Abstract: PyTorch Profiler is a tool that allows the collection of the performance metrics during the training and inference. The Profiler's context API can be used to better understand what model operators are the most expensive, examine their input shapes and stack traces, study device kernel activity and visualize the execution trace. In this talk, Sabrina Smai (Program Manager, Microsoft) shares the most recent updates to PyTorch Profiler, a demo and tips for leveraging the Profiler API to help you quickly locate and address common bottlenecks, as well as a look into what's to come.
Bio: Sabrina Smai is a Product Manager in Microsoft’s AI Frameworks team. She works with all things PyTorch and ONNX Runtime.