{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"Nevergrad is a gradient-free optimization library that finds optimal parameter values for functions without requiring derivatives, supporting continuous, discrete, and mixed variable spaces.","skillfed_tags":["optimization","hyperparameter-tuning","black-box"],"use_cases":["Tuning hyperparameters (learning rate, batch size, architecture choice) for neural networks without access to gradient information.","Optimizing simulator or expensive-to-evaluate function outputs where finite differences are impractical.","Finding optimal configurations for complex systems with mixed continuous and categorical parameters.","Benchmarking and comparing multiple gradient-free algorithms on the same optimization problem.","Automating parameter search for legacy code or proprietary black-box functions."],"what_it_does":"Nevergrad is a Python optimization library designed for problems where gradients are unavailable or expensive to compute. It implements multiple gradient-free algorithms (including evolutionary strategies, Bayesian optimization, and CMA-ES variants) and lets you specify complex search spaces with bounded continuous variables, discrete choices, and mixtures of both through its Instrumentation API. You define a function to minimize, describe the parameter space, set a budget, and the optimizer explores and returns the best point found.\n\nThe library is maintained by Facebook Research and actively developed. It's commonly used for hyperparameter tuning in machine learning, algorithm configuration, and any black-box optimization task where you can evaluate a function but cannot compute gradients. Dependencies include numpy for numerics, cma for covariance-matrix adaptation, and bayesian-optimization for probabilistic search strategies.","worth_installing":"Yes. Nevergrad is actively maintained, has no known vulnerabilities, low install friction, and a permissive MIT license. It's well-suited if you need gradient-free optimization with support for mixed variable types. Install it if you're doing hyperparameter tuning, black-box optimization, or algorithm configuration without access to gradients."},"id":"nevergrad","links":{"html":"https://skillfed.io/packages/nevergrad","md":"https://skillfed.io/packages/nevergrad.md","pypi":"https://pypi.org/project/nevergrad/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-04-23","license_spdx":null,"license_treatment":"permissive","name":"nevergrad","python_support":"supports_current","summary":"A Python toolbox for performing gradient-free optimization"},"popularity":{"monthly_downloads":482272,"position":6423,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.12"}