Describe datacenter architecture for AI workloads: Worked Example — Administration (NVIDIA-Certified Professional: AI Operations)

Datacenter Architecture for AI Workloads: Worked Example

Designing and administering a datacenter architecture optimized for AI workloads is a critical skill for the NVIDIA-Certified Professional: AI Operations certification. This example walks through a realistic scenario illustrating the step-by-step process of architecting an AI datacenter environment that supports high-performance GPU compute, efficient resource scheduling, and scalability.

Scenario

A research organization plans to deploy an AI datacenter to support multiple teams running diverse deep learning workloads. The infrastructure must maximize GPU utilization, support multi-tenant environments, and enable flexible workload scheduling. The goal is to design a scalable architecture integrating NVIDIA GPUs with Slurm cluster management, Run:AI platform for workload orchestration, Kubernetes for containerized services, and Multi-Instance GPU (MIG) configuration for workload isolation.

Step 1: Define Hardware and Network Topology

• GPU Selection: Choose NVIDIA A100 GPUs with MIG capability to partition each GPU into multiple isolated instances, enabling concurrent workloads.
• Compute Nodes: Deploy multiple compute nodes, each equipped with 8x A100 GPUs, high-speed NVLink interconnects, and sufficient CPU and memory resources.
• Networking: Use a high-bandwidth, low-latency network fabric (e.g., InfiniBand) to connect compute nodes, storage, and management servers.

Step 2: Configure Multi-Instance GPU (MIG)

• Enable MIG Mode: On each A100 GPU, activate MIG mode to partition GPUs into smaller instances (e.g., 7 MIG instances per GPU).
• Assign MIG Profiles: Define MIG profiles based on workload requirements, balancing compute power and memory allocation.
• Verify MIG Instances: Use NVIDIA System Management Interface (nvidia-smi) to confirm MIG instances are active and properly configured.

Step 3: Deploy Slurm Cluster for Resource Scheduling

• Install Slurm: Set up Slurm on all compute nodes and a dedicated controller node.
• Configure GPU Scheduling: Integrate GPU resources and MIG instances into Slurm’s resource management to allow fine-grained scheduling.
• Partitioning: Define partitions in Slurm to segregate resources for different teams or projects.

Step 4: Integrate Run:AI Platform

• Install Run:AI: Deploy Run:AI on top of the Slurm cluster to provide AI workload orchestration and scheduling with AI-specific optimizations.
• Resource Pooling: Use Run:AI to create virtual GPU pools abstracting physical GPUs and MIG instances for flexible allocation.
• Quota Management: Configure user quotas and priorities to manage multi-tenant access efficiently.

Step 5: Deploy Kubernetes for Containerized AI Services

• Set up Kubernetes Cluster: Deploy a Kubernetes cluster for hosting AI inference services, model registries, and monitoring tools.
• GPU Integration: Install NVIDIA device plugin to expose GPUs and MIG instances to Kubernetes pods.
• Namespace Isolation: Use Kubernetes namespaces and RBAC to isolate workloads and manage permissions.

Step 6: Validate and Optimize Architecture

• Performance Testing: Run representative AI training and inference workloads to validate GPU utilization and network throughput.
• Monitoring: Deploy monitoring tools (e.g., NVIDIA DCGM, Prometheus) to track GPU health, MIG instance usage, and cluster performance.
• Adjust Configuration: Tune Slurm scheduling policies, Run:AI quotas, and MIG profiles based on observed workload characteristics.

Worked Example Summary

Problem: Architect a datacenter environment to support concurrent AI workloads with efficient GPU utilization and multi-tenant scheduling.

Solution Steps:

1. Selected NVIDIA A100 GPUs with MIG to enable GPU partitioning.
2. Configured MIG instances on each GPU to isolate workloads.
3. Deployed Slurm cluster with GPU-aware scheduling.
4. Integrated Run:AI platform for AI workload orchestration and quota management.
5. Set up Kubernetes cluster with NVIDIA device plugin for containerized AI services.
6. Validated architecture through performance testing and monitoring.

This approach ensures scalable, flexible, and efficient AI datacenter operations aligned with the requirements of the NVIDIA-Certified Professional: AI Operations exam.

More in this topic

Configure Multi-Instance GPU (MIG) — Administration (NVIDIA-Certified Professional: AI Operations)Describe datacenter architecture for AI workloads: Common Mistakes — Administration (NVIDIA-Certified Professional: AI Operations)Describe datacenter architecture for AI workloads — Administration (NVIDIA-Certified Professional: AI Operations)Configure Multi-Instance GPU (MIG): Practice Questions — Administration (NVIDIA-Certified Professional: AI Operations)Administer Slurm clusters: Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)Administer Run:ai platforms — Administration (NVIDIA-Certified Professional: AI Operations)Configure Multi-Instance GPU (MIG): Worked Example — Administration (NVIDIA-Certified Professional: AI Operations)Administer Slurm clusters: Practice Questions — Administration (NVIDIA-Certified Professional: AI Operations)Configure Multi-Instance GPU (MIG): Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)Administer Slurm clusters: Common Mistakes — Administration (NVIDIA-Certified Professional: AI Operations)Administer Slurm clusters — Administration (NVIDIA-Certified Professional: AI Operations)Administration — NVIDIA-Certified Professional: AI OperationsConfigure Multi-Instance GPU (MIG): Common Mistakes — Administration (NVIDIA-Certified Professional: AI Operations)Describe datacenter architecture for AI workloads: Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)Administer Slurm clusters: Worked Example — Administration (NVIDIA-Certified Professional: AI Operations)Describe datacenter architecture for AI workloads: Practice Questions — Administration (NVIDIA-Certified Professional: AI Operations)Administer Kubernetes environments — Administration (NVIDIA-Certified Professional: AI Operations)