An open source framework that provides a simple, universal API for building distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library.
Library for Semi-Automated Data Science
A Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
Coursera Deep Learning Specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
Asynchronous Distributed Hyperparameter Optimization.
A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Toolbox for Bayesian Optimization and Model-Based Optimization in R
An automatic ML model optimization tool.
Rl Baselines3 Zoo
A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Gradient based hyperparameter optimization & meta-learning package for TensorFlow
A simple, extensible library for developing AutoML systems
Time Series Cross-Validation -- an extension for scikit-learn
mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
State-of-the art Automated Machine Learning python library for Tabular Data
[UNMAINTAINED] Automated machine learning for analytics & production
Bilevel Optimization Library in Python for Multi-Task and Meta Learning
A toolset for black-box hyperparameter optimisation.
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
A fully decentralized hyperparameter optimization framework
A general, modular, and programmable architecture search framework
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Hyperopt Keras Cnn Cifar 100
Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
Python library for CMA Evolution Strategy.
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Determined: Deep Learning Training Platform
Purely functional genetic algorithms for multi-objective optimisation
Simple, but essential Bayesian optimization package
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Rl Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Gradient Free Optimizers
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Awesome Automl And Lightweight Models
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.
Python library to easily log experiments and parallelize hyperparameter search for neural networks
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Sequential Model-based Algorithm Configuration
Tuning hyperparams fast with Hyperband
Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
A hyperparameter optimization framework
a distributed Hyperband implementation on Steroids
Experimental Global Optimization Algorithm
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.
AutoGluon: AutoML for Text, Image, and Tabular Data
Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Awesome Automl Papers
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Sequential model-based optimization with a `scipy.optimize` interface