How can large-scale datasets be handled in PyTorch?

When dealing with large-scale datasets, PyTorch offers several methods.

  1. Utilizing DataLoader: DataLoader is a tool in PyTorch used for loading data, allowing for batch processing and shuffling of datasets. Parameters can be set to control batch size and data shuffling. By using DataLoader, it is efficient to load large-scale datasets and perform batch processing during the training process.
  2. Utilizing the Dataset class: It is possible to load large-scale datasets by customizing the Dataset class. This class allows for customizing the way data is loaded, such as reading data from files or databases. By customizing the Dataset class, various data formats can be flexibly handled.
  3. PyTorch also includes some built-in datasets, such as MNIST and CIFAR-10. These datasets have been preprocessed and can be easily loaded by calling `torchvision.datasets`. By using these built-in datasets, you can quickly and conveniently load common datasets for training and testing.

In conclusion, PyTorch offers various methods to handle large datasets, enabling users to choose the appropriate way to load data according to their specific needs. By effectively utilizing DataLoader, custom Dataset classes, and built-in datasets, users can efficiently process large datasets and conduct training.

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