Python Multiprocessing Tutorial | Use Python Multiprocessing Pool | Multiprocessing vs Threading

Python Multiprocessing is used to run the python processes in parallel and hence it is used to speed up your program.

Python Multiprocessing bypasses the Global Interpreter Lock or GIL in Python and help you to run the threads in parallel and use all the CPUs of your Computer.

We will also be checking out multiprocessing on a real world example.

I have also covered synchronous vs asynchronous tasks and IO bound vs CPU bound tasks.

We will also learn to easily switch from Process Pool Executor to Thread pool executor and hence learn when to use processing vs threading.

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Tags:
Python MultiProcessing
ProcessPoolExecutor
MultiProcessing vs Threading

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#pythonmultiprocessing

[uditvashisht]

Very detailed and helpful video. Keep it up....

[palvimalik]

What at a wonderful tutorial. You made it look so easy. Thanks....

[BharatLudhiana]

Great tutorial.... Especially theoretical explanation in beginning... For understanding completely I need to try your examples first. Next another thing in my mind is.... If somehow we can split our input file and assign program execution them to automatically available free CPUs. I have also four cores hoping it will work four times faster. Please suggest.

[BharatLudhiana]

Which method you prefer... Multiprocessing or processPoolExecuter... clearly In the 1-10 range example multiprocessing took less time....

[dsreekanth]

One requirement
Need to compare files in linux server files that need to be downloaded at the same time. 10 processors are working then 10 ssh connections You need to create and download the file.
I tried but got a socket error while running in the linux system.

[BharatLudhiana]

Why you use time.sleep(1). Does it pause job for 1 millisecond.