
If initializer is not None then each worker process will call initializer(initargs) when it starts.
maxtasksperchild is the number of tasks a worker process can complete before it will exit and be replaced with a fresh worker process, to enable unused resources to be freed. The default maxtasksperchild is None, which means worker processes will live as long as the pool.
context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function multiprocessing.Pool or the Pool method of a context object. In both cases context is set appropriately. If None, calling this function will have the side effect of setting the current global start method if it has not been set already. See the get_context function.
Note that the methods of the pool object should only be called by the process which created the pool.
The class of the result returned by Pool.apply_async and Pool.map_async.
The following example demonstrates the use of a pool
from multiprocessing import Pool import time
if name == 'main': with Pool(processes=4) as pool: # start 4 worker processes result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process print(result.get(timeout=1)) # prints "100" unless your computer is very slow
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Python Documentation — Doc/library/multiprocessing.rst :: Process Pools ↗Revision f10166035d60 · PSF-2.0 and attribution