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Top 93 hyperparameter-optimization open source projects

Ray
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.
Bayesian Optimization
Python code for bayesian optimization using Gaussian processes
Cornell Moe
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
Hyperas
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
Mlrmbo
Toolbox for Bayesian Optimization and Model-Based Optimization in R
Rl Baselines3 Zoo
A collection of pre-trained RL agents using Stable Baselines3, training and hyperparameter optimization included.
Far Ho
Gradient based hyperparameter optimization & meta-learning package for TensorFlow
Btb
A simple, extensible library for developing AutoML systems
Tscv
Time Series Cross-Validation -- an extension for scikit-learn
Mlmodels
mlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
Deep architect legacy
DeepArchitect: Automatically Designing and Training Deep Architectures
Milano
Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.
Pbt
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
Boml
Bilevel Optimization Library in Python for Multi-Task and Meta Learning
Hypertunity
A toolset for black-box hyperparameter optimisation.
Deephyper
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
Chocolate
A fully decentralized hyperparameter optimization framework
Talos
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
Hord
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.
Cmaes
Python library for CMA Evolution Strategy.
Xcessiv
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Mgo
Purely functional genetic algorithms for multi-objective optimisation
Bayeso
Simple, but essential Bayesian optimization package
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.
Test Tube
Python library to easily log experiments and parallelize hyperparameter search for neural networks
Atm
Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
Simple
Experimental Global Optimization Algorithm
Neuraxle
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.
Hypersearch
Hyperparameter optimization for PyTorch.
Sherpa
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
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