Cluster Management for Large Scale AI and GPUs: Challenges and Opportunities

Overview

Join us as we explore the intricate challenges and innovative solutions involved in managing large-scale GPU clusters for artificial intelligence workloads. This session will cover key areas including maximizing resource utilization, implementing effective fault monitoring systems, and leveraging Kubernetes for native automation.

Discover strategies for health checks and optimal workload steering to ensure efficient AI cluster management.

Syllabus

• Introduction to Cluster Management for AI
Overview of GPU Clusters in AI Workloads
Importance of Effective Cluster Management
• Understanding GPU Hardware and Architecture
GPU Architecture Basics
GPU Performance Metrics
• Challenges in Large Scale AI Cluster Management
Scalability Issues
Resource Allocation and Scheduling
Fault Tolerance and Recovery
• Effective Utilization of GPU Clusters
Methods for Monitoring and Optimization
Load Balancing Techniques
• Fault Monitoring and Management
Identifying and Diagnosing Failures
Automated Systems for Fault Detection
Case Studies of Fault Management Strategies
• Kubernetes for AI Workloads
Introduction to Kubernetes Basics
Kubernetes-native Automation Tools
• Health Checks and Workload Steering
Implementing Health Checks in Kubernetes
Strategies for Dynamic Workload Steering
• Tools and Technologies for Cluster Management
Overview of Key Tools (e.g., Prometheus, Grafana)
Technology Stack for Cluster Automation
• Opportunities and Future Trends
Innovations in GPU Cluster Technology
Emerging Solutions in Cluster Management
• Hands-on Lab and Real-world Case Studies
Practical Exercises in Cluster Management
Analysis of Successful Cluster Deployments
• Final Project and Assessment
Design a Cluster Management Strategy
Presentation of Findings and Solutions

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Subjects

Computer Science