What do you forget to do with data on CUDA that can lead to memory overflow during training?

When working with CUDA, it is common to forget to free the memory allocated on the GPU after using the data. If you do not call the appropriate memory freeing functions (such as cudaFree in C++ or delete tensors on the GPU in PyTorch), the memory on the graphics card gradually fills up, leading to overflow and errors during model training.

Additionally, when loading data onto the GPU, sometimes people forget to transfer it back to the CPU or clear intermediate buffers, which also increases memory consumption.

Example in PyTorch:

import torch

tensor = torch.randn(1000, 1000).cuda()  # allocate memory on GPU
# ... use tensor

del tensor  # delete reference
torch.cuda.empty_cache()  # clear CUDA cache

It is important to control memory allocation and freeing, especially during cyclic training, to avoid leaks and overflow.