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Open Cluster Management (OCM) addresses the challenges of managing multiple Kubernetes distributions, providing open APIs for cluster registration, workload distribution, dynamic placement of policies, and more. The placement concept allows dynamic selection of clusters, enabling users to replicate Kubernetes resources or run advanced workloads across member clusters. For instance, as an application developer, I can deploy workloads to clusters with the most available memory and CPU. With the rise of AI technology, there's an increasing need to schedule AI workloads based on GPU/TPU resources. In this talk, we will demonstrate how to utilize the extensible placement scheduling mechanism and a GPU/TPU resource collector addon. Using an addon template, this setup can provide an AddonPlacementScore, facilitating placement decisions based on GPU/TPU resources. This approach enables OCM API consumers to intelligently schedule AI workloads to the most optimal clusters.
