From b77224ba86c4fc23cfe93fd9017ace5e9952fcf1 Mon Sep 17 00:00:00 2001 From: Vijay Janapa Reddi Date: Sun, 9 Nov 2025 14:42:08 -0500 Subject: [PATCH] docs: add December 2024 release documentation - DECEMBER_2024_RELEASE.md: Release announcement template - RELEASE_CHECKLIST.md: Pre-release checklist and validation steps - STUDENT_VERSION_TOOLING.md: Documentation for untested student generation tooling --- DECEMBER_2024_RELEASE.md | 213 ++++++++++++++++++++++++++++++ RELEASE_CHECKLIST.md | 263 +++++++++++++++++++++++++++++++++++++ STUDENT_VERSION_TOOLING.md | 110 ++++++++++++++++ 3 files changed, 586 insertions(+) create mode 100644 DECEMBER_2024_RELEASE.md create mode 100644 RELEASE_CHECKLIST.md create mode 100644 STUDENT_VERSION_TOOLING.md diff --git a/DECEMBER_2024_RELEASE.md b/DECEMBER_2024_RELEASE.md new file mode 100644 index 00000000..ed1287ec --- /dev/null +++ b/DECEMBER_2024_RELEASE.md @@ -0,0 +1,213 @@ +# TinyTorch December 2024 Release + +## πŸŽ‰ Announcement: TinyTorch is Ready for Community Review + +**TL;DR**: Complete ML systems course (20 modules: Tensors β†’ Transformers β†’ Optimization) now available for pedagogical review. Seeking feedback on implementation quality, learning progression, and systems thinking approach. + +--- + +## What is TinyTorch? + +A Harvard University course teaching ML systems engineering by building a complete framework from scratchβ€”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. + +--- + +## What's Released (December 2024) + +### βœ… 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 + +### πŸ“š Complete Documentation + +- **Jupyter Book**: Full course website with learning guides +- **Inline Tests**: Immediate validation in every module +- **Historical Milestones**: 6 demos (1957 Perceptron β†’ 2024 Systems) +- **CLI System**: `tito` command-line tool for student workflow + +### πŸ”§ Infrastructure + +- NBGrader integration for classroom deployment +- Comprehensive testing suite (200+ tests) +- Student version generation tooling (untested) +- GitHub Actions for book deployment + +--- + +## What We're Seeking Feedback On + +### 1. **Pedagogical Progression** +- Do modules build logically? (Tensor β†’ Autograd β†’ CNNs β†’ Transformers) +- Are learning objectives clear? +- Does "Build β†’ Use β†’ 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 + +--- + +## How to Review + +### Quick Look (15 minutes) +```bash +# Browse the Jupyter Book +open https://mlsysbook.github.io/TinyTorch/ +``` + +### Deep Dive (2-4 hours) +```bash +# 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 + +--- + +## 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. + +--- + +## 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](STUDENT_VERSION_TOOLING.md) for classroom strategies. + +--- + +## 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 + +--- + +## Who Should Review This? + +### βœ… 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 + +--- + +## Acknowledgments + +**Created by**: [Prof. Vijay Janapa Reddi](https://vijay.seas.harvard.edu), Harvard University + +**Inspired by**: FastAI (pedagogy), MiniTorch (Cornell), micrograd (Karpathy), tinygrad (Hotz) + +**Community**: Thanks to early testers and feedback providers + +--- + +## 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 + +--- + +## Quick Facts + +- **20 modules** (Tensor β†’ Capstone) +- **6 historical milestones** (1957 Perceptron β†’ 2024 Systems) +- **200+ tests** (integration + unit) +- **Zero external ML dependencies** (only NumPy) +- **MIT License** (open source) +- **Harvard course** (academic-quality materials) + +--- + +## Call to Action + +**We need your feedback to make TinyTorch exceptional.** + +- πŸ“– Read the book: https://mlsysbook.github.io/TinyTorch/ +- πŸ’» Try the code: `git clone https://github.com/mlsysbook/TinyTorch.git` +- πŸ’¬ Share feedback: GitHub Issues or Discussions +- 🌟 Star the repo: Help others discover it +- πŸ“’ Spread the word: Share with ML educators and engineers + +**Goal**: Build the best ML systems education materials through community collaboration. + +--- + +**Thank you for helping us improve ML systems education!** + +β€” Prof. Vijay Janapa Reddi, Harvard University + diff --git a/RELEASE_CHECKLIST.md b/RELEASE_CHECKLIST.md new file mode 100644 index 00000000..b10cc6bb --- /dev/null +++ b/RELEASE_CHECKLIST.md @@ -0,0 +1,263 @@ +# December 2024 Release Checklist + +## Pre-Release (Complete Before Pushing) + +### Code & Documentation +- [ ] All 20 modules have complete implementations +- [ ] All inline tests pass when running modules +- [ ] README.md updated with December release notice +- [ ] STUDENT_VERSION_TOOLING.md created (explains untested tooling) +- [ ] DECEMBER_2024_RELEASE.md created (announcement template) +- [ ] Academic Integrity section added to README + +### Repository Cleanup +- [ ] Remove any temporary/debug files +- [ ] Update .gitignore if needed +- [ ] Verify no sensitive data in commits +- [ ] Clean up any TODOs or FIXMEs in visible code + +### Testing +- [ ] Run key module tests: `pytest tests/01_tensor tests/05_autograd tests/09_spatial` +- [ ] Verify book builds locally: `cd book && jupyter-book build .` +- [ ] Check that setup script works: `./setup-environment.sh` +- [ ] Test at least one milestone: `python milestones/03_1986_mlp_revival/mlp_mnist.py` + +--- + +## Release Day (Execution) + +### 1. Merge to Main (30 minutes) +```bash +cd /Users/VJ/GitHub/TinyTorch + +# 1. Ensure you're on your working branch +git status + +# 2. Commit all changes +git add README.md STUDENT_VERSION_TOOLING.md DECEMBER_2024_RELEASE.md RELEASE_CHECKLIST.md +git commit -m "Prepare December 2024 release with complete implementations" + +# 3. Switch to main and merge +git checkout main +git merge optimization-tier-restructure --no-ff -m "Release December 2024: Complete 20-module implementation" + +# 4. Push to GitHub +git push origin main + +# 5. Verify GitHub Actions triggered +# Go to: https://github.com/mlsysbook/TinyTorch/actions +# Confirm "Deploy TinyTorch Jupyter Book" workflow started +``` + +### 2. Verify Deployment (10 minutes) +```bash +# Wait 5-10 minutes for GitHub Actions to complete + +# Check deployment status +open https://github.com/mlsysbook/TinyTorch/actions + +# Verify book is live +open https://mlsysbook.github.io/TinyTorch/ + +# Test these critical pages: +# - Home page loads +# - Chapter navigation works +# - At least 3 module chapters render correctly +``` + +### 3. Create GitHub Release (15 minutes) +```bash +# Go to: https://github.com/mlsysbook/TinyTorch/releases/new + +Tag: v0.1.0-alpha +Release title: TinyTorch December 2024 Release - Community Review +Description: Copy from DECEMBER_2024_RELEASE.md + +Key sections: +- What's released +- What we're seeking feedback on +- How to review +- What's not ready yet +- Links to book and discussions + +Mark as: "Pre-release" (this is alpha quality) +``` + +### 4. Enable GitHub Discussions (5 minutes) +```bash +# Go to: https://github.com/mlsysbook/TinyTorch/settings + +# Enable: +- Discussions tab +- Issues (should already be enabled) +- Wiki (optional) + +# Create initial discussion categories: +- πŸ’¬ General Feedback +- πŸ“š Pedagogy & Learning Design +- πŸ’» Implementation Quality +- πŸ› Bugs & Issues +- πŸ’‘ Feature Suggestions +- πŸŽ“ Classroom Use (Future) +``` + +--- + +## Announcement (1-2 hours) + +### Prepare Announcement Text + +**Short Version (Twitter/LinkedIn - 280 chars)**: +``` +πŸš€ TinyTorch December Release: Build ML systems from scratch! + +20 modules: Tensors β†’ Transformers β†’ Optimization +Goal: CIFAR-10 CNNs @ 75%+ with YOUR code (no PyTorch!) + +Seeking feedback on pedagogy & implementation. + +πŸ“š Book: https://mlsysbook.github.io/TinyTorch/ +πŸ’» Repo: https://github.com/mlsysbook/TinyTorch + +#MachineLearning #MLSystems #Education +``` + +**Medium Version (Blog post - 500 words)**: +Use DECEMBER_2024_RELEASE.md sections: +- What is TinyTorch +- What's released +- What we're seeking feedback on +- How to review +- Links + +**Long Version (Academic announcement - 1000 words)**: +Full DECEMBER_2024_RELEASE.md content + +### Distribution Channels + +#### Academic +- [ ] Harvard SEAS mailing list +- [ ] ML education forums (e.g., MLSys community) +- [ ] Academic Twitter/X + +#### Technical Community +- [ ] Hacker News (Show HN: TinyTorch - Build ML systems from scratch) +- [ ] Reddit r/MachineLearning (appropriate day/time) +- [ ] LinkedIn post (tag relevant educators/engineers) +- [ ] Twitter/X thread (break down into tweet storm) + +#### Direct Outreach +- [ ] Email to ML educator colleagues (personal note) +- [ ] Reach out to PyTorch/FastAI communities +- [ ] Contact MiniTorch maintainers (Cornell) - as peer project +- [ ] Share with Karpathy, George Hotz communities (related projects) + +--- + +## Post-Release Monitoring (First Week) + +### Daily Tasks +- [ ] Check GitHub Issues (respond within 24 hours) +- [ ] Monitor Discussions (participate actively) +- [ ] Track analytics (GitHub stars, book views if available) +- [ ] Respond to Twitter/LinkedIn comments +- [ ] Collect feedback in organized notes + +### Weekly Tasks +- [ ] Summarize feedback themes +- [ ] Identify critical bugs vs. enhancements +- [ ] Prioritize based on community input +- [ ] Update project roadmap based on feedback + +### What to Look For +1. **Critical bugs** - breaks setup or core modules β†’ fix immediately +2. **Pedagogical gaps** - unclear instructions, missing context +3. **Technical issues** - implementation problems, incorrect code +4. **Feature requests** - nice-to-have but not blocking + +--- + +## Success Metrics (First Month) + +### Quantitative +- [ ] GitHub Stars: Target 100+ in first month +- [ ] Issues/Discussions: Active engagement (20+ threads) +- [ ] Book views: Analytics showing page visits +- [ ] Forks: Community interest in contributing + +### Qualitative +- [ ] Positive feedback on pedagogical approach +- [ ] Constructive technical feedback incorporated +- [ ] Interest from other instructors +- [ ] Community contributions (PRs, issues) + +--- + +## Contingency Plans + +### If Book Doesn't Deploy +```bash +# Manual deployment +cd book +jupyter-book clean . && jupyter-book build . +# Upload _build/html/ to GitHub Pages manually +``` + +### If Critical Bug Found +```bash +# Hot fix workflow +git checkout main +git checkout -b hotfix-issue-123 +# Make fix +git commit -m "Fix critical bug in tensor operations" +git push origin hotfix-issue-123 +# Merge immediately to main +``` + +### If Negative Reception +- Don't panic +- Listen to feedback +- Acknowledge legitimate concerns +- Focus on improvement, not defense +- Remember: this is alpha, feedback is the goal + +--- + +## After First Month + +### Review & Plan +- [ ] Compile all feedback into summary doc +- [ ] Identify patterns in feedback +- [ ] Create prioritized improvement roadmap +- [ ] Decide on timeline for next release + +### Next Steps Decision +1. **If feedback is mostly positive**: + - Focus on polishing existing modules + - Begin student version testing + +2. **If significant issues found**: + - Address critical problems first + - Delay student version work + +3. **If little engagement**: + - Re-evaluate announcement strategy + - Reach out to specific communities + - Consider why adoption is slow + +--- + +## Notes + +**Philosophy**: Ship early, get feedback, iterate based on real use. + +**Goal**: Not perfection, but improvement through community input. + +**Timeline**: December release β†’ January-March refinement β†’ Spring validation β†’ Fall classroom (maybe) + +**Mindset**: Academic software development is iterative. First release exposes blind spots. + +--- + +**Ready to ship?** Check off items above and execute! πŸš€ + diff --git a/STUDENT_VERSION_TOOLING.md b/STUDENT_VERSION_TOOLING.md new file mode 100644 index 00000000..e799c06f --- /dev/null +++ b/STUDENT_VERSION_TOOLING.md @@ -0,0 +1,110 @@ +# Student Version Generation Tooling + +## Status: 🚧 Available but Untested + +TinyTorch includes tooling to generate student versions from complete implementations, but these are **not yet validated for classroom use**. + +## Current State + +### βœ… What Exists +- **NBGrader integration**: `tito nbgrader generate [module]` +- **Student notebook generator**: `bin/generate_student_notebooks.py` +- **Solution markers**: Modules use `### BEGIN SOLUTION` / `### END SOLUTION` +- **Release workflow**: `tito nbgrader release [module]` + +### 🚧 What's Not Ready +- Student versions have not been tested with actual students +- NBGrader autograding rubrics need validation +- Generated notebooks may need manual review + +## Planned Workflow (Untested) + +### For Instructors (Future) + +```bash +# 1. Generate student version of a module +tito nbgrader generate 01_tensor + +# 2. Release to students (removes solutions) +tito nbgrader release 01_tensor + +# 3. Collect student submissions +tito nbgrader collect 01_tensor + +# 4. Auto-grade submissions +tito nbgrader autograde 01_tensor + +# 5. Generate feedback +tito nbgrader feedback 01_tensor +``` + +### Current Best Practice + +**December 2024 Release**: Focus on complete implementations for pedagogical review + +**Spring 2025**: Validate student version generation with pilot users + +**Fall 2025**: Full classroom deployment with tested workflows + +## Student Version Features (Planned) + +When mature, student versions will: +- Remove solutions between `### BEGIN SOLUTION` / `### END SOLUTION` markers +- Replace with `raise NotImplementedError()` stubs +- Preserve tests and validation cells +- Include hints and learning guidance +- Support NBGrader auto-grading + +## Contributing + +If you're interested in helping validate student version workflows: +1. Try generating student versions for modules 01-07 +2. Report issues with missing hints, unclear instructions, or broken tests +3. Suggest improvements to the pedagogical flow + +## Architecture + +### Markers System +```python +### BEGIN SOLUTION +# Complete implementation here +# This will be removed in student version +### END SOLUTION + +### BEGIN HIDDEN TESTS +# Tests hidden from students +# Used for auto-grading +### END HIDDEN TESTS +``` + +### Generation Process +1. Parse `_dev.py` files in `modules/source/` +2. Convert to notebooks using Jupytext +3. Remove solution blocks +4. Add NBGrader metadata +5. Output to `assignments/release/` + +## Why Not Ship Student Versions Now? + +1. **Focus on pedagogy first**: Get feedback on complete implementations +2. **Avoid half-baked releases**: Student versions need proper testing +3. **Community validation**: Let others help identify gaps before classroom use +4. **Honest communication**: Better to say "untested" than to ship broken workflows + +## Timeline + +- **December 2024**: Release complete implementations + this documentation +- **January-March 2025**: Community testing of student version generation +- **April 2025**: Validated student version workflows +- **Fall 2025**: Full classroom deployment + +--- + +**For December reviewers**: Please focus feedback on: +- Pedagogical progression (modules 01-20) +- Implementation quality and correctness +- Documentation clarity +- Learning objectives and systems thinking + +Student version feedback is welcome but secondary for now. +