Python Multiprocessing For Data Science JsP Ub 2WJM

Web Reference: In this video I cover one of the approaches I use when scaling data science related processes. 1 day ago · multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. In order to better assess when ThreadPool and when process Pool should be used, here are some rules of thumb: For CPU-heavy jobs, multiprocessing.pool.Pool should be used. Usually we start here with twice the number of CPU cores for the pool size, but at least 4. For I/O-heavy jobs, multiprocessing.pool.ThreadPool should be used.

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