Overview
Explore the challenges and solutions for managing heterogeneous AI infrastructure in Kubernetes clusters through this 40-minute conference talk. Dive into the HAMi project, designed to address the complexities of integrating diverse AI devices like NVIDIA, Intel, and Huawei Ascend. Learn about unified scheduling, observability, and strategies to improve resource utilization of expensive AI hardware. Discover how to implement GPU sharing, ensure QoS for high-priority tasks, and support flexible scheduling policies. Gain insights from real-world case studies and explore integration with other projects such as Volcano and scheduler-plugin. Understand the current challenges and future roadmap for optimizing heterogeneous AI device management in Kubernetes environments.
Syllabus
Unlocking Heterogeneous AI Infrastructure K8s Cluster: Leveraging the Po... Xiao Zhang & Mengxuan Li
Taught by
Linux Foundation