Advanced Debugging Techniques in AI Toolkit

Debugging is an essential skill for developing robust AI applications. Here are key strategies to master advanced debugging:

1. Interactive Debugging Tools

• Jupyter Debugger 🐍
  Use %debug magic command to inspect variables and execution flow
  

  

  Interactive_Debugging
  
  Learn more about Jupyter debugging

• TensorBoard 🧠
  Visualize training metrics and debug model performance
  

  

  TensorBoard_Overview
  
  Explore TensorBoard features

2. Logging Best Practices

• Implement structured logging with logging module
• Use pdb.set_trace() for runtime inspections
• Analyze error logs using grep/awk for pattern detection
  
  

  Error_Logs

3. Unit Testing Frameworks

• Write test cases using pytest or unittest
• Mock dependencies with unittest.mock
• Check test coverage with coverage.py
  
  

  Unit_Testing

4. Profiling Tools

• Use cProfile for performance analysis
• Monitor memory usage with memory_profiler
• Optimize code with py-spy for CPU tracing
  
  

  Profiling_Tools

For deeper insights, check out our debugging fundamentals guide to build a solid foundation before advancing.