266 Open source projects that are alternatives of or similar to syne-tune

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

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

Generalized and Efficient Blackbox Optimization System.

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Automated Machine Learning with scikit-learn

a distributed Hyperband implementation on Steroids

Sequential Model-based Algorithm Configuration

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

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

Sequential model-based optimization with a `scipy.optimize` interface

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

A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.

Parallel Hyperparameter Tuning in Python

Python library for Bayesian hyper-parameters optimization

Yes, it's a camel case.

A library for performing hyperparameter optimization

Learnable Embedding Space for Efficient Neural Architecture Compression

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

Machine Learning Tool Box

Tuning hyperparams fast with Hyperband

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

Simple, but essential Bayesian optimization package

Experimental Global Optimization Algorithm

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.

Purely functional genetic algorithms for multi-objective optimisation

Determined: Deep Learning Training Platform

Hyperparameter optimization in Julia.

Black box hyperparameter optimization made easy.

The standard and parallel multiobjective EGO algorithms

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

A toolset for black-box hyperparameter optimisation.

AutoOED: Automated Optimal Experimental Design Platform

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

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Distribution transparent Machine Learning experiments on Apache Spark

This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.

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

An automatic ML model optimization tool.

Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. 比HyperOpt更强的分布式异步超参优化库。

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

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

Generalized and Efficient Blackbox Optimization System [SIGKDD'21].

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

A fully decentralized hyperparameter optimization framework

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

A general, modular, and programmable architecture search framework

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

DeepArchitect: Automatically Designing and Training Deep Architectures

Library for Semi-Automated Data Science

A general-purpose pythonic genetic algorithm.

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

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

Bayesian Optimization using GPflow

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

A drop-in replacement for Scikit-Learn’s GridSearchCV / RandomizedSearchCV -- but with cutting edge hyperparameter tuning techniques.

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

Toolbox for Bayesian Optimization and Model-Based Optimization in R

A scikit-learn compatible implementation of hyperband

Hyperparameter optimization package of the mlr3 ecosystem