NVIDIA explains reusable workload maps

NVIDIA’s walkthrough addresses a platform problem: different AI frameworks describe their computing tasks differently. Workload Map captures their component groups, pod templates, replica counts and status in reusable definitions. Pods are the units Kubernetes runs. Inspection tools, KAI Scheduler and Run:ai can consume that description. The map supplies grouping information; KAI makes scheduling decisions, and its native plugins retain priority over this fallback.

The project documentation explains how definitions locate fields through jq expressions, which select data inside structured objects. A Go library uses them to extract information or update pod specifications, labels and annotations. For installation, the command-line tool supports Homebrew on macOS and Linux, or platform-specific release archives: check the supplied checksums and put the executable on PATH. The project explicitly does not support installing the CLI with go install. It remains in development before version one, and its API can change between minor releases.

Elotl’s July account illustrates a separate consumer: Nova uses the mapping to read resource requirements, constraints and replica counts when placing workloads across clusters. The order matters. Nova first distributes the workload framework and its resource definitions to target clusters. It then places a submitted workload resource according to policy, considering available resources when that policy requests it. After placement, the framework’s controller on the selected cluster handles the workload as usual.