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

A Python implementation of global optimization with gaussian processes.

Automated Machine Learning with scikit-learn

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

A modular active learning framework for Python

Notebooks about Bayesian methods for machine learning

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

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

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.

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

Experimental Global Optimization Algorithm

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

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

Parallel Hyperparameter Tuning in Python

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

Generalized and Efficient Blackbox Optimization System