An End-to-End Assurance Framework for AI/ML Workloads in Datacenters

TL;DR

This paper presents an end-to-end assurance framework for AI/ML workloads in datacenters, utilizing SaaS-based observability and automated troubleshooting across multiple system layers to improve performance diagnosis and remediation.

Contribution

It introduces a comprehensive SaaS-based system that integrates cross-layer telemetry and logs for automated root cause analysis and performance assurance of distributed AI/ML workloads.

Findings

Effective root cause analysis for AI/ML workload failures

Demonstrated use cases for end-to-end assurance

Improved troubleshooting accuracy and speed

Abstract

Modern machine learning workloads such as large language model training, fine-tuning jobs are highly distributed and span across hundreds of systems with multiple GPUs. Job completion time for these workloads is the artifact of the application, compute, network and storage performance. In case of failure or degraded performance it is imperative to understand the root cause and possible remediation for the problem for end-to-end assurance. This demo showcases SaaSbased observability and automated troubleshooting for AI/ML workload performance issues using cross-layer telemetry and logs (e.g., Application telemetry, Collective communication logs, GPU Health metrics, Network Flow Data, NIC ROCEv2 telemetry). Different use cases are demonstrated for end-to-end assurance such as Cross-layer Dependency Graph, Cross-layer Service Level Expectations, Automated Root Cause Analysis, GPU-toGPU application path tracing.