Nvidia has released KAI Scheduler as an open-source graphics processing unit management tool for Kubernetes, aimed at optimizing AI workloads. It manages dynamic GPU demands, reducing wait times and providing resource guarantees. The tool supports the entire AI lifecycle, allowing seamless transitions from light to heavy GPU usage without manual adjustments. With features like continuous fair-share recalculation and gang scheduling, KAI Scheduler improves resource allocation and fairness across applications, ultimately enhancing performance for machine learning engineers within Kubernetes clusters.
You might need only one GPU for interactive work (for example, for data exploration) and then suddenly require several GPUs for distributed training or multiple experiments... Traditional schedulers struggle with such variability.
KAI Scheduler continuously recalculates fair-share values, and adjusts quotas and limits in real time, automatically matching the current workload demands. This dynamic approach helps ensure efficient GPU allocation without constant manual intervention from administrators.
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