Monitoring ML Chips in Amazon EKS Cluster with Open Source Observability Pattern for AWS Inferentia

Machine learning (ML) is a rapidly evolving field, with recent developments leading to the creation of increasingly large models that require massive computational resources for training and inference. This has led to the need for advanced observability tools to monitor model performance and optimize resource usage. In distributed environments with ML chips like AWS Inferentia, observability becomes crucial for fine-tuning models and reducing costs.

The Open Source Observability pattern for AWS Inferentia provides a solution for monitoring ML chips in an Amazon EKS cluster. By deploying Amazon EKS with node groups containing Inf1 instances, and using the NeuronX runtime, users can access ML chips from Kubernetes with the AWS Neuron device plugin. Metrics are collected by the neuron-monitor DaemonSet and exposed to Amazon Managed Service for Prometheus for storage and visualization in Amazon Managed Grafana.

The AWS CDK Observability Accelerator offers a set of reusable patterns for setting up observability in Amazon EKS clusters. By deploying the open source observability pattern for AWS Inferentia, users can monitor the performance of ML chips and optimize resource allocation for ML workloads. The solution architecture diagram illustrates how the components work together to collect and visualize metrics from ML chips.

Setting up the environment and deploying the solution involves configuring AWS CDK context, bootstrapping the CDK environment, and deploying the pattern using the provided commands. Users can validate the solution by checking the running DaemonSets and executing commands to view Neuron devices and cores. The Grafana Neuron dashboard provides a visual representation of the collected metrics for monitoring ML chip performance.

In conclusion, the Open Source Observability pattern for AWS Inferentia enables users to introduce observability into EKS clusters with Inf1 instances using open source tools. By following the steps outlined in this post, users can monitor and optimize the performance of ML chips and improve infrastructure efficiency. Exploring additional observability patterns in the AWS Observability Accelerator for CDK GitHub repo can provide further insights into enhancing ML chip monitoring and capacity planning. For more information on Neuron devices, users can refer to the AWS Neuron Documentation.

About the author, Riccardo Freschi is a Senior Solutions Architect at AWS specializing in application modernization. With a focus on helping partners and customers transform their IT landscapes on the AWS Cloud, Riccardo brings expertise in refactoring existing applications and building new ones. His work in the field of ML observability showcases the importance of monitoring and optimizing ML workloads in distributed environments with advanced computational resources.