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OverviewFull Product DetailsAuthor: Guanhua WangPublisher: Packt Publishing Limited Imprint: Packt Publishing Limited ISBN: 9781801815697ISBN 10: 1801815690 Pages: 284 Publication Date: 22 July 2022 Audience: General/trade , General Format: Paperback Publisher's Status: Active Availability: In stock  We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsTable of Contents Splitting Input Data Parameter Server and All-Reduce Building a Data Parallel Training and Serving Pipeline Bottlenecks and Solutions Splitting the Model Pipeline Input and Layer Split Implementing Model Parallel Training and Serving Workflows Achieving Higher Throughput and Lower Latency A Hybrid of Data and Model Parallelism Federated Learning and Edge Devices Elastic Model Training and Serving Advanced Techniques for Further Speed-UpsReviewsAuthor InformationGuanhua Wang is a final-year Computer Science PhD student in the RISELab at UC Berkeley, advised by Professor Ion Stoica. His research lies primarily in the Machine Learning Systems area including fast collective communication, efficient in-parallel model training and real-time model serving. His research gained lots of attention from both academia and industry. He was invited to give talks to top-tier universities (MIT, Stanford, CMU, Princeton) and big tech companies (Facebook/Meta, Microsoft). He received his master's degree from HKUST and bachelor's degree from Southeast University in China. He also did some cool research on wireless networks. He likes playing soccer and runs half-marathon multiple times in the Bay Area of California. Tab Content 6Author Website:Countries AvailableAll regions |
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