103 Open source projects that are alternatives of or similar to optuna-dashboard

OptKeras: wrapper around Keras and Optuna for hyperparameter optimization

Python library for CMA Evolution Strategy.

⚡️ AllenNLP plugin for adding subcommands to use Optuna, making hyperparameter optimization easy

Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models

An automatic ML model optimization tool.

A general, modular, and programmable architecture search framework

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

Purely functional genetic algorithms for multi-objective optimisation

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

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

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

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

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

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.

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

Gradient based hyperparameter optimization & meta-learning package for TensorFlow

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

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

DeepArchitect: Automatically Designing and Training Deep Architectures

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

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

Automated Machine Learning with scikit-learn

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

Population Based Training (in PyTorch with sqlite3). Status: Unsupported

Tuning hyperparams fast with Hyperband

A toolset for black-box hyperparameter optimisation.

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

A fully decentralized hyperparameter optimization framework

A Clojure machine learning library

Hyperparameter Optimization for TensorFlow, Keras and PyTorch

Toolbox for Bayesian Optimization and Model-Based Optimization in R

Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

A library for doing Bayesian Optimization using Gaussian Processes (blackbox optimizer) in Go/Golang.

Python library for CMA Evolution Strategy.

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

Determined: Deep Learning Training Platform

Python code for bayesian optimization using Gaussian processes

Simple, but essential Bayesian optimization package

A simple, extensible library for developing AutoML systems

Scalable, structured, dynamically-scheduled hyperparameter optimization.

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

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

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

Library for Semi-Automated Data Science

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

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Sequential Model-based Algorithm Configuration

A scikit-learn compatible implementation of hyperband

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

State-of-the art Automated Machine Learning python library for Tabular Data

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

A hyperparameter optimization framework

Notes for Deep Learning Specialization Courses led by Andrew Ng.

a distributed Hyperband implementation on Steroids

Experimental Global Optimization Algorithm

[UNMAINTAINED] Automated machine learning for analytics & production

Parallel Hyperparameter Tuning in Python

Hyperparameter optimization package of the mlr3 ecosystem