TinyTorch/RELEASE_CHECKLIST.md

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

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Release Day (Execution)

1. Merge to Main (30 minutes)

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)

# 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)

# 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)

# Go to: https://github.com/mlsysbook/TinyTorch/settings

# Enable:
- Discussions tab
- Issues (should already be enabled)
- Wiki (optional)

# Create initial discussion categories:
- 💬 General Feedback
- 📚 Pedagogy & Learning Design
- 💻 Implementation Quality
- 🐛 Bugs & Issues
- 💡 Feature Suggestions
- 🎓 Classroom Use (Future)

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Announcement (1-2 hours)

Prepare Announcement Text

Short Version (Twitter/LinkedIn - 280 chars):

🚀 TinyTorch December Release: Build ML systems from scratch!

20 modules: Tensors → Transformers → Optimization
Goal: CIFAR-10 CNNs @ 75%+ with YOUR code (no PyTorch!)

Seeking feedback on pedagogy & implementation.

📚 Book: https://mlsysbook.github.io/TinyTorch/
💻 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)

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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 → 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

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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

# Manual deployment
cd book
jupyter-book clean . && jupyter-book build .
# Upload _build/html/ to GitHub Pages manually

If Critical Bug Found

# 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

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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

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Notes

Philosophy: Ship early, get feedback, iterate based on real use.

Goal: Not perfection, but improvement through community input.

Timeline: December release → January-March refinement → Spring validation → Fall classroom (maybe)

Mindset: Academic software development is iterative. First release exposes blind spots.

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Ready to ship? Check off items above and execute! 🚀