Module Standardization:
- Applied consistent introduction format to all 17 modules
- Every module now has: Welcome, Learning Goals, Build→Use→Reflect, What You'll Achieve, Systems Reality Check
- Focused on systems thinking, performance, and production relevance
- Consistent 5 learning goals with systems/performance/scaling emphasis
Agent Structure Fixes:
- Recreated missing documentation-publisher.md agent
- Clear separation: Documentation Publisher (content) vs Educational ML Docs Architect (structure)
- All 10 agents now present and properly defined
- No overlapping responsibilities between agents
Improvements:
- Consistent Build→Use→Reflect pattern (not Understand or Analyze)
- What You'll Achieve section (not What You'll Learn)
- Systems Reality Check in every module
- Production context and performance insights emphasized
Enhancements for achieving 75% accuracy on CIFAR-10:
Module 08 (DataLoader):
- Add download_cifar10() function for real dataset downloading
- Implement CIFAR10Dataset class for loading real CV data
- Simple implementation focused on educational value
Module 11 (Training):
- Add model checkpointing (save_checkpoint/load_checkpoint)
- Enhanced fit() with save_best parameter
- Add evaluation tools: compute_confusion_matrix, evaluate_model
- Add plot_training_history for tracking progress
These minimal changes enable students to:
1. Download and load real CIFAR-10 data
2. Train CNNs with checkpointing
3. Evaluate model performance
4. Achieve our north star goal of 75% accuracy
This comprehensive update ensures all TinyTorch modules follow consistent NBGrader
formatting guidelines and proper Python module structure:
- Fix test execution patterns: All test calls now wrapped in if __name__ == "__main__" blocks
- Add ML Systems Thinking Questions to modules missing them
- Standardize NBGrader formatting (BEGIN/END SOLUTION blocks, STEP-BY-STEP, etc.)
- Remove unused imports across all modules
- Fix syntax errors (apostrophes, special characters)
- Ensure modules can be imported without running tests
Affected modules: All 17 development modules (00-16)
Agent workflow: Module Developer → QA Agent → Package Manager coordination
Testing: Comprehensive QA validation completed
- Fixed test functions to only run when modules executed directly
- Added proper __name__ == '__main__' guards to all test calls
- Fixed syntax errors from incorrect replacements in Module 13 and 15
- Modules now import properly without executing tests
- ProductionBenchmarkingProfiler (Module 14) and ProductionMLSystemProfiler (Module 16) fully working
- Other profiler classes present but require full numpy environment to test completely
- Add documentation for test_unit_dataset_interface function
- Add documentation for test_unit_dataloader function
- Add documentation for test_unit_simple_dataset function
- Add documentation for test_unit_dataloader_pipeline function
- Ensures every code function has preceding explanatory markdown cell
- Maintains educational clarity and structure
- Insert ## 🔧 DEVELOPMENT header before first test function
- Organizes module according to educational structure guidelines
- Maintains all existing functionality and test execution
- Improves readability and navigation for educational use
Stops the automatic execution of the integration test.
This change prevents the test from running every time the module is loaded,
allowing for more focused and controlled testing.
Removes redundant "DEVELOPMENT" headers from several notebook files.
These headers are no longer necessary and declutter the notebook content, improving readability and focus on the core content and testing sections.
- Added ## 🔧 DEVELOPMENT section before Step 1 where development begins
- Added ## 🤖 AUTO TESTING section before auto testing block
- Updated to ## 🎯 MODULE SUMMARY: Data Loading Systems
Improves notebook organization without changing any code logic or content.
Vijay Janapa Reddi