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.

Library for Semi-Automated Data Science

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

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

🔨 Malet (Machine Learning Experiment Tool) is a tool for efficient machine learning experiment execution, logging, analysis, and plot making.

Tree-of-Parzen-estimators hyperparameter optimization

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

Hyperparameter selection on machine learning models using Particle Swarm Optimization

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

Distribution transparent Machine Learning experiments on Apache Spark

A lightweight custom automl library.

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning 🚀

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

Automated Machine Learning with scikit-learn

Determining hyperparameter equivalency classes for dimensionality reduction algorithms based on the topology of the embedding space.

Hyperparameter search wrapper that uses multiple GPUs.

Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.