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

AI Cluster Orchestration and Job Scheduling: Quick Reference

This quick reference summarizes the essential concepts and practices for AI cluster orchestration and job scheduling within AI infrastructure, as covered in the NVIDIA-Certified Associate: AI Infrastructure and Operations exam.

Key Definitions

• AI Cluster Orchestration: Automated management and coordination of multiple AI compute nodes and resources to efficiently run AI workloads.
• Job Scheduling: The process of assigning AI training or inference jobs to available compute resources based on priority, resource requirements, and policies.

Core Objectives of AI Cluster Orchestration

• Maximize utilization of GPU and CPU resources across the cluster.
• Ensure workload isolation and resource fairness.
• Enable scalability and fault tolerance of AI workloads.
• Automate deployment, scaling, and lifecycle management of AI jobs.

Common Orchestration Tools

• Kubernetes: Widely used container orchestration platform supporting GPU scheduling via device plugins.
• NVIDIA GPU Operator: Simplifies deployment and management of GPU drivers and monitoring in Kubernetes clusters.
• Slurm: Popular HPC job scheduler adapted for AI workloads on GPU clusters.

Job Scheduling Principles

• Resource Matching: Schedule jobs only on nodes with sufficient GPU memory, compute capability, and other resources.
• Priority and Fairness: Implement priority queues and fair-share policies to balance workload demands.
• Preemption: Ability to pause or cancel lower priority jobs to free resources for higher priority tasks.
• Backfilling: Efficiently utilize idle resources by running smaller jobs ahead of queued larger jobs when possible.

GPU Monitoring Metrics Relevant to Scheduling

• GPU Utilization: Percentage of GPU compute resources actively used.
• Memory Usage: Amount of GPU memory allocated and free.
• Temperature and Power: Thermal and power status to avoid hardware throttling.
• Process Status: Active processes using GPU resources.

Considerations for Virtualized Accelerated Infrastructure

• Ensure orchestration supports GPU virtualization technologies (e.g., NVIDIA vGPU) for resource sharing.
• Account for overhead and performance impact of virtualization in scheduling decisions.
• Maintain isolation and security between virtualized workloads.

Example: Scheduling an AI Training Job on a Kubernetes Cluster

Scenario: A deep learning training job requires 2 GPUs with at least 16GB memory each.

Steps:

1. Check node labels and GPU availability using kubectl get nodes -L gpu-memory.
2. Define resource requests and limits in the job manifest specifying nvidia.com/gpu: 2 and memory requirements.
3. Submit the job to the cluster scheduler.
4. Monitor job status and GPU utilization with kubectl describe pod [pod-name] and NVIDIA GPU metrics exporter.

This ensures the job is scheduled only on nodes meeting the GPU resource criteria, optimizing cluster utilization and job performance.

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