TinyTorch/DECEMBER_2024_RELEASE.md

TinyTorch December 2024 Release

🎉 Announcement: TinyTorch is Ready for Community Review

TL;DR: Complete ML systems course (20 modules: Tensors → Transformers → Optimization) now available for pedagogical review. Seeking feedback on implementation quality, learning progression, and systems thinking approach.

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What is TinyTorch?

A Harvard University course teaching ML systems engineering by building a complete framework from scratch—no PyTorch or TensorFlow dependencies. Students implement every component: tensors, autograd, optimizers, CNNs, transformers, and optimization systems.

North Star Goal: Train CNNs on CIFAR-10 to 75%+ accuracy using only your own code + NumPy.

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What's Released (December 2024)

✅ Complete Implementation (All 20 Modules)

Part I: Neural Network Foundations (01-07)

• Tensors, Activations, Layers, Losses, Autograd, Optimizers, Training
• Milestone: Train XOR solver and MNIST classifier

Part II: Computer Vision (08-09)

• DataLoader, Spatial Convolutions (Conv2d, MaxPool2d)
• Milestone: CIFAR-10 @ 75%+ accuracy

Part III: Language Models (10-14)

• Tokenization, Embeddings, Attention, Transformers, KV-Caching
• Milestone: TinyGPT text generation

Part IV: System Optimization (15-20)

• Profiling, Acceleration, Quantization, Compression, Benchmarking, Capstone
• Milestone: TinyMLPerf optimization competition

📚 Complete Documentation

• Jupyter Book: Full course website with learning guides
• Inline Tests: Immediate validation in every module
• Historical Milestones: 6 demos (1957 Perceptron → 2024 Systems)
• CLI System: tito command-line tool for student workflow

🔧 Infrastructure

• NBGrader integration for classroom deployment
• Comprehensive testing suite (200+ tests)
• Student version generation tooling (untested)
• GitHub Actions for book deployment

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What We're Seeking Feedback On

1. Pedagogical Progression

• Do modules build logically? (Tensor → Autograd → CNNs → Transformers)
• Are learning objectives clear?
• Does "Build → Use → Understand" framework work?

2. Implementation Quality

• Code clarity and readability
• Educational value of inline tests
• Balance of guidance vs. challenge

3. Systems Thinking

• Memory management lessons
• Performance analysis integration
• Real-world ML engineering patterns

4. Documentation

• Jupyter Book clarity
• Module README completeness
• Getting started experience

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How to Review

Quick Look (15 minutes)

# Browse the Jupyter Book
open https://mlsysbook.github.io/TinyTorch/

Deep Dive (2-4 hours)

# Clone and run implementations
git clone https://github.com/mlsysbook/TinyTorch.git
cd TinyTorch
./setup-environment.sh
source activate.sh

# Try building a module
cd modules/source/01_tensor
python tensor_dev.py

# Check a milestone
cd ../../../milestones/03_1986_mlp_revival
python mlp_mnist.py

Provide Feedback

• GitHub Issues: Specific bugs or improvements
• GitHub Discussions: General feedback, pedagogical suggestions
• Email: vijay@seas.harvard.edu for detailed reviews

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What's NOT Ready (Yet)

🚧 Student Version Tooling

• Scripts exist to generate student versions (remove solutions)
• Not yet validated with real students
• Planned for testing: January-March 2025

🚧 Classroom Deployment

• NBGrader workflows need instructor testing
• Grading rubrics need validation
• First classroom use: Fall 2025 (tentative)

🚧 Known Issues

• Modules 15-20 (optimization tier) are functional but need polish
• Some inline tests could use better explanations
• Book could use more cross-referencing

We're being honest: This release prioritizes complete implementations for review over polished student experience.

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Why Solutions Are Public

Philosophy: Modern ML education values pedagogy over secrecy.

For Reviewers: You need to see complete implementations to evaluate educational quality.

For Students: TinyTorch's progressive complexity makes copying ineffective. Module 05 (Autograd) exposes shallow understanding from earlier modules. Learning comes from struggle, not copying.

For Instructors: See STUDENT_VERSION_TOOLING.md for classroom strategies.

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Timeline

• December 2024: Community review of complete implementations
• January-March 2025: Incorporate feedback, test student version tooling
• April-May 2025: Finalize classroom workflows
• Fall 2025: Potential first classroom deployment

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Who Should Review This?

✅ Perfect For:

• ML educators considering systems-focused courses
• ML engineers evaluating educational materials
• Students interested in deep understanding (not just API usage)
• Open-source contributors wanting to improve ML education

⚠️ Not Yet For:

• Instructors needing classroom-ready materials immediately
• Students expecting polished MOOC experience
• Organizations requiring production-ready framework

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Acknowledgments

Created by: Prof. Vijay Janapa Reddi, Harvard University

Inspired by: FastAI (pedagogy), MiniTorch (Cornell), micrograd (Karpathy), tinygrad (Hotz)

Community: Thanks to early testers and feedback providers

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Links

• Jupyter Book: https://mlsysbook.github.io/TinyTorch/
• GitHub: https://github.com/mlsysbook/TinyTorch
• Issues: https://github.com/mlsysbook/TinyTorch/issues
• Discussions: https://github.com/mlsysbook/TinyTorch/discussions

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Quick Facts

• 20 modules (Tensor → Capstone)
• 6 historical milestones (1957 Perceptron → 2024 Systems)
• 200+ tests (integration + unit)
• Zero external ML dependencies (only NumPy)
• MIT License (open source)
• Harvard course (academic-quality materials)

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Call to Action

We need your feedback to make TinyTorch exceptional.

• 📖 Read the book: https://mlsysbook.github.io/TinyTorch/
• 💻 Try the code: git clone https://github.com/mlsysbook/TinyTorch.git
• 💬 Share feedback: GitHub Issues or Discussions
• 🌟 Star the repo: Help others discover it
• 📢 Spread the word: Share with ML educators and engineers

Goal: Build the best ML systems education materials through community collaboration.

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Thank you for helping us improve ML systems education!

— Prof. Vijay Janapa Reddi, Harvard University