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Aligned website milestone documentation with the comprehensive README files: 1. Updated milestone naming consistency: - M06: '2024 Systems Age' → '2018 MLPerf' (historically accurate) - Updated Acts table to reflect correct module ranges 2. Fixed all script paths to match new naming convention: - M01: perceptron_trained.py → 01_rosenblatt_forward.py + 02_rosenblatt_trained.py - M02: xor_crisis folder → xor folder, updated script names - M05: vaswani_shakespeare.py → 01_vaswani_generation.py + 02_vaswani_dialogue.py - M06: optimize_models.py → 01_baseline_profile.py + 02_compression.py + 03_generation_opts.py 3. Enhanced M06 (MLPerf) section: - Added historical context (2018 MLCommons establishment) - Explained systematic optimization methodology - Included quantitative results (8-16× compression, 12-40× speedup) - Shows 3-script progressive optimization workflow 4. Maintained excellent 'Two Dimensions' framing: - Pedagogical Acts (WHY you're learning) - Historical Milestones (WHAT you can build) - Connection table showing how they relate Documentation hierarchy: Milestone READMEs are canonical source, website milestones.md provides overview + navigation.
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 book
jupyter-book build .
Output: _build/html/index.html
Option 2: PDF (Simple Method - Recommended)
No LaTeX installation required!
cd book
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 book
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
book/
├── _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):
- MLPerf® Edu 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 book book/_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.