THE ANNOTATED PYTORCH TRAINING LOOP

Summary from the TLDR Dev newsletter

To build an effective training loop in PyTorch, it's necessary to understand the order and placement of various operations, as misplaced lines can lead to issues such as failed convergence or excessive memory usage. This includes setting up the model, loss function, optimizer, and data loader, followed by executing the training and validation phases while managing gradients through techniques like gradient clipping and learning rate scheduling.