Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler
Source
huggingface.co
Date
Context
A slow AI operation does not necessarily mean its GPU is spending the time calculating. The CPU must first schedule the programs that run on the GPU, and that preparation can dominate a small workload. This PyTorch profiling tutorial explains how to distinguish the two using a simple matrix calculation.
The profiler's table summarizes time spent in operations, while its timeline shows CPU and GPU activity in order. Reading both helps distinguish expensive calculations from delays launching them or gaps between them. That gives a developer a concrete starting point for optimization: identify where time goes before changing the model's computation.
