Python Multiprocessing Pool Apply
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Python Multiprocessing Pool Apply
These examples are extracted from open source projects. You should not block as this will make the user interface unresponsive. Multiprocessing.pool is cool to do parallel jobs in python.
You’ll import the os module in order to add some more logging to your transform () function so you can see what’s going on behind the scenes. Replace = options.get ('replace') for route in args: Map is a higher level abstraction for apply, applying each element in an iterable for a same function.
Time.sleep (2) # wait 2 seconds print (x*x) p = pool (8) p.map (f, [1, 2, 3, 4]) p.close () p.join () run time python3 main.py >> 1 >> 4 >> 9 >> 16 >> python3 main.py 0.08s user 0.06s system 6% cpu 2.180 total
You can also specify a callback function that is called when the task completes. You can also specify a callback function that is called when the task completes. When you need to run a function in another process for some reason (and you want to use a process pool instead of creating a new process to run the function). Multiprocessing.pool is cool to do parallel jobs in python.
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