tqdm-joblib is a Python library that provides a context manager to easily integrate `tqdm` progress bars with `joblib.Parallel` execution. It addresses the challenge of displaying accurate and non-interfering progress updates when performing parallel computations with `joblib`. The current version is 0.0.5, and it appears to be actively maintained with recent releases.
pip install tqdm-joblibVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to use the `tqdm_joblib` context manager to wrap a `joblib.Parallel` call, automatically adding a progress bar for the parallel execution. The `desc` and `total` parameters are passed directly to `tqdm`.
Consider custom logging handlers with explicit locks for parallel processes, or use `tqdm.write()` from within parallel tasks if possible, ensuring it's properly configured for multiprocessing.
Always use the `tqdm_joblib` context manager as shown in the quickstart. If multiple independent progress bars are strictly needed in complex scenarios, consider assigning `position` arguments to each `tqdm` instance and ensuring proper locking mechanisms.
Ensure you are using `joblib` version 0.12 or newer, which defaults to the `loky` backend. If issues persist, try setting the `JOBLIB_START_METHOD` environment variable to 'forkserver' (on non-Windows systems) or `backend='threading'` if your tasks are I/O bound.