Documentation updates across the codebase:
Root documentation:
- README.md: Updated references from book/ to site/
- CONTRIBUTING.md: Updated build and workflow instructions
- .shared-ai-rules.md: Updated AI assistant rules for new structure
GitHub configuration:
- Issue templates updated for new module locations
- Workflow references updated from book/ to site/
docs/ updates:
- STUDENT_QUICKSTART.md: New paths and structure
- module-rules.md: Updated module development guidelines
- NBGrader documentation: Updated for module restructuring
- Archive documentation: Updated references
Module documentation:
- modules/17_memoization/README.md: Updated after reordering
All documentation now correctly references:
- site/ instead of book/
- modules/XX_name/ instead of modules/source/
- Replace all 'python bin/tito.py' references with correct 'tito' commands
- Update command structure to use proper subcommands (tito system info, tito module test, etc.)
- Add virtual environment activation to all workflows
- Update Makefile to use correct tito commands with .venv activation
- Update activation script to use correct tito path and command examples
- Add Tiny🔥Torch branding to activation script header
- Update documentation to reflect correct CLI usage patterns
- 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
- Delete _proc/ directory (duplicate of modules/ with processed files)
- Delete bin/tito.py (old CLI implementation, replaced by tito/ structure)
- Delete temporary log files (tito-cli.log, tinytorch-cli.log)
- Update bin/tito wrapper to use new CLI structure
- All CLI functionality preserved with new architecture
- 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.
- Moved tools/py_to_notebook.py to bin/py_to_notebook.py
- Updated tito.py to reference the new location
- Made py_to_notebook.py executable for direct invocation
- Removed empty tools/ directory
- Updated documentation to reflect new location
- All tools now consolidated in bin/ directory for consistency
Benefits:
- Conventional organization (bin/ for executables)
- Can invoke tools directly: ./bin/py_to_notebook.py
- Cleaner project structure
- Consistent with other tools (tito.py, generate_student_notebooks.py)
- 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
- Added 'tito notebooks' command to build all module notebooks from *_dev.py files
- Added 'tito notebooks --module <name>' for building specific module notebooks
- Uses existing tools/py_to_notebook.py for conversion
- Provides clear success/error feedback with Rich formatting
- Includes helpful next steps guidance after generation
��️ 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.
- 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
- 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
✅ 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.
Extends the sync command to allow users to synchronize
specific modules instead of the entire project. This
improves efficiency by reducing the scope of the nbdev
export process. Adds argument parsing for module selection.
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.
Introduces a command-line interface (CLI) named 'tito'
to streamline project setup, testing, and information retrieval.
Includes a setup script to automate virtual environment
creation and dependency installation.
Improves the user experience by providing clear instructions
and status indicators within the CLI.
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.
