- Restored tools/py_to_notebook.py as a focused, standalone tool
- Updated tito notebooks command to use subprocess to call the separate tool
- Maintains clean separation of concerns: tito.py for CLI orchestration, py_to_notebook.py for conversion logic
- Updated documentation to use 'tito notebooks' command instead of direct tool calls
- Benefits: easier debugging, better maintainability, focused single-responsibility modules
- Restore complete setup module with system information and developer profiles
- Add NBDev educational patterns with #| export and #| hide directives
- Implement SystemInfo class for platform detection and compatibility checking
- Add DeveloperProfile class with ASCII art customization and personalization
- Include comprehensive educational content with step-by-step learning progression
- Expand test suite to 20 comprehensive tests covering all functionality:
- Function execution and output validation
- Arithmetic operations (basic, negative, floating-point)
- System information collection and compatibility
- Developer profile creation and customization
- ASCII art file loading and fallback behavior
- Error recovery and integration testing
- Update README with complete feature documentation and usage examples
- Convert to executed notebook showing rich output and system information
- Maintain file-based ASCII art system with tinytorch_flame.txt
- All tests passing with professional pytest structure
- Replace complex setup module with simple hello_tinytorch() function
- Keep only the ASCII art file (tinytorch_flame.txt) for visual appeal
- Simplify tests to just verify function runs and file exists
- Update README to reflect simplified purpose and usage
- Remove complex developer profiles and system info classes
- Focus on minimal introduction to TinyTorch workflow
🔧 Notebook Compatibility:
- Fix __file__ NameError in notebook environment
- Add try/except for file path detection
- Handle both script and notebook execution contexts
- Use os.getcwd() fallback when __file__ not available
📓 Notebook Execution:
- Execute setup_dev.ipynb with all outputs generated
- Beautiful ASCII art displays properly in notebook
- All test cells show expected results
- Student-friendly interactive experience
🎨 ASCII Art Display:
- Stunning flame ASCII art renders perfectly in notebook
- Full profile display with Tiny🔥Torch branding
- Interactive testing cells with clear outputs
- Students can see the ASCII art immediately
🧪 Testing:
- All 17 tests still pass after notebook conversion
- File loading works in both environments
- Comprehensive coverage maintained
- Ready for student use
Students can now run the setup_dev.ipynb notebook and immediately see the beautiful ASCII art output, making their first TinyTorch experience memorable and engaging
🎯 Student Version (Visible):
- All functions raise NotImplementedError('Student implementation required')
- Clear TODO instructions with specific guidance
- Test cells handle NotImplementedError gracefully
- Students must implement everything from scratch
🔧 Instructor Version (Hidden with #|hide):
- Complete working implementations
- Same function signatures as student version
- Production-ready code for package export
📚 Added MODULE_GENERATION_GUIDE.md:
- Clear instructions for creating student/instructor modules
- NBDev workflow documentation
- Module structure patterns and templates
- Generation commands and checklist
🔄 Generation Flow:
1. Write .py with student stubs + hidden instructor solutions
2. Convert to notebook: jupytext --to notebook module_dev.py
3. Export package: python bin/tito.py sync --module name
4. Package gets instructor solutions, students see exercises
Perfect foundation for all TinyTorch modules
- Focused on original purpose: just setting up development environment
- Students don't see NBDev educational features explanations
- Simple workflow: hello world, basic class, export, test, progress
- NBDev #|hide directive works behind scenes for instructor solutions
- Clean and simple, just like the original but with hidden solutions
Back to basics: setup is about setup, not teaching NBDev features
SINGLE SOURCE approach: One notebook serves both instructors and students
🎯 Key Features:
- #|hide directive hides complete solutions from students
- #|code-fold creates collapsible sections for details
- #|export with educational metadata
- Progressive learning: simple → ML-relevant complexity
- Vector operations (add, dot product) as ML foundations
- ML-aware SystemInfo class with library checking
- Comprehensive testing for both student/instructor versions
🔄 Workflow:
- Students see exercises with TODOs and hints
- Instructors see complete solutions (hidden by default)
- Package exports get instructor (complete) implementations
- Single notebook maintains both audiences
This establishes the pattern for all TinyTorch modules
