Monitor TAS and gang scheduling for AI training in Kubernetes

Distributed AI training workloads challenge Kubernetes’s default pod-by-pod scheduling model because workers require both low-latency topology-aware placement and simultaneous startup to avoid communication bottlenecks and idle GPU capacity. The post describes how Kueue provides admission control and topology-aware scheduling by assigning jobs to labeled hardware domains such as hosts, racks, or blocks, while the Coscheduling plugin implements gang scheduling by reserving resources until a job’s required set of pods can bind together. It explains how cluster topology affects GPU communication through NVLink, PCIe, and network fabrics, with cross-rack placement often increasing latency and reducing training throughput, especially for large distributed jobs. Effective validation and troubleshooting require correlating Kueue admission and eviction metrics, PodGroup gang states, workload topology assignments, GPU and network bandwidth, and framework-level throughput metrics. Datadog’s Kubernetes and GPU monitoring tools are presented as a way to centralize these signals, detect queueing, placement, hardware, or assembly issues, and retain diagnostic data for frameworks such as Ray, PyTorch, Kubeflow, and Horovod. Kubernetes is also adding native gang scheduling, workload-aware preemption, and topology-aware scheduling capabilities in versions 1.35 through 1.37, but these remain early-stage or disabled by default, leaving Kueue and Coscheduling as the described production approach.

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