GPU training, but datasets are on the CPU
What is your question?
I am running GPU training, but it is not much faster. My batch_size is 1024. I see that the model's datasets are on the CPU. Also, so are the model parameters. Why?
How can I preload them all onto the GPU for faster training? They will fit in GPU memory.
I see 18 it/sec on CPU and 30 it/sec on GPU
Code
for i, (x, y, d) in enumerate(model.train_dataloader()):
    print(x.device)
    print(d.device)
    break

cpu
cpu
for p in model.parameters():
    # p.requires_grad: bool
    # p.data: Tensor
    print(p.device)

cpu
...
What have you tried?
Applying .to(device) on the dataset tensors.
Unfortunately, it is not so obvious to me from the pytorch lighning docs how to debug whether something is on the GPU or not.
What's your environment?

OS: Ubuntu
Packaging pip3
Version 0.8.1