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 hyperparameter optimization framework

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.

Large scale and asynchronous Hyperparameter and Architecture Optimization at your fingertips.

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

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

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

Fast and Accurate ML in 3 Lines of Code

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

Purely functional genetic algorithms for multi-objective optimisation

We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.

🐟Tunny🐟 is Grasshopper's optimization component using Optuna, an open source hyperparameter auto-optimization framework.

Describe and optimize data

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.

Your local Flux surgeon

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

Relentless mutation!!

Evolve Flux networks from scratch!

SEML: Slurm Experiment Management Library

Real-time Web Dashboard for Optuna.