KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

ML applications often require specialized hardware and additional configuration to run efficiently and reliably on Kubernetes. However, managing the cluster lifecycle, the diversity and complexity of hardware configuration across nodes can be challenging. How can we simplify and automate this process to ensure a smooth experience for kubernetes users? Kubernetes Operators offer a great solution. In this session, we will go over operators and demonstrate how they can help automate the installation, configuration, and lifecycle management of AI-ready infra end to end from cluster provisioning and k8s node configuration to deep learning model deployments. We will demo a fine-tuning LLM workload, to showcase how existing operators in the ecosystem such as Cluster API Operator, GPU Operator, Network Operator, and the Kubernetes AI Toolchain Operator, can be used to simplify the infra. Finally, we will discuss challenges and best practices of using operators in production.

ML 应用通常需要专门的硬件和额外的配置才能在 Kubernetes 上高效可靠地运行。然而，管理集群生命周期、节点间硬件配置的多样性和复杂性可能具有挑战性。我们如何简化和自动化这个过程，以确保 Kubernetes 用户的顺畅体验？ Kubernetes 运算符提供了一个很好的解决方案。在本场演讲中，我们将介绍运算符，并演示它们如何帮助自动化 AI-ready 基础架构的安装、配置和生命周期管理，从集群提供和 k8s 节点配置到深度学习模型部署。我们将演示一个微调 LLM 工作负载，展示生态系统中现有运算符（如 Cluster API Operator、GPU Operator、Network Operator 和 Kubernetes AI Toolchain Operator）如何简化基础架构。最后，我们将讨论在生产环境中使用运算符的挑战和最佳实践。