Deploy inference workloads with Kubernetes and Run:ai: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)

Deploying Inference Workloads with Kubernetes and Run:AI: A Worked Example In the NVIDIA-Certified Professional: AI Operations exam, workload...

Deploying Inference Workloads with Kubernetes and Run:AI: A Worked Example

In the NVIDIA-Certified Professional: AI Operations exam, workload management is a critical topic, with a significant focus on deploying inference workloads using Kubernetes and Run:AI. This worked example provides a detailed, step-by-step guide to deploying an AI inference workload in a realistic enterprise environment.

Scenario Overview

Your AI operations team needs to deploy a scalable inference service for a computer vision model that classifies images in real-time. The deployment environment uses a Kubernetes cluster integrated with Run:AI to optimize GPU resource allocation across multiple teams.

Step 1: Prepare the Inference Container

Obtain the model container: Use an NVIDIA NGC container optimized for inference, such as the TensorRT Inference Server container.
Verify container compatibility: Ensure the container supports GPU acceleration and is compatible with your Kubernetes environment.

Step 2: Configure Kubernetes Cluster for GPU Workloads

GPU node setup: Confirm that Kubernetes nodes have NVIDIA GPUs installed and the NVIDIA device plugin is deployed to expose GPUs to pods.
Namespace creation: Create a dedicated Kubernetes namespace for the inference workload to isolate resources.

Step 3: Integrate Run:AI for Resource Scheduling

Install Run:AI: Deploy the Run:AI platform components on the Kubernetes cluster to enable advanced GPU scheduling and quota management.
Define team quotas: Allocate GPU resources to your AI operations team through Run:AI’s quota system to ensure fair resource sharing.

Step 4: Create Kubernetes Deployment Manifest

Define deployment YAML: Specify the container image, GPU resource requests, and limits in the pod specification.
Annotate for Run:AI: Add Run:AI-specific annotations to the pod spec to enable Run:AI’s scheduling and priority features.
Example snippet:

{ "apiVersion": "apps/v1", "kind": "Deployment", "metadata": { "name": "inference-service", "namespace": "ai-inference" }, "spec": { "replicas": 3, "selector": { "matchLabels": { "app": "inference" } }, "template": { "metadata": { "labels": { "app": "inference" }, "annotations": { "runai.com/priority": "high", "runai.com/team": "ai-ops-team" } }, "spec": { "containers": [ { "name": "inference-container", "image": "nvcr.io/nvidia/tensorrtserver:latest", "resources": { "limits": { "nvidia.com/gpu": 1 } }, "ports": [ { "containerPort": 8000 } ] } ] } } } }

Step 5: Deploy and Monitor

Apply deployment: Use kubectl apply -f deployment.yaml to deploy the inference service.
Verify pod status: Check pods are running and GPUs are allocated using kubectl get pods -n ai-inference and Run:AI dashboard.
Monitor logs and performance: Use kubectl logs and Run:AI monitoring tools to ensure inference requests are processed efficiently.

Step 6: Troubleshooting Common Issues

Pod pending due to GPU shortage: Check Run:AI quotas and adjust team allocations or scale down other workloads.
Inference latency spikes: Analyze GPU utilization and pod resource limits; consider increasing replicas or GPU allocation.

Worked Example Summary

By following these steps, you successfully deploy a GPU-accelerated inference workload using Kubernetes and Run:AI. This approach ensures efficient resource sharing across teams, scalable inference service deployment, and integration with NVIDIA’s optimized containers from NGC.

This practical deployment scenario reflects the knowledge required for the NVIDIA-Certified Professional: AI Operations exam’s workload management section, specifically focusing on inference workload deployment.

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