Enhances the notebook by replacing some unicode characters with more standard and universally compatible symbols, improving the overall readability and user experience.
- Add template section to tensor, layers, activations, and cnn modules
- Create docs/development/module-template.md for future reference
- Clarify learning vs building structure consistently
- Show students where their code will live in the final package
- Decouple learning modules from production organization
- Add cnn_dev.py with NBDev educational pattern, Conv2D for-loop TODO, and all scaffolding
- Add README.md explaining learning goals, what is implemented vs provided, and rationale
- Add tests/test_cnn.py for basic correctness and shape tests
- Generate cnn_dev.ipynb for notebook workflow
- Add matmul_naive function with for-loop implementation for learning
- Update Dense layer to support both NumPy (@) and naive matrix multiplication
- Add comprehensive tests comparing both implementations (correctness & performance)
- Include step-by-step computation visualization for 2x2 matrices
- Fix missing imports in tensor.py and activations.py
- Export both tensor and activations modules to package
This provides students with immediate success using NumPy while allowing them to
understand the underlying computation through explicit for-loops. The scaffolding
includes performance comparisons and educational insights about why NumPy is faster.
- Add modules/networks/networks_dev.py and networks_dev.ipynb (Jupytext/nbdev educational pattern)
- Add comprehensive visualizations: architecture, data flow, layer analysis, network comparison
- Add modules/networks/README.md with learning goals, usage, and visualization docs
- Add modules/networks/tests/test_networks.py with thorough tests for composition, MLPs, and visualizations
- Register 'networks' in CLI info and test commands
- Update CLI info command to check layers/networks status
- This module focuses on forward pass only (no training yet)
- 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
��️ Major architectural improvement implementing clean separation of concerns:
✨ NEW: Activations Module
- Complete activations module with ReLU, Sigmoid, Tanh implementations
- Educational NBDev structure with student TODOs + instructor solutions
- Comprehensive testing suite (24 tests) with mathematical correctness validation
- Visual learning features with matplotlib plotting (disabled during testing)
- Clean export to tinytorch.core.activations
🔧 REFACTOR: Layers Module
- Removed duplicate activation function implementations
- Clean import from activations module: 'from tinytorch.core.activations import ReLU, Sigmoid, Tanh'
- Updated documentation to reflect modular architecture
- Preserved all existing functionality while improving code organization
🧪 TESTING: Comprehensive Test Coverage
- All 24 activations tests passing ✅
- All 17 layers tests passing ✅
- Integration tests verify clean architecture works end-to-end
- CLI testing with 'tito test --module' works for both modules
📦 ARCHITECTURE: Clean Dependency Graph
- activations (math functions) → layers (building blocks) → networks (applications)
- Separation of concerns: pure math vs. neural network components
- Reusable components across future modules
- Single source of truth for activation implementations
�� PEDAGOGY: Enhanced Learning Experience
- Week-sized chunks: students master activations, then build layers
- Clear progression from mathematical foundations to applications
- Real-world software architecture patterns
- Modular design principles in practice
This establishes the foundation for scalable, maintainable ML systems education.
✨ Features:
- Dense layer with Xavier initialization (y = Wx + b)
- Activation functions: ReLU, Sigmoid, Tanh
- Layer composition for building neural networks
- Comprehensive test suite (17 passed, 5 skipped stretch goals)
- Package-level integration tests (14 passed)
- Complete documentation and examples
🎯 Educational Design:
- Follows 'Build → Use → Understand' pedagogical framework
- Immediate visual feedback with working examples
- Progressive complexity from simple layers to full networks
- Students see neural networks as function composition
🧪 Testing Architecture:
- Module tests: 17/17 core tests pass, 5 stretch goals available
- Package tests: 14/14 integration tests pass
- Dual testing supports both learning and validation
📚 Complete Implementation:
- Dense layer with proper weight initialization
- Numerically stable activation functions
- Batch processing support
- Real-world examples (image classification network)
- CLI integration: 'tito test --module layers'
This establishes the fundamental building blocks students need
to understand neural networks before diving into training.
- Fixed module-level tests to work with current implementation
- Added stretch goal testing pattern with pytest.skip()
- Created comprehensive testing documentation
- Both test levels now pass: 29 package tests + 22 module tests
- 11 stretch goals available for student implementation
Package tests (integration): ✅ 29/29 passed
Module tests (development): ✅ 22/22 passed, 11 skipped (stretch goals)
This provides immediate functionality for students while offering
clear implementation targets for advanced features.
- 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
- Fix CLI tool to look for tests in modules/{module}/tests/ instead of tests/
- Update test imports to use parent directory module imports
- Update Cursor rules to reflect new test structure:
* Project structure shows tests/ subdirectory
* Testing patterns show correct paths and import patterns
* Development workflow shows updated test locations
- Test imports now work: from tensor_dev import Tensor
- CLI commands now find tests in correct locations
Tests are now properly organized and discoverable
- 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
- Create tensor_nbdev_educational.py with NBDev directives
- Demonstrate #|hide, #|code-fold, #|filter_stream features
- Convert using standard Jupytext (# %% markers)
- Add comprehensive guides and comparisons
- Show superior approach vs custom generator
Key insight: NBDev already has mature educational capabilities!
No need to build custom tools when industry standards exist.
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.
Updates references to the development notebook naming convention from `[module].ipynb` to `[module]_dev.ipynb` in documentation. This change ensures consistency across the project and aligns with the intended naming scheme for development notebooks.
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.
Vijay Janapa Reddi