TinyTorch/quickstart.md

🚀 TinyTorch Quick Start Guide

Get up and running with TinyTorch in 10 minutes! This guide will walk you through setting up your environment and implementing your first ML component.

📋 Prerequisites

• Python 3.8+ (check with python --version)
• Git for cloning the repository
• Basic Python knowledge (functions, classes, imports)
• Jupyter familiarity (we'll install it for you)

⚡ 5-Minute Setup

Step 1: Clone and Navigate

git clone https://github.com/tinytorch/TinyTorch.git
cd TinyTorch

Step 2: Create Virtual Environment

# Create isolated environment
python -m venv .venv

# Activate it
source .venv/bin/activate  # Linux/Mac
# OR: .venv\Scripts\activate  # Windows

Step 3: Install Dependencies

# Install all required packages
pip install -r requirements.txt

# This installs: numpy, matplotlib, jupyter, nbdev, pytest, and more

Step 4: Verify Installation

# Check TinyTorch CLI
python bin/tito.py --version

# Check environment
python bin/tito.py info

✅ If you see system information, you're ready to go!

🎯 Your First Module: Setup

Let's implement your first TinyTorch component to understand the workflow.

Step 1: Navigate to Setup Module

cd modules/setup/

Step 2: Read the Module Overview

# This explains what you'll build
cat README.md

Step 3: Open the Development Notebook

# Start Jupyter Lab
jupyter lab setup_dev.ipynb

# The notebook will open in your browser at http://localhost:8888

Step 4: Follow the Notebook

The notebook guides you through:

1. Environment check - Verify everything works
2. Hello function - Implement hello_tinytorch()
3. Export process - Learn the #| export directive
4. Testing - Run interactive tests

Step 5: Export Your Code

# Back in terminal (new tab/window):
cd /path/to/TinyTorch  # Navigate back to root

# Export notebook code to Python package
python bin/tito.py sync

This creates tinytorch/core/utils.py with your function!

Step 6: Test Your Implementation

# Run automated tests
python bin/tito.py test --module setup

# Should show: ✅ All tests passed!

Step 7: Verify Integration

# Test that your function is importable
python -c "from tinytorch.core.utils import hello_tinytorch; print(hello_tinytorch())"

🎉 Congratulations! You've completed your first module!

🔄 The TinyTorch Development Workflow

Now you understand the core workflow that you'll use for every module:

graph LR
    A[Read README] --> B[Open Notebook]
    B --> C[Implement Code]
    C --> D[Mark #|export]
    D --> E[Test in Notebook]
    E --> F[Export with tito sync]
    F --> G[Run Tests]
    G --> H{Tests Pass?}
    H -->|No| C
    H -->|Yes| I[Next Module]

Key Commands Reference

Command	Purpose	When to Use
cat README.md	Read module overview	Start of each module
jupyter lab [module]_dev.ipynb	Open development environment	Implement code
python bin/tito.py sync	Export notebooks to package	After coding in notebook
python bin/tito.py test --module [name]	Test your implementation	Verify correctness
python bin/tito.py info	Check system status	Anytime

🧗 Next Steps: Building Your First Tensor

Ready for the real challenge? Let's build the foundation of TinyTorch!

Step 1: Move to Tensor Module

cd modules/tensor/

Step 2: Read the Overview

cat README.md

Step 3: Start Building

jupyter lab tensor_dev.ipynb

In this module, you'll implement:

• Tensor class - Multi-dimensional arrays
• Arithmetic operations - Addition, multiplication
• Utility methods - Reshape, transpose, sum
• Error handling - Robust edge cases

Expected Timeline

• Setup module: 15-30 minutes
• Tensor module: 2-4 hours
• Complete course: 40-80 hours (over several weeks)

🛠️ Development Environment Tips

Jupyter Lab Shortcuts

• Shift + Enter: Run cell and move to next
• Ctrl + Enter: Run cell and stay
• A: Insert cell above
• B: Insert cell below
• DD: Delete cell

TinyTorch Best Practices

1. Read module READMEs first - Understand objectives
2. Test frequently - Don't implement everything at once
3. Use #| export correctly - Only mark code for the package
4. Write docstrings - Document your functions
5. Check tests - They show exactly what's expected

Common Issues and Solutions

"ModuleNotFoundError"

# Make sure you've activated your environment
source .venv/bin/activate

# And exported your notebooks
python bin/tito.py sync

"Command 'tito' not found"

# Use the full path
python bin/tito.py [command]

# Make sure you're in the TinyTorch root directory
pwd  # Should end with /TinyTorch

"Tests failing"

# Run with verbose output to see details
python bin/tito.py test --module setup -v

# Check the specific error messages
# Fix in the notebook, then re-export and test

"Jupyter not starting"

# Make sure it's installed
pip install jupyter jupyterlab

# Try classic notebook instead
jupyter notebook modules/setup/setup_dev.ipynb

📚 Learning Path

Beginner Track (Start Here)

1. Setup (30 min) - Learn the workflow
2. Tensor (4 hours) - Core data structures
3. MLP (6 hours) - Basic neural networks

Intermediate Track

4. Autograd (8 hours) - Automatic differentiation
5. CNN (6 hours) - Convolutional networks
6. Training (4 hours) - Training loops and optimizers

Advanced Track

7. Data (3 hours) - Efficient data loading
8. Kernels (10 hours) - Custom GPU operations
9. Compression (6 hours) - Model optimization

Expert Track

10. Profiling (4 hours) - Performance analysis
11. Benchmarking (4 hours) - Systematic evaluation
12. Config (2 hours) - Configuration management
13. MLOps (8 hours) - Production systems

🎯 Success Metrics

You'll know you're succeeding when:

After Each Module

• ✅ All tests pass: python bin/tito.py test --module [name]
• ✅ Code is importable: from tinytorch.core.X import Y
• ✅ You understand what you built
• ✅ Ready for the next module

After Major Milestones

• Tensor complete: Can create and manipulate multi-dimensional arrays
• MLP complete: Can build and train simple neural networks
• CNN complete: Can implement convolutional architectures
• Course complete: You've built a complete ML framework!

💡 Getting Help

Self-Help Resources

1. Module READMEs - Detailed explanations and tips
2. Test files - Show exactly what's expected
3. Notebook examples - See reference implementations
4. Error messages - Often contain helpful guidance

Debugging Workflow

1. Read the error - Understand what failed
2. Check the test - See what was expected
3. Review the notebook - Look for implementation issues
4. Test incrementally - Don't implement everything at once
5. Use print statements - Debug your logic

Community Support

• GitHub Issues - Report bugs or ask questions
• Discussions - Share tips and solutions
• Study Groups - Find fellow learners

🚀 Ready to Build?

You now have everything you need to start building ML systems from scratch!

# Quick reminder of the workflow:
cd modules/setup/          # Navigate to module
cat README.md             # Read overview
jupyter lab setup_dev.ipynb   # Start implementing
# [work in notebook]
python bin/tito.py sync   # Export to package
python bin/tito.py test --module setup  # Test implementation

Welcome to TinyTorch! Let's build something amazing! 🔥

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📖 What's Next?

After completing the QUICKSTART:

• Dive deeper: Read COURSE_GUIDE.md for the complete curriculum
• Understand the structure: Review STRUCTURE_PROPOSAL.md for architecture details
• Stay updated: Check README.md for the latest information

Happy coding! 🎉