AI/ML and batch workloads introduce unique scheduling challenges that go beyond simple Pod-by-Pod scheduling. In Kubernetes v1.35, we introduced the first tranche of workload-aware scheduling improvements, featuring the foundational Workload API alongside basic gang scheduling support built on a Pod-based framework, and an opportunistic batching feature to efficiently process identical Pods. Kubernetes v1.36 introduces a significant architectural evolution by cleanly separating API concerns: the Workload API acts as a static template, while the new PodGroup API handles the runtime state.
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