How does memory management work in PyTorch?
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How does memory management work in PyTorch?
Memory management in PyTorch involves dynamic allocation and deallocation of tensors, optimizing GPU memory usage.
What are the best practices for managing memory in PyTorch?
Best practices include using torch.no_grad() for inference, deleting unused tensors, and using torch.cuda.empty_cache().
Can you explain how to monitor memory usage in PyTorch?
You can monitor memory usage using torch.cuda.memory_allocated() and torch.cuda.memory_reserved() functions.
What happens if I run out of GPU memory?
If you run out of GPU memory, PyTorch will raise a RuntimeError indicating that it cannot allocate the required memory.
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