In this talk, we will present our effort to run Ray on YARN, and the integration of Ray on LinkedIn’s open-sourced offline infrastructure: Azkaban (workflow scheduling service) and TonY (Tensorflow on Yarn). We will provide a demo of running a Ray job end-to-end, discuss the architectural decisions and talk about our cooperation with the Ray team on this effort. In the later half of the talk, we will share how we used Ray Tune on Kubernetes in a real world use case. Tune helped us identify promising model configurations, using state of the art Bayesian optimization algorithms like TPE & PBT, with minimal supervision. The data pipeline is also optimized using Tune to extract the maximum throughput and we were able to train 2x faster by reducing the GPU idle times.
