Launch a multi-node distributed hyperparameter sweep in less than 10 lines of code.

Supports any machine learning framework, including PyTorch, XGBoost, MXNet, and Keras.

Automatically manages checkpoints and logging to TensorBoard.

Choose among state of the art algorithms such as Population Based Training (PBT), BayesOptSearch, HyperBand/ASHA.

Move your models from training to serving on the same infrastructure with Ray Serve.