Open-source alternatives to Botorch

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

Use evolutionary algorithms instead of gridsearch in scikit-learn

Sequential model-based optimization with a scipy.optimize interface

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

An open source python library for scalable Bayesian optimisation.

Adaptive Experimentation Platform

Nevergrad is a gradient-free optimization library and hyperparameter optimization framework designed to find the minimum of objective functions without using derivatives. It serves as an asynchronous optimization engine that decouples parameter suggestions from result reporting to support parallel function evaluations. The project specializes in multi-objective optimization to identify Pareto fronts for competing goals and provides a suite for benchmarking the performance and convergence of different optimization routines. It supports black-box system optimization, enabling the tuning of exte

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Bayesian Optimization using GPflow

scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

Autograd is an automatic differentiation library and numerical gradient engine for Python. Its primary purpose is to compute the gradients of mathematical functions to enable numerical optimization and the training of mathematical models. The library automates the calculation of derivatives to simplify the implementation of optimization algorithms. This supports activities such as machine learning research, gradient-based learning, and the optimization of numerical models.

Spearmint Bayesian optimization codebase

A Hyperparameter Tuning Library for Keras

Automated Machine Learning on Kubernetes

A research toolkit for particle swarm optimization in Python

Keras Hyperopt: A very simple wrapper for convenient hyperparameter optimization

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

Advanced evolutionary computation library built directly on top of PyTorch, created at NNAISENSE.