Hi Juan,
that might be the case yes (it’s a “small” pretrained autoencoder model I am loading next to the one I want to train). Are there any best practices/examples how to evenly use GPU memory when using more than 1 GPU?
I also had situations where output was on one GPU but target on the other one.
When building the model, is there any good example to avoid those kind of situations?