An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

Automated Machine Learning with scikit-learn

A Python implementation of global optimization with gaussian processes.

Sequential model-based optimization with a `scipy.optimize` interface

A modular active learning framework for Python

Notebooks about Bayesian methods for machine learning

Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

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

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

a distributed Hyperband implementation on Steroids

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Experimental Global Optimization Algorithm

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

Generalized and Efficient Blackbox Optimization System

Parallel Hyperparameter Tuning in Python