{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/6"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/7"}],"enrichment":{"capability":"Registers Apache Spark as a distributed backend for joblib's parallel task execution, allowing joblib-compatible code to offload work to a Spark cluster.","skillfed_tags":["spark-integration","distributed-computing"],"use_cases":["Distribute parallel training workloads across a Spark cluster to speed up model tuning.","Scale joblib-based custom parallel loops to a Spark cluster without rewriting code.","Offload CPU-intensive training tasks from a single machine to a multi-node cluster.","Run joblib-parallelized code on Spark without changing application code."],"what_it_does":"Joblibspark bridges joblib's parallel execution framework and Apache Spark, allowing you to run joblib-parallelized code on a Spark cluster instead of a single machine. It works by registering Spark as a backend that joblib can dispatch tasks to, so parallel training routines and other joblib-compatible code automatically scale across cluster nodes.\n\nThe package is lightweight\u2014it depends only on joblib\u2014and integrates cleanly with joblib's `parallel_backend` context manager. However, it has documented limitations: it accelerates training loops but does not parallelize model inference or feature engineering steps, which continue to run locally. This makes it most useful for training workflows where the training loop itself is the bottleneck.","worth_installing":"Yes, if you have a Spark cluster and need to scale joblib-parallelized training workloads. Install friction is low and the integration is straightforward. No, if you only do model inference or feature engineering in parallel\u2014the package explicitly does not accelerate those. Verify that your PySpark version is compatible and that your use case fits the training-loop parallelization model."},"id":"joblibspark","links":{"html":"https://skillfed.io/packages/joblibspark","md":"https://skillfed.io/packages/joblibspark.md","pypi":"https://pypi.org/project/joblibspark/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-04-07","license_spdx":null,"license_treatment":"permissive","name":"joblibspark","python_support":"unspecified","summary":"Joblib Apache Spark Backend"},"popularity":{"monthly_downloads":303304,"position":7814,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.0"}