KubeRay best practices focus on production scale and cluster failures
Anyscale outlines production KubeRay practices for logs, GPU health, autoscaler tuning and head-node resilience, with Connect in private preview.
KubeRay positions Ray workloads inside Kubernetes
An AnyScale explainer says KubeRay brings RayCluster, RayJob and RayService to Kubernetes-native lifecycle management and autoscaling.
KubeRay v1.7 Launches With Upgraded Features for Ray on Kubernetes
KubeRay v1.7 introduces major upgrades like History Server beta, enhanced RayJob management, and stronger security for Ray workloads on Kubernetes.
Enhancing Ray Clusters with NVIDIA KAI Scheduler for Optimized Workload Management
NVIDIA's KAI Scheduler integrates with KubeRay, enabling advanced scheduling features for Ray clusters, optimizing resource allocation and workload prioritization.
KubeRay v1.3.0 Launch: Enhancing Observability and Reliability for Kubernetes
Anyscale releases KubeRay v1.3.0, bringing significant improvements in observability and reliability for Ray on Kubernetes, addressing key challenges in scalability and usability.