RayAn 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.
LaleLibrary for Semi-Automated Data Science
Cornell MoeA Python library for the state-of-the-art Bayesian optimization algorithms, with the core implemented in C++.
Coursera Deep Learning SpecializationNotes, 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
OrionAsynchronous Distributed Hyperparameter Optimization.
HyperactiveA hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.
HyperasKeras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
MlrmboToolbox for Bayesian Optimization and Model-Based Optimization in R
AuptimizerAn automatic ML model optimization tool.
Rl Baselines3 ZooA collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Far HoGradient based hyperparameter optimization & meta-learning package for TensorFlow
BtbA simple, extensible library for developing AutoML systems
TscvTime Series Cross-Validation -- an extension for scikit-learn
Mlmodelsmlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
MilanoMilano is a tool for automating hyper-parameters search for your models on a backend of your choice.
PbtPopulation Based Training (in PyTorch with sqlite3). Status: Unsupported
Automl alexState-of-the art Automated Machine Learning python library for Tabular Data
Auto ml[UNMAINTAINED] Automated machine learning for analytics & production
Boml Bilevel Optimization Library in Python for Multi-Task and Meta Learning
HypertunityA toolset for black-box hyperparameter optimisation.
DeephyperDeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
ChocolateA fully decentralized hyperparameter optimization framework
Deep architectA general, modular, and programmable architecture search framework
TalosHyperparameter Optimization for TensorFlow, Keras and PyTorch
NniAn open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
HordEfficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Hyperopt Keras Cnn Cifar 100Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.
CmaesPython library for CMA Evolution Strategy.
XcessivA web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
DeterminedDetermined: Deep Learning Training Platform
MgoPurely functional genetic algorithms for multi-objective optimisation
BayesoSimple, but essential Bayesian optimization package
TpotA Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
Mljar SupervisedAutomated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning 🚀
Rl Baselines ZooA collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
Gradient Free OptimizersSimple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
Awesome Automl And Lightweight ModelsA 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.
Test TubePython library to easily log experiments and parallelize hyperparameter search for neural networks
Hyperparameter hunterEasy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
Smac3Sequential Model-based Algorithm Configuration
HyperbandTuning hyperparams fast with Hyperband
AtmAuto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
OptunaA hyperparameter optimization framework
Hpbandstera distributed Hyperband implementation on Steroids
SimpleExperimental Global Optimization Algorithm
NeuraxleA 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.
AutogluonAutoGluon: AutoML for Text, Image, and Tabular Data
SherpaHyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.
Awesome Automl PapersA curated list of automated machine learning papers, articles, tutorials, slides and projects
Scikit OptimizeSequential model-based optimization with a `scipy.optimize` interface