Solution overview
This lab provides an intensive, hands-on deep dive into the NVIDIA GPU Operator and Multi-Instance GPU (MIG) partitioning, designed to bridge the gap between standard Kubernetes administration and high-performance AI/ML infrastructure. It transforms a single physical GPU into a flexible, multi-tenant resource, ensuring engineers can maximize hardware utilization while maintaining strict performance isolation.
What This Lab Offers
End-to-End GPU Lifecycle Management: Participants move from raw infrastructure to a fully automated GPU stack using the NVIDIA GPU Operator to manage drivers, container toolkits, and device plugins.
Hardware-Level Partitioning (MIG): Practical experience in reconfiguring A100/H100 GPUs into up to seven independent hardware instances, providing dedicated compute and memory for varied workloads.
Production-Ready Workload Patterns: Hands-on deployment of PyTorch batch jobs and persistent inference services, complete with resource limit optimization and node targeting.
Infrastructure-as-Code Mastery: Usage of Helm and Kubernetes Custom Resources (ClusterPolicy) to define and enforce GPU configurations across a cluster.