Administer Kubernetes environments: Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)

Administer Kubernetes Environments — Quick Reference

This quick reference provides essential facts and best practices for administering Kubernetes environments within NVIDIA AI Operations, focusing on managing AI workloads efficiently.

Key Concepts

• Kubernetes Cluster: A set of nodes that run containerized applications, managed by a control plane.
• Nodes: Worker machines (physical or virtual) where pods run.
• Pods: The smallest deployable units, encapsulating one or more containers.
• Namespaces: Logical partitions within a cluster to isolate resources.
• Deployments: Manage stateless applications by defining desired state and scaling.
• StatefulSets: Manage stateful applications requiring stable identities and storage.
• ConfigMaps and Secrets: Store configuration data and sensitive information separately from container images.

Administration Essentials

• Cluster Setup: Use NVIDIA GPU Operator to automate GPU driver and runtime installation for GPU nodes.
• Resource Management: Define resource requests and limits for GPU, CPU, and memory to optimize workload scheduling.
• Scheduling: Use nodeSelector, taints, and tolerations to assign AI workloads to GPU-enabled nodes.
• Multi-Instance GPU (MIG) Configuration: Enable MIG on supported GPUs to partition a single GPU into multiple instances for workload isolation and efficiency.
• Monitoring: Integrate NVIDIA DCGM Exporter and Prometheus for GPU metrics monitoring within Kubernetes.
• Security: Use Role-Based Access Control (RBAC) to restrict permissions and secure cluster operations.

Common Commands

• kubectl get nodes — List all nodes and their status.
• kubectl describe node NODE_NAME — Detailed info about a node, including GPU resources.
• kubectl get pods -n NAMESPACE — List pods in a namespace.
• kubectl apply -f FILE.yaml — Deploy or update resources from a YAML file.
• kubectl logs POD_NAME — View logs for troubleshooting.
• kubectl top nodes and kubectl top pods — Show resource usage.

Best Practices

• Use NVIDIA GPU Operator: Simplifies deployment and management of GPU drivers, device plugin, and monitoring tools.
• Namespace Segmentation: Isolate AI workloads by namespaces to improve resource control and security.
• Automate Scaling: Use Horizontal Pod Autoscaler (HPA) with custom metrics for AI workload demand.
• Enable MIG: Partition GPUs for multi-tenant environments to maximize utilization and workload isolation.
• Regular Updates: Keep Kubernetes, NVIDIA drivers, and GPU Operator components up to date for security and performance.

Troubleshooting Tips

• Check GPU visibility with nvidia-smi inside GPU-enabled pods.
• Verify device plugin logs for errors: kubectl logs -n kube-system -l name=nvidia-device-plugin-daemonset.
• Confirm MIG configuration using nvidia-smi -L on nodes.
• Monitor pod events for scheduling issues: kubectl describe pod POD_NAME.

References

• NVIDIA Kubernetes Documentation
• NVIDIA GPU Operator Guide
• Kubernetes Concepts
• NVIDIA MIG User Guide

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