Recently, Bitnami announced significant changes to its container image distribution here. As part of this update, the Bitnami public catalog (docker.io/bitnami) will be permanently deleted on September 29th.
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.
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.
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.
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.
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.
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.
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.
Note:
This enhancement applies to namespace-based aggregation in chargeback reports (not namespace-label-based aggregation). This is because if a primary label value (e.g., cost_center) is the same across multiple namespaces but secondary label values (e.g., team) differ, the report will not be able to aggregate on primary labels in such cases.
Modern enterprises rarely run applications in a single cluster. A production fleet might include on-prem clusters in Singapore and London, a regulated environment in AWS us-east-1, and a developer sandbox in someone’s laptop. GitOps with Argo CD is the natural way to keep all those clusters in the desired state—but the moment clusters live in different security domains (fire-walled data centers, private VPCs, or even air-gapped networks) the simple argocd cluster add story breaks down:
Bespoke bastion hosts or VPN tunnels for every hop
Long-lived bearer-token Secrets stashed in Argo’s namespace
High latency between the GitOps engine and far-flung clusters, turning reconciliations into a slog
Rafay’s Zero-Trust Kubectl Access (ZTKA) solves all three problems in one stroke. By front-loading the connection with a hardened Kube API Access Proxy—and issuing just-in-time (JIT), short-lived ServiceAccounts inside every cluster.
In this blog, we will describe how Rafay Zero Trust Kubectl Access Proxy gives Argo CD a secure path to every cluster in the fleet, even when those clusters sit deep behind corporate firewalls.
When developers are halfway around the world from their clusters, every kubectl get pods can feel like it’s moving through molasses. Rafay’s Zero-Trust Kubectl (ZTKA) service fixes the security risks and the lag by adding a network of regional proxies between the user and the cluster.
Zero-Trust Kubectl in a Nutshell
Rafay ZTKA routes all CLI and web-terminal traffic through its Kube API Access Proxy. The key design goals are:
Friction-free for users (“vanilla kubectl”),
Zero infrastructure to manage for platform teams,
Centralized RBAC + audit, and “great performance” even for clusters behind firewalls. 
Under the hood, users authenticate to Rafay; Rafay spins up just-in-time service accounts inside the target cluster and tears them down after idle timeouts, eliminating credential sprawl.
Many organizations typically rely on pull-based GitOps tools (e.g. Argo CD) to detect and remediate drift on their Kubernetes clustes. This approach allows clusters to diverge before reconciling them on the next polling interval. For the last 4 years, Rafay customers have benefited from an architecturally different approach that focuses on true drift prevention, backed by robust detection capabilities across both cluster blueprints and application workloads.
Info
In a previous blog, we discussed how ArgoCD's reconcilation works and its best practices.
ArgoCD is a powerful GitOps controller for Kubernetes, enabling declarative configuration and automated synchronization of workloads. One of its core functions is reconciliation, a continuous process by which ArgoCD ensures that the live state of a Kubernetes cluster matches the desired state defined in a Git repository.
While this might sound straightforward, reconciliation plays a critical role in the GitOps lifecycle, and its default behavior can be surprisingly aggressive. In this blog post, we’ll explore:
What reconciliation in ArgoCD actually does
Why it exists and how it ensures cluster integrity
The pitfalls of the default timer
Best practices for tuning reconciliation to balance responsiveness and resource efficiency
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In a related blog, we describe how customers using Rafay are able to Block Drift in the first place.
In the modern era of containerized machine learning and AI infrastructure, GPUs are a critical and expensive asset. Kubernetes makes scheduling and isolation easier—but managing GPU utilization efficiently requires more than just assigning something like
nvidia.com/gpu:1
In this blog post, we will explore what custom GPU resource classes are, why they matter, and when to use them for maximum impact. Custom GPU resource classes are a powerful technique for fine-grained GPU management in multi-tenant, cost-sensitive, and performance-critical environments.
