joblib

Joblib is a Python library that provides lightweight pipelining and easy-to-use parallel computing utilities. It focuses on efficient serialization, caching, and simple parallel execution, with particular emphasis on NumPy arrays. The project was created by Gael Varoquaux and is maintained as part of the scientific Python ecosystem; it is released under the BSD license.

Key features include a high-performance persistence interface via dump and load for Python objects, with specialized handling for large NumPy arrays to avoid unnecessary data copies. Memory is a separate utility that caches the result of expensive function calls to a directory on disk (and optionally keeps results in memory) so repeated runs can reuse prior computations.

For parallel computing, joblib provides Parallel and delayed to express and execute a collection of tasks in parallel. It supports multiple backends (such as multiprocessing and threading backends) and exposes an n_jobs parameter to control the number of workers. This makes it convenient for accelerating data processing, model evaluation, and cross-validation workflows, notably within scikit-learn.

Because of its focus on numpy data and simple APIs, joblib is a common dependency in machine learning projects. It complements numpy and SciPy, offering robust, portable serialization, caching, and parallelism without the complexity of more general distributed systems.

Installation is straightforward through PyPI (pip install joblib). The project continues to be maintained by the open-source community with ongoing updates and compatibility improvements for modern Python versions.