SkyRay: Seamlessly Extending KubeRay to Multi-Cluster Multi-Cloud Operation - Anne Holler, Elotl
Ray is a unified framework for scaling AI applications from a laptop to a cluster. KubeRay supports the creation, deletion, and scaling of Ray clusters on K8s, along with managing Ray jobs and services on the Ray clusters. This talk introduces SkyRay, in which KubeRay is extended towards the Sky computing model via interoperation with a multi-cluster fleet manager. With SkyRay, each Ray cluster is seamlessly scheduled onto a cloud K8s cluster suited to the Ray cluster's resource needs and policy requirements.
The policies can capture a variety of cluster characteristics, e.g., desired cloud provider, region, K8s version, service quality, and GPU type availability. Fleet manager policy updates can be used to trigger automatic migration of Ray clusters between K8s workload clusters. The talk presents several example use cases for SkyRay, including cluster selection for resource needs, service availability, development vs production cluster configuration, and K8s version upgrade.