How do you use memory-mapped files to efficiently load and handle large datasets in PyTorch

Question

Can you show how to use memory-mapping files in PyTorch to load and handle large datasets efficiently using Python programming?

Answer 1

You can use memory-mapped files to efficiently load and handle large datasets in PyTorch by referring to below:

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In the code above, we are using memory mapping, in which the (np. mem map)function maps the file into memory. The data is not fully loaded into RAM but can be accessed efficiently without excessive memory usage, Dataset class to custom MemoryMappedDataset class inherits from torch.utils.data.Dataset, allowing it to work seamlessly with a DataLoader and DataLoader to efficiently load batches of data during training without loading the entire dataset into memory at once.

Hence, the above strategies can be used to efficiently load and handle large datasets in PyTorch using memory-mapped files.