The torch.profiler module identifies execution bottlenecks by tracking CPU and GPU activity. This guide explains how to wrap training loops to analyze operator latency and memory usage. It provides a baseline for optimizing model throughput. Developers can now pinpoint specific kernels causing slowdowns to improve hardware utilization during large-scale training runs.