KubeCon + CloudNativeCon India 2024

Large Language Model (LLM) finetuning on enterprise private data has emerged as an important strategy for enhancing model performance on specific downstream tasks. This process however demands substantial compute resources, and presents some unique challenges in Kubernetes environments. This session offers a practical, step-by-step guide to implementing multi-node LLM finetuning on Kubernetes clusters with GPUs, utilizing PyTorch FSDP and the Kubeflow training operator. We'll cover - preparing a Kubernetes cluster for LLM finetuning, optimizing cluster, system, and network configurations, and comparing performance of various network topologies including pod networking, secondary networks, and GPU Direct RDMA over ethernet for peak performance. By the end of this session, the audience will have a comprehensive understanding of the intricacies involved in multi-node LLM finetuning on Kubernetes empowering them to introduce the same in their own production Kubernetes environments.

This session will explore the AI/ML Framework (AIMLFW) within O-RAN SC (O-RAN Software Community) community, designed for dynamic and efficient 5G network management. Key Topics: - Introduction to O-RAN SC and AIMLFW: Overview of O-RAN’s architecture and mission. - AI/ML Use Cases in O-RAN: Real-world applications like traffic prediction and anomaly detection, supported by AIMLFW’s scalable platform. - Architecture and Components of AIMLFW: * Kubeflow for Model Training * KServe for Model Deployment * O-RAN Specification for AI/ML Workload Deployment * Core ML Lifecycle Components - Challenges and Solutions in AI/ML Deployment: Addressing common challenges in distributed 5G environments. - Future Directions and Community Collaboration: Potential integration with Flyte and MLflow for enhanced AI/ML workflow management.

In the era of digital transformation, PepsiCo is leading the way in integrating edge computing to ensure real-time data processing across its network. Utilizing lightweight Kubernetes solutions like K3s and RKE2, PepsiCo has built a platform that boosts computational capabilities at edge locations. Supported by Rancher and Longhorn, this platform enables efficient microservices deployment, providing the agility needed to meet dynamic market demands. A key component is the deployment of advanced ML models for camera and video inferences, which need substantial GPU resources. PepsiCo employs cutting-edge GPU sharing techniques to optimize these costly assets, improving performance and scalability while reducing costs. Join us to explore PepsiCo's edge computing strategy, its use of lightweight Kubernetes, and innovative GPU sharing techniques. Learn how PepsiCo is harnessing edge computing to drive operational excellence, sales growth and maintain a competitive edge.

Enterprises are eager to adopt Generative AI to increase their productivity. OPEA, the Open Platform for Enterprise AI, is a new project with the Linux Foundation. It provides a framework of composable microservices for state-of-the-art GenAI systems including LLMs, data stores, and prompt engines. It provides blueprints of end-to-end workflows for popular usage such as ChatQnA, CodeGen, and RAG systems. OPEA applications leverage cloud-native architecture to simplify deployment. It even includes a friendly high-level pipeline definition language for deployment on Kubernetes. This session will introduce OPEA, key component microservices, and how GenAI applications can be composed using those microservices. The attendees will learn to start with OPEA by deploying their GenAI application on a k8s cluster. Explicit contribution opportunities will be shared with the attendees. You'll also see an open source OPEA playground running on k8s, and how to contribute your components to it.

Showcasing AstraZeneca’s cutting-edge Enterprise Cloud Native Machine Learning Platform. It is built on Kubernetes and integrates a diverse array of cloud-native tools, enabling seamless development, deployment, and management of machine learning workflows. My presentation will delve into the architecture, key components, real-world applications, and the integration with Cloudability for cost management, highlighting its role in empowering data science teams and accelerating innovation within AstraZeneca. The tech stack involves Kubeflow, Weights & Biases, Ray, Volcano Scheduler, Grafana, Prometheus, ELK, Harbor, NetApp Ontap FSx, Kyverno, GitHub Actions, ArgoCD, Argo Rollouts, Milvus, etc

As organisations increasingly integrate AI solutions, the demand for environmentally sustainable practices within this space has never been more critical. This presentation delves into the collaborative effort between the Cloud Native Computing Foundation (CNCF) AI WG and the TAG Environmental Sustainability to define a repeatable design approach aimed at fostering sustainable AI in cloud-native environments. Our discussion will outline the crucial considerations in such approach, including efficient management of compute resources, storage optimisation, and advanced networking solutions. Attendees will gain insights into the lifecycle of AI/ML deployments, from inception through operation, emphasising resilience, scalability, and resource efficiency. By highlighting innovative "green" strategies, this session will provide actionable best practices and recommendations, alongside a forward-looking perspective on future trends and research directions in sustainable AI.

Machine learning platforms aim to streamline the workflow for ML practitioners, allowing them to focus on developing their models while the platform handles repetitive tasks like packaging code, dependencies and configurations. Traditional methods using Dockerfiles require ML engineers to navigate complex Linux processes and maintain multiple Dockerfiles for different projects, which can be time-consuming and prone to errors. Additionally, security mandates for regular patching and updates adding further to the complexity. Join this talk to explore how Cloud Native Buildpacks can simplify and secure MLOps deployments. By automating the packaging of ML projects, including custom libraries and hardware specifications, Buildpacks enhance flexibility, maintainability and security. This approach reduces the operational burden on both developers and security teams, ensuring a more efficient and scalable MLOps deployment process

Deploying large language models (LLMs) is inherently complex, challenging, and expensive. This case study demonstrates how Kubernetes, specifically Kserve with Modelcar OCI storage backend, simplifies the deployment and management of private LLM services. First, we explore how Kserve enables efficient and scalable model serving within a Kubernetes environment, allowing seamless integration and optimized GPU utilization. Second, we delve into how Modelcar OCI artifacts streamline artifact delivery beyond container images, reducing duplicate storage usage, increasing download speeds, and minimizing governance overhead. The session will cover implementation details, benefits, best practices, and lessons learned. Walk away learning how to leverage Kubernetes, Kserve, and OCI artifacts to enhance your MLOps journey, achieving significant efficiency gains and overcoming common challenges in deploying and scaling private LLM services.