The Top 54 Hyperparameter Optimization Open Source Projects

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The Top 54 Hyperparameter Optimization Open Source Projects

Categories > Machine Learning > Hyperparameter Optimization

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.

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

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

Auto Sklearn ⭐5,024

Automated Machine Learning with scikit-learn

Optuna ⭐3,637

A hyperparameter optimization framework

Autogluon ⭐2,779

AutoGluon: AutoML for Text, Image, and Tabular Data

Awesome Automl Papers ⭐2,744

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

Hyperas ⭐2,052

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

Auto_ml ⭐1,504

[UNMAINTAINED] Automated machine learning for analytics & production

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Xcessiv ⭐1,256

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

Determined ⭐951

Determined: Deep Learning Training Platform

Rl Baselines Zoo ⭐761

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

Awesome Automl And Lightweight Models ⭐662

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.

Test Tube ⭐634

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

Hyperparameter_hunter ⭐630

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

Mljar Supervised ⭐577

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

Hyperband ⭐541

Tuning hyperparams fast with Hyperband

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

Hpbandster ⭐424

a distributed Hyperband implementation on Steroids

Hypersearch ⭐373

Hyperparameter optimization for PyTorch.

Hyperparameter Optimization Of Machine Learning Algorithms ⭐349

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

Neuraxle ⭐328

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.

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

Awesome Distributed Deep Learning ⭐260

A curated list of awesome Distributed Deep Learning resources.

Bayesian Optimization ⭐235

Python code for bayesian optimization using Gaussian processes

Gpflowopt ⭐218

Bayesian Optimization using GPflow

My Data Competition Experience ⭐214

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

Cornell Moe ⭐185

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

Asynchronous Distributed Hyperparameter Optimization.

Library for Semi-Automated Data Science

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

A simple, extensible library for developing AutoML systems

Auptimizer ⭐152

An automatic ML model optimization tool.

Mlmodels ⭐144

mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...

Deep_architect_legacy ⭐143

DeepArchitect: Automatically Designing and Training Deep Architectures

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

Time Series Cross-Validation -- an extension for scikit-learn

Hypertunity ⭐119

A toolset for black-box hyperparameter optimisation.

Bilevel Optimization Library in Python for Multi-Task and Meta Learning

Chocolate ⭐111

A fully decentralized hyperparameter optimization framework

Deephyper ⭐101

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

Deep_architect ⭐100

A general, modular, and programmable architecture search framework

Hyperopt Keras Cnn Cifar 100 ⭐95

Auto-optimizing a neural net (and its architecture) on the CIFAR-100 dataset. Could be easily transferred to another dataset or another classification task.

Rl Baselines3 Zoo ⭐86

A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.

Purely functional genetic algorithms for multi-objective optimisation

Simple, but essential Bayesian optimization package

Deep_traffic ⭐48

MIT DeepTraffic top 2% solution (75.01 mph) 🚗.

Coursera Deep Learning Specialization ⭐41

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

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