Monitoring GPUs at Scale for AI - ML and HPC Clusters

Overview

Learn how NVIDIA monitors GPU clusters for AI/ML workloads using open-source tools, addressing deployment, maintenance, security, and scale challenges for various user personas.

Syllabus

• Introduction to GPU Monitoring
Importance of GPU monitoring in AI/ML and HPC clusters
Overview of NVIDIA's approach to GPU monitoring
• Understanding GPU Architectures and Performance Metrics
Basics of GPU architecture relevant to AI/ML workloads
Key performance metrics for monitoring GPUs
• Tools for Monitoring NVIDIA GPUs
Introduction to open-source tools
Overview of NVIDIA-s specific tools
• Deployment of Monitoring Solutions at Scale
Strategies for deploying monitoring tools in large clusters
Automation in deployment
• Maintenance and Updates
Routine maintenance practices
Handling updates and upgrades in a monitored environment
• Security Considerations in GPU Monitoring
Identifying potential security threats
Implementing security measures for monitoring solutions
• Scaling GPU Monitoring Solutions
Challenges of scale in GPU monitoring
Solutions and best practices for scalable monitoring
• Addressing User Personas in GPU Monitoring
Different user personas in GPU monitoring (e.g., Admins, Engineers, Data Scientists)
Tailoring monitoring solutions to different user needs
• Case Studies and Real-world Examples
Examination of real-world implementations
Lessons learned from industry examples
• Practical Exercises and Lab Sessions
Hands-on exercises with open-source monitoring tools
Setting up a small-scale monitoring solution
• Conclusion and Future Trends
Summary of key takeaways
Emerging trends in GPU monitoring for AI/ML and HPC clusters
• Q&A and Course Wrap-up

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Subjects

Conference Talks