🤖 AI & ML Recipes

Run AI and ML workloads on Kubernetes with GPU scheduling, NVIDIA KAI Scheduler, model serving frameworks, and distributed training patterns.

5 recipes available

Deploy KAI Scheduler for optimized GPU resource allocation in Kubernetes AI/ML clusters with hierarchical queues and batch scheduling

Configure hierarchical queues in KAI Scheduler for multi-tenant GPU clusters with quotas, limits, and Dominant Resource Fairness (DRF)

Maximize GPU utilization using KAI Scheduler's GPU sharing, fractional GPUs, and intelligent bin packing strategies

Implement gang scheduling for distributed training jobs using KAI Scheduler PodGroups to ensure all-or-nothing pod scheduling

Optimize GPU workload placement using KAI Scheduler's Topology-Aware Scheduling (TAS) for NVLink, NVSwitch, and disaggregated serving architectures

Our book includes an entire chapter dedicated to ai & ml with dozens more examples.