NVIDIA开源Kubernetes工作负载描述组件Workload Map
NVIDIA在一篇技术博客文章中介绍了Workload Map。该开源组件采用Apache 2.0许可证，为不同框架中的Kubernetes工作负载结构提供可复用的声明式描述。同一个JobSet定义可用于工作负载检查、成组调度（gang scheduling）、状态解释、提交、变更及平台可见性。NVIDIA表示，KAI Scheduler将Workload Map作为没有原生插件的工作负载类型的回退方案，NVIDIA Run:ai已在生产环境中使用该组件，Elotl Nova则在多集群调度中使用它来解析工作负载结构和资源需求。
这种通用描述模型使调度器、控制器和平台无需维护独立的框架专用解析代码，即可解释受支持的Kubernetes工作负载类型。NVIDIA还表示，Elotl Nova通过使用预定义的工作负载描述，可减少框架专用解析逻辑的维护。
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.
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