Simplify AI Infrastructure with Kubernetes Operators - Ganeshkumar Ashokavardhanan & Tariq Ibrahim

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Simplify AI Infrastructure with Kubernetes Operators | 使用Kubernetes Operators简化AI基础设施 - Ganeshkumar Ashokavardhanan, Microsoft & Tariq Ibrahim US, NVIDIA
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 the GPU 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，管理集群生命周期以及节点之间的硬件配置的多样性和复杂性可能是一项挑战。我们如何简化和自动化这一过程，以确保Kubernetes用户的顺畅体验？Kubernetes Operators 提供了一个很好的解决方案。在本次会议中，我们将讨论Operators，并演示它们如何帮助从集群配置和K8s节点配置到深度学习模型部署，端到端地自动化AI准备基础设施的安装、配置和生命周期管理。我们将展示一个微调LLM工作负载，以展示生态系统中的现有Operators，如GPU Operator和Kubernetes AI Toolchain Operator，如何简化基础设施。最后，我们将讨论在生产环境中使用Operators的挑战和最佳实践。