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Nvidia releases Workload Map to standardize AI workload parsing on Kubernetes
Nvidia introduces Workload Map, an open-source tool providing prebuilt definitions to interpret distributed AI frameworks without custom parsers.
Summary
Nvidia released Workload Map, an open-source Kubernetes component featuring a catalog of prebuilt definitions for frameworks like JobSet, PyTorchJob, and RayCluster. The tool provides a common structural description so that schedulers, controllers, and platforms can interpret distributed AI workloads without framework-specific parsing code. Developers can use the Workload Map CLI to inspect workloads or integrate the definitions into NVIDIA Run:ai and Elotl Nova.
What changed
- Nvidia released Workload Map as an open-source component available through the NVIDIA DSX AI Factory GitHub organization.
- The tool includes a prebuilt catalog for JobSet, PyTorchJob, RayCluster, RayJob, RayService, KServe, Knative, MPIJob, NVIDIA NIM, LeaderWorkerSet, Milvus, Grove PodCliqueSet, and NVIDIA Dynamo.
- The Workload Map CLI enables inspecting distributed workloads and viewing resource totals.
- Elotl Nova and NVIDIA Run:ai integrated Workload Map to interpret workload structure and resource requirements.
Availability
Available now“NVIDIA Workload Map is an open source component available through the NVIDIA DSX AI Factory GitHub organization .”
Founder takeaway
If your team runs distributed AI workloads on Kubernetes using multiple custom resource definitions, adopt Workload Map to eliminate the need for writing custom framework-specific parsing adapters.
Source
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