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.
In the process of training a Deep Learning model, the dataset needs to be read from memory and pre-processed, before it can be passed as input to the model. This operation requires loading the data into memory all at once. In most cases and, especially, with large datasets, a memory shortage arises due to the limited amount of it available in the system, which also deteriorates the system’s response time.
In the process of training a Deep Learning model, the dataset needs to be read from memory and pre-processed, before it can be passed as input to the model. This operation requires loading the data into memory all at once. In most cases and, especially, with large datasets, a memory shortage arises due to the limited amount of it available in the system, which also deteriorates the system’s response time.
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