Training was stopped with the following message: DefaultCPUAllocator: not enough memory: you tried to allocate 58720256 bytes: Buy new RAM!.
However, I assigned 1) my network (binary image classification), 2) input image  (N*C*D*H*W = 32*1*7*256*256), and 2) label (32*1) to my GPU (2080Ti). I think the training shouldn’t demand much CPU memory since DataSet (including random image augmentation) and DataLoader are the only processes for CPU.
From Task Manager, I found that the training requires about 24GB from CPU memory.
Why does the training requires much memory from CPU while I used CUDA for training? Thanks in advance!!