Fast and efficient hyperparameter tuning with Ray Tune

We will showcase many Ray Tune highlights, including how to: 

• Set up distributed hyperparameter search in under 10 lines of code

• Scale from a single machine to a cluster with minimal code changes

• Trial leading search methods (ASHA, BOHB, PBT, etc) with built-in access

• Visualize results with TensorBoard or MLflow

We will also share stories of users that are finding the most-performant models, while saving compute costs and maximizing CPU/GPU utilization with Ray Tune. 

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