Introducing KubeRay v1.4

KubeRay v1.4 is the latest release from the KubeRay team, enhancing the deployment of Ray on Kubernetes with new features such as the KubeRay API Server V2 and Ray Autoscaler V2, both of which improve reliability and observability. The update introduces Service Level Indicator (SLI) metrics, offering insights into Ray cluster health and performance, and a new experimental KubeRay Dashboard for visualizing resources via a web interface. The release addresses challenges in transitioning from Proof of Concept (POC) to production environments by streamlining interfaces for data scientists, simplifying operations, and facilitating dynamic workload management. Additional improvements include support for gang scheduling with scheduler plugins, streamlined Python package management with uv, and Helm chart unit testing to ensure deployment reliability. The update also highlights the integration of large language model (LLM) workloads, allowing users to deploy and manage distributed GPU resources efficiently. The KubeRay community is encouraged to provide feedback and participate in ongoing development efforts.

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