TinyTorch/docs

📚 TinyTorch Documentation

Complete documentation for the TinyTorch ML Systems course.

🎯 Quick Navigation

For Students 👨‍🎓

• Project Guide - Complete course navigation and progress tracking
• Start Here: modules/setup/README.md - First module setup

For Developers 👨‍💻

• Development Guide - Complete methodology and best practices
• Quick Reference - Commands and essential patterns
• Creation Checklist - Step-by-step module creation

For Instructors 👨‍🏫

• Pedagogical Principles - Educational philosophy and learning theory
• Testing Architecture - Assessment and verification strategy

📁 Documentation Structure

Development (development/)

For module developers and contributors

• module-development-guide.md - Complete development methodology
• quick-module-reference.md - Fast reference for commands and patterns
• module-creation-checklist.md - Comprehensive step-by-step process
• module-template.md - Reusable template snippets

Students (students/)

For course participants

• project-guide.md - Course navigation and module progression

Pedagogy (pedagogy/)

For instructors and educational design

• pedagogical-principles.md - Educational philosophy and learning theory
• testing-architecture.md - Assessment strategy and testing patterns
• vision.md - Course vision and goals

🚀 Quick Commands Reference

System Commands

tito system info              # System information and course navigation
tito system doctor            # Environment diagnosis
tito system jupyter           # Start Jupyter Lab

Module Development

tito module status            # Check all module status
tito module test --module X   # Test specific module
tito module notebooks --module X  # Convert Python to notebook

Package Management

tito package sync            # Export notebooks to package
tito package sync --module X # Export specific module
tito package reset           # Reset package to clean state

🎓 Educational Philosophy

TinyTorch follows a "Build → Use → Understand → Repeat" methodology where students:

1. Build - Implement core ML components from scratch
2. Use - Apply their implementations to real problems
3. Understand - Reflect on design decisions and trade-offs
4. Repeat - Apply learnings to increasingly complex systems

Key Principles

• Real Data, Real Systems - Use production datasets and realistic constraints
• Progressive Complexity - Build understanding step by step
• Systems Thinking - Connect to production ML engineering practices
• Immediate Feedback - Students see their code working quickly

🛠️ Development Workflow

For New Modules

1. Plan - Choose real datasets, define learning objectives
2. Implement - Write complete working version first
3. Structure - Add educational content and TODO guidance
4. Test - Comprehensive testing with real data
5. Export - Convert to notebooks and export to package

For Students

1. Setup - Complete environment setup in modules/setup/
2. Develop - Work in modules/{name}/{name}_dev.py files
3. Export - Use tito package sync to build package
4. Test - Use tito module test to verify implementation
5. Progress - Use tito module status to track completion

📊 Course Structure

TinyTorch is organized into progressive modules:

• Setup - Development environment and workflow
• Tensor - Core data structures and operations
• Layers - Neural network building blocks
• Networks - Complete model architectures
• Training - Optimization and learning algorithms
• Advanced - Production systems and MLOps

Each module builds on previous ones, creating a complete ML systems engineering curriculum.

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💡 Pro Tip: Start with the Project Guide if you're a student, or the Development Guide if you're creating modules.