{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/3"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/4"}],"enrichment":{"capability":"MPIRE provides a faster, more feature-rich alternative to Python's standard multiprocessing.Pool, with an API designed for easy parallelization of embarrassingly parallel tasks.","skillfed_tags":["parallel-execution","worker-pool","progress-tracking"],"use_cases":["Parallelize CPU-bound tasks like batch processing across multiple workers with a progress bar.","Load a large model or dataset once per worker during initialization, then reuse it across many tasks without serialization overhead.","Profile multiprocessing efficiency by enabling worker insights to diagnose bottlenecks in startup, waiting, and working time.","Run embarrassingly parallel tasks with automatic task chunking to speed up processing of small task queues.","Parallelize lambdas and complex objects in Jupyter notebooks by enabling dill serialization through the multiprocess backend."],"what_it_does":"MPIRE is a multiprocessing library that wraps Python's standard multiprocessing.Pool with a more convenient API and additional features. It offers map-like functions (map, imap, apply, apply_async) similar to the standard library but combines them with copy-on-write shared objects, worker state management, progress bar integration via tqdm, and automatic task chunking. The package is designed to reduce boilerplate for parallel tasks without requiring users to learn a completely new multiprocessing syntax.\n\nThe library supports worker initialization and exit functions, timeouts, graceful exception handling, worker insights for profiling efficiency, and nested worker pools. It integrates with tqdm for progress tracking and can optionally use dill for serialization to handle more complex objects like lambdas in Jupyter notebooks. Runtime dependencies include tqdm for progress bars, pygments for syntax highlighting, pywin32 for Windows support, and importlib-resources for resource access.","worth_installing":"Yes, if you need multiprocessing with a gentler learning curve and built-in progress tracking. The low install friction, permissive MIT license, and feature set (worker state, progress bars, insights) make it a solid drop-in replacement for multiprocessing.Pool. Caveat: maintenance is dormant (last release 2024-05-07), so consider your tolerance for slow bug fixes or compatibility updates."},"id":"mpire","links":{"html":"https://skillfed.io/packages/mpire","md":"https://skillfed.io/packages/mpire.md","pypi":"https://pypi.org/project/mpire/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-05-07","license_spdx":null,"license_treatment":"permissive","name":"mpire","python_support":"unspecified","summary":"A Python package for easy multiprocessing, but faster than multiprocessing"},"popularity":{"monthly_downloads":4303676,"position":2339,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.10.2"}