Describe AI cluster orchestration and job scheduling: Worked Example — AI Operations (NVIDIA-Certified Associate: AI Infrastructure and Operations)

AI Cluster Orchestration and Job Scheduling: A Worked Example

In the context of the NVIDIA-Certified Associate: AI Infrastructure and Operations certification, understanding AI cluster orchestration and job scheduling is essential. This process ensures efficient utilization of AI resources, particularly GPUs, within datacenters. Below is a detailed, step-by-step worked example illustrating how to orchestrate an AI cluster and schedule jobs effectively in a realistic scenario.

Scenario Overview

An AI research team operates a cluster with 10 GPU nodes running Kubernetes with NVIDIA GPU Operator installed. They need to schedule multiple AI training jobs with varying GPU requirements and priorities. The goal is to maximize GPU utilization while respecting job priorities and resource constraints.

Step 1: Define Cluster Resources and Job Requirements

• Cluster: 10 nodes, each with 4 NVIDIA GPUs (total 40 GPUs)
• Jobs to schedule:
• Job A: Requires 8 GPUs, high priority, estimated runtime 4 hours
• Job B: Requires 12 GPUs, medium priority, estimated runtime 6 hours
• Job C: Requires 4 GPUs, low priority, estimated runtime 2 hours
• Job D: Requires 10 GPUs, high priority, estimated runtime 5 hours

Step 2: Configure Kubernetes Cluster for GPU Scheduling

The cluster uses the NVIDIA GPU Operator to manage GPU resources. Kubernetes nodes are labeled with GPU capacity, and the device plugin exposes GPUs as schedulable resources.

• Ensure nvidia.com/gpu resource is available on each node.
• Set up taints and tolerations if needed to isolate GPU workloads.
• Configure priority classes for jobs to enforce scheduling order.

Step 3: Define Job Specifications with Resource Requests and Priorities

Each job is defined as a Kubernetes Pod or Job manifest specifying:

• Number of GPUs requested using resources.requests.nvidia.com/gpu
• Priority class (e.g., high-priority, medium-priority, low-priority)
• Estimated runtime as metadata for monitoring

Step 4: Submit Jobs to the Cluster

Jobs are submitted in the order of priority:

1. Submit Job A (8 GPUs, high priority)
2. Submit Job D (10 GPUs, high priority)
3. Submit Job B (12 GPUs, medium priority)
4. Submit Job C (4 GPUs, low priority)

Step 5: Scheduler Evaluates Resource Availability and Priorities

Kubernetes scheduler evaluates the available GPUs and job priorities:

• Total GPUs: 40
• Job A + Job D require 18 GPUs (high priority)
• Job B requires 12 GPUs (medium priority)
• Job C requires 4 GPUs (low priority)

Since 18 GPUs are free, Jobs A and D are scheduled immediately.

Step 6: Job Scheduling and Resource Allocation

• Job A assigned 8 GPUs across 2 nodes (4 GPUs per node)
• Job D assigned 10 GPUs across 3 nodes (4, 4, and 2 GPUs)
• Remaining GPUs: 40 - 18 = 22 GPUs

Job B (12 GPUs) can also be scheduled immediately, leaving 10 GPUs free.

Job C (4 GPUs, low priority) is scheduled last, utilizing the remaining GPUs.

Step 7: Monitor Job Progress and GPU Utilization

Use monitoring tools such as NVIDIA DCGM and Kubernetes metrics-server to track:

• GPU utilization per node
• Job runtime progress
• Queue status for pending jobs

Step 8: Handle Job Completion and Resource Reallocation

As jobs complete, GPUs are freed and the scheduler assigns resources to pending or new jobs based on priority and availability.

Summary of Key Points

• Cluster orchestration leverages Kubernetes with NVIDIA GPU Operator for managing GPU resources.
• Job scheduling respects GPU resource requests and job priorities to optimize utilization.
• Monitoring ensures visibility into GPU usage and job status.
• Dynamic resource allocation allows efficient handling of job queues and cluster capacity.

This worked example demonstrates the practical steps involved in AI cluster orchestration and job scheduling, a critical skill area for the NVIDIA-Certified Associate: AI Infrastructure and Operations exam.

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