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

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

Automated Machine Learning with scikit-learn

SkyPilot: Run AI and batch jobs on any infra (Kubernetes or 12+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.

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 Network…

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

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

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

Automated Machine Learning on Kubernetes

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

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

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

EvalML is an AutoML library written in python.

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

The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.

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

Implementation/Tutorial of using Automated Machine Learning (AutoML) methods for static/batch and online/continual learning

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…

Tuning hyperparams fast with Hyperband