# ipython/ipyparallel

IPython Parallel: Interactive Parallel Computing in Python

Repository: https://github.com/ipython/ipyparallel
Canonical: https://ross.abutalabs.com/products/ipyparallel
Homepage: https://ipyparallel.readthedocs.io/
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: parallel, jupyter, python
Last push: 2026-08-12T03:44:41+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 4164, "days_push": 21, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2647, forks 1008 (observed 2026-08-28T04:07:06.817836+00:00)

## What it is
IPython Parallel is a Python package and set of CLI scripts for controlling clusters of IPython engine processes, built on the Jupyter protocol. It enables interactive parallel computing from Python or Jupyter notebooks, with tools like ipcluster, ipcontroller, and ipengine.

## Use cases
- run a function across multiple Python processes in parallel
- start and manage a cluster of IPython engines
- execute parallel map operations from a Jupyter notebook
- distribute computation across machines on a network
- interactively parallelize data processing workloads

## When to choose
- you want interactive parallel computing tightly integrated with Jupyter/IPython
- you need to control a cluster of Python engines from a REPL or notebook
- you want simple async/parallel map over many tasks

## When to avoid
- you need heavy-duty distributed job scheduling like SLURM or Dask's full ecosystem
- you need GPU or multi-node orchestration beyond Python engine processes
- you want a non-interactive batch HPC scheduler

## Facets
- artifact type: library
- maturity: active
- function: concurrency, developer-tools, cli
- domain: developer-tools, microservices, data-science
- platform: python, cli, cross-platform
- tags: parallel-computing, jupyter, ipython, cluster-computing, distributed-execution

## Member repositories
- ipython/ipyparallel (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:06.817836+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 2026-08-30T02:18:57.216018+00:00, confidence not recorded.
  - readme: https://github.com/ipython/ipyparallel (fetched 2026-08-28T04:07:06.817836+00:00, sha a82c4986bf38)
- Data as of 2026-08-30T08:39:29.467469+00:00.