What are some advanced options for PyTorch DataLoader?
PyTorch DataLoader offers several advanced options like custom collate functions, multi-threading, and data shuffling. Would you like to know more about a specific option?
Can you explain how to use a custom collate function?
Sure! A custom collate function allows you to define how to combine a list of samples into a mini-batch. You can pass it to the DataLoader using the collate_fn
argument.
What about using multiple workers for loading data?
You can set the num_workers
parameter in DataLoader to use multiple subprocesses for data loading, which can speed up the process significantly.
Are there any best practices for using DataLoader?
Yes, some best practices include using pin_memory
for faster data transfer to GPU, and ensuring your dataset is well-optimized for loading.
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