torchutilsdataDataLoader
torchutilsdataDataLoader is a fundamental component of the PyTorch deep learning framework, designed to facilitate efficient data loading and preprocessing during model training and evaluation. It provides an iterable over a dataset, enabling batching, shuffling, and parallel data loading to optimize training performance.
The primary purpose of DataLoader is to abstract and streamline the process of fetching individual data samples,
DataLoader works in conjunction with a dataset object, which can be any subclass of torch.utils.data.Dataset. This
Efficiency features of DataLoader include multi-process data loading and prefetching, which reduce data bottlenecks in training
Overall, torch.utils.data.DataLoader is an essential utility for scalable and flexible data management in PyTorch projects, enabling