Nvidia tackles graphics processing unit hogging | Computer Weekly
Summary
The GitHub repository for KAI Scheduler said it supports the entire AI lifecycle, from small, interactive jobs that require minimal resources to large training and inference, all in the same cluster. According to Dar and Karabulut, this dynamic approach helps ensure efficient GPU allocation without constant manual intervention from administrators. To optimise for fluctuating demand of GPU and CPU resources, Dar and Karabulut said that KAI Scheduler uses what Nvidia calls bin packing and consolidation. They said this maximises compute utilisation by combating resource fragmentation, and achieves this by packing smaller tasks into partially used GPUs and CPUs. KAI Scheduler provides what Nvidia calls a built-in podgrouper that automatically detects and connects with tools and frameworks such as Kubeflow, Ray, Argo and the Training Operator, which it said reduces configuration complexity and helps to speed up development.
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