Chapter 18: Debugging and Visualization with PyTorch
Abstract: Debugging and visualization are crucial for developing and optimizing PyTorch models. Several tools and techniques facilitate these processes: Debugging: Standard Python Debuggers: Integrated Development Environments (IDEs) like VS Code or PyCharm offer robust Python debugging capabilities. This includes setting breakpoints, stepping through code, inspecting variables, and evaluating expressions. To debug into PyTorch source code, the justMyCode setting in the Python configuration might need to be set to false . Printing and Logging: Simple print() statements or logging libraries can be used to inspect tensor values, shapes, and other relevant information at different stages of the model's execution. PyTorch Hooks: Forward and backward hooks can be registered on modules or tensors to inspect and even modify activations or gradients during the forward and backward passes. This is particularly use...