- Remove redundant fields from module.yaml files: exports_to, files, components
- Keep only essential system metadata: name, title, description, dependencies
- Export command now reads actual export targets from dev files (#| default_exp directive)
- Status command updated to use dev files as source of truth for export targets
- Export command shows detailed source → target mapping for better clarity
- Dependencies field retained as it's useful for CLI module ordering and prerequisites
- Eliminates duplication between YAML and dev files - dev files are the real truth
- Add Module Info sections with difficulty ratings to all README.md files
- Use consistent 4-star difficulty scale: ⭐ Beginner, ⭐⭐ Intermediate, ⭐⭐⭐ Advanced, ⭐⭐⭐⭐ Expert
- Include time estimates, prerequisites, and next steps for each module
- Maintain clear separation: README.md = student experience, module.yaml = system metadata
- Difficulty progression: Setup (⭐) → Tensor/Activations/Layers (⭐⭐) → Networks/CNN/DataLoader (⭐⭐⭐) → Transformer (⭐⭐⭐⭐)
- Help students plan their learning journey and set appropriate expectations
- Add new CleanCommand for cleaning up module directories
- Supports cleaning notebooks (*.ipynb) and cache files (__pycache__, *.pyc)
- Can clean specific modules or all modules
- Provides preview of files to be cleaned with confirmation
- Includes --force flag to skip confirmation
- Integrates with module command group as 'tito module clean'
- Preserves Python source files (*_dev.py) and other important files
- Fixes issue with duplicate file removal from __pycache__ directories
- Delete bin/py_to_notebook.py and tito/tools/py_to_notebook.py
- Update notebooks command to use Jupytext directly
- Jupytext is already configured in all *_dev.py files
- Simpler, more standard workflow using established tools
- Better integration with NBDev ecosystem
Benefits:
- Eliminates duplicate conversion tools
- Uses industry-standard Jupytext instead of custom tool
- Reduces maintenance burden
- Better error handling and compatibility
- Remove version field from all module.yaml files
- Update template generator to exclude version field
- Further simplify metadata to focus on system information only
- Status remains dynamically determined by test results
- Reduce module.yaml files from 100+ lines to ~25 lines focused on system needs
- Remove pedagogical details (learning objectives, difficulty, time estimates)
- Keep only essential fields: name, title, description, status, dependencies, exports, files, components
- Update status command to work with simplified metadata format
- Update metadata generation script to create simplified templates
- Focus on system metadata for CLI tools and build systems, not educational content
Before: Verbose pedagogical metadata with 20+ fields
After: Concise system metadata with 8 core fields
This aligns with the principle that module.yaml should be for systems, not pedagogy.
- Changed all module titles to be short and clean (e.g., 'Autograd' not 'Autograd - Automatic Differentiation')
- Updated metadata generation template to use concise titles by default
- Fixed CLI reference in metadata generator to use new hierarchical structure
- Titles are now consistent: just the module name capitalized
- Detailed descriptions remain in the description field where they belong
- Add module.yaml files for setup, tensor, activations, layers, and autograd modules
- Enhanced tito status command with --metadata flag for rich information display
- Created metadata schema with learning objectives, dependencies, components, and more
- Added metadata generation script (bin/generate_module_metadata.py)
- Comprehensive documentation in docs/development/module-metadata-system.md
- Status command now shows module status, difficulty, time estimates, and detailed metadata
- Supports dependency tracking, component-level status, and educational information
- Enables rich CLI experience with structured module information
- 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 manual testing with pytest test classes and assertions
- Add comprehensive test coverage:
- Function execution without errors
- Correct output content validation
- ASCII art file existence and content checks
- Error handling and fallback behavior
- Missing file graceful handling
- Update README with pytest testing instructions
- Make testing consistent with other TinyTorch modules
- All 5 tests passing with proper pytest integration
- 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
🎨 File-Based ASCII Art System:
- Add beautiful tinytorch_flame.txt with sophisticated flame design
- Dynamic file loading with fallback to simple flame
- Easy customization - students can edit the file directly
- Proper error handling for missing files
🔥 Enhanced Visual Experience:
- Stunning detailed flame ASCII art from file
- 'Tiny🔥Torch' branding with 'Build ML Systems from Scratch!' tagline
- Much more sophisticated design than hardcoded version
- Students can easily customize by editing tinytorch_flame.txt
📚 Educational Benefits:
- Two customization options: parameter override or file editing
- Teaches file I/O and error handling concepts
- Encourages creative expression and ownership
- Professional development practices
🧪 Testing:
- Updated all tests to work with file-based system
- All 17 tests pass successfully
- Comprehensive coverage of file loading and fallback
- Verified both custom and default ASCII art functionality
Students can now easily create their own ASCII art by editing the tinytorch_flame.txt file or providing custom art via parameters
🎨 ASCII Art Features:
- Add beautiful TinyTorch flame ASCII art as default
- Support custom ASCII art for student personalization
- New get_ascii_art() and get_full_profile() methods
- Enhanced visual appeal and student engagement
👨💻 Profile Updates:
- Update instructor info: vj@eecs.harvard.edu, @profvjreddi
- Add comprehensive ASCII art examples and ideas
- Encourage creative student customization
- Professional signature generation with visual flair
🧪 Testing:
- Add 2 new ASCII art tests (17 total tests pass)
- Test default flame art and custom art functionality
- Verify full profile display with proper formatting
- Comprehensive coverage of all new features
Students can now create truly personalized TinyTorch experiences with custom ASCII art, making their learning journey unique and memorable
- Add DeveloperProfile class with customizable developer information
- Default to course instructor (Vijay Janapa Reddi) but allow student customization
- Include professional signature generation for code attribution
- Add comprehensive test coverage (6 new tests, 15 total tests pass)
- Encourage student ownership and personalization of their TinyTorch journey
- Maintain educational structure with TODO sections and hidden solutions
- Fix syntax errors in setup_dev.py test cells (proper indentation)
- Add comprehensive test suite for setup module (test_setup.py)
- Test coverage: functions, SystemInfo class, integration tests
- Custom test runner (no pytest dependency required)
- All 9 tests pass successfully
- Update .gitignore to allow modules/ directory for educational structure
- Move test_tensor.py to modules/tensor/tests/test_tensor.py
- Create tests/ directories in setup and tensor modules
- Clean up setup module: remove setup_dev.html, setup_dev_files/, __init__.py
- Establish clean module structure pattern:
* {module}_dev.py - Main development file
* {module}_dev.ipynb - Generated notebook
* README.md - Module documentation
* tests/ - Test directory with test_{module}.py
This makes it crystal clear to students where tests are located.
🎯 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
- Removed modules/setup/setup_nbdev_educational.* files
- Keep only the clean setup_dev.* files
- Setup module now focused and clean
- NBDev features work behind scenes without clutter
- 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
Introduces a README with project overview, setup instructions,
and course structure.
Adds a VISION document outlining the project's goals, conventions,
and architecture.
Includes updates to the setup module's README to clarify module to
package mapping.
✅ Setup Module Implementation:
- Created comprehensive setup_dev.ipynb with TinyTorch workflow tutorial
- Added hello_tinytorch(), add_numbers(), and SystemInfo class
- Updated README with clear learning objectives and development workflow
- All 11 tests passing for complete workflow validation
🔧 CLI Enhancements:
- Added --module flag to 'tito sync' for module-specific exports
- Implemented 'tito reset' command with --force option
- Smart auto-generated file detection and cleanup
- Interactive confirmation with safety preservations
📚 Documentation Updates:
- Updated all references to use [module]_dev.ipynb naming convention
- Enhanced test coverage for new functionality
- Clear error handling and user guidance
This establishes the foundation workflow that students will use throughout TinyTorch development.
Introduces a standardized module structure with README, notebooks, tutorials, tests, and solutions.
Refactors the project to emphasize a modular learning path, enhancing clarity and consistency across the TinyTorch course.
Changes the virtual environment path to ".venv".
Updates the project to use `.venv` as the standard virtual environment directory. This change:
- Updates `.gitignore` to ignore `.venv/`.
- Modifies the activation script to create and activate `.venv`.
- Adjusts the `tito.py` script to check for `.venv`'s existence and activation.
- Updates documentation and setup scripts to reflect the new virtual environment naming convention.
This change streamlines environment management and aligns with common Python practices.
