Use system management tools for troubleshooting: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)

Using System Management Tools for Troubleshooting in AI Workload Management

Effective workload management in AI operations requires robust troubleshooting capabilities to ensure optimal performance and resource utilization. This worked example demonstrates how to use system management tools to diagnose and resolve issues within an NVIDIA AI infrastructure environment.

Scenario Overview

An AI operations team has deployed multiple inference workloads using Kubernetes and Run:ai on a cluster of NVIDIA GPUs. Recently, users have reported degraded inference performance and occasional job failures. The goal is to identify the root cause using system management tools and resolve the issue to restore optimal workload execution.

Step 1: Identify Symptoms and Gather Initial Data

• Monitor job status and logs via Kubernetes dashboard and Run:ai CLI to confirm failures and performance drops.
• Check for error messages or warnings related to GPU utilization or resource contention.

Reasoning: Understanding the symptoms helps focus troubleshooting on relevant system components.

Step 2: Use NVIDIA System Management Interface (nvidia-smi) to Inspect GPU Status

• Run nvidia-smi on affected nodes to check GPU utilization, temperature, memory usage, and running processes.
• Look for GPUs with high memory usage or processes stuck in an unresponsive state.

Reasoning: nvidia-smi provides real-time visibility into GPU health and workload distribution, critical for diagnosing hardware or driver-related issues.

Step 3: Analyze Kubernetes Node and Pod Metrics

• Use kubectl top nodes and kubectl top pods to view CPU and memory usage across nodes and pods.
• Identify any nodes or pods exhibiting resource saturation or anomalies.

Reasoning: Resource bottlenecks at the node or pod level can cause workload degradation or failures.

Step 4: Examine Run:ai Scheduler and Resource Allocation

• Check Run:ai dashboard or CLI to review GPU allocation and job queue status.
• Verify if resource contention or misallocation is causing delays or failures.

Reasoning: Run:ai manages GPU sharing and scheduling; misconfigurations here can impact workload stability.

Step 5: Inspect System Logs for Errors

• Access system logs on affected nodes (journalctl -u kubelet, docker logs for container runtimes).
• Look for errors related to container runtime, GPU drivers, or Kubernetes components.

Reasoning: Logs often reveal underlying issues such as driver crashes, container failures, or communication errors.

Step 6: Apply Corrective Actions

• If GPU memory leaks or stuck processes are detected, restart the affected GPU processes or nodes.
• Adjust Run:ai resource quotas or priorities to alleviate contention.
• Update GPU drivers or Kubernetes components if bugs are identified.
• Redeploy affected pods to clear transient faults.

Reasoning: Targeted remediation based on diagnostic findings restores system stability and workload performance.

Step 7: Verify Resolution

• Monitor workloads post-fix to ensure performance returns to expected levels.
• Confirm no recurring errors in system logs or Run:ai scheduler.

Reasoning: Continuous monitoring validates the effectiveness of troubleshooting and prevents future disruptions.

Summary of the Worked Example

1. Gather workload failure symptoms from Kubernetes and Run:ai interfaces.
2. Use nvidia-smi to check GPU health and utilization.
3. Analyze Kubernetes node and pod resource metrics.
4. Review Run:ai scheduler for resource allocation issues.
5. Inspect system and container logs for error messages.
6. Apply fixes such as restarting processes, adjusting quotas, or updating drivers.
7. Verify workload stability and performance after remediation.

This systematic approach leverages NVIDIA system management tools and platform-specific interfaces to efficiently troubleshoot AI workload issues, a critical skill for the NVIDIA-Certified Professional: AI Operations certification.

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