ppft
distributed and parallel Python
Decision gist · record as of 2026-08-14
Yes, if you need straightforward job-based parallelism for CPU-bound Python code across multiple cores or machines. The low install friction, active maintenance, permissive license, and no runtime dependencies make it a practical choice. Consider it especially if you want to avoid the complexity of other frameworks or if you need both local and remote execution in one model. Not necessary if you're already using other multiprocessing libraries that fit your use case.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.9; if Parallel Python is already installed, uninstall it first to avoid import conflicts.
- Low friction installation via pip with no runtime dependencies.
- Actively maintained with recent commits and a stable release history since 2015.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and disclaimer.
last release 2026-01-19 (207 days) · last repo commit 2026-06-22 · 92 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 17,937,089 downloads/mo, #1,097 on PyPI
Alternatives
Verify before relying
pip install ppft
import ppft as pp
import math
job_server = pp.Server()
f1 = job_server.submit(math.sin, (math.pi/2,), (), ('math',))
result = f1()
job_server.print_stats()- Whether enhanced serialization using dill.source is automatic or requires explicit opt-in beyond installing ppft[dill]
- Performance characteristics and overhead compared to other multiprocessing frameworks for typical workloads
- Network security details beyond SHA-based authentication mentioned in the description
What it is and what it does
ppft is a fork of Parallel Python that brings the original package into modern Python with pip and setuptools support and enhanced serialization. It provides a job-server model for parallel execution: you create a Server, submit functions with their arguments and dependencies, and retrieve results asynchronously. Internally, ppft uses separate processes and inter-process communication to work around Python's Global Interpreter Lock, enabling true parallelism on multi-core systems and across networked computers.
The package handles the complexity of process management, load balancing, and fault tolerance automatically. Jobs can run locally on detected processor cores, on remote nodes via ppserver daemons, or both with dynamic load balancing. It supports function serialization by source code extraction, automatic processor detection, SHA-based network authentication, and statistics reporting on job execution.
Use it for
- Distribute CPU-intensive computations across multiple cores on a single machine without threading bottlenecks.
- Execute long-running tasks on remote cluster nodes and retrieve results from a local client.
- Run data analysis or scientific calculations in parallel with automatic load balancing across heterogeneous hardware.
- Scale batch processing jobs across a network of computers with fault tolerance and dynamic resource allocation.
- Parallelize embarrassingly parallel workloads with minimal code changes.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need straightforward job-based parallelism for CPU-bound Python code across multiple cores or machines.
The low install friction, active maintenance, permissive license, and no runtime dependencies make it a practical choice. Consider it especially if you want to avoid the complexity of other frameworks or if you need both local and remote execution in one model. Not necessary if you're already using other multiprocessing libraries that fit your use case.
Install
ppft on PyPI
Before you install
Low friction installation via pip with no runtime dependencies. Actively maintained with recent commits and a stable release history since 2015.
Requires Python >=3.9; if Parallel Python is already installed, uninstall it first to avoid import conflicts.
License in practice
BSD-3-Clause permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and disclaimer.
Quickstart
pip install ppft
import ppft as pp
import math
job_server = pp.Server()
f1 = job_server.submit(math.sin, (math.pi/2,), (), ('math',))
result = f1()
job_server.print_stats()Verify before relying
- Whether enhanced serialization using dill.source is automatic or requires explicit opt-in beyond installing ppft[dill]
- Performance characteristics and overhead compared to other multiprocessing frameworks for typical workloads
- Network security details beyond SHA-based authentication mentioned in the description
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 207 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 17,937,089 / month, #1,097 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | 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 |
Evidence: ppft-1.7.8-py3-none-any.whl
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