Administer Kubernetes environments: Worked Example — Administration (NVIDIA-Certified Professional: AI Operations)

{ "title": "NVIDIA-Certified Professional: AI Operations – Administer Kubernetes Environments: Worked Example", "category": "NVIDIA-Certified Professional: AI Operations", "hashtags": "NVIDIA, AIOperations, Kubernetes, MIG, RunAI", "content": "

Administering Kubernetes Environments: A Worked Example for NVIDIA-Certified Professional: AI Operations

In the NVIDIA-Certified Professional: AI Operations exam, administration of Kubernetes environments is a critical skill. This worked example demonstrates the step-by-step process to administer a Kubernetes cluster optimized for AI workloads, focusing on GPU resource management, including Multi-Instance GPU (MIG) configuration, and integration with Run:AI platform.

Scenario

You are tasked with deploying and managing an AI workload on a Kubernetes cluster equipped with NVIDIA GPUs. The cluster must efficiently allocate GPU resources using MIG, ensure proper scheduling of AI jobs, and integrate with Run:AI for workload orchestration.

Step 1: Verify Kubernetes Cluster and NVIDIA GPU Setup

• Ensure the Kubernetes cluster is up and running with nodes that have NVIDIA GPUs installed.
• Install the NVIDIA device plugin for Kubernetes to expose GPUs to the cluster. This plugin enables Kubernetes to schedule GPU resources.

Command example:

kubectl apply -f https://raw.githubusercontent.com/NVIDIA/k8s-device-plugin/v0.11.0/nvidia-device-plugin.yml

Step 2: Configure Multi-Instance GPU (MIG)

To maximize GPU utilization, configure MIG on supported NVIDIA GPUs (e.g., A100). MIG partitions a single GPU into multiple isolated instances.

• Enable MIG mode on each GPU node by running:

sudo nvidia-smi -i GPU_ID -mig 1

• Create MIG instances as needed. For example, to create a 1g.5gb instance:

sudo nvidia-smi mig -cgi 19,19,19 -C

• Restart the NVIDIA device plugin to detect MIG instances as separate resources.

Step 3: Label Nodes for GPU and MIG Resources

Label each node to indicate available GPU and MIG resources for Kubernetes scheduling.

kubectl label nodes NODE_NAME nvidia.com/gpu.present=true

For MIG, custom labels or extended resources can be used to represent MIG instances.

Step 4: Deploy AI Workload with GPU and MIG Resource Requests

Create a Kubernetes pod specification that requests GPU resources. For MIG, specify the exact MIG resource profile if supported.

apiVersion: v1 kind: Pod metadata: name: ai-workload spec: containers: - name: ai-container image: ai-image:latest resources: limits: nvidia.com/gpu: 1 # Request one GPU or MIG instance

Step 5: Integrate with Run:AI Platform

Run:AI provides advanced scheduling and resource orchestration for AI workloads.

• Install Run:AI agents on the Kubernetes cluster.
• Configure Run:AI to recognize GPU and MIG resources.
• Submit AI jobs through Run:AI CLI or UI, which schedules jobs considering GPU availability and priorities.

Step 6: Monitor and Troubleshoot

• Use kubectl commands to monitor pod status and GPU allocation.
• Use nvidia-smi on nodes to verify MIG instance usage.
• Check Run:AI dashboard for job queue and resource utilization.

Worked Example Summary

Problem: Deploy an AI training job on a Kubernetes cluster with NVIDIA A100 GPUs using MIG and Run:AI.

Solution steps:

1. Verify Kubernetes and GPU setup; install NVIDIA device plugin.
2. Enable MIG mode and create MIG instances on GPU nodes.
3. Label nodes to reflect GPU/MIG resources.
4. Create pod spec requesting GPU/MIG resources.
5. Deploy Run:AI agents and submit jobs via Run:AI.
6. Monitor GPU usage and job status with kubectl, nvidia-smi, and Run:AI tools.

This approach ensures efficient GPU utilization, workload orchestration, and scalability for AI operations in Kubernetes environments.

" }

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