Building and training models represents significant progress, but often, the initial attempts don't yield the desired results or run smoothly. Models might converge slowly, generate nonsensical outputs, or encounter runtime errors. This chapter addresses the practical necessity of monitoring training and debugging PyTorch applications.

We will cover systematic techniques for diagnosing and resolving frequent problems, including tensor shape incompatibilities and errors related to CPU/GPU device allocation. You will learn how to inspect gradients to detect training stability issues like vanishing or exploding gradients. Additionally, this chapter introduces methods for monitoring training dynamics, specifically using TensorBoard to visualize metrics such as loss and accuracy over time. We will also discuss integrating basic logging and utilizing the Python debugger (pdb) for step-by-step code examination. By the end of this chapter, you'll have a toolkit for troubleshooting and observing your PyTorch models effectively.