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dc.contributor.authorZhang, Jiaruien_US
dc.identifier.citationZhang, J. (2021). Performance profiling and optimizations in distributed deep learning frameworks. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractDeep learning has been a very popular topic in Artificial Intelligent industry these years and can be applied to many fields, such as computer vision, natural language processing and so forth. However, training a deep learning model usually takes lots of time. It is necessary to identify the bottleneck of the deep learning process and implement optimizations on them to improve the training efficiency, especially the training speed. Usually, optimizations are implemented in two aspects: data processing and model training. In this work, multiple optimization methods are studied and conducted to check their corresponding effect. Regarding data processing, optimizations such as parallelization of multiple transforming processes, dataset caching, prefetching of data samples are implemented. Regarding training, data parallelism of distributed training is especially studied, and two current popular frameworks are utilized to achieve it. Experiments are conducted to compare the two frameworks and analyze possible influencing factors’ effect on the training speed.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titlePerformance profiling and optimizations in distributed deep learning frameworksen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Zhipingen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.organizationYITU Pte Ltden_US
dc.contributor.supervisor2Wang Lien_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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