Webinar
Fast and efficient hyperparameter tuning with Ray Tune
Wednesday, October 20, 4:00PM UTCHyperparameter tuning or optimization is used to find the best performing machine learning (ML) model by exploring and optimizing the model hyperparameters (eg. learning rate, tree depth, etc). It is a compute-intensive problem that lends itself well to distributed execution.
Ray Tune is a Python library, built on Ray, that allows you to easily run distributed hyperparameter tuning at scale. Ray Tune is framework-agnostic and supports all the popular training frameworks including PyTorch, TensorFlow, XGBoost, LightGBM, and Keras.
Join this webinar with Will Drevo, product manager for Ray machine learning libraries, for an overview of Ray Tune and demo of using it for tuning a deep learning model.
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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Q&A summary >>>
Speakers
Will Drevo
Product Manager, Anyscale
Will is a Product Manager for ML at Anyscale. Previously, he was the first ML Engineer at Coinbase, and ran a couple of ML-related startups, one in the data labeling space and the other in the pharmaceutical space. He has a BS in CS and Music Composition from MIT, and did his master's thesis at MIT in machine learning systems. In his spare time, he produces electronic music, travels, and tries to find the best Ethiopian food in the Bay Area.