Deploy containers from NGC: Quick Reference — Workload Management (NVIDIA-Certified Professional: AI Operations)

Deploy Containers from NGC — Quick Reference

This quick reference covers the essential facts and steps for deploying containers from the NVIDIA GPU Cloud (NGC) registry, a key skill for the NVIDIA-Certified Professional: AI Operations certification under Workload Management.

Key Definitions

• NGC (NVIDIA GPU Cloud): A comprehensive catalog of GPU-optimized containers, pre-trained models, and AI resources.
• Container: A lightweight, standalone, executable package that includes everything needed to run a piece of software.
• Registry: A repository where container images are stored and distributed, such as NGC.

Prerequisites

• Access to an NGC account with API key for authentication.
• Docker or compatible container runtime installed on the deployment system.
• Configured Kubernetes cluster or local environment for container deployment.

Authentication and Setup

• Obtain your NGC API key from the NGC website.
• Log in to NGC container registry:docker login nvcr.io and enter your API key as the password.
• Verify access by listing available containers:docker pull nvcr.io/nvidia/container-name:tag

Basic Deployment Commands

• Pull container image:docker pull nvcr.io/nvidia/container-name:tag
• Run container locally:docker run --gpus all -it nvcr.io/nvidia/container-name:tag
• Deploy container on Kubernetes:Use a Deployment YAML manifest referencing the NGC image:image: nvcr.io/nvidia/container-name:tag

Best Practices

• Always use the latest stable tag or specify a tested version to ensure compatibility.
• Leverage GPU resource requests and limits in Kubernetes manifests for efficient scheduling.
• Use namespaces and labels to organize deployments by team or project.
• Regularly update containers to benefit from security patches and performance improvements.

Troubleshooting Tips

• Verify network connectivity to nvcr.io when pulling images.
• Check Docker daemon and Kubernetes node GPU availability.
• Inspect container logs for runtime errors:kubectl logs <pod-name>
• Ensure correct API key permissions if authentication fails.

Additional Resources

• NGC Container Catalog
• NGC User Guide
• NVIDIA Container Toolkit Installation

More in this topic

Deploy inference workloads with Kubernetes and Run:ai — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy containers from NGC: Common Mistakes — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy training workloads with Slurm and Run:ai: Practice Questions — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy containers from NGC — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy training workloads with Slurm and Run:ai — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy training workloads with Slurm and Run:ai: Quick Reference — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy training workloads with Slurm and Run:ai: Common Mistakes — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy containers from NGC: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)Allocate resources between teams across platforms — Workload Management (NVIDIA-Certified Professional: AI Operations)Workload Management — NVIDIA-Certified Professional: AI OperationsDeploy containers from NGC: Practice Questions — Workload Management (NVIDIA-Certified Professional: AI Operations)Use system management tools for troubleshooting — Workload Management (NVIDIA-Certified Professional: AI Operations)Deploy training workloads with Slurm and Run:ai: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)