## Kubernetes v1.34 for Rafay MKS As part of our continuous effort to bring the latest Kubernetes versions to our users, support for [Kubernetes v1.34](https://kubernetes.io/blog/2025/08/27/kubernetes-v1-34-release/) will be added soon to the Rafay Operations Platform for MKS cluster types. Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that **every feature** in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. This will be promoted from Preview to Production in a few days and will be made available to all customers. ## Deploy Workload using DRA ResourceClaim in Kubernetes In the [**first blog**](https://docs.rafay.co/blog/2025/08/23/introduction-to-dynamic-resource-allocation-dra-in-kubernetes/) in the DRA series, we introduced the concept of Dynamic Resource Allocation (DRA) that recently went GA in [Kubernetes v1.34](https://kubernetes.io/blog/2025/08/27/kubernetes-v1-34-release/) which was released end of August 2025. In the [**second blog**](https://docs.rafay.co/blog/2025/08/28/enable-dynamic-resource-allocation-dra-in-kubernetes/), we installed a Kuberneres v1.34 cluster and deployed an example DRA driver on it with "simulated GPUs". In **this blog**, we’ll will deploy a few workloads on the DRA enabled Kubernetes cluster to understand how "Resource Claim" and "ResourceClaimTemplates" work. Info We have optimized the steps for users to experience this on their laptops in less than 5 minutes. The steps in this blog are optimized for macOS users. ## GPU/Neo Cloud Billing using Rafay’s Usage Metering APIs Cloud providers offering GPU or Neo Cloud services need accurate and automated mechanisms to track resource consumption. Usage data becomes the foundation for billing, showback, or chargeback models that customers expect. The Rafay Platform provides usage metering APIs that can be easily integrated into a provider’s billing system. In this blog, we’ll walk through how to use these APIs with a **sample Python script** to generate detailed usage reports. ## Upstream Kubernetes on RHEL 10 using Rafay Our [upcoming release](https://docs.rafay.co/releasenotes/preview/upcoming/) update will add support for a number of new features and enhancements. This blog is focused on the upcoming support for Upstream Kubernetes on nodes based on [Red Hat Enterprise Linux (RHEL) v10.0](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/10/html/10.0_release_notes/index). Both new cluster provisioning and in-place upgrades of Kubernetes clusters will be supported for lifecycle management. ## Support for Parallel Execution with Rafay's Integrated GitOps Pipeline At Rafay, we are continuously evolving our platform to deliver powerful capabilities that streamline and accelerate the software delivery lifecycle. One such enhancement is the recent update to our **GitOps pipeline engine**, designed to optimize execution time and flexibility — enabling a better experience for platform teams and developers alike. ### Integrated Pipeline for Diverse Use Cases Rafay provides a tightly integrated pipeline framework that supports a range of common operational use cases, including: - **System Synchronization**: Use Git as the single source of truth to orchestrate controller configurations - **Application Deployment**: Define and automate your app deployment process directly from version-controlled pipelines - **Approval Workflows**: Insert **optional approval gates** to control when and how specific pipeline stages are triggered, offering an added layer of governance and compliance This comprehensive design empowers platform teams to standardize delivery patterns while still accommodating organization-specific controls and policies. ### From Sequential to Parallel Execution with DAG Support Historically, Rafay’s GitOps pipeline executed all stages **sequentially**, regardless of interdependencies. While effective for simpler workflows, this model imposed time constraints for more complex operations. With our latest update, the pipeline engine now supports **Directed Acyclic Graphs (DAGs)** — allowing stages to execute **in parallel**, wherever dependencies allow. ## Important Update: Changes to Bitnami Public Catalog Recently, Bitnami announced significant changes to its container image distribution [here](https://github.com/bitnami/charts/issues/35164). As part of this update, the **Bitnami public catalog (`docker.io/bitnami`) will be permanently deleted on September 29th**. ### What’s Changing - All existing container images (including older or versioned tags such as `2.50.0`, `10.6`, etc.) will be moved from the public catalog (`docker.io/bitnami`) to a **Bitnami Legacy repository (`docker.io/bitnamilegacy`)**. - The **legacy catalog will no longer receive updates or support**. It is intended **only as a temporary migration solution** to give users time to transition. ## Simplifying Day-2 Operations with Agent Pools Implementing Day-2 Operations such as **agent replacement** is cumbersome today because every configuration tied to a previous agent must be reconfigured manually. This makes tasks like scaling, retiring agents, or handling failures both error-prone and time-consuming. To address this pain point, we are introducing the concept of an **Agent Pool**. ### Why Agent Pools? Instead of binding configurations directly to individual agents, customers can now attach multiple agents to a shared **Agent Pool**. Configurations such as **Environment Templates** and **Resource Templates** reference the pool, rather than a single agent. This simple shift brings significant operational benefits: - **Seamless Failover and Replacement**: Add or remove agents from a pool without reconfiguring existing associations. - **Simplified Day-2 Operations**: Manage scaling, upgrades, and retirements without disruption. - **Load Balancing**: Distribute load across multiple agents within a pool for higher availability and performance. ## Enable Dynamic Resource Allocation (DRA) in Kubernetes In the previous [**blog**](https://docs.rafay.co/blog/2025/08/23/introduction-to-dynamic-resource-allocation-dra-in-kubernetes/), we introduced the concept of Dynamic Resource Allocation (DRA) that just went GA in [Kubernetes v1.34](https://kubernetes.io/blog/2025/08/27/kubernetes-v1-34-release/) which was released in August 2025. In this blog post, we’ll will configure DRA on a Kubernetes 1.34 cluster. Info We have optimized the steps for users to experience this on their macOS or Windows laptops in less than 15 minutes. The steps in this blog are optimized for macOS users. ## NVIDIA Performance Reference Architecture: An Introduction Artificial intelligence (AI) and high-performance computing (HPC) workloads are evolving at unprecedented speed. Enterprises today require infrastructure that can scale elastically, provide consistent performance, and ensure secure multi-tenant operation. NVIDIA’s Performance Reference Architecture (PRA), built on HGX platforms with Shared NVSwitch GPU Passthrough Virtualization, delivers precisely this capability. This is the introductory blog in a multi part series. In this blog, we explain **why PRA is critical** for modern enterprises and service providers, highlight the benefits of adoption, and outline the key steps required to successfully deploy and support the PRA design/architecture. ## Deep Dive into `nvidia-smi`: Monitoring Your NVIDIA GPU with Real Examples Whether you're training deep learning models, running simulations, or just curious about your GPU's performance, **`nvidia-smi`** is your go-to command-line tool. Short for **NVIDIA System Management Interface**, this utility provides essential real-time information about your NVIDIA GPU’s health, workload, and performance. In this blog, we’ll explore what `nvidia-smi` is, how to use it, and walk through a **real output** from a system using an **NVIDIA T1000 8GB GPU**. ### What is `nvidia-smi`? `nvidia-smi` is a CLI utility bundled with the NVIDIA driver. It enables: - Real-time **GPU monitoring** - **Driver and CUDA version** discovery - Process visibility and control - GPU configuration and performance tuning You can execute it using: ``` nvidia-smi ```