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Implements automated testing, code quality checks, and dependency management for continuous integration and deployment. GitHub Actions Workflows: - Code quality & linting (YAPF, Black, isort, mypy) - CPU-based unit tests for Python 3.10 and 3.11 - Security scanning (safety, bandit) - Package building and validation - Documentation building Pre-commit Hooks: - File checks (trailing whitespace, EOF, YAML/JSON validation) - Code formatting (YAPF, Black) - Import sorting (isort) - Linting (flake8) - Type checking (mypy) - Security checks (bandit) - Docstring coverage (interrogate) - Markdown linting Dependabot Configuration: - Weekly dependency updates for Python packages - Grouped updates for related ecosystems (PyTorch, Transformers) - Automatic PR creation with labels and reviewers - Security-focused update strategy Type Checking: - mypy.ini with gradual typing configuration - External dependency stub configuration - Per-module strictness levels Files Added: - .github/workflows/ci.yml - CI/CD pipeline - .github/dependabot.yml - Dependency updates - .github/pull_request_template.md - PR template - .github/ISSUE_TEMPLATE/bug_report.yml - Bug report template - .github/ISSUE_TEMPLATE/feature_request.yml - Feature request template - .pre-commit-config.yaml - Pre-commit hooks - mypy.ini - Type checking configuration Benefits: - Automated code quality enforcement - Early detection of bugs and security issues - Consistent code style across contributors - Reduced manual review burden
2.8 KiB
2.8 KiB
Description
Type of Change
- Bug fix (non-breaking change which fixes an issue)
- New feature (non-breaking change which adds functionality)
- Breaking change (fix or feature that would cause existing functionality to not work as expected)
- Documentation update
- Performance improvement
- Code refactoring
- Test addition/modification
- CI/CD changes
- Dependency update
Related Issues
Closes # Relates to #
Changes Made
Testing
Test Environment
- Python version:
- PyTorch version:
- CUDA version:
- GPU type:
- Number of GPUs:
Testing Performed
- All existing tests pass
- Added new unit tests
- Added new integration tests
- Manual testing completed
- Tested on CPU
- Tested on GPU
- Tested with 14B model
- Tested with 1.3B model
Test Results
pytest output here
Performance Impact
- Inference speed:
- Memory usage:
- GPU utilization:
Breaking Changes
Documentation
- README.md updated
- INSTALL.md updated
- Code comments added/updated
- Docstrings added/updated
- API documentation updated
- CHANGELOG.md updated
- No documentation needed
Checklist
- My code follows the project's style guidelines (YAPF/Black formatted)
- I have performed a self-review of my code
- I have commented my code, particularly in hard-to-understand areas
- I have made corresponding changes to the documentation
- My changes generate no new warnings
- I have added tests that prove my fix is effective or that my feature works
- New and existing unit tests pass locally with my changes
- Any dependent changes have been merged and published
- I have run
make formatto format the code - I have checked my code with
mypyfor type errors - I have updated type hints where necessary
- Pre-commit hooks pass
Screenshots/Videos
Additional Notes
Reviewer Notes
For Maintainers:
- Code review completed
- Tests pass in CI
- Documentation is adequate
- Ready to merge