Login Register

multiprocessing.pool.Pool.apply()

apply(func[, args[, kwds]])

Call func with arguments args and keyword arguments kwds. It blocks until the result is ready. Given this blocks, apply_async() is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.

Links:

https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.pool.Pool.apply

doc_python

2016-10-07 17:37:52

Comments

Leave a Comment

Please login to continue.

Popular Articles

multiprocessing.pool.Pool.apply_async() apply_async(func[, args[, kwds[, callback[, error_callback]]]]) A variant of the apply() method which returns a result object. If callback is spec

multiprocessing.pool.Pool class multiprocessing.pool.Pool([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]]) A process pool object which controls a pool

multiprocessing.pool.Pool.imap() imap(func, iterable[, chunksize]) A lazier version of map(). The chunksize argument is the same as the one used by the map() method. For very long

multiprocessing.pool.Pool.close() close() Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.

multiprocessing.pool.Pool.join() join() Wait for the worker processes to exit. One must call close() or terminate() before using join().