A blog post discusses how to visualize and manage GPU memory usage when training models with PyTorch. It suggests using tools like `nvidia-smi` and `torch.cuda.memory_stats()` to monitor memory consumption, and provides a code example for visualizing memory usage over time. The author recommends setting aside some extra memory for the model's weights and gradients, as well as allocating sufficient memory for the input data. --- Why it matters: This matters because managing GPU memory is crucial for efficient training of deep learning models, and PyTorch users need to be aware of these best practices to avoid running out of memory or experiencing slow performance. Source: https://huggingface.co/blog/train_memory