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Pool class can be used for parallel execution of a function for different input data. The multiprocessing. Pool() class spawns a set of processes called workers and can submit tasks using the methods apply/apply_async and map/map_async . For parallel mapping, you should first initialize a multiprocessing.
First, we create two Process objects and assign them the function they will execute when they start running, also known as the target function. Second, we tell the processes to go ahead and run their tasks. And third, we wait for the processes to finish running, then continue with our program.
First we create a tuple with the name of all scripts that we wish to run in parallel. We will also need a function to execute a system command. In this case we will use the standard library os and the method . system , that allows you to run commands to your operational system just as if you are in any terminal.
I am doing a data mining project in Python, and during the experiment phase I have to run many experiments at the same time. How could I create n processes, so that each process is dedicated to an experiment? Which module I should use?
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