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

Deploying Inference Workloads with Kubernetes and Run:ai – Quick Reference This quick reference sheet covers the essential facts and procedures for...

Deploying Inference Workloads with Kubernetes and Run:ai – Quick Reference

This quick reference sheet covers the essential facts and procedures for deploying AI inference workloads using Kubernetes and Run:ai, key components in the NVIDIA-Certified Professional: AI Operations certification.

Key Concepts

Kubernetes: An open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.
Run:ai: A virtualization and orchestration platform designed to optimize GPU resource allocation and workload scheduling for AI workloads on Kubernetes clusters.
Inference Workloads: AI tasks that apply trained models to new data for prediction or classification, typically requiring low latency and high throughput.

Core Steps for Deploying Inference Workloads

Containerize the Inference Application: Package the inference model and runtime environment into a container image, preferably from NVIDIA GPU Cloud (NGC) containers optimized for inference.
Configure Kubernetes Deployment: Define a Kubernetes deployment YAML manifest specifying:
- Container image and version
- Resource requests and limits (GPUs, CPU, memory)
- Replica count for scaling
- Service definitions for network access
Integrate Run:ai: Use Run:ai's Kubernetes scheduler plugin to:
- Enable dynamic GPU resource virtualization and sharing
- Set priority and QoS policies for inference workloads
- Allocate GPUs efficiently across multiple teams or projects
Deploy the Workload: Apply the Kubernetes manifests with kubectl apply -f and verify pod status with kubectl get pods.
Monitor and Troubleshoot: Use Kubernetes tools (kubectl logs, kubectl describe pod) and Run:ai dashboards to monitor GPU utilization, pod health, and inference latency.

Important Commands and Tools

kubectl apply -f deployment.yaml – Deploy or update inference workload
kubectl get pods – Check pod status
kubectl logs POD_NAME – View container logs for troubleshooting
Run:ai CLI and dashboard – Manage GPU allocation and workload priorities

Best Practices

Resource Requests: Accurately specify GPU and CPU requests to avoid resource contention.
Use NGC Containers: Leverage NVIDIA-optimized inference containers for performance and compatibility.
Enable GPU Sharing: Configure Run:ai to maximize GPU utilization by sharing resources across inference workloads.
Implement Autoscaling: Use Kubernetes Horizontal Pod Autoscaler with Run:ai to scale inference pods based on demand.
Monitor Latency: Continuously monitor inference latency to ensure SLAs are met.

Summary

Deploying inference workloads with Kubernetes and Run:ai involves containerizing the model, defining Kubernetes manifests with appropriate resource specifications, leveraging Run:ai for GPU virtualization and scheduling, and monitoring the deployment for performance and reliability. Mastery of these steps is critical for efficient AI operations in NVIDIA GPU environments.

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