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

Quick Reference: Using System Management Tools for Troubleshooting in NVIDIA AI Operations

Effective troubleshooting is critical for maintaining optimal AI workload performance and reliability. This quick reference summarizes key system management tools and best practices for troubleshooting within the scope of Workload Management for the NVIDIA-Certified Professional: AI Operations certification.

1. Key System Management Tools

• nvidia-smi: Command-line utility to monitor GPU status, utilization, temperature, and memory usage. Essential for identifying hardware bottlenecks or failures.
• DCGM (Data Center GPU Manager): Provides health monitoring, diagnostics, and telemetry data for NVIDIA GPUs at scale. Useful for automated alerting and performance tracking.
• kubectl: Kubernetes CLI tool to inspect pod status, logs, and resource usage when deploying inference workloads on Kubernetes clusters.
• Run:ai CLI and Dashboard: Tools for managing AI workload scheduling, resource allocation, and monitoring across multi-tenant environments.
• Slurm commands (squeue, sinfo, scontrol): Used to monitor and manage training workloads on Slurm clusters, including job status and resource allocation.
• NGC CLI: For managing container images and deployments from NVIDIA GPU Cloud, ensuring correct container versions and dependencies.

2. Common Troubleshooting Tasks

• Check GPU health and utilization: Use nvidia-smi to verify GPU availability and detect errors or memory leaks.
• Inspect workload status: Use kubectl get pods or squeue to confirm jobs are running or identify failures.
• Analyze logs: Retrieve logs via kubectl logs or Slurm job output files to diagnose application-level issues.
• Resource contention: Monitor resource usage with DCGM or Run:ai dashboards to detect overcommitment or starvation.
• Container issues: Verify container images and versions with NGC CLI; ensure compatibility with deployed hardware and software stack.

3. Best Practices and Rules

• Regular monitoring: Schedule automated health checks using DCGM to proactively detect anomalies.
• Granular logging: Enable detailed logs for critical workloads to facilitate root cause analysis.
• Resource quotas: Use Run:ai or Slurm to enforce fair resource allocation and prevent noisy neighbor effects.
• Version control: Maintain consistent container versions via NGC to avoid environment drift.
• Collaborative troubleshooting: Share monitoring dashboards and logs across teams for faster resolution.

4. Troubleshooting Workflow Summary

1. Identify symptoms: performance degradation, job failures, or hardware alerts.
2. Gather data: use nvidia-smi, DCGM, kubectl, Slurm commands, and Run:ai tools.
3. Analyze logs and metrics to pinpoint bottlenecks or errors.
4. Apply fixes: restart pods/jobs, adjust resource allocations, update containers.
5. Verify resolution and document findings for future reference.

Mastering these system management tools and troubleshooting steps is essential for efficient workload management and maintaining NVIDIA AI infrastructure health.

More in this topic

Deploy containers from NGC: Quick Reference — Workload Management (NVIDIA-Certified Professional: AI Operations)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)Use system management tools for troubleshooting: Common Mistakes — Workload Management (NVIDIA-Certified Professional: AI Operations)Use system management tools for troubleshooting: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)Use system management tools for troubleshooting: Practice Questions — 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)