RayTune is a product of Ray (others include RLlib, RaySGD, and RayServe) – a simple, universal API that lets you build distributed applications. Tune is an open-source framework that allows for hyperparameter optimization to quickly increase your model performance. Tune leverages a variety of optimization algorithms, reducing the cost of tuning by aggressively terminating bad hyperparameter evaluations, intelligently choosing better parameters to evaluate, or even changing the hyperparameters during training to optimize hyperparameter schedules
- Launch a multi-node distributed hyperparameter sweep in just a few lines of code
- Supports any machine learning framework (PyTorch, XGBoost, MXNet, Keras, etc.)
- Automatically manages checkpoints and logging to TensorBoard.
- Offers a choice from state-of-the-art algorithms such as Population Based Training (PBT), BayesOptSearch, HyperBand/ASHA.
- Allows moving models from training to serving on the same infrastructure with Ray Serve.
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