Debug TensorFlow Models with tfdbg

TensorFlow Debugger (tfdbg) is a specialized tool designed to facilitate the debugging of machine learning models within Google's open-source TensorFlow library. By addressing the complexity of debugging during the TensorFlow runtime, where standard Python debuggers fall short, tfdbg offers a command-line interface (CLI) that allows users to inspect and interact with dataflow graph nodes, visualize execution histories, and identify issues such as bad numerical values like infinities and NaNs. The tool enhances the debugging experience by enabling stepper mode for detailed exploration of node outputs, and supports conditional breakpoints for efficient problem identification. Compared to alternative methods, tfdbg requires fewer code changes and provides more comprehensive graph coverage, accelerating model development and debugging workflows. It includes features for offline debugging and integrates with tf.contrib.learn, with a minimum TensorFlow version requirement of 0.12.1. Users can find documentation, report bugs on GitHub, and seek help on StackOverflow, with the project benefiting from contributions by Google's TensorFlow Core/API and Applied Machine Intelligence Teams.