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Contributing to Deep Learning Model Scaling Analysis

Thank you for your interest in contributing! This document provides guidelines for contributing to the project.

Development Setup

  1. Clone the repository:

    git clone https://github.com/0DevDutt0/deep-learning-model-scaling-analysis.git
cd deep-learning-model-scaling-analysis

  2. Create a virtual environment:

    python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

  3. Install the package in development mode:

    pip install -e ".[dev]"

  4. Install pre-commit hooks:

    pre-commit install

Development Workflow

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov

# Run specific test file
pytest tests/unit/test_models.py

# Run specific test
pytest tests/unit/test_models.py::test_small_cnn_parameter_count

Code Quality

We use several tools to maintain code quality:

# Format code
ruff format .

# Lint code
ruff check .

# Fix auto-fixable issues
ruff check --fix .

# Type check
mypy src tests

Pre-commit Hooks

Pre-commit hooks run automatically before each commit. To run manually:

pre-commit run --all-files

Code Style

  • Follow PEP 8 guidelines
  • Use type hints for all function signatures
  • Write Google-style docstrings
  • Keep line length to 100 characters
  • Use meaningful variable names

Example:

def train_model(
    model: nn.Module,
    data_loader: DataLoader,
    epochs: int,
    learning_rate: float = 0.001,
) -> tuple[float, float]:
    """Train a PyTorch model on the provided data.

    Args:
        model: The neural network model to train.
        data_loader: DataLoader containing training data.
        epochs: Number of training epochs.
        learning_rate: Learning rate for the optimizer. Defaults to 0.001.

    Returns:
        A tuple containing (final_accuracy, training_time).

    Raises:
        ValueError: If epochs is less than 1.
    """
    # Implementation
    pass

Testing Guidelines

  • Write tests for all new features
  • Maintain test coverage above 80%
  • Use descriptive test names
  • Use fixtures for common setup
  • Mock external dependencies

Test Structure:

def test_feature_name_expected_behavior():
    """Test that feature_name produces expected behavior."""
    # Arrange
    input_data = create_test_data()
    
    # Act
    result = function_under_test(input_data)
    
    # Assert
    assert result == expected_value

Pull Request Process

  1. Create a feature branch from main:

    git checkout -b feature/your-feature-name

  2. Make your changes and commit:

    git add .
git commit -m "feat: add your feature description"

  3. Push to your fork:

    git push origin feature/your-feature-name

  4. Open a Pull Request with:

    • Clear description of changes
    • Link to related issues
    • Test results
    • Documentation updates

Commit Message Convention

We follow Conventional Commits:

  • feat: New feature
  • fix: Bug fix
  • docs: Documentation changes
  • test: Test additions or changes
  • refactor: Code refactoring
  • perf: Performance improvements
  • chore: Maintenance tasks

Reporting Issues

When reporting issues, please include:

  • Python version
  • Operating system
  • Steps to reproduce
  • Expected vs actual behavior
  • Relevant code snippets or error messages

Questions?

Feel free to open an issue for questions or clarifications!