Kubespray is one of the most popular projects in the SIG-Cluster-Lifecycle community of Kubernetes, often used in a bare-metal environment. As AI workloads are rapidly increasing, bare metal can provide superior performance. Therefore, this session will share features and best practices of using Kubespray to build an AI-optimized cluster.
In the first half of the session, we will demo and discuss the most main features of Kubespray, and we'll also share useful tips and best practices from Kubespray.
In the second half of the session, we will highlight enhanced features and share best practices to support AI workloads. This will include insights on GPU support, scheduler enhancement, batch job queuing, RDMA network, DRA driver, GPU monitoring, and more.
Lastly, we aim to delve deeper into community engagement and open a discussion about progressing the project further. We will then allocate a substantial amount of time for questions.