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

Overview

Explore the challenges and solutions for managing large GPU clusters for AI workloads, including effective utilization, fault monitoring, and Kubernetes-native automation for health checks and workload steering.

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

Subjects

Computer Science