multiprocess

better multiprocessing and multithreading in Python

multiprocess Permissive license BSD-3-Clause Active ★ 701 v0.70.19 released 2026-01-19

Install

multiprocess on PyPI

pip

pip install multiprocess

uv

uv add multiprocess

poetry

poetry add multiprocess

Package facts

License	BSD-3-Clause (permissive)
Python support	supports the current Python release (>=3.9)
Install friction	low — pure-Python wheel
Runtime dependencies	1 — dill
Maintenance	actively maintained — 206 days since the last release
Last repo commit	2026-08-09
First released	2015-06-20
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: multiprocess-0.70.19-py310-none-any.whl; multiprocess-0.70.19-py311-none-any.whl; multiprocess-0.70.19-py312-none-any.whl; multiprocess-0.70.19-py313-none-any.whl; multiprocess-0.70.19-py314-none-any.whl; multiprocess-0.70.19-py39-none-any.whl

Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Scientific/EngineeringTopic :: Software Development

About multiprocess

from the package's own PyPI description — quoted content, verbatim

multiprocess: better multiprocessing and multithreading in Python

About Multiprocess

multiprocess is a fork of multiprocessing. multiprocess extends multiprocessing to provide enhanced serialization, using dill. multiprocess leverages multiprocessing to support the spawning of processes using the API of the Python standard library's threading module. multiprocessing has been distributed as part of the standard library since Python 2.6.

multiprocess is part of pathos, a Python framework for heterogeneous computing. multiprocess is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/multiprocess/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.

Major Features

multiprocess enables:

- objects to be transferred between processes using pipes or multi-producer/multi-consumer queues
- objects to...

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

multiprocess extends Python's standard multiprocessing with enhanced serialization via dill, enabling better inter-process communication and object transfer across spawned processes and thread-like APIs.

Low friction: pure-Python wheels available for Python 3.9–3.14, single lightweight runtime dependency (dill >=0.4.1), and active maintenance with recent releases.

BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; attribution and license notice required in distributions.

Usage

from multiprocess import Process, Queue

def worker(q):
    q.put('result')

if __name__ == '__main__':
    q = Queue()
    p = Process(target=worker, args=[q])
    p.start()
    print(q.get())
    p.join()

Python >=3.9 required; if __name__ == '__main__' guard needed on Windows and macOS to avoid spawning loops.

Verdict: Production-stable fork of multiprocessing with active maintenance, no known vulnerabilities, and permissive licensing. Low install friction and broad Python version support (3.9–3.14) make it suitable for projects needing robust inter-process serialization beyond the standard library.

Needs verification

Whether dill's serialization capabilities cover the specific object types your application needs to transfer between processes.
Performance overhead compared to standard multiprocessing for your workload's typical message sizes and process counts.

multiprocessing with better serializationinter-process communication pythonprocess pool with dillspawn processes like threadsserialize complex objects between processespython parallel executionmultiprocess queue and pipeenhanced multiprocessing fork