{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"}],"enrichment":{"capability":"ppft executes Python functions in parallel across multiple processors or networked computers, bypassing the GIL by using separate processes and inter-process communication.","skillfed_tags":["parallel-computing","distributed-execution","cluster-computing"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"ppft","links":{"html":"https://skillfed.io/packages/ppft","md":"https://skillfed.io/packages/ppft.md","pypi":"https://pypi.org/project/ppft/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-01-19","license_spdx":null,"license_treatment":"permissive","name":"ppft","python_support":"supports_current","summary":"distributed and parallel Python"},"popularity":{"monthly_downloads":17937089,"position":1097,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.7.8"}