The Hugging Face blog provides a beginner's guide to using the profiling tools in PyTorch, specifically focusing on the `torch.profiler` module. The article is part of a series and aims to help users understand how to profile their models and improve performance. Profiling involves measuring the time it takes for a model to run through a dataset, allowing developers to identify bottlenecks and optimize their code. The guide covers setting up profiling, understanding the output, and interpreting results. --- Why it matters: Understanding how to profile PyTorch models is crucial for researchers and engineers who want to improve the performance of their AI applications. By identifying areas where optimization is needed, developers can reduce training time and costs, making it easier to deploy and maintain complex models. Source: https://huggingface.co/blog/torch-profiler