Enhancing and monitoring network performance when running ML Inference on Amazon EKS

This article demonstrates how to use Container Network Observability in Amazon EKS to monitor and optimize ML inference workloads, using a Stable Diffusion image generation example.

• Container Network Observability enables visualization of pod communication and network performance metrics
• Features include Service Map for traffic visualization, Flow Table for detailed metrics, and Performance Metrics collection
• Network Flow Monitor Agent runs as DaemonSet on worker nodes, exposing metrics in Open Metrics format
• Integrates with Amazon Managed Prometheus and Grafana for custom observability stack integration
• Identifies network bottlenecks like bandwidth limits causing packet retransmissions during model weight downloads
• Helps troubleshoot latency issues by correlating network health with inference performance metrics
• Supports topology-aware routing optimization and inter-AZ traffic pattern analysis

Container Network Observability provides Kubernetes-enriched network insights for ML inference workloads, enabling data-driven optimization and faster troubleshooting of network-related performance issues.