Lilly Tech Systems
GPU Monitoring & Management
Master the tools and techniques for monitoring and managing GPU infrastructure for AI workloads. From nvidia-smi fundamentals to enterprise-grade DCGM monitoring, Grafana GPU dashboards, and intelligent scheduling strategies, this course covers everything you need to maximize GPU utilization and minimize costs.
What You'll Learn
Comprehensive GPU monitoring from command-line tools to enterprise dashboards.
nvidia-smi Mastery
Understand every nvidia-smi metric, from GPU utilization and memory to ECC errors and PCIe throughput.
DCGM Enterprise
Deploy NVIDIA DCGM for fleet-wide GPU health monitoring, diagnostics, and policy management.
Grafana GPU Dashboards
Build production-grade GPU dashboards with real-time utilization, thermal maps, and error tracking.
Smart Scheduling
Implement GPU scheduling strategies including MIG, time-slicing, and topology-aware placement.
Course Lessons
Follow the lessons in order for complete GPU monitoring mastery.
1. Introduction
GPU architecture basics for monitoring, key metrics to track, and why GPU monitoring differs from CPU monitoring.
2. nvidia-smi
Deep dive into nvidia-smi: every flag, metric interpretation, query commands, and automation scripts.
3. DCGM
Deploy NVIDIA Data Center GPU Manager for enterprise monitoring, health checks, diagnostics, and policy enforcement.
4. Grafana GPU
Build Grafana dashboards for GPU fleets: utilization heatmaps, memory tracking, error rates, and thermal monitoring.
5. Scheduling
GPU scheduling strategies: MIG partitioning, time-slicing, topology-aware scheduling, and fair-share queuing.
6. Best Practices
Production GPU management: fleet maintenance, driver updates, RMA workflows, and cost optimization strategies.
Prerequisites
What you need before starting this course.
- Basic understanding of GPU hardware (CUDA cores, memory, PCIe)
- Familiarity with Linux command-line tools
- Experience with Kubernetes (helpful but not required)
- Access to a system with NVIDIA GPUs for hands-on practice