Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.

Automated Machine Learning with scikit-learn

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

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

Determined: Deep Learning Training Platform

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

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

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

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

The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps enviro…

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

Library for Semi-Automated Data Science

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

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

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

Distribution transparent Machine Learning experiments on Apache Spark

Black-box optimization library

🔬 Some personal research code on analyzing CNNs. Started with a thorough exploration of Stanford's Tiny-Imagenet-200 dataset.

Hyperparameter search for AllenNLP - powered by Ray TUNE

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