Machine learning models are becoming more advanced, requiring significant computing power for efficient training. Many organizations are using GPU-accelerated Kubernetes clusters for training and inference. Monitoring GPU usage is essential for optimizing performance and infrastructure utilization. Understanding how models utilize GPU resources over time is crucial for cluster optimization. Machine learning experts need observability solutions to monitor GPU and Elastic Fabric Adapter (EFA) metrics to correlate model behavior and infrastructure performance.
In the past, organizations had to install multiple agents and create custom dashboards for monitoring GPUs and EFAs. Amazon CloudWatch now offers Container Insights for Amazon EKS to monitor NVIDIA GPUs and EFAs automatically. It collects critical health and performance metrics, making them available on curated dashboards. By using Container Insights, users can monitor GPU temperature, utilization, memory utilization, and more.
Furthermore, EFA metrics help evaluate inter-node communication during distributed model training. Container Insights leverages file system counter metrics to gather and share EFA metrics with CloudWatch. Understanding EFA metrics helps monitor traffic impact and latency-sensitive training jobs.
To set up GPU and EFA monitoring on Amazon EKS, users can follow a step-by-step process. This involves creating an Amazon EKS cluster with GPU support, deploying GPU load using the “gpuburn” utility, and generating EFA traffic through container images with EFA software. Container Insights provides detailed views of GPU and EFA metrics at the cluster, node, pod, container, and GPU device levels. Users can analyze aggregated metrics and drill down to find issues and optimize resource allocation efficiently.
Container Insights dashboards offer out-of-the-box visualizations for GPU and EFA metrics. Users can easily monitor resource consumption and optimize allocation for machine learning workloads. The dashboards provide a unified view of cluster health and infrastructure metrics required to optimize GPU and EFA performance.
In conclusion, setting up observability for GPU workloads in an accelerated compute environment on Amazon EKS is essential for optimizing performance. By leveraging Container Insights with CloudWatch, users can gain detailed visibility into GPU and EFA metrics at various levels. This allows for better optimization, troubleshooting, and performance monitoring of machine learning workloads.
Article Source
https://aws.amazon.com/blogs/mt/gain-operational-insights-for-nvidia-gpu-workloads-using-amazon-cloudwatch-container-insights/
