Hi everyone,
I am working with large images + DenseNet for a medical experiment.  Unfortunately I have to scale the images down to fit them in Titan X memory.  If I try full size images, of course the network runs out of GPU memory and stops.
I was wondering which of the following methods is more feasible in PyTorch to implement:

Is there a way to shard a large image in multiple tiles across multiple GPUs?  OR

Is there something like “GPU SWAP” memory, where large image can reside in main memory and only portions being convolved get swapped in CUDA memory?  Naturally this will be slower, but it is acceptable for my experiment purposes.

Thank you!