Topic: "Debugging and Optimization of PyTorch Models"Speaker: Collin Wilson, SHARCNETVideo link --- Deep learning models are often viewed as uninterpretable "black boxes". As researchers, we often extend this thinking to the memory and compute utilization of such models. Using PyTorch Profiler, we can identify model bugs and bottlenecks to understand how to improve model performance from an efficiency perspective. This will improve training scaling and allow completion of large hyperparameter optimizations more efficiently. Here we will dicuss the usage of PyTorch Profiler, including some case studies of real training examples, and discuss possible optimizations based on profiler results.---The Compute Ontario Colloquia are weekly Zoom presentations on Advanced Research Computing, High Performance Computing, Research Data Management, and Research Software topics, delivered by staff from three Compute Ontario consortia (CAC, SciNet, SHARCNET) and guest speakers. The series began January 2023 and superseded similar series previously delivered by individual consortia (e.g. General Interest Seminars by SHARCNET or User Group Meeting TechTalks by SciNet). The colloquia are one hour long and include time for questions. No registration is required. Presentations are usually recorded and uploaded to the hosting consortium video channel (colloquia hosted by SHARCNET go to our youtube channel).