78b669329f
Add JavaScript to dynamically fix GIF image paths in hero carousel for both main site and dev subdirectory deployments. The fix detects the current page directory and prepends it to relative _static paths.
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:
- Edit chapter files in
chapters/ - Test your changes:
jupyter-book build . - Preview in browser: Open
_build/html/index.html - 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:
- Host locally: Build and serve on your institution's server
- Customize content: Modify chapters for your course
- Generate PDFs: Distribute offline reading material
- 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
Build ML systems from scratch. Understand how things work.