Why your Kubernetes scheduler can't handle AI workloads

The text discusses the challenges of distributed training jobs in Kubernetes environments, emphasizing the limitations of the default kube-scheduler, which does not support gang scheduling or multi-node fabric topology awareness, leading to inefficiencies like partial-scheduling deadlocks. It introduces three alternative schedulers tailored for AI workloads: Kueue, KAI Scheduler, and Volcano, each offering unique strengths such as multi-tenant governance, GPU-aware resource allocation, and mature gang scheduling, respectively. Kueue, which manages job queues and quotas without replacing kube-scheduler, is best for organizations facing resource contention, while KAI Scheduler and Volcano are suited for optimizing NVIDIA GPU clusters and handling distributed training at scale. The text highlights that choosing the right scheduler depends on the specific needs of an organization, such as the type of workloads, machine learning frameworks, and cluster topology, and recommends a strategic evaluation to optimize cluster scheduling for modern AI infrastructure.

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