TinyTorch/site

TinyTorch Course Book

This directory contains the TinyTorch course content built with Jupyter Book.

🌐 View Online

Live website: https://mlsysbook.github.io/TinyTorch/

📚 Build Options

Option 1: HTML (Default Website)

cd site
jupyter-book build .

Output: _build/html/index.html

Option 2: PDF (Simple Method - Recommended)

No LaTeX installation required!

cd site
make install-pdf    # Install dependencies
make pdf-simple     # Build PDF

Output: _build/tinytorch-course.pdf

Option 3: PDF (LaTeX Method - Professional Quality)

Requires LaTeX installation (texlive, mactex, etc.)

cd site
make pdf

Output: _build/latex/tinytorch-course.pdf

🚀 Quick Commands

Using the Makefile (recommended):

make html        # Build website
make pdf-simple  # Build PDF (no LaTeX needed)
make pdf         # Build PDF via LaTeX
make clean       # Remove build artifacts
make install     # Install dependencies
make install-pdf # Install PDF dependencies

Using scripts directly:

./build_pdf_simple.sh  # PDF without LaTeX
./build_pdf.sh         # PDF with LaTeX

📖 Detailed Documentation

See PDF_BUILD_GUIDE.md for:

• Complete setup instructions
• Troubleshooting guide
• Configuration options
• Build performance details

🏗️ Structure

site/
├── _config.yml              # Jupyter Book configuration
├── _toc.yml                 # Table of contents
├── chapters/                # Course chapters (01-20)
├── _static/                 # Images, CSS, JavaScript
├── intro.md                 # Book introduction
├── quickstart-guide.md      # Quick start for students
├── tito-essentials.md       # CLI reference
└── ...                      # Additional course pages

🎯 Content Overview

📚 20 Technical Chapters

Foundation Tier (01-07):

• Tensor operations, activations, layers, losses, autograd, optimizers, training

Architecture Tier (08-13):

• DataLoader, convolutional networks (CNNs), tokenization, embeddings, attention, transformers

Optimization Tier (14-19):

• Profiling, memoization (KV caching), quantization, compression, acceleration, benchmarking

Capstone (20):

• Torch Olympics Competition project

🔧 Development

Local Development Server

jupyter-book build . --path-output ./_build-dev
python -m http.server 8000 -d _build-dev/html

Visit: http://localhost:8000

Auto-rebuild on Changes

pip install sphinx-autobuild
sphinx-autobuild site site/_build/html

🤝 Contributing

To contribute to the course content:

1. Edit chapter files in chapters/
2. Test your changes: jupyter-book build .
3. Preview in browser: Open _build/html/index.html
4. Submit PR with your improvements

📦 Dependencies

Core dependencies are in requirements.txt:

• jupyter-book
• numpy, matplotlib
• sphinxcontrib-mermaid
• rich (for CLI output)

PDF dependencies (optional):

• pyppeteer (HTML-to-PDF, no LaTeX)
• LaTeX distribution (for pdflatex method)

🎓 For Instructors

Using this book for teaching:

1. Host locally: Build and serve on your institution's server
2. Customize content: Modify chapters for your course
3. Generate PDFs: Distribute offline reading material
4. Track progress: Use the checkpoint system for assessment

See instructor guide for more details.

📝 License

MIT License - see LICENSE file in repository root

🐛 Issues

Report issues: https://github.com/mlsysbook/TinyTorch/issues

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