Files
TinyTorch/modules/setup
Vijay Janapa Reddi b9c2c6de6d Add ASCII art and enhance developer personalization
🎨 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
2025-07-10 19:11:41 -04:00
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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 SystemInfo class
  • 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 #| export to 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