Ray on Public-Cloud Kubernetes: experiments, lessons learned, and suggested best practices
Anne Holler, Chi Su, Madhuri Yechuri
Ray is an increasingly popular distributed execution framework for scaling applications and leveraging state of the art machine learning libraries. With the availability of GPU compute shapes on public clouds, deploying Ray on the public cloud is an attractive option over deploying it on bespoke on-prem compute resources. This talk explores suitability of Kubernetes on public cloud as a deployment platform for Ray, shares experiments with Ray deployed on Nodeless Kubernetes, lessons learned, and suggested best practices.
