Appendix G: Troubleshooting and Debugging in PyTorch
Abstract: Troubleshooting and debugging in PyTorch involves identifying and resolving issues that arise during model development, training, and deployment. This can encompass a range of problems, from incorrect model behavior and performance bottlenecks to memory errors and unexpected numerical instability. Common Troubleshooting Areas: Data Issues: Incorrect data loading or preprocessing: Verify dataset integrity, transformations, and batching. Data starvation: Use tools like nvidia-smi to monitor GPU utilization and identify if the data loader is a bottleneck. Model Issues: Incorrect model architecture or layer implementation: Carefully review the nn.Module definitions and ensure correct parameter handling (e.g., using nn.ModuleList for lists of modules). Weight initialization problems: Investigate the impact of different initialization schemes. Gradient issues: Check for exploding or vanishing gradient...