The TensorFlow ecosystem can only grow through the contributions of this community. Thanks so much for your enthusiasm and your work—we appreciate everything you do!
In the interest of fostering an open and welcoming environment, contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone—regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
Examples of behaviors that contribute to creating a positive environment include:
- Use welcome and inclusive language.
- Be respectful of differing viewpoints and experiences.
- Gracefully accept constructive criticism.
- Foster what's best for the community.
- Show empathy for other community members.
Decisions are made based on technical merit and consensus. The TensorFlow community aspires to treat everyone equally, and to value all contributions. For more information on best practices in the TensorFlow community, please review our Code of Conduct.
Make your first contribution
There are many ways to contribute to TensorFlow! You can contribute code, make improvements to the TensorFlow API documentation, or add your Jupyter notebooks to the tensorflow/examples repo. This guide provides everything you need to get started. Our most common contributions include code, documentation, and community support.
- Write code.
- Improve tests.
- Improve documentation.
- Answer questions on Stack Overflow.
- Participate in the discussion on the TensorFlow forums.
- Contribute example notebooks.
- Investigate bugs and issues on GitHub.
- Review and comment on pull requests from other developers.
- Report an issue.
- Give a “thumbs up” 👍 on issues that are relevant to you.
- Reference TensorFlow in your blogs, papers, and articles.
- Talk about TensorFlow on social media.
- ... even just starring/forking the repos you like on GitHub!
TensorFlow was originally developed by researchers and engineers from the Google Brain team within Google's AI organization. Google open sourced TensorFlow in the hope of sharing technology with the external community and encouraging collaboration between researchers and industry. Since then, TensorFlow has grown into a thriving ecosystem of products, on a wide range of platforms. But our goal is still to make machine learning accessible to anyone, anywhere.