I’ve had success modifying the RNN Names Classifier example from the PyTorch tutorials for my own purposes with minimal modification of parameters: https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html 1
I’m using it to input a string of something called SMILES (a way of writing a structure of a chemical in text form where each character is important) and output a single value.
Recently I put the model on GPU using .to(device), but the GPU performance is about half the speed of the CPU, which is very unexpected given it’s a Nvidia 1080 vs a middling i5. I was told this may be because this example doesn’t use a dataloader, so I’ve been trying to implement one without success.
So I must ask you all, is it true that a dataloader with the right batch size will speed up the training of this kind of model on GPU?
And how do you implement a data loader that takes characters from a string and turns them into one hot vectors? Most every dataloader example I’ve found can do images (not useful here) or does text in a way that has vocabs and thousands of words. My total unique character count is around 80 so those examples needlessly complicate things.
Thanks all.