I\'m trying to use multiprocessing\'s Pool.map() function to divide out work simultaneously. When I use the following code, it works fine:
I have GUI that will interact with a postgres database, using psycopg2.I have db connection in a multiprocessing process, and send SQL via a multiprocessing queue, and receive via another queue.
When ever I use the following code the pool result always returns a timeout, is there something logicall开发者_如何学编程y incorrect I am doing?
I want to know how to distribute N independent tasks to exactly M processors on a machine that has L cores, where L>M.I don\'t want to use all the processors because I still want to have I/O available
The values that .get() returns from my priority queue are ~70% of the time 100% correctly sorted, but ~30% of the time they will only be ~70% sorted correctly, with most of the elements correct but a
I am trying to run a function in a loop many times using parallel multiprocessing. When I run this simple code:
I have the following function: def Upscale(path_to_image): img = cv2.imread(path_to_image) sr = cv2.dnn_superres.DnnSuperResImpl_create()
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