RunTime Error, Cuda
Hi, I am training a pretrained model resnet-18 imported from torchvision.models with dataset containing 1050 images of size 3x240x320. After training, I am testing with 399 samples, but I am getting RunTime CUDA Error : Out of Memory. Also, I have ported the test dataset to CUDA and volatile attribute is set to True. The model is in GPU and after training nvidia-smi output is 3243MiB/4038MiB Is there any way available to free the GPU memory so that I can do the testing?
srv902
it’s possible that moving both the training and test dataset over to CUDA doesn’t give enough space for the network to forward-prop through. Loading your dataset into GPU memory itself takes 1.3 GB of memory. Are you giving a large batch size as input to your network? Can you reduce the batch size at inference time?
smth
Yes I was giving a large batch size, so I reduced the batch size at inference time. It is working fine now. Thank you very much.
srv902
apaszke