KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

In the dynamic landscape of AI/ML, deploying and orchestrating large open-source inference models on Kubernetes has become paramount. This talk delves into the intricacies of automating the deployment of heavyweight models like Falcon and Llama 2, leveraging Kubernetes Custom Resource Definitions (CRDs) to manage large model files seamlessly through container images. The deployment is streamlined with an HTTP server facilitating inference calls using the model library. This session will explore eliminating manual tuning of deployment parameters to fit GPU hardware by providing preset configurations. Learn how to auto-provision GPU nodes based on specific model requirements, ensuring optimal utilization of resources. We'll discuss empowering users to deploy their containerized models effortlessly by allowing them to provide a pod template in the workspace custom resource inference field. The controller dynamically, in turn, creates deployment workloads utilizing all GPU nodes.

在AI/ML不断发展的领域中，在Kubernetes上部署和编排大型开源推理模型变得至关重要。本次演讲将深入探讨自动化部署像Falcon和Llama 2这样的重型模型的复杂性，利用Kubernetes自定义资源定义（CRDs）通过容器镜像无缝管理大型模型文件。部署通过HTTP服务器简化，以便使用模型库进行推理调用。 本场演讲将探讨通过提供预设配置来消除手动调整部署参数以适应GPU硬件的需求。了解如何根据特定模型要求自动配置GPU节点，确保资源的最佳利用。我们将讨论如何赋予用户轻松部署其容器化模型的能力，允许他们在工作区自定义资源推理字段中提供一个pod模板。控制器动态地创建部署工作负载，利用所有GPU节点。