A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

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 hyperparameter optimization framework

A curated list of automated machine learning papers, articles, tutorials, slides and projects

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

[UNMAINTAINED] Automated machine learning for analytics & production

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

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

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

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Python library to easily log experiments and parallelize hyperparameter search for neural networks

Tuning hyperparams fast with Hyperband

Determined: Deep Learning Training Platform

Sequential Model-based Algorithm Configuration

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

Experimental Global Optimization Algorithm

✨ Automates Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀

a distributed Hyperband implementation on Steroids

Hyperparameter optimization for PyTorch.

A Sklearn-like Framework for Hyperparameter Tuning and AutoML in Deep Learning projects. Finally have the right abstractions and design patterns to properly do AutoML. Let your pipeline steps have hyperparameter spaces. Enable checkpoints to cut duplicate calculations. Go from research to production environment easily.

A curated list of awesome Distributed Deep Learning resources.

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

Python code for bayesian optimization using Gaussian processes

Bayesian Optimization using GPflow

A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.

Asynchronous Distributed Hyperparameter Optimization.

Toolbox for Bayesian Optimization and Model-Based Optimization in R