Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

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

A hyperparameter optimization framework

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Fast and Accurate ML in 3 Lines of Code

Automated Machine Learning with scikit-learn

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

A Hyperparameter Tuning Library for Keras

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

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

[UNMAINTAINED] Automated machine learning for analytics & production

Hyperparameter Experiments with TensorFlow and Keras

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

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

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

A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.

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)