- Update test, export, and clean commands to use positional arguments
- Change from 'tito module test --module dataloader' to 'tito module test dataloader'
- Eliminates redundant --module flag within module command group
- Update help text and examples to reflect new syntax
- Maintains backward compatibility with --all flag
- More intuitive and consistent CLI design
- 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
- Rename modules/data/ → modules/dataloader/
- Rename data_dev.py → dataloader_dev.py
- Update NBDev export target: core.data → core.dataloader
- Rename test files: test_data.py → test_dataloader.py
- Update package exports to tinytorch.core.dataloader
- Update module imports and internal references
This makes the module name more descriptive and aligned with ML industry standards.
- 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.
- Remove 14 empty/unused directories from tinytorch/ package
- Keep only essential directories: core/, datasets/, configs/
- All directories removed contained only empty __init__.py files or were completely empty
- CLI functionality preserved and tested working
- Cleaner package structure for development
- Ported all commands from bin/tito.py to new tito/ CLI architecture
- Added InfoCommand with system info and module status
- Added TestCommand with pytest integration
- Added DoctorCommand with environment diagnosis
- Added SyncCommand for nbdev export functionality
- Added ResetCommand for package cleanup
- Added JupyterCommand for notebook server
- Added NbdevCommand for nbdev development tools
- Added SubmitCommand and StatusCommand (placeholders)
- Fixed missing imports in tinytorch/core/tensor.py
- All commands now work with 'tito' command in shell
- Maintains professional architecture while restoring full functionality
Commands restored:
✅ info - System information and module status
✅ test - Run module tests with pytest
✅ doctor - Environment diagnosis
✅ sync - Export notebooks to package
✅ reset - Clean tinytorch package
✅ nbdev - nbdev development commands
✅ jupyter - Start Jupyter server
✅ submit - Module submission
✅ status - Module status
✅ notebooks - Build notebooks from Python files
The CLI now has both the professional architecture and all original functionality.
- 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.
Introduces a Tensor class that wraps numpy arrays, enabling
fundamental ML operations like addition, subtraction,
multiplication, and division.
Adds utility methods such as reshape, transpose, sum, mean, max,
min, item, and numpy to the Tensor class.
Updates tests to accommodate both scalar and Tensor results
when checking mean values.
✅ 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.
Sets up the foundational project structure for the TinyTorch ML system, including the CLI entry point, project directories, and setup scripts.
This commit introduces the `tito` CLI for project management, testing, and information display.
It also includes setup scripts to automate environment creation and verification, along with initial documentation.
Introduces the foundational CLI structure and core components for the TinyTorch project.
This initial commit establishes the command-line interface (CLI) using `argparse` for training, evaluation, benchmarking, and system information. It also lays out the basic directory structure and essential modules, including tensor operations, autograd, neural network layers, optimizers, data loading, and MLOps components.
