To make my code more “pythonic” and faster, I use multiprocessing
and a map function to send it a) the function and b) the range of iterations.
The implanted solution (i.e., calling tqdm
directly on the range tqdm.tqdm(range(0, 30))
) does not work with multiprocessing (as formulated in the code below).
The progress bar is displayed from 0 to 100% (when python reads the code?) but it does not indicate the actual progress of the map function.
How can one display a progress bar that indicates at which step the ‘map’ function is ?
from multiprocessing import Pool import tqdm import time def _foo(my_number): square = my_number * my_number time.sleep(1) return square if __name__ == '__main__': p = Pool(2) r = p.map(_foo, tqdm.tqdm(range(0, 30))) p.close() p.join()
Any help or suggestions are welcome…
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Answer
Solution found. Be careful! Due to multiprocessing, the estimation time (iteration per loop, total time, etc.) could be unstable, but the progress bar works perfectly.
Note: Context manager for Pool
is only available in Python 3.3+.
from multiprocessing import Pool import time from tqdm import * def _foo(my_number): square = my_number * my_number time.sleep(1) return square if __name__ == '__main__': with Pool(processes=2) as p: max_ = 30 with tqdm(total=max_) as pbar: for _ in p.imap_unordered(_foo, range(0, max_)): pbar.update()