The Release Check workflow is a comprehensive quality assurance system that validates TinyTorch meets all educational, technical, and documentation standards before any release.

Workflow Structure

The workflow consists of 6 parallel quality gates that run sequentially to ensure comprehensive validation:

Educational Standards → Implementation Standards → Testing Standards
        ↓                         ↓                       ↓
Package Integration → Documentation → Systems Analysis → Release Report

Quality Gates

1. Educational Validation

✅ Module structure and learning objectives
✅ Progressive disclosure patterns (no forward references)
✅ Cognitive load management
✅ NBGrader compatibility

2. Implementation Validation

✅ Time estimate consistency (LEARNING_PATH.md ↔ ABOUT.md)
✅ Difficulty rating consistency
✅ Testing patterns (test_unit_*, test_module())
✅ Dependency chain validation
✅ NBGrader metadata

3. Test Validation

✅ All unit tests passing
✅ Integration tests passing
✅ Checkpoint validation
✅ Test coverage ≥80%

4. Package Validation

✅ Export directives correct
✅ Import paths consistent
✅ Package builds successfully
✅ Installation works

5. Documentation Validation

✅ ABOUT.md files consistent
✅ Checkpoint markers in long modules
✅ Jupyter Book builds successfully

6. Systems Analysis Validation

✅ Memory profiling present
✅ Performance analysis included
✅ Production context provided

Triggering the Workflow

Manual Trigger (Recommended for Releases)

# Via GitHub UI:
# 1. Go to Actions → TinyTorch Release Check
# 2. Click "Run workflow"
# 3. Select:
#    - Release Type: patch | minor | major
#    - Check Level: quick | standard | comprehensive

Automatic Trigger (PRs)

The workflow runs automatically on:

Pull requests to main or dev branches
When PRs are opened or synchronized

Check Levels

Quick (5-10 minutes)

Essential validations only
Time estimates, difficulty ratings, testing patterns
Good for: Small fixes, documentation updates

Standard (15-20 minutes) - Default

All quality gates
Complete validation suite
Good for: Regular releases, feature additions

Comprehensive (30-40 minutes)

Extended testing
Performance benchmarks
Full documentation rebuild
Good for: Major releases, significant changes

Running Locally

You can run individual validation scripts before pushing:

# Time estimates
python .github/scripts/validate_time_estimates.py

# Difficulty ratings
python .github/scripts/validate_difficulty_ratings.py

# Testing patterns
python .github/scripts/validate_testing_patterns.py

# Checkpoint markers
python .github/scripts/check_checkpoints.py

Validation Scripts

Located in .github/scripts/:

Core Validators (Fully Implemented)

validate_time_estimates.py - Time consistency across docs
validate_difficulty_ratings.py - Star rating consistency
validate_testing_patterns.py - test_unit_* and test_module() patterns
check_checkpoints.py - Checkpoint markers in long modules (8+ hours)

Stub Validators (To Be Implemented)

validate_educational_standards.py - Learning objectives, scaffolding
check_learning_objectives.py - Objective alignment
check_progressive_disclosure.py - No forward references
validate_dependencies.py - Module dependency chain
validate_nbgrader.py - NBGrader metadata
validate_exports.py - Export directive validation
validate_imports.py - Import path consistency
validate_documentation.py - ABOUT.md validation
validate_systems_analysis.py - Memory/performance/production analysis

Release Report

After all gates pass, the workflow generates a comprehensive Release Readiness Report:

# TinyTorch Release Readiness Report

✅ Educational Standards
✅ Implementation Standards
✅ Testing Standards
✅ Package Integration
✅ Documentation
✅ Systems Analysis

Status: APPROVED FOR RELEASE

The report is:

✅ Uploaded as workflow artifact
✅ Posted as PR comment (if applicable)
✅ Includes quality metrics and module inventory

Integration with Agent Workflow

This GitHub Actions workflow complements the manual agent review process:

Agent-Driven Reviews (Pre-Release)

TPM coordinates:
├── Education Reviewer → Pedagogical validation
├── Module Developer → Implementation review
├── Quality Assurance → Testing validation
└── Package Manager → Integration check

Automated CI/CD (Every Commit/PR)

GitHub Actions runs:
├── Educational Validation
├── Implementation Validation
├── Test Validation
├── Package Validation
├── Documentation Validation
└── Systems Analysis Validation

Failure Handling

If any quality gate fails:

Workflow stops at the failed gate
Error details are displayed in the job log
PR is blocked (if configured)
Notifications sent to team

To fix:

Review the failed job log
Run the specific validation script locally
Fix the identified issues
Push changes
Workflow re-runs automatically

Configuration

Branch Protection

Recommended settings for main and dev branches:

# In GitHub Repository Settings → Branches
- Require status checks to pass before merging
  ✓ TinyTorch Release Check / educational-validation
  ✓ TinyTorch Release Check / implementation-validation
  ✓ TinyTorch Release Check / test-validation
  ✓ TinyTorch Release Check / package-validation
  ✓ TinyTorch Release Check / documentation-validation

Workflow Permissions

The workflow requires:

✅ Read access to repository
✅ Write access to pull requests (for comments)
✅ Artifact upload permissions

Continuous Improvement

The validation scripts are designed to evolve:

Adding New Validators

Create script in .github/scripts/
Add to appropriate job in release-check.yml
Update this README
Test locally before committing

Enhancing Existing Validators

Update script logic
Add tests for the validator itself
Document new checks in README
Version the changes

Success Metrics

Educational Excellence

All modules have consistent metadata
Progressive disclosure maintained
Cognitive load appropriate

Technical Quality

All tests passing
Package builds and installs correctly
Integration validated

Documentation Quality

All ABOUT.md files complete
Checkpoint markers in place
Jupyter Book builds successfully

Troubleshooting

Common Issues

"Time estimate mismatch"

Check LEARNING_PATH.md and module ABOUT.md
Ensure format: "X-Y hours" (with space)

"Missing test_module()"

Add integration test at end of module
Must be named exactly test_module()

"Checkpoint markers recommended"

Informational only for modules 8+ hours
Add 2+ checkpoint markers in ABOUT.md

"Build failed"

Check for Python syntax errors
Verify all dependencies in requirements.txt

Maintained by: TinyTorch Team Last Updated: 2024-11-24 Version: 1.0.0

README.md

TinyTorch Release Check Workflow

Overview