How does PyTorch handle memory management for tensors?
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PyTorch provides a context manager called 'torch.cuda.amp.autocast' that reduces memory usage for certain operations by using lower precision data types whenever possible.
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PyTorch has a feature called 'torch.autograd' that automatically performs memory optimizations. It reuses memory for intermediate results, reducing the need for memory allocation and deallocation during computation.
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PyTorch uses a caching memory allocator to manage GPU memory. It allocates memory on the GPU in larger chunks and then assigns smaller sections to tensors when needed. This helps reduce memory fragmentation and improves memory allocation speed.
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