🔥 TinyTorch: Build ML Systems from Scratch

A complete Machine Learning Systems course where students build their own ML framework.

🎯 What You'll Build

• Complete ML Framework: Build your own PyTorch-style framework from scratch
• Real Applications: Use your framework to classify CIFAR-10 images
• Production Skills: Learn ML systems engineering, not just algorithms
• Immediate Feedback: See your code working at every step

🚀 Quick Start (2 minutes)

Students

git clone https://github.com/your-org/tinytorch.git
cd TinyTorch
make install                                    # Install dependencies
tito system doctor                              # Verify setup
cd assignments/source/00_setup                  # Start with setup
jupyter lab setup_dev.py                       # Open first assignment

Instructors

# System check
tito system info                                # Check course status
tito system doctor                              # Verify environment  

# Assignment management
tito nbgrader generate 00_setup                 # Create student assignments
tito nbgrader release 00_setup                  # Release to students
tito nbgrader autograde 00_setup                # Auto-grade submissions

📚 Course Structure

Core Assignments (6+ weeks of proven 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

Advanced Features (in development)

• 01_tensor (22/33 tests) - Tensor arithmetic
• 07-13 - Autograd, optimizers, training, MLOps

🛠️ Development Workflow

NBGrader (Assignment Creation & Testing)

tito nbgrader generate 00_setup     # Create student assignments
tito nbgrader release 00_setup      # Release to students
tito nbgrader collect 00_setup      # Collect submissions
tito nbgrader autograde 00_setup    # Auto-grade with pytest

nbdev (Package Export & Building)

tito module export 00_setup         # Export to tinytorch package
tito module test 00_setup           # Test package integration

📈 Student Success Path

Build → Use → Understand → Repeat

1. Build: Implement ReLU() function from scratch
2. Use: from tinytorch.core.activations import ReLU - your own code!
3. Understand: See how it works in real neural networks
4. Repeat: Each assignment builds on previous work

Example: First Assignment

# You implement this:
def hello_tinytorch():
    print("Welcome to TinyTorch!")

# Then immediately use it:
from tinytorch.core.utils import hello_tinytorch
hello_tinytorch()  # Your code working!

🎓 Educational Philosophy

Real Data, Real Systems

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

Build Everything from Scratch

• No black boxes or "magic" functions
• Understanding through implementation
• Connect every concept to production systems
• See your code working immediately

📁 Repository Structure

TinyTorch/
├── assignments/source/XX/          # Assignment source files
│   ├── XX_dev.py                   # Development assignment
│   └── tests/                      # Assignment tests
├── tinytorch/                      # Your built framework
│   └── core/                       # Exported student code
├── tito/                           # CLI tools
└── docs/                           # Documentation

🔧 Technical Requirements

• Python 3.8+
• Jupyter Lab for development
• PyTorch for comparison and final projects
• NBGrader for assignment management
• nbdev for package building

🎯 Getting Started

Students

1. System Check: tito system doctor
2. First Assignment: cd assignments/source/00_setup && jupyter lab setup_dev.py
3. Build & Test: Follow the notebook, export when complete
4. Use Your Code: from tinytorch.core.utils import hello_tinytorch

Instructors

1. Course Status: tito system info
2. Assignment Management: tito nbgrader generate 00_setup
3. Student Release: tito nbgrader release 00_setup
4. Auto-grading: tito nbgrader autograde 00_setup

📊 Success Metrics

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:

• ✅ Student Journey: receive assignment → implement → export → use
• ✅ Instructor Journey: create → release → collect → grade
• ✅ Package Integration: All core imports work correctly

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