## 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 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 ``` ## Introduction to Dynamic Resource Allocation (DRA) in Kubernetes In the previous [**blog**](https://docs.rafay.co/blog/2025/08/20/rethinking-gpu-allocation-in-kubernetes/), we reviewed the limitations of Kubernetes GPU scheduling. These often result in: 1. **Resource fragmentation** – large portions of GPU memory remain idle and unusable. 2. **Topology blindness** – multi-GPU workloads may be scheduled suboptimally. 3. **Cost explosion** – teams overprovision GPUs to work around scheduling inefficiencies. In this post, we’ll look at how a new GA feature in [Kubernetes v1.34](https://kubernetes.io/blog/2025/07/28/kubernetes-v1-34-sneak-peek/) — **Dynamic Resource Allocation (DRA)** — aims to solve these problems and transform GPU scheduling in Kubernetes. ## Rethinking GPU Allocation in Kubernetes Kubernetes has cemented its position as the de-facto standard for orchestrating containerized workloads in the enterprise. In recent years, its role has expanded beyond web services and batch processing into one of the most demanding domains of all: **AI/ML workloads**. Organizations now run everything from lightweight inference services to massive, distributed training pipelines on Kubernetes clusters, relying heavily on GPU-accelerated infrastructure to fuel innovation. **But there’s a problem**. In this blog, we will explore why the current model falls short, what a more advanced GPU allocation approach looks like, and how it can unlock efficiency, performance, and cost savings at scale. ## Enhancing Namespace Chargeback Reports with Custom Label-Based Metadata in Rafay In the world of **FinOps**, precise cost allocation is more than just a “nice to have”, it’s the foundation for accurate chargeback, accountability, and informed decision-making. With Rafay’s latest release, **Chargeback Summary Reports** aggregated by **namespace** now support **custom label-based metadata enrichment**. This enhancement empowers FinOps teams to add business-relevant metadata (like `team` or `cost_center`) directly into their cost reports making it easier to trace expenses to the right owners and justify resource consumption. ### Why This Matters for FinOps In large, multi-tenant Kubernetes environments, namespaces often represent workloads owned by different teams, applications, or business units. Without enriched metadata, a FinOps practitioner might see “Namespace A” incurring costs, but need extra steps to figure out which **team** or **cost center** is responsible. Now, you can define **specific label keys** (e.g., `team`, `cost_center`) in the **chargeback report configuration**, and Rafay will automatically include them as **additional columns** in the report—populated with values from the namespace labels. This directly embeds organizational context into your cost visibility.