# joblib/joblib

Computing with Python functions.

Repository: https://github.com/joblib/joblib
Canonical: https://ross.abutalabs.com/products/joblib
Homepage: http://joblib.readthedocs.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, parallel-computing, caching, multiprocessing, threading, memoization
Last push: 2026-08-24T11:06:49+00:00

## Health v2 (maintenance only)
Score: 78/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 37, longevity 100
- inputs: {"age_days": 5962, "days_push": 9, "days_rel": 261, "gap_med": 96, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4388, forks 473 (observed 2026-08-28T04:08:47.185597+00:00)

## What it is
Joblib is a Python library for lightweight pipelining: running Python functions in parallel with multiprocessing/threading backends and memoizing their results to disk. It also provides efficient serialization of large numpy arrays and optional Dask distributed execution.

## Use cases
- parallelize a python function across cpu cores
- cache function results to disk in python
- memoize expensive computations
- dump and load large numpy arrays efficiently
- distribute python function execution with dask
- speed up embarrassingly parallel loops in python

## When to choose
- you need simple parallel execution of Python functions without rewriting code
- you want transparent on-disk caching of function outputs
- you work with large numpy arrays and need fast persistence
- you want a lightweight alternative to full task-queue systems

## When to avoid
- you need distributed task queues across many machines with a broker like Celery provides
- you need GPU or async-native parallelism
- your workload is I/O-bound network tasks better served by asyncio

## Facets
- artifact type: library
- maturity: stable
- function: caching, serialization, concurrency
- domain: developer-tools, data-science, performance
- platform: python, cross-platform
- tags: parallel-computing, memoization, multiprocessing, numpy-serialization, pipelining

## Member repositories
- joblib/joblib (main) score 78

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:47.185597+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 2026-08-29T18:21:14.010177+00:00, confidence not recorded.
  - readme: https://github.com/joblib/joblib (fetched 2026-08-28T04:08:47.185597+00:00, sha 81137b118801)
  - registry_pypi: https://pypi.org/pypi/joblib/json (fetched 2026-08-29T09:09:51.775282+00:00, sha 4fedf3b6347e)
- Data as of 2026-08-30T08:39:29.467469+00:00.