TensorFlow Contributing Guidelines

This document provides an overview of how to contribute to the TensorFlow project. Contributions to TensorFlow are welcome from the community and are essential for the project's growth and improvement.

How to Contribute

  1. Fork the TensorFlow Repository: If you wish to contribute a new feature or fix a bug, the first step is to fork the TensorFlow repository on GitHub.
  2. Create a New Branch: Create a new branch in your fork for your specific contribution.
  3. Follow Best Practices: Make sure your code follows the TensorFlow coding style and includes comprehensive tests.
  4. Document Your Changes: Include comments in your code explaining your changes, and update the relevant documentation if necessary.
  5. Submit a Pull Request: When you are done with your contribution, submit a pull request to the TensorFlow main repository.

Coding Style

  • Follow the Style Guide: TensorFlow follows a Python style guide that you should adhere to when contributing code.
  • Use PEP 8: Adhere to PEP 8 for Python code style.
  • Code Linting: Ensure your code passes linting checks.

Testing

  • Unit Tests: Write unit tests for your code to ensure that it works as expected.
  • Integration Tests: Test your changes in the context of the larger TensorFlow ecosystem.
  • Continuous Integration: Your contribution will be tested against a variety of environments automatically.

Contributing to Documentation

  • Read the Documentation Guidelines: Follow the guidelines outlined in the TensorFlow Documentation Guidelines.
  • Update Existing Content: Improve or correct existing documentation as needed.
  • Add New Content: Write new documentation to cover new features or improvements.

Community Resources

  • Join the TensorFlow Community: Engage with the TensorFlow community through TensorFlow forums or Stack Overflow.
  • Contribute to TensorFlow Tutorials: Help improve TensorFlow tutorials and examples.

Images

Here is an image of a TensorFlow logo: (center) TensorFlow Logo (center)

Conclusion

Your contribution is valuable to the TensorFlow project. By following these guidelines, you can help improve TensorFlow and make it better for everyone.