Visualize GPU Memory in PyTorch with PyTorch Visualizer

AI Impact Summary

This tutorial guides users on visualizing and understanding GPU memory usage in PyTorch during training, specifically using the PyTorch visualizer. The analysis demonstrates how memory is allocated and deallocated during model creation, input tensor creation, forward passes, and backpropagation. Key insights include the persistent memory usage of the model parameters and activations, as well as the temporary memory allocation during gradient calculations and optimizer steps. The example highlights the importance of understanding memory leaks and how to release unused tensors to avoid out-of-memory errors.