Adobe Firefly: Simplified observability with Amazon Managed Prometheus

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Adobe has used Amazon Web Services (AWS) since 2008. Adobe Firefly powers creative features across applications including Photoshop and Illustrator. Adobe operates a GPU-based training infrastructure built on Amazon Elastic Kubernetes Service (Amazon EKS) to support Firefly. The infrastructure enables teams to run model training jobs across thousands of compute nodes and GPUs, designed to scale with growing demand. The team initially relied on a self-hosted Prometheus infrastructure, sending data to a remote endpoint for long-term retention. As Firefly’s adoption increased and training jobs scaled, Adobe needed an observability solution that could deliver fast query performance over large metric volumes, remain highly available and scalable, and give infrastructure users self-service access to the infrastructure metrics they need to monitor and troubleshoot training jobs independently. This post describes how Adobe evolved its observability architecture — from a self-managed Prometheus deployment for in-cluster metrics to Amazon Managed Service for Prometheus for critical metrics — and the measurable improvements in query performance, infrastructure reliability, and scale. The challenge: GPU observability at scale Monitoring GPU-based training infrastructure presents unique challenges that differ from traditional application monitoring. GPU training clusters generate high-cardinality telemetry across multiple dimensions like GPU health and performance metrics, compute and memory metrics and more. Unlike CPU workloads where a single utilization metric may suffice, GPU …

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