GPU Clusters and Containers for Distributed AI

Modern artificial intelligence and deep learning workloads require immense computational power, making GPU clusters and containerization essential skills for developers today. This comprehensive written course guides you through the fundamentals of managing high-performance computing resources to scale your machine learning models efficiently. You will transition from running simple local scripts to understanding how to deploy distributed training workloads across multiple GPUs. By understanding the core mechanics of containerization and cluster orchestration, you will learn to optimize resource utilization and accelerate model training times. What you'll learn: Understand foundational GPU architecture, memory management, and clustering concepts; Package deep learning models and dependencies into portable containers; Configure GPU-accelerated runtimes to access hardware resources efficiently; Orchestrate multi-container workloads on GPU clusters using Kubernetes; Apply modern MLOps principles to scale distributed training and model inference; Monitor cluster performance, memory usage, and workload distribution. This course begins with core definitions of GPU hardware and containerization basics before moving into orchestration strategies and distributed training workflows. It is designed for software engineers, data scientists, and system administrators who want to build a solid foundation in modern AI infrastructure. Master the essentials of high-performance computing and scale your AI workloads.