# sybrenjansen/mpire

A Python package for easy multiprocessing, but faster than multiprocessing

Repository: https://github.com/sybrenjansen/mpire
Canonical: https://ross.abutalabs.com/products/mpire
Language: Python
License: MIT
License Family: permissive
Last push: 2024-07-31T11:36:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2186, "days_push": 763, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2064, forks 40 (observed 2026-08-28T04:06:10.023729+00:00)

## What it is
MPIRE (MultiProcessing Is Really Easy) is a Python library providing a faster, more feature-rich alternative to the standard multiprocessing package. It offers pool-based map/apply functions with copy-on-write shared objects, per-worker state, progress bars, timeouts, and worker insights.

## Use cases
- parallelize map operations across CPU cores in Python
- load a large model once per worker process
- show a progress bar for multiprocessing tasks
- run parallel tasks with timeouts and graceful exception handling
- parallelize lambdas and functions in Jupyter notebooks
- reduce memory usage when running many parallel tasks

## When to choose
- you need multiprocessing that is faster and easier than Python's multiprocessing.Pool
- you want per-worker state, init/exit hooks, or copy-on-write shared objects
- you want built-in progress bars, dashboards, and worker efficiency insights
- you need to parallelize exotic objects or lambdas via dill

## When to avoid
- you need distributed or multi-machine parallelism rather than a single machine
- you rely on the spawn start method and need copy-on-write sharing
- your workload is I/O-bound and better served by asyncio or threading

## Facets
- artifact type: library
- maturity: active
- function: concurrency, developer-tools
- domain: developer-tools, performance, cross-platform
- platform: python, windows
- tags: multiprocessing, parallel-processing, worker-pool, progress-bar, tqdm, dill, cpu-pinning, linux, macos

## Member repositories
- sybrenjansen/mpire (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:10.023729+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:57:09.611002+00:00, confidence not recorded.
  - readme: https://github.com/sybrenjansen/mpire (fetched 2026-08-28T04:06:10.023729+00:00, sha 1455b47df802)
  - registry_pypi: https://pypi.org/pypi/mpire/json (fetched 2026-08-29T10:37:24.272940+00:00, sha ba6a10f31510)
- Data as of 2026-08-30T08:39:29.467469+00:00.