{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/20"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Generates hyperparameter sweep suggestions using Bayesian optimization, grid search, and other methods, and identifies runs to early-terminate based on performance.","skillfed_tags":["hyperparameter-optimization","machine-learning","bayesian-search"],"use_cases":["Suggest next hyperparameters in a machine learning training loop using Bayesian optimization to balance exploration and exploitation.","Implement early stopping in parallel hyperparameter searches by identifying underperforming runs via Hyperband.","Validate and parse sweep configurations to ensure they conform to the expected schema before running experiments.","Integrate hyperparameter optimization into a local training pipeline without relying on external services.","Grid search over discrete parameter combinations with deterministic parameter assignment."],"what_it_does":"Sweeps is a hyperparameter optimization engine that powers Weights & Biases' sweep functionality. It takes a sweep configuration (defining the optimization method, parameter ranges, and metric to optimize) and a list of completed runs, then suggests the next hyperparameter values to try. It supports multiple search strategies including Bayesian optimization and grid search, and can identify runs to stop early using methods like Hyperband to save compute resources.\n\nThe package validates configurations against a JSON schema, routes to the appropriate search algorithm, and returns structured suggestions. It's designed as a backend component but is also available as a standalone library for local hyperparameter tuning workflows. Dependencies include numpy, scipy, scikit-learn for numerical computation, and pydantic for configuration validation.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and offers low-friction installation. It solves a concrete problem\u2014suggesting hyperparameters and early-stopping decisions\u2014with established algorithms. Install it if you need programmatic hyperparameter optimization or are integrating with Weights & Biases sweeps locally."},"id":"sweeps","links":{"html":"https://skillfed.io/packages/sweeps","md":"https://skillfed.io/packages/sweeps.md","pypi":"https://pypi.org/project/sweeps/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2022-08-15","license_spdx":null,"license_treatment":"permissive","name":"sweeps","python_support":"supports_current","summary":"Weights and Biases Hyperparameter Sweeps Engine."},"popularity":{"monthly_downloads":103140,"position":12828,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.2.0"}