Ray Meetup @ Data + AI Denver Meetup: Ray, a framework for scaling and distributing Python & ML applications

This talk will cover Ray’s overview, architecture, core concepts, and primitives, such as remote Tasks and Actors; briefly discuss Ray native libraries (Ray Tune, Ray Train, Ray Serve, Ray Datasets, RLlib); and Ray’s growing ecosystem.

Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference—from a single node to a cluster, with tangible performance difference when using Ray.

The takeaways from this talk are :

• Learn Ray architecture, core concepts, and Ray primitives and patterns

• Why Distributed computing will be the norm not an exception

• How to scale your ML workloads with Ray libraries:

• Training on a single node vs. Ray cluster, using XGBoost with/without Ray

• Hyperparameter search and tuning, using XGBoost with Ray and Ray Tune

• Inferencing at scale, using XGBoost with/without Ray

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