joblib 1.3.0 is out in the wild!

joblib is a library that provides an generic way to call into thread-based, process-based and distributed parallelism (via external backends) + a way to cache expensive computation in repeated function calls on disk.

https://joblib.readthedocs.io

This new release provides several major new features, inclusing a `return_as="generator"` argument to the `Parallel`class to make it possible to aggregate parallel results when ready (preserving the submission order).

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Joblib: running Python functions as pipeline jobs — joblib 1.5.3 documentation

In the future this will also be extended to `return_as="unordered_generator"` to optionally make it possible to aggregate results as soon as ready.

This release also includes a new `parallel_config` context manager as an extension to `parallel_backend` to make it possible to configure all the arguments of the `Parallel` class and not just the backend using a context manager idiom.

Detailed changelog:
https://github.com/joblib/joblib/blob/master/CHANGES.rst#release-130----20230628

2/4

joblib/CHANGES.rst at master · joblib/joblib

Computing with Python functions. Contribute to joblib/joblib development by creating an account on GitHub.

GitHub

As a side benefit of this refactoring, the traceback of an exception raised in sequential mode (`n_jobs=1`) is now flatter.

3/4

And thanks to everybody involved in making this happen, and Thomas as the release manager in particular.

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