Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.

Open Source version of SigOpt API, performing hyperparameter optimization and visualization

Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

Hyperparameter optimization package of the mlr3 ecosystem

Black-box optimization framework for R.

Implementation of a comparative analysis to find the best ML model for classifying dry eye disease from healthy controls using metabolomics datasets.

A hyperparameter optimization framework

Fast and Accurate ML in 3 Lines of Code

Real-time Web Dashboard for Optuna.

A framework for the comparative training and evaluation of statistical and deep learning models for multi-feature categorical sequence modeling, utilizing feature fusion and automated with MLflow and Optuna integration.

Examples for https://github.com/optuna/optuna

Distributed hyperparameter optimization made easy

This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.

MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle

Polyaxon Core Client & CLI to streamline MLOps

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

Propulate is an asynchronous evolutionary optimization algorithm and software package for global optimization and hyperparameter search on high-performance computers.

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (https://arxiv.org/abs/2310.08252)