Open-source alternatives to BayesianOptimization

Optim.jl is a numerical optimization library for the Julia programming language, providing a comprehensive framework for minimizing or maximizing univariate and multivariate functions. It offers a suite of tools for solving both constrained and unconstrained mathematical problems, utilizing a variety of gradient-based, derivative-free, and stochastic search methods. The library distinguishes itself through a modular architecture that leverages language-level multiple dispatch to automatically select efficient solvers based on input data types and objective function properties. It supports com

Sequential model-based optimization with a scipy.optimize interface

Hyper-parameter optimization for sklearn

Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces. The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc

This project is an educational collection of computational notebooks and tutorials focused on Bayesian machine learning and probabilistic programming. It provides a framework for building predictive models that represent uncertainty by defining probability distributions over parameters rather than relying on single point estimates. The repository serves as a library of statistical methods for estimating parameter distributions, performing regression, and quantifying confidence levels in predictive systems. It covers a range of techniques including Gaussian process regression, Markov chain Mon

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

This project is a support vector machine library implemented in C, providing an engine for classification and regression tasks. It functions as a machine learning kernel library and a statistical model validator used to categorize data points and predict continuous numerical values. The library allows for the definition of custom kernel functions to calculate similarity between data points in specialized datasets. It also includes tools for probabilistic modeling, such as estimating class membership, data density, and distribution boundaries. Broad capabilities cover model training for multi

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

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.

NNI is an AutoML toolkit designed to automate machine learning lifecycles. It functions as a hyperparameter optimization framework, a neural architecture search tool, and a model compression suite. The project provides a distributed training orchestrator to manage machine learning workloads across local machines, remote servers, and cloud platforms. It enables the discovery of efficient model structures through reinforcement learning and one-shot optimization methods, while utilizing Bayesian and evolutionary algorithms to automate hyperparameter tuning. Additional capabilities include tools

PyCaret is a Python AutoML platform and MLOps lifecycle manager designed to automate machine learning workflows. It functions as a low-code environment that leverages a scikit-learn native engine to execute preprocessing, training, and evaluation for tabular data. The platform distinguishes itself as an LLM-powered ML copilot, using large language model agents to analyze datasets, design experiment configurations, and explain model results. It also serves as a Kubernetes ML orchestrator and model registry, enabling the versioning of trained pipelines and their promotion to production API endp

This is a Python automated machine learning framework designed to automate the design and optimization of machine learning pipelines. It functions as a genetic programming pipeline optimizer and an automated feature selection tool, using evolutionary search to discover the most effective sequences of data processing and model steps. The project focuses on multi-objective optimization to balance competing performance metrics simultaneously. It employs a genetic selection process to identify impactful variables and remove noise from raw datasets, ensuring the resulting machine learning solution

Ludwig is a declarative machine learning framework designed for training neural networks and large language models using configuration files instead of manual coding. It functions as a multimodal model builder and a low-code tool for supervised fine-tuning, allowing users to build models that process mixed inputs of text, images, audio, and tabular data. The project distinguishes itself through an automated hyperparameter optimizer and a system for large language model fine-tuning using parameter-efficient adapters. It features a multimodal data pipeline and the ability to automatically gener

Use evolutionary algorithms instead of gridsearch in scikit-learn

optimization routines for hyperparameter tuning

Hyperparameter Experiments with TensorFlow and Keras

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

Bayesian optimization in PyTorch

An open source python library for scalable Bayesian optimisation.

Automated Machine Learning on Kubernetes

Spearmint Bayesian optimization codebase

A Hyperparameter Tuning Library for Keras

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

Open-source implementation of Google Vizier for hyper parameters tuning

GPyTorch is a GPU-accelerated probabilistic framework and PyTorch library for implementing scalable Gaussian process models. It provides a system for Gaussian process modeling and uncertainty estimation, designed to perform efficient matrix operations on graphics hardware. The framework features a modular kernel system for constructing custom covariance functions and modeling complex data dependencies. It specifically integrates Gaussian processes with deep neural networks to create hybrid models for regression and classification. The system employs numerical linear algebra techniques, inclu

Penrose is a compiler that transforms structured mathematical notation into optimized SVG diagrams. It uses a three-stage pipeline of separate domain, substance, and style files to define mathematical objects, relationships, and visual presentation, then solves continuous optimization problems with user-defined spatial constraints and objectives to automatically arrange diagram elements. The system separates diagram content from visual style using distinct declarative languages, and provides a typed domain language with subtype hierarchies for mathematical objects. It supports embedding compi

Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c