YoVDO

Efficient Multi-Cluster GPU Workload Management with Karmada and Volcano

Offered By: CNCF [Cloud Native Computing Foundation] via YouTube

Tags

Kubernetes Courses Distributed Systems Courses GPU Computing Courses Cloud Native Computing Courses Volcano Courses Karmada Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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

Advanced Operating Systems
Georgia Institute of Technology via Udacity High Performance Computing
Georgia Institute of Technology via Udacity GT - Refresher - Advanced OS
Georgia Institute of Technology via Udacity Distributed Machine Learning with Apache Spark
University of California, Berkeley via edX CS125x: Advanced Distributed Machine Learning with Apache Spark
University of California, Berkeley via edX