We’re delighted to announce the release of the beta of TensorFlow 2.0 today. It can be installed using:
> pip install tensorflow==2.0.0-beta0
- Keras as a high-level API for quick and easy model design and training
- Eager execution as a default for fast, intuitive development and debugging
@tf.functionfor graph performance and portability
We are already seeing how these usability improvements in the Alpha release are helping users get started, and are thrilled to see how the TensorFlow community is growing. Over 130,000 students have enrolled in the deeplearning.ai and Udacity courses that launched alongside the alpha, and the GitHub repository has gotten over 128,000 stars and been forked over 75,000 times.
In this beta release, we have completed renaming and deprecating symbols for the 2.0 API. This means the current API is final and is also available as a v2 compatibility module inside the TensorFlow 1.14 release. (A list of all symbol changes can be found here.) We have also added 2.0 support for Keras features like model subclassing, simplified the API for custom training loops, added distribution strategy support for most kinds of hardware, and lots more.
Core components of TensorFlow product ecosystem such as TensorBoard, TensorFlow Hub, TensorFlow Lite, and TensorFlow.js work with the Beta. Support for TensorFlow Extended (TFX) components and end to end pipelines is still in progress.
We’ve closed over 100 issues you reported against the alpha release, and we continue to iterate on what’s left. We value all your feedback as it has helped get where we are today. Please keep it coming!
Between beta and the release candidate (RC) for the final 2.0 version, we will be completing Keras model support on Cloud TPUs and TPU pods, further work on performance, and closing even more issues. A list of known open TensorFlow 2.0 issues is on the issue tracker, and you can track our progress in the release notes.
We are aiming to reach RC sometime this summer. In the meantime, please test the beta out and provide your feedback! For more on TensorFlow 2.0, join our developers mailing list, file issues with the 2.0 tag, and check out our docs.