jobliber

Jobliber is a Python library designed to simplify parallel computing and persistent storage for Python objects. Its primary aim is to make it easier for developers to leverage multiple CPU cores for computationally intensive tasks and to save and load large datasets efficiently.

One of the core features of jobliber is its parallel execution capabilities. It provides simple-to-use interfaces for distributing tasks across multiple processes, effectively bypassing Python's Global Interpreter Lock (GIL) limitations for CPU-bound operations. This allows for significant speedups in tasks such as data processing, machine learning model training, and simulations. Jobliber offers different backends for parallelism, including multiprocessing and threading, allowing users to choose the most suitable option for their specific needs.

Another key aspect of jobliber is its efficient disk-caching mechanism. It allows developers to cache the results of expensive function calls or computations. When a function is called with the same arguments again, jobliber can load the pre-computed result from disk instead of re-executing the function. This is particularly useful for iterative development or when dealing with large datasets that are time-consuming to process. The library uses a specialized serialization format that is optimized for NumPy arrays and other common Python data structures, leading to faster loading and saving times compared to standard pickling. Jobliber is widely used in scientific computing and machine learning communities.