Efficient Multi-Cluster GPU Workload Management with Karmada and Volcano
Offered By: CNCF [Cloud Native Computing Foundation] via YouTube
Course Description
Overview
Explore efficient multi-cluster GPU workload management using Karmada and Volcano in this informative conference talk. Discover solutions to critical challenges faced when running AI/ML workloads on large-scale, heterogeneous GPU environments across multiple Kubernetes clusters. Learn about intelligent GPU workload scheduling, cluster failover support for seamless workload migration, two-level scheduling consistency and efficiency, and balancing utilization and QoS for resource sharing among workloads with different priorities. Gain insights into addressing resource fragmentation, operational costs, and cross-resource scheduling in cloud native AI platforms spanning multiple data centers and diverse GPU types.
Syllabus
Efficient Multi-Cluster GPU Workload Management with Karmada and Volcano - Kevin Wang, Huawei
Taught by
CNCF [Cloud Native Computing Foundation]
Related Courses
Karmada Cross-Cluster Elastic Scaling: Scenarios and Implementation AnalysisCNCF [Cloud Native Computing Foundation] via YouTube Simplifying Multi-cluster Kubernetes Management with Karmada
CNCF [Cloud Native Computing Foundation] via YouTube Managing Multi-Cluster with Karmada - Session 6
CNCF [Cloud Native Computing Foundation] via YouTube Karmada and ErieCanal Multi-Cluster Scheduling - Session 4
CNCF [Cloud Native Computing Foundation] via YouTube Sailing Multi-Cloud Traffic Management with Karmada
CNCF [Cloud Native Computing Foundation] via YouTube