TensorFlow 2.0 is coming. Here's what we can expect.

The last couple of months have seen TensorFlow releases coming thick and fast. Clearly, the Google team are working hard to ship new updates for a framework that seems to be defining deep learning as we know it. But TensorFlow 2.0 remains on the horizon - and that, really, is the release we've all been waiting for. Amid speculation and debate, we now have the first inkling of what we can expect thanks to a post by Google Brain engineer Martin Wicke.

In a somewhat unassuming post on Google Groups, Wicke said that work was underway on TensorFlow 2.0, with a preview version expected later this year.

The big changes that the team are working towards include:

• Making TensorFlow easier to learn and use by putting eager execution (TensorFlow's programming environment) at the center of the new release
• Support for more platforms and languages
• Removing deprecated APIs

How you can support the TensorFlow 2.0 design process

Wicke writes that TensorFlow 2.0 still needs to go through a public review process. To do this, the project will be running a number of public design reviews that run through the proposed changes in detail and give users the opportunity to give feedback and communicate their views.

What TensorFlow 2.0 means for the TensorFlow project

Once TensorFlow 2.0 is released migration will be essential - Wicke explains that "We do not anticipate any further feature development on TensorFlow 1.x once a final version of TensorFlow 2.0 is released" and that the project "will continue to issue security patches for the last TensorFlow 1.x release for one year after TensorFlow 2.0’s release date."

The end of tf.contrib?

TensorFlow 2.0 will bring an end (of sorts) to tf.contrib, the repository where code contributed to TensorFlow sits, waiting to be merged. "TensorFlow’s contrib module has grown beyond what can be maintained and supported in a single repository." Wicke writes. "Larger projects are better maintained separately, while we will incubate smaller extensions along with the main TensorFlow code." However, Wicke promises that TensorFlow will help the owners of contributed code to migrate appropriately. Some modules could be integrated into the core project, others moved into another, separate repository, and others simply removed entirely.

If you have any questions about TensorFlow 2.0 you can get in touch with the team directly by emailing discuss@tensorflow.org. 

TensorFlow has also set up a mailing list for anyone interested in regular updates - simply subscribe to developers@tensorflow.org.

Read next

Why Twitter (finally!) migrated to Tensorflow

Python, Tensorflow, Excel and more – Data professionals reveal their top tools

Can a production ready Pytorch 1.0 give TensorFlow a tough time?