Containers

In this post, we demonstrate how to use a Raspberry Pi 5 as an Amazon EKS hybrid node to process edge workloads while maintaining cloud connectivity. We show how to set up an EKS cluster that connects cloud and edge infrastructure, secure connectivity using WireGuard VPN, enable container networking with Cilium, and implement a real-world IoT application using an ultrasonic sensor that demonstrates edge-cloud integration.

In this post, we explore the migration path from AWS CodeDeploy to Amazon ECS for blue/green deployments, discussing key architectural differences and implementation considerations. We examine three different migration approaches – in-place update, new service with existing load balancer, and new service with new load balancer – along with their respective trade-offs in terms of complexity, risk, downtime, and cost.

In this post, we explore how Amazon ECS’s native support for blue/green deployments can be extended using lifecycle hooks to integrate test suites, manual approvals, and metrics into deployment pipelines.

In this post, we introduce an automated, GitOps-driven approach to resource optimization in Amazon EKS using AWS services such as Amazon Managed Service for Prometheus and Amazon Bedrock. The solution helps optimize Kubernetes resource allocation through metrics-driven analysis, pattern-aware optimization strategies, and automated pull request generation while maintaining GitOps principles of collaboration, version control, and auditability.

In this post, we explore how to build highly available Kubernetes applications using Amazon EKS Auto Mode by implementing critical features like Pod Disruption Budgets, Pod Readiness Gates, and Topology Spread Constraints. Through various test scenarios including pod failures, node failures, AZ failures, and cluster upgrades, we demonstrate how these implementations maintain service continuity and maximize uptime in EKS Auto Mode environments.

In this post, we show you how to swiftly deploy inference workloads on EKS Auto Mode and demonstrate key features that streamline GPU management. We walk through a practical example by deploying open weight models from OpenAI using vLLM, while showing best practices for model deployment and maintaining operational efficiency.

In this post, we demonstrate how to utilize the Kubecost Amazon EKS add-on to reduce infrastructure costs and enhance Kubernetes efficiency through Container Request Right Sizing, which helps identify and fix inefficient container resource configurations. We explore how to review Kubecost’s right sizing recommendations and implement them through either one-time updates or scheduled automated resizing within Amazon EKS environments for continuous resource optimization.

In this post, we explore how Amazon EC2 P6e-GB200 UltraServers are transforming distributed AI workload through seamless Kubernetes integration, featuring NVIDIA GB200 Grace Blackwell architecture that enables memory-coherent domains of up to 72 GPUs. The post demonstrates how Dynamic Resource Allocation (DRA) on Amazon EKS enables sophisticated GPU topology management and cross-node GPU communication through IMEX channels, making it possible to efficiently train and deploy trillion-parameter AI models at scale.

In this post, we demonstrate how to generate comprehensive reports for Amazon ECR repositories that include cost breakdowns, usage metrics, security scan results, and compliance status across all repositories. The solution provides two types of reports: a Repository Summary report containing attributes for tracking and optimizing cost, usage, and OS vulnerabilities, and an Image-Level report for detailed analysis of specific repository images.

In this post, we explore how SOCI Parallel Pull Mode transforms container image pulls through configurable parallelization strategies, addressing performance bottlenecks in both download and unpacking phases. The solution demonstrates significant improvements in pull times, showing nearly 60% acceleration when tested with a 10GB Deep Learning Container image, making it particularly valuable for AI/ML workloads with large, complex images.