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I want to run a function in parallel, and wait until all parallel nodes are done, using joblib. Like in the example:
from math import sqrt from joblib import Parallel, delayed Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in range(10)) But, I want that the execution will be seen in a single progressbar like with tqdm, showing how many jobs has been completed.
How would you do that?
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Parallel provides a special handling for large arrays to automatically dump them on the filesystem and pass a reference to the worker to open them as memory map on that file using the numpy. memmap subclass of numpy. ndarray . This makes it possible to share a segment of data between all the worker processes.
tqdm(range(0, 30)) does not work with multiprocessing (as formulated in the code below).
TL;DR - it preserves order for both backends.
I think tqdm is meant for long loops, not short loops that takes a lot of time. That is because tqdm estimates the ETA based on the average time it took a cycle to complete, so it wont be that useful.
Just put range(10) inside tqdm(...)! It probably seemed too good to be true for you, but it really works (on my machine):
from math import sqrt from joblib import Parallel, delayed from tqdm import tqdm result = Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in tqdm(range(100000))) I've created pqdm a parallel tqdm wrapper with concurrent futures to comfortably get this done, give it a try!
To install
pip install pqdm and use
from pqdm.processes import pqdm # If you want threads instead: # from pqdm.threads import pqdm args = [1, 2, 3, 4, 5] # args = range(1,6) would also work def square(a): return a*a result = pqdm(args, square, n_jobs=2) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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