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Update README to reflect current repository state
- Add Harvard University badge and attribution - Document professional academic design improvements - Update quick start with virtual environment setup - Add Jupyter Book website information - Include instructor grading workflow with NBGrader - Add prerequisites and learning resources section - Update contributing and support information - Add citation format for academic use - Reflect 95% component reuse for TinyGPT - Clean title format (TinyTorch with fire emoji)
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Vijay Janapa Reddi
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README.md
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README.md
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# Tiny🔥Torch
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# TinyTorch 🔥
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**Build ML Systems From First Principles. From Computer Vision to Language Models.**
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[](https://python.org)
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[](https://mybinder.org/v2/gh/MLSysBook/TinyTorch/main)
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[](https://mlsysbook.github.io/TinyTorch/)
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[](https://harvard.edu)
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📚 **[Read the Interactive Course →](https://mlsysbook.github.io/TinyTorch/)**
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📚 **[Interactive Course Website →](https://mlsysbook.github.io/TinyTorch/)** | 🎓 **[Instructor Resources →](https://mlsysbook.github.io/TinyTorch/instructor-guide.html)**
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---
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## ✨ **New in v0.1**
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### **🎓 Professional Academic Design**
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- **Clean Typography**: Inter font family optimized for extended reading sessions
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- **Academic Styling**: Professional appearance matching top university courses
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- **Enhanced Readability**: Improved spacing, contrast, and visual hierarchy
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- **Responsive Design**: Beautiful on all devices from mobile to desktop
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### **🎯 Interactive ML Systems Learning**
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- **Interactive Questions**: Write 150-300 word reflections on ML systems design
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- **NBGrader Integration**: Automated assessment with instructor feedback
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- **Checkpoint System**: Track your capability progression through 16 checkpoints
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- **TinyGPT Module**: Build transformers showing 70% framework component reuse
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- **TinyGPT Module**: Build transformers showing 95% framework component reuse
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### **🛠️ Simplified Instructor Tools**
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- **`tito grade`**: Complete grading workflow wrapped in simple CLI commands
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- **Module Management**: Export, test, and validate all modules with one command
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- **Progress Tracking**: Visual checkpoint timeline showing student achievements
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- **Jupyter Book**: Professional course website with automatic chapter generation
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---
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## 🎓 **A Harvard University Course**
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Created by [Prof. Vijay Janapa Reddi](https://vijay.seas.harvard.edu) at Harvard University, TinyTorch provides Ivy League-quality education in ML systems engineering. This course represents the culmination of years of teaching experience and research in machine learning systems.
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## 🔬 **Why Build Your Own ML Framework?**
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**Most ML courses teach you to use frameworks. TinyTorch teaches you to understand them through systems engineering.**
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### 📊 **Step 1: Setup & System Check**
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```bash
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# Clone the repository
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git clone https://github.com/mlsysbook/TinyTorch.git
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cd TinyTorch
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pip install -r requirements.txt # Install all dependencies (numpy, jupyter, pytest, etc.)
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pip install -e . # Install TinyTorch package in editable mode
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tito system doctor # Verify your setup
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# Create virtual environment (recommended)
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python -m venv .venv
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source .venv/bin/activate # On macOS/Linux
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# OR: .venv\Scripts\activate # On Windows
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# Install dependencies and TinyTorch
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pip install -r requirements.txt # Install all dependencies
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pip install -e . # Install TinyTorch in editable mode
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# Verify your setup
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tito system doctor # Comprehensive system check
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```
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### 🎯 **Step 2: Start with Module 0 - Introduction**
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# ⏳ 02: Intelligence - "Can I add nonlinearity - the key to neural network intelligence?"
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```
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### 👩🏫 **Instructors**
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### 📚 **Step 5: Access the Interactive Course Website**
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```bash
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# System check
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tito system info
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tito system doctor
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# Build and view the Jupyter Book website locally
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tito book build # Generate the course website
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tito book serve # Launch local server (http://localhost:8000)
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# Module workflow with checkpoint integration
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tito module complete 01_setup # Export + test capability
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tito checkpoint status --detailed # Student progress overview
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tito checkpoint test 01 # Validate specific checkpoint
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# Or access online:
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# https://mlsysbook.github.io/TinyTorch/
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```
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# Traditional workflow (still available)
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tito export 01_setup
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tito test 01_setup
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tito nbdev build # Update package
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### 👩🏫 **For Instructors**
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```bash
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# Grading workflow with NBGrader
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tito grade setup # Configure NBGrader
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tito grade assign 02_tensor # Create student version
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tito grade collect # Collect submissions
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tito grade autograde 02_tensor # Automatic grading
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tito grade feedback 02_tensor # Generate feedback
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# Module management
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tito module complete 01_setup # Export + test capability
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tito checkpoint status --detailed # Student progress overview
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# Book management
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tito book build # Build course website
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tito book deploy # Deploy to GitHub Pages
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```
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---
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---
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## 📋 **Prerequisites & Learning Resources**
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### **Required Knowledge**
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- **Python Programming**: Intermediate level (classes, functions, NumPy basics)
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- **Linear Algebra**: Matrices, vectors, basic operations
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- **Calculus**: Derivatives and chain rule (for backpropagation)
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- **Basic ML Concepts**: What neural networks are (implementation not required)
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### **Recommended Resources**
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- 📖 **[Machine Learning Systems Book](https://mlsysbook.ai)** - Comprehensive ML systems context
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- 🎥 **Course Videos** - Coming soon on the course website
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- 💬 **Community Discord** - Join discussions with other learners
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- 📝 **[NBGrader Documentation](https://nbgrader.readthedocs.io)** - For instructors using autograding
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### **Time Commitment**
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- **Complete Course**: 60-80 hours (full implementation + exercises)
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- **Quick Exploration**: 10-15 hours (understand core concepts)
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- **Module Pace**: 3-5 hours per module average
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---
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## 🚀 **Your Learning Journey: Vision to Language**
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**TinyTorch demonstrates that the same mathematical foundations power both computer vision AND language models.**
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---
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</details>
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---
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## 🤝 **Contributing & Support**
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### **Getting Help**
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- 📚 **Course Website**: [mlsysbook.github.io/TinyTorch](https://mlsysbook.github.io/TinyTorch/)
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- 💬 **Issues**: [GitHub Issues](https://github.com/mlsysbook/TinyTorch/issues)
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- 📧 **Contact**: Course instructors through the website
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### **Contributing**
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We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for:
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- Code style guidelines
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- Testing requirements
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- Documentation standards
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- Pull request process
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### **Citation**
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If you use TinyTorch in your research or teaching, please cite:
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```bibtex
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@software{tinytorch2024,
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title = {TinyTorch: Build ML Systems From First Principles},
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author = {Reddi, Vijay Janapa},
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year = {2024},
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institution = {Harvard University},
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url = {https://github.com/mlsysbook/TinyTorch}
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}
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```
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### **License**
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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### **Acknowledgments**
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- Harvard University for supporting this educational initiative
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- Students who provided feedback and helped improve the course
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- The ML systems community for inspiration and guidance
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---
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**Built with ❤️ at Harvard University**
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_Start Small. Go Deep. Build ML Systems._
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