If your problem consists of many parts, you could split the parts into k
subgroups, run each subgroup in parallel and update the progressbar in between, resulting in k
updates of the progress.
This is demonstrated in the following example from the documentation.
>>> with Parallel(n_jobs=2) as parallel:
... accumulator = 0.
... n_iter = 0
... while accumulator < 1000:
... results = parallel(delayed(sqrt)(accumulator + i ** 2)
... for i in range(5))
... accumulator += sum(results) # synchronization barrier
... n_iter += 1
https://pythonhosted.org/joblib/parallel.html#reusing-a-pool-of-workers
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