AMD publishes AI PyTorch profiling workflow Part 7

This guide outlines a systematic performance profiling workflow for PyTorch training workloads on AMD GPUs, using TinyOpenFold as a case study. It demonstrates how to establish a baseline, capture whole-system traces with rocprof-sys, annotate phases using ROCTx, and analyze hardware counters with rocprof-compute. Additionally, the author evaluates three optimization strategies including PyTorch kernel fusion, torch.compile, and custom Triton kernels to improve throughput and reduce launch overhead. Open article →