How to Debug and Optimize Multi-GPU Training in TensorFlow

This guide walks you through using the TensorFlow Profiler with TensorBoard to identify and fix GPU performance bottlenecks in both single- and multi-GPU setups. It covers profiling workflows, diagnosing input pipeline issues, increasing GPU utilization, reducing kernel launch delays, optimizing op placement, and enabling mixed precision with XLA for faster, more efficient training. By following these steps, you can eliminate idle GPU time, minimize CPU-GPU data transfer overhead, and achieve consistently high training throughput.