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CONTRIBUTING.md

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Contributing to PyTorchLabFlow 🧪

First off, thank you for considering contributing to PyTorchLabFlow! We're thrilled you're here. This project is built by the community, for the community, and we welcome any contribution, from fixing typos to implementing major new features.

This document provides guidelines to help make the contribution process easy and effective for everyone involved.

Code of Conduct

By participating in this project, you are expected to uphold our Code of Conduct. Please read it to understand the standards we follow to ensure our community is welcoming and inclusive.

How Can I Contribute?

There are many ways to contribute, and all of them are valuable.

  • 🐛 Reporting Bugs: If you find a bug, please open an issue and provide as much detail as possible.
  • 💡 Suggesting Enhancements: Have an idea for a new feature or an improvement to an existing one? Open an issue to start a discussion.
  • 📝 Improving Documentation: If you find parts of the documentation unclear or incorrect, you can suggest changes or submit a pull request.
  • 💻 Writing Code: Help us fix bugs or add new features by submitting a pull request.

Your First Contribution

Unsure where to begin? A great place to start is by looking for issues tagged good first issue or help wanted. These are typically well-defined and a great way to get familiar with the codebase.

🚀 Submitting a Pull Request (PR)

Ready to contribute code or documentation? Here’s how to set up your environment and submit a pull request.

1. Fork the Repository

Click the "Fork" button at the top right of the PyTorchLabFlow GitHub page to create your own copy.

2. Clone Your Fork

Clone your forked repository to your local machine.

git clone https://github.com/YOUR_USERNAME/PyTorchLabFlow.git

cd PyTorchLabFlow

3. Create a New Branch

Create a descriptive branch name for your changes. This keeps your work separate from the main branch.

# Example for a new feature
git checkout -b feature/add-new-visualization-plot

# Example for a bug fix
git checkout -b fix/resolve-config-save-error

4. Set Up Your Development Environment

We recommend using a virtual environment.

# Create a virtual environment
python -m venv venv

# Activate it (on Windows)
# venv\Scripts\activate
# Activate it (on macOS/Linux)
source venv/bin/activate

# Install dependencies, including testing tools
pip install -r requirements.txt
pip install pytest

5. Make Your Changes

Now, write your code! Make your changes to the codebase, and remember to follow our style guidelines.

6. Run Tests

Before submitting, make sure your changes haven't broken anything. Run the full test suite from the root directory.

pytest

If you've added new functionality, please add new tests to cover it!

7. Commit Your Changes

Commit your changes with a clear and descriptive message. We follow the Conventional Commits specification.

# Example commit message
git commit -m "feat: Add performance_plot for multi-pipeline comparison"

8. Push to Your Fork

Push your new branch to your forked repository on GitHub.

git push origin feature/add-new-visualization-plot

9. Open a Pull Request

Go to your forked repository on GitHub. You will see a prompt to create a pull request from your new branch. Click it, and fill out the pull request template with details about your changes.

  • Link to the issue if your PR addresses one.
  • Describe your changes clearly.
  • Explain the "why" behind your changes.

Once you submit the PR, a project maintainer will review your code. We may suggest some changes or improvements. Thank you for your contribution!

Style Guides

Git Commit Messages

  • Use the present tense ("Add feature" not "Added feature").
  • Use the imperative mood ("Move file to..." not "Moves file to...").
  • Limit the first line to 72 characters or less.
  • Reference issues and pull requests liberally in the body of the commit message.

Python Code

  • Please follow the PEP 8 style guide for Python code.
  • We recommend using a code formatter like black to automatically format your code.

Thank you again for your interest in making PyTorchLabFlow better! ❤️