{"categories":[{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing/2"}],"enrichment":{"capability":"Manages clusters of IPython processes for parallel computing, providing CLI tools and a Python API to distribute computations across multiple engines via the Jupyter protocol.","skillfed_tags":["parallel-computing","jupyter-ecosystem","cluster-management"],"use_cases":["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"],"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.\n\nYou 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.","worth_installing":"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."},"id":"ipyparallel","links":{"html":"https://skillfed.io/packages/ipyparallel","md":"https://skillfed.io/packages/ipyparallel.md","pypi":"https://pypi.org/project/ipyparallel/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-12","license_spdx":null,"license_treatment":"permissive","name":"ipyparallel","python_support":"supports_current","summary":"Interactive Parallel Computing with IPython"},"popularity":{"monthly_downloads":192162,"position":9867,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"9.2.0"}