From HPC to Edge: A Web-Based Workflow for AI Model Testing and Deployment

Abstract

Artificial intelligence (AI) workflows increasingly span heterogeneous environments, from centralized high-performance computing (HPC) systems to resource-constrained edge devices. A primary hurdle in these pipelines is the configuration mismatch between development and deployment, often requiring researchers to manually rewrite scripts for specific edge requirements. This poster demonstrates an integrated management framework—showcasing both web-based and standalone user interface (UI) elements—powered by Tapis for the systematic testing and deployment of machine learning models. Building on existing cyberinfrastructure including the ML Field Planner for configuration, PATRA for model management, and the Cyberinfrastructure Knowledge Network (CKN) for telemetry, the framework provides a unified control interface. By utilizing the ML Edge Server as a consistent, pluggable runtime across the edge-to-center continuum, the system ensures that configurations and telemetry translate directly to field execution. This demonstration shows how a unified UI-driven workflow streamlines the transition from initial evaluation to deployment, ensuring operational consistency and reproducibility without manual script porting.