KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

Enterprises keep invested in AI. But once GPU are installed in a data center, a challenge arises: how to construct an "AI cloud" atop bare-metal. Even when K8S is recognized as the foundational infrastructure for AI, But K8S only is merely the initial step. Organizations may face challenges: - Maximizing GPU utilization - Unifying multi-arch accelerators/GPUs (k8s DRA) - Organization quotas and cost management - Resource isolation among organizations - Smarter scheduling, tiered GPU allocation, task prioritization.. - Sharing GPU clusters between VMs & containers - Harnessing the full potential of high-speed networks , Storage optimization and dataset orchestration Leveraging open source stacks in Linux Foundation and CNCF, we've experience in building AI clouds for IDC or internal usage. We can share experiences to empower communities' journey towards constructing the 10x efficiency of cloud-native AI. Refer to `Additional resources` chapter for more details

企业继续投资于人工智能。但是一旦在数据中心安装了GPU，就会面临一个挑战：如何在裸金属之上构建一个“AI云”。即使K8S被认为是AI的基础基础设施，但K8S只是一个起步。 组织可能面临的挑战包括： - 最大化GPU利用率 - 统一多架构加速器/GPU（k8s DRA） - 组织配额和成本管理 - 组织之间的资源隔离 - 更智能的调度，分层GPU分配，任务优先级... - 在虚拟机和容器之间共享GPU集群 - 充分利用高速网络的潜力，优化存储和数据集编排 利用Linux基金会和CNCF中的开源堆栈，我们在为IDC或内部使用构建AI云方面有经验。我们可以分享经验，以赋予社区构建云原生AI的效率提升10倍的旅程。 有关更多详细信息，请参考“附加资源”章节。