In this paper, researchers highlight dataloaders as key to improving ML training, comparing libraries for functionality, usability, and performance.
Authors: Iason Ofeidis, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution}; Diego Kiedanski, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven {Equal contribution}; Leandros TassiulasLevon Ghukasyan, Activeloop, Mountain View, CA, USA, Department of Electrical Engineering, and Yale Institute for Network Science, Yale University, New Haven.
This section describes several efforts in the community to benchmark deep learning libraries, models, and frameworks. A large body of work exists towards benchmarking deep learning tools and methods. MLPerf is arguably the most popular ML benchmarking project for modern ML workloads that targets both training and inference, spanning a variety of AI tasks. The authors use as their objective metric the training time required to reach a given accuracy level.
This section describes several efforts in the community to benchmark deep learning libraries, models, and frameworks. A large body of work exists towards benchmarking deep learning tools and methods. MLPerf is arguably the most popular ML benchmarking project for modern ML workloads that targets both training and inference, spanning a variety of AI tasks. The authors use as their objective metric the training time required to reach a given accuracy level.
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