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Testing TensorFlow and Reporting Issues

📢 How to Report Issues

Over the last few years, and with the extremely productive involvement of our community (thank you!), the TensorFlow development team has reviewed RFCs, added many new features, and implemented most of what will be TensorFlow 2.0 - a significant milestone for the framework, with a focus on ease of use.

TensorFlow is truly a community effort, and we would love to have your feedback on how we've been doing so far, as well as your suggestions for ways that we can improve!

📝 What is a Good Issue?

🐞 Report a Bug

Please submit all bugs, errors, and pecularities on GitHub. Differences between documentation and implementation, lack of documentation, performance issues, or compatibility problems are all fair game. Please be specific and include all information that would be helpful to debug the issue using our issue templates:

If you have a general question, you can submit it to StackOverflow with the tag tensorflow, or to our discuss@ mailing group. Our engineering team tries to answer as many of these questions as possible, but we appreciate help from end users!

Submit a Feature Request

As members of the TensorFlow community, your recommendations and suggestions are highly valued, and we are honored to have them. Please submit all feature requests as an issue on GitHub:

🤔 Send an Experience Report

If you would like to submit general feedback about TensorFlow (and in particular, about TensorFlow 2.0), consider submitting a friction log!

Friction logs are documents that describe the frustrations and delights of a product, focused around a specific use case (for example, creating an LSTM model for text classification). They're also intended to be brutally honest - feel free to vent or to praise! 😊

An template and example of a TensorFlow friction log can be found here.

Once you have completed such a document, please email it to our testing team.

🛠 How to Get Involved

Between now and the preview launch for TensorFlow 2.0, we will be actively maintaining a discussion group for any questions, comments, suggestions, or issues that arise. We will be holding a weekly stand-up for TF 2.0 testing via Hangouts that will be announced through the TensorFlow Testing Discussion Group.

Please subscribe to to stay up-to-date.

Special Interest Groups (SIGs)

TensorFlow's Special Interest Groups (SIGs) support community collaboration on particular projects. Members of these groups work together to build and support specific parts of TensorFlow or TensorFlow-related projects.

To join the discussion on a specific topic, subscribe to one of our SIG mailing lists:

  • TensorBoard: Plug-in development, discussion, and contribution to TensorFlow visualization tooling.
  • Networking: Adding network protocols other than gRPC.
  • I/O: Support for file systems and formats not available in core TensorFlow.
  • Add-ons: Extensions to TensorFlow that conform to the stable API.
  • Build: Discussion on TensorFlow distribution and packaging.