You can also optimize the training time by using PyTorch's built-in DataLoader class, which helps with efficient data loading and preprocessing. Additionally, using GPU-accelerated operations and techniques like model freezing and gradient checkpointing can further improve training performance.

Another way is to make use of distributed training, allowing you to train your models across multiple GPUs or even multiple machines. This can significantly reduce the training time by parallelizing the computations.

One way to optimize training time in PyTorch is by utilizing mixed precision training. This technique combines the use of both single and half precision floating point numbers to speed up computations without sacrificing much accuracy.