Redesigning Scheduling in Ray to Improve Cost-Efficiency at Scale
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the key challenges and solutions in building a cost-efficient scheduling system for AI applications at scale in this 30-minute talk from Anyscale. Dive into Ray's scheduling features, including placement groups, graceful node draining, and label-based scheduling, and learn how they optimize performance for diverse AI workloads. Examine two Ray AI applications - model serving and data preprocessing for distributed training - to understand how Ray scheduling enhances speed and reduces costs. Gain insights on leveraging various Ray scheduling features to meet specific requirements and improve the efficiency of AI applications. Access the accompanying slide deck for a comprehensive visual guide to the concepts discussed.