By Alon Lavian
Publication Date: 2026-11-02 16:00:00
NVIDIA DSX AI Factory Platform brings together technologies to help the ecosystem design, build, and operate more efficient AI factories, including open source, modular software components built to support multi-tenant operations and scaling.
Operating an AI factory requires a shared understanding of distributed workloads: their components, pod templates, replica counts, and status. On Kubernetes, each framework encodes that information differently. JobSet, LeaderWorkerSet, and other custom resources often require separate adapters before a tool can interpret and manage them.
NVIDIA Workload Map provides a common description of Kubernetes workload structure that users can reuse across frameworks. As part of the NVIDIA DSX AI Factory Platform, Workload Map enables integrated schedulers, controllers, and platforms to interpret supported workload types without maintaining separate framework-specific parsing code.
This post shows how to describe a distributed JobSet workload once, then reuse that definition for workload inspection, gang scheduling, and platform visibility.
Why every workload type needs its own adapter
On Kubernetes, a real workload is rarely a single pod. A distributed training job or a multi-node inference server is a small tree of resources: groups of pods, each with a role, a replica count, a pod template, and a status that depends on the workload type.
Teams building schedulers, controllers, dashboards, or cost tools that must work…
