# Workload Map

NVIDIA has moved the structural description of distributed Kubernetes jobs out of adapters compiled into tools and into a readable definition. Workload Map, a custom resource plus a Go library, gives JobSet, PyTorchJob, RayCluster and other frameworks one structural view. Previously each custom resource needed its own adapter to interpret roles, turn status into phases and edit pod templates without breaking the job, so support for a new type often waited for a release. Definitions state template paths, replica counts and status mappings in YAML, shared by schedulers and platforms; a worked JobSet resolves into one leader and two workers totaling 12 GPUs.

NVIDIA has moved the structural description of distributed Kubernetes jobs out of adapters compiled into tools and into a readable definition. Workload Map, a custom resource plus a Go library, gives JobSet, PyTorchJob, RayCluster and other frameworks one structural view. Previously each custom resource needed its own adapter to interpret roles, turn status into phases and edit pod templates without breaking the job, so support for a new type often waited for a release. Definitions state template paths, replica counts and status mappings in YAML, shared by schedulers and platforms; a worked JobSet resolves into one leader and two workers totaling 12 GPUs.

NVIDIA has released the complete Run:ai platform as open source software within its DSX AI Factory platform, giving customers, developers and ecosystem partners access to production-grade AI workload and GPU orchestration. The platform previously shipped as a commercial product that dynamically allocated GPU resources on Kubernetes and managed workloads across the full lifecycle, from data processing and training to inference. The release includes four components that can also be adopted independently: KAI Scheduler, GPU Fractioning Operator, KAI Resource Management and Workload Map, so partners can take the whole platform, build on it, or integrate single pieces.

NVIDIA has released the complete Run:ai platform as open source software within its DSX AI Factory platform, giving customers, developers and ecosystem partners access to production-grade AI workload and GPU orchestration. The platform previously shipped as a commercial product that dynamically allocated GPU resources on Kubernetes and managed workloads across the full lifecycle, from data processing and training to inference. The release includes four components that can also be adopted independently: KAI Scheduler, GPU Fractioning Operator, KAI Resource Management and Workload Map, so partners can take the whole platform, build on it, or integrate single pieces.