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🔧 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
Module 0: Setup
Learning Objectives
This module teaches you the TinyTorch development workflow. By the end, you'll be comfortable with:
- Writing code in Jupyter notebooks using nbdev conventions
- Exporting notebook code to Python modules
- Running tests and using the TinyTorch CLI
- Understanding the development rhythm you'll use for all modules
What You'll Build
A simple "Hello World" system that demonstrates the complete development cycle:
- Basic utility functions
- A simple
SystemInfoclass - Tests to verify everything works
- Experience with the full notebook → export → test workflow
Module Structure
modules/setup/
├── setup_dev.ipynb # 📓 Main development notebook
├── README.md # 📖 This guide
└── __init__.py # 📦 Module marker
Development Workflow
1. Work in the Notebook
cd modules/setup
jupyter lab setup_dev.ipynb
2. Export Your Code
python bin/tito.py sync
3. Test Your Implementation
python bin/tito.py test --module setup
4. Check Your Progress
python bin/tito.py info
Key Concepts
- nbdev workflow: Write in notebooks, export to Python
- Export directive: Use
#| exportto mark code for export - Module → Package mapping: This module exports to
tinytorch/core/utils.py - Teaching vs. Building: Learn by modules, build by function (see VISION.md)
- Test integration: Tests run automatically via CLI
- Module development: Each module is self-contained
Success Criteria
✅ All tests pass
✅ Code exports cleanly to tinytorch/core/utils.py
✅ You understand the development rhythm
✅ Ready to tackle the Tensor module
Next Module: Tensor - Core data structures and operations