ipyparallel
Interactive Parallel Computing with IPython
Decision gist · record as of 2026-08-14
Yes, if you need interactive or programmatic parallel computing within the Jupyter ecosystem. The package is actively maintained, has low install friction, uses a permissive license, and integrates cleanly with IPython and Jupyter. No known vulnerabilities. Best suited for developers and researchers already using Jupyter who want to scale computations beyond a single process without adopting heavier distributed frameworks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- The cluster startup (ipcluster start) depends on system-level process management and network configuration.
- Low friction installation with a pure Python wheel.
License · maintenance · safety
permissive license (permissive) — Modified BSD License (permissive). You may use, modify, and distribute this software freely in commercial and private projects, provided you retain the copyright notice and disclaimer.
last release 2026-05-12 (94 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 192,162 downloads/mo, #9,867 on PyPI
Alternatives
Verify before relying
pip install ipyparallel
import ipyparallel as ipp
cluster = ipp.Cluster(n=4)
with cluster as rc:
ar = rc[:].apply_async(os.getpid)
pid_map = ar.get_dict()- Whether the package works reliably with modern Jupyter Lab versions beyond the stated 4.0 support
- Performance characteristics and scalability limits for typical cluster sizes
- Whether all 10 runtime dependencies are always required or if some are optional
What it is and what it does
IPython Parallel is a framework for controlling and coordinating clusters of IPython processes, allowing you to distribute Python computations across multiple machines or cores. It builds on Jupyter's protocol and provides both command-line tools (ipcluster, ipcontroller, ipengine) and a Python API for submitting work to the cluster and collecting results.
You start a cluster using the CLI, then connect from Python code to submit tasks asynchronously across all available engines. It's designed for interactive exploratory computing and batch-style parallel workloads, integrating with Jupyter Notebook and Jupyter Lab as extensions. The package handles the complexity of process management, communication via pyzmq, and result aggregation.
Use it for
- Distribute parameter sweeps or simulations across multiple cores in a single machine or cluster
- Run embarrassingly parallel data processing tasks on large datasets split across engines
- Interactive exploration with live feedback from parallel computations in a Jupyter notebook
- Batch job submission and monitoring from Python scripts without manual process orchestration
- Scientific computing workflows requiring coordinated multi-process execution with result collection
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need interactive or programmatic parallel computing within the Jupyter ecosystem.
The package is actively maintained, has low install friction, uses a permissive license, and integrates cleanly with IPython and Jupyter. No known vulnerabilities. Best suited for developers and researchers already using Jupyter who want to scale computations beyond a single process without adopting heavier distributed frameworks.
Install
ipyparallel on PyPI
Before you install
Low friction installation with a pure Python wheel. Actively maintained as of 94 days ago. Depends on 10 runtime packages including core Jupyter components (ipykernel, jupyter-client) and infrastructure libraries (pyzmq, tornado), all widely used and stable.
Requires Python 3.10 or later. The cluster startup (ipcluster start) depends on system-level process management and network configuration.
License in practice
Modified BSD License (permissive). You may use, modify, and distribute this software freely in commercial and private projects, provided you retain the copyright notice and disclaimer.
Quickstart
pip install ipyparallel
import ipyparallel as ipp
cluster = ipp.Cluster(n=4)
with cluster as rc:
ar = rc[:].apply_async(os.getpid)
pid_map = ar.get_dict()Verify before relying
- Whether the package works reliably with modern Jupyter Lab versions beyond the stated 4.0 support
- Performance characteristics and scalability limits for typical cluster sizes
- Whether all 10 runtime dependencies are always required or if some are optional
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesdecoratoripykernelipythonjupyter-clientpsutilpython-dateutilpyzmqtornadotqdmtraitlets |
| Maintenance | Actively maintained 94 days since the last release |
| First released | |
| Downloads | 192,162 / month, #9,867 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Framework :: JupyterFramework :: Jupyter :: JupyterLabFramework :: Jupyter :: JupyterLab :: 4Framework :: Jupyter :: JupyterLab :: ExtensionsFramework :: Jupyter :: JupyterLab :: Extensions :: PrebuiltIntended Audience :: DevelopersIntended Audience :: Science/ResearchIntended Audience :: System AdministratorsLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python |
Evidence: ipyparallel-9.2.0-py3-none-any.whl
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