{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering"},{"label":"Distributed Computing","url":"https://skillfed.io/packages/category/system-distributed-computing"}],"enrichment":{"capability":"Distributed provides a scheduler and runtime for parallel and distributed computation using Dask, enabling you to scale Python workloads across multiple machines or cores.","skillfed_tags":["parallel-computing","cluster-scheduling","dask-ecosystem"],"use_cases":["Scale data processing pipelines across a cluster without rewriting code for distributed execution.","Run machine learning training or inference jobs in parallel across multiple GPUs or nodes.","Execute long-running batch computations that would timeout or exhaust memory on a single machine.","Coordinate complex workflows with dependencies between tasks across a heterogeneous cluster.","Monitor and debug distributed workloads through the scheduler's built-in diagnostics and web dashboard."],"what_it_does":"Distributed is a scheduler and runtime that coordinates parallel and distributed computation for Dask. It manages task scheduling, data movement, and fault tolerance across a cluster of workers, allowing you to run computations on multiple machines or cores as if they were a single pool. The package handles the low-level coordination\u2014worker lifecycle, task graph execution, communication\u2014so you focus on expressing your computation in Dask.\n\nYou typically use it by creating a Client that connects to a cluster (local or remote), then submitting Dask graphs or collections through that client. It depends on Tornado for async networking, Cloudpickle for serialization, and several utility libraries (toolz, sortedcontainers, locket, msgpack) to manage scheduling state and inter-process communication. The package is production-stable and actively maintained.","worth_installing":"Yes. Distributed is production-stable, actively maintained, and widely used for scaling Dask workloads. Install friction is low, dependencies are well-established, and there are no known vulnerabilities. Choose it if you need to parallelize or distribute computation beyond a single machine; if you only need local parallelism, Dask alone may suffice."},"id":"distributed","links":{"html":"https://skillfed.io/packages/distributed","md":"https://skillfed.io/packages/distributed.md","pypi":"https://pypi.org/project/distributed/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-14","license_spdx":"BSD-3-Clause","license_treatment":"permissive","name":"distributed","python_support":"supports_current","summary":"Distributed scheduler for Dask"},"popularity":{"monthly_downloads":8413098,"position":1625,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2026.7.1"}