NVIDIA open-sources Workload Map for Kubernetes workloads
In a technical blog post, NVIDIA introduced Workload Map, an open-source component licensed under Apache 2.0 that provides a reusable declarative description of Kubernetes workload structures across frameworks. A JobSet definition can support workload inspection, gang scheduling, status interpretation, submission, mutation and platform visibility. NVIDIA says KAI Scheduler uses Workload Map as a fallback for workload types without native plugins, NVIDIA Run:ai uses it in production, and Elotl Nova uses it to interpret workload structures and resource requirements during multi-cluster scheduling.
The common description model lets schedulers, controllers and platforms interpret supported Kubernetes workload types without maintaining separate framework-specific parsing code. NVIDIA also says Elotl Nova can reduce framework-specific parsing logic by using prebuilt definitions.
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.
#NVIDIA#NVIDIA Workload Map#NVIDIA DSX AI Factory Platform#Kubernetes#JobSet#LeaderWorkerSet#KAI Scheduler#NVIDIA Run:ai#Elotl Nova