Distributed Computing

Introduction Large Language Models (LLMs) such as GPT‑4, LLaMA, and BLOOM have transformed natural‑language processing, but training and serving them at scale demands massive GPU resources, high‑speed networking, and sophisticated orchestration. Amazon Elastic Kubernetes Service (EKS) provides a managed, production‑grade Kubernetes platform that can run distributed GPU workloads, while integrating tightly with AWS services for security, observability, and cost management. This article walks you through end‑to‑end optimization of distributed GPU workloads for LLMs on Amazon EKS. We’ll cover: ...

Large Language Models (LLMs) power everything from chatbots to code generation, but scaling them for training, fine-tuning, and inference demands distributed computing expertise. Ray, an open-source framework, simplifies this with libraries like Ray LLM, Ray Serve, Ray Train, and Ray Data, enabling efficient handling of massive workloads across GPU clusters.[1][5] This guide takes you from zero knowledge to hero status, covering installation, core concepts, hands-on examples, and production deployment. What is Ray and Why Use It for LLMs? Ray is a unified framework for scaling AI and Python workloads, eliminating the need for multiple tools across your ML pipeline.[5] For LLMs, Ray LLM builds on Ray to optimize training and serving through distributed execution, model parallelism, and high-performance inference.[1] ...

Introduction As artificial intelligence (AI) and machine learning (ML) models grow in size and complexity, the need for scalable and efficient computing frameworks becomes paramount. Ray, an open-source Python framework, has emerged as a powerful tool for distributed and parallel computing, enabling developers and researchers to scale their ML workloads seamlessly. This article explores Python Ray, its ecosystem, and how it specifically relates to the development, training, and deployment of Large Language Models (LLMs). ...