Monitoring GPUs at Scale for AI - ML and HPC Clusters

Overview

Join us as we delve into how NVIDIA effectively monitors GPU clusters tailored for AI and machine learning workloads. This comprehensive session will guide you through the application of open-source tools to address the major challenges of deployment, maintenance, security, and scaling in heterogeneous user environments.

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