Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

This post introduces torch.profiler, a PyTorch profiling tool for identifying performance bottlenecks in deep learning workloads.

• •The profiler exports two artifacts: a statistical table showing time consumption and a temporal trace showing when/why operations occur
• •Uses matrix multiplication and bias addition as a simple example to demonstrate profiling workflow
• •Distinguishes between CPU overhead-bound and GPU compute-bound regimes through profile metrics
• •Shows how to interpret profiler tables with columns like 'Self CPU/CUDA time' and 'total CPU/CUDA time'
• •Demonstrates using Perfetto UI to visualize profiler traces and understand kernel dispatch chains

This summary was automatically generated by AI based on the original article and may not be fully accurate.