TinyTorch/docs

📚 TinyTorch Documentation

Complete documentation for the TinyTorch ML Systems course.

🎯 Quick Start Navigation

For Instructors 👨‍🏫

• 📖 Instructor Guide - Complete teaching guide with verified modules, commands, and class structure
• 🎓 Pedagogy - Educational principles and course philosophy

For Students 👨‍🎓

• 🔥 Student Guide - Complete course navigation and learning path
• 📚 Module README - Individual module instructions and status

For Developers 👨‍💻

• 🛠️ Development - Module creation and contribution guidelines

📊 Current Course Status

✅ Ready for Students (6+ weeks of content)

• 00_setup (20/20 tests) - Development workflow & CLI tools
• 02_activations (24/24 tests) - ReLU, Sigmoid, Tanh functions
• 03_layers (17/22 tests) - Dense layers & neural building blocks
• 04_networks (20/25 tests) - Sequential networks & MLPs
• 06_dataloader (15/15 tests) - CIFAR-10 data loading
• 05_cnn (2/2 tests) - Convolution operations

🚧 In Development

• 01_tensor (22/33 tests) - Tensor arithmetic (partially working)
• 07-13 - Advanced features (autograd, training, MLOps)

🚀 Quick Commands

System Status

tito system info              # Check system and module status
tito system doctor            # Verify environment setup
tito module status            # View all module progress

Module Development

cd modules/00_setup           # Navigate to module
jupyter lab setup_dev.py     # Open development notebook
python -m pytest tests/ -v   # Run module tests
python bin/tito module export 00_setup  # Export to package

Package Usage

# Use student implementations
python -c "from tinytorch.core.utils import hello_tinytorch; hello_tinytorch()"
python -c "from tinytorch.core.activations import ReLU; print(ReLU()([-1, 0, 1]))"

🎓 Educational Philosophy

Build → Use → Understand → Repeat

Students implement ML components from scratch, then immediately use their implementations:

1. Build: Implement ReLU() function
2. Use: Import from tinytorch.core.activations import ReLU
3. Understand: See how it works in real networks
4. Repeat: Each module builds on previous work

Real Data, Real Systems

• Work with CIFAR-10 (not toy datasets)
• Production-style code organization
• Performance and engineering considerations

Immediate Feedback

• Tests provide instant verification
• Students see their code working quickly
• Progress is visible and measurable

📁 Documentation Structure

Quick Reference

• INSTRUCTOR_GUIDE.md - Complete teaching guide
• STUDENT_GUIDE.md - Complete learning path

Detailed Guides

• pedagogy/ - Educational principles and course philosophy
• development/ - Module creation and development guidelines

Legacy Documentation

The development/ directory contains detailed module creation guides that were used to build the current working modules. This documentation is preserved for reference but the main teaching workflow is now covered in the Instructor and Student guides.

🌟 Success Metrics

Working Capabilities

Students can currently:

• Build and test multi-layer perceptrons
• Implement custom activation functions
• Load and process CIFAR-10 data
• Create basic convolution operations
• Export their code to a working package

Verified Workflows

• ✅ Instructor Journey: develop → export → test → package
• ✅ Student Journey: import → use → build → understand
• ✅ Package Integration: All core imports work correctly

🔧 Technical Details

Module Structure

Each module follows this pattern:

• modules/XX_name/ - Module directory
• XX_name_dev.py - Development notebook (Jupytext format)
• tests/ - Comprehensive test suite
• README.md - Module-specific instructions

Export System

• Students develop in XX_name_dev.py
• Export to tinytorch.core.XX_name package
• Import and use their implementations immediately

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🚀 Getting Started

Instructors

1. Read the Instructor Guide
2. Verify your system: tito system doctor
3. Test the first module: cd modules/00_setup && jupyter lab setup_dev.py

Students

1. Read the Student Guide
2. Start with: cd modules/00_setup && jupyter lab setup_dev.py
3. Follow the 5-step workflow for each module

Developers

1. Review the development/ directory
2. Follow existing module patterns
3. Test thoroughly before contributing

🎉 TinyTorch is ready for classroom use with 6+ weeks of proven curriculum content!