I have the code below and I don’t understand why the memory increase twice then stops I searched the forum and can not find answer env: PyTorch 0.4.1, Ubuntu16.04, Python 2.7, CUDA 8.0/9.0 from torchvision.models import vgg16 import torch import pdb net = vgg16().cuda() data1 = torch.rand(16,3,224,224).cuda() for i in range(10): pdb.set_trace ...

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    RuntimeError: CUDA out of memory. Tried to allocate 144.00 MiB (GPU 0; 2.00 GiB total capacity; 1.21 GiB already allocated; 43.55 MiB free; 1.23 GiB reserved in total ...

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    The memory usage for the CUDA context might differ from different CUDA versions. The model itself should not use more or less memory. asha97 June 14, 2020, 5:38amI figured out where I was going wrong. I am posting the solution as an answer for others who might be struggling with the same problem. Basically, what PyTorch does is that it creates a computational graph whenever I pass the data through my network and stores the computations on the GPU memory, in case I want to calculate the gradient during backpropagation.

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