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.
Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs. Recent advances have shown promise not only by considerably decreasing training time but also by offering new features such as loading data from remote storage like S3.
Training a machine learning model requires a dataset, a model, and the hardware, which for real problems involves one or more GPUs. We are always interested in reducing the total computational time required to train a model. This is desirable for several reasons: lower costs, easier to iterate, and more accessible for smaller teams, among other things.
Dataloaders, in charge of moving data from storage into GPUs while training machine learning models, might hold the key to drastically improving the performance of training jobs. Recent advances have shown promise not only by considerably decreasing training time but also by offering new features such as loading data from remote storage like S3.
Training a machine learning model requires a dataset, a model, and the hardware, which for real problems involves one or more GPUs. We are always interested in reducing the total computational time required to train a model. This is desirable for several reasons: lower costs, easier to iterate, and more accessible for smaller teams, among other things.
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