NVIDIA says Workload Map provides a shared way to describe Kubernetes workloads across frameworks, with support for JobSet, KAI Scheduler and Run:ai.

NVIDIA has outlined Workload Map, an open-source component in its DSX AI Factory Platform, as a way to describe Kubernetes workloads once and reuse that structure across frameworks and tools. The company said the approach is designed to reduce the need for separate framework-specific parsers and adapters when managing distributed AI jobs.

According to NVIDIA, operating an AI factory on Kubernetes requires a common understanding of distributed workloads, including pod templates, replica counts and status. The company said that workload types such as JobSet and LeaderWorkerSet often require separate custom adapters before schedulers, controllers or dashboards can interpret them. Workload Map is intended to provide a reusable structural description that can be consumed across those systems.

NVIDIA used a JobSet example to show the issue. In the manifest described in the post, a distributed training job includes one leader job and two worker jobs, with each pod requesting four GPUs. The company said the manifest represents a total request of 12 GPUs. It also said kubectl shows the JobSet, its three Jobs and their pods as separate resource entries, while a platform still needs a definition to understand the roles and aggregate the resource request.

NVIDIA said Workload Map, formerly called Karta, ships as a Kubernetes CRD and a Go library that interprets workload definitions. Each definition specifies where to find pod templates, replica counts and status fields for a particular workload type.

Source link