About UnitSeeker

Hi! This PR is part of the UnitSeeker project, a human-guided initiative to help Python repositories establish testing infrastructure.

Key points:

• Human-approved: Every PR is manually approved before work begins
• Semi-automated with oversight: Created and controlled via a homegrown wrapper around Claude Code with human quality control
• Infrastructure only: This PR intentionally contains only the testing setup without actual unit tests
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Set up comprehensive Python testing infrastructure

Summary

This PR establishes a complete testing infrastructure for the musubi-tuner project, providing developers with all the tools needed to write and run tests effectively.

Changes Made

Package Management & Dependencies

• ✅ Detected existing UV package manager and kept current configuration
• ✅ Added testing dependencies to pyproject.toml:
  • pytest>=8.0.0 - Main testing framework
  • pytest-cov>=4.0.0 - Coverage reporting
  • pytest-mock>=3.12.0 - Mocking utilities
• ✅ Set up UV script commands for easy test execution

Testing Configuration

• ✅ Comprehensive pytest configuration in pyproject.toml:
  • Test discovery patterns for files, classes, and functions
  • Coverage settings with 80% threshold requirement
  • HTML and XML coverage report generation
  • Custom markers: unit, integration, slow
  • Strict configuration options for better test quality
  • Warning filters for cleaner test output

Directory Structure

• ✅ Created organized test directories:

  tests/
  ├── __init__.py
  ├── conftest.py
  ├── unit/
  │   └── __init__.py
  ├── integration/
  │   └── __init__.py
  └── test_setup_validation.py
  

Test Fixtures & Utilities

• ✅ Comprehensive conftest.py with ML/AI-specific fixtures:
  • Temporary directories and files
  • Mock configurations, models, datasets, dataloaders
  • Mock HuggingFace components (models, tokenizers, text encoders)
  • LoRA configuration fixtures
  • Environment setup for testing
  • Logging capture utilities

Development Environment

• ✅ Updated .gitignore with testing-related exclusions:
  • .pytest_cache/, .coverage, htmlcov/, coverage.xml
  • Build artifacts, IDE files, OS files
• ✅ Environment configuration for isolated testing

Validation

• ✅ Created validation tests to verify infrastructure works
• ✅ Tested basic functionality - core tests passing
• ✅ Verified pytest plugins are correctly installed

How to Run Tests

Using UV (Recommended)

# Install test dependencies
uv sync --group test

# Run all tests
uv run test
# or
uv run tests

# Run specific test categories
uv run pytest -m unit
uv run pytest -m integration
uv run pytest -m "not slow"

Using Python directly

# Create virtual environment and install dependencies
python3 -m venv .venv
source .venv/bin/activate
pip install pytest pytest-cov pytest-mock

# Run tests
PYTHONPATH=src pytest

Coverage Reporting

The infrastructure generates multiple coverage report formats:

• Terminal: Real-time coverage summary with missing lines
• HTML: Detailed interactive reports in htmlcov/ directory
• XML: Machine-readable reports in coverage.xml for CI/CD

Configuration Highlights

• 80% coverage threshold - Tests will fail if coverage drops below this
• Exclusions configured for external/generated code files
• Custom markers for organizing different test types
• Strict pytest configuration for better test quality
• ML/AI-specific fixtures ready for testing machine learning components

Next Steps

The testing infrastructure is now ready for development. Developers can:

1. Start writing unit tests in tests/unit/
2. Add integration tests in tests/integration/
3. Use provided fixtures for common ML/AI testing scenarios
4. Run tests locally before committing changes
5. Monitor coverage to ensure code quality

Notes

• The project uses UV as the package manager (detected from existing pyproject.toml)
• No actual unit tests were written for the codebase - only infrastructure setup
• Validation tests verify that the testing environment works correctly
• Coverage excludes external model files that are copied from other projects