How can I optimize memory usage with PyTorch DataLoader?
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How can I optimize memory usage with PyTorch DataLoader?
You can optimize memory usage in PyTorch DataLoader by using smaller batch sizes, enabling pin_memory, and using num_workers.
What does enabling pin_memory do?
Enabling pin_memory allows the DataLoader to copy tensors into pinned memory, which can speed up data transfer to the GPU.
Are there any other tips for reducing memory usage?
Yes, consider using data augmentation techniques that don't require storing additional copies of the data in memory.
How can I monitor memory usage while training?
You can use tools like PyTorch's built-in memory profiler or external libraries like memory_profiler to monitor memory usage.
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