I want to duplicate multiple models and train each with a different dataloader.
Simply cloning the model is expected to waste that much memory.
So I’m looking for a way to use memory efficiently.
I know that TensorFlow has an option called reuse like variable_scope(reuse=True).
Is there any way I can use it similarly in Pytorch??
Thank you.