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.
Anyscale launches SIG for Ray on accelerators
Anyscale announced a Special Interest Group to expand Ray support for GPUs, TPUs and other accelerators across testing, scheduling and transport.
Anyscale, Google Cloud add native Ray support for PyTorch on TPUs
Anyscale and Google Cloud said Ray now supports PyTorch on Google TPUs natively on GKE, with one worker per TPU chip and TPU-aware scheduling.
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.
Anyscale Debuts GPU Health Observability for AI Workloads
Anyscale introduces GPU Health Observability, bridging hardware and ML workloads with real-time monitoring to reduce failures.
Ray 2.58 Unveils Native gVisor Sandboxing for Scalable AI Workloads
Ray 2.58 introduces native gVisor sandboxing, enabling scalable, isolated environments for reinforcement learning and agentic AI workloads.
NVIDIA GB300 NVL72 Enhances AI with Ray Placement Groups
NVIDIA GB300 NVL72 leverages Ray's NVLink Domain-Aware Placement Groups for optimized multi-node GPU scheduling, boosting AI performance.
Anyscale to Merge with Nscale, Boosting AI Infrastructure
Anyscale and Nscale unite to advance AI infrastructure, focusing on open-source Ray, GPU capacity, and multi-cloud flexibility.
Ray's Resource Isolation Enhances Cluster Stability with cgroup v2
Ray's new Resource Isolation feature leverages Linux's cgroup v2 to improve stability under heavy workloads, cutting node failures to zero.