TensorFlow 2.0 is coming
Martin Wicke
Since the open-source release in 2015, TensorFlow has become the world’s most widely adopted machine learning framework, catering to a broad spectrum of users and use-cases. In this time, TensorFlow has evolved along with rapid developments in computing hardware, machine learning research, and commercial deployment.
Reflecting these rapid changes, we have started work on the next major version of TensorFlow. TensorFlow 2.0 will be a major milestone, with a focus on ease of use. Here are some highlights of what users can expect with TensorFlow 2.0:
Eager execution will be a central feature of 2.0. It aligns users’ expectations about the programming model better with TensorFlow practice and should make TensorFlow easier to learn and apply.
Support for more platforms and languages, and improved compatibility and parity between these components via standardization on exchange formats and alignment of APIs.
We will remove deprecated APIs and reduce the amount of duplication, which has caused confusion for users.
We are planning to release a preview version of TensorFlow 2.0 later this year.
Public 2.0 design process
Shortly, we will hold a series of public design reviews covering the planned changes. This process will clarify the features that will be part of TensorFlow 2.0, and allow the community to propose changes and voice concerns. Please join devel...@tensorflow.org if you would like to see announcements of reviews and updates on process. We hope to gather user feedback on the planned changes once we release a preview version later this year.
Compatibility and continuity
TensorFlow 2.0 is an opportunity to correct mistakes and to make improvements which are otherwise forbidden under semantic versioning.
To ease the transition, we will create a conversion tool which updates Python code to use TensorFlow 2.0 compatible APIs, or warns in cases where such a conversion is not possible automatically. A similar tool has helped tremendously in the transition to 1.0.
Not all changes can be made fully automatically. For example, we will be deprecating APIs, some of which do not have a direct equivalent. For such cases, we will offer a compatibility module (tensorflow.compat.v1) which contains the full TensorFlow 1.x API, and which will be maintained through the lifetime of TensorFlow 2.x.
We do not anticipate any further feature development on TensorFlow 1.x once a final version of TensorFlow 2.0 is released. We will continue to issue security patches for the last TensorFlow 1.x release for one year after TensorFlow 2.0’s release date.
On-disk compatibility
We do not intend to make breaking changes to SavedModels or stored GraphDefs (i.e., we plan to include all current kernels in 2.0). However, the changes in 2.0 will mean that variable names in raw checkpoints might have to be converted before being compatible with new models.
tf.contrib
TensorFlow’s contrib module has grown beyond what can be maintained and supported in a single repository. Larger projects are better maintained separately, while we will incubate smaller extensions along with the main TensorFlow code. Consequently, as part of releasing TensorFlow 2.0, we will stop distributing tf.contrib. We will work with the respective owners on detailed migration plans in the coming months, including how to publicise your TensorFlow extension in our community pages and documentation. For each of the contrib modules we will either a) integrate the project into TensorFlow; b) move it to a separate repository or c) remove it entirely. This does mean that all of tf.contrib will be deprecated, and we will stop adding new tf.contrib projects today. We are looking for owners/maintainers for a number of projects currently in tf.contrib, please contact us (reply to this email) if you are interested.
Next steps
For questions about development of or migration to TensorFlow 2.0, contact us at dis...@tensorflow.org. To stay up to date with the details of 2.0 development, please subscribe to devel...@tensorflow.org, and participate in related design reviews.
On behalf of the TensorFlow team,
Martin