TensorFlow Addons Newsletter: March2020

[Sean Morgan]

Hi SIG,

As mentioned, we'll be sending out a monthly newsletter describing the state of the repo, top relevant issues, and the road-map going forward. A big reason for this is to boost community input so please comment on the below information if you have any thoughts! 

Summary:

A lot has happened since our last newsletter. We've release a 0.8 version of Addons which dropped support for Python2 and implemented type checking across the repo. We've standardized our code formatting using Black, and a ton of work has gone into easing development and restructuring.  Automating tasks such as tagging proxy maintainers and backporting fixes have been great additions to ease maintenance burdens. A huge thank you to Gabriel de Marmiesse for spear heading all of this. 

Daily Downloads for package TFA:

Landmark Issues:

Below are some of the landmark issues that we're aiming to address in the coming month(s):

• Benchmark activations using different implementations of python and custom ops. We're attempting to reduce maintenance burden of supporting custom-ops which are no more performant than TF composite python ops. We would very much appreciate help in setting up our benchmarking framework

• Migrate GELU to TensorFlow core. There is a lot of interest in moving this activation to core and we've been asked to author a Keras RFC for the move. This very much related to the activation benchmarks.

• Support Python3.8. TF-nightly has begun supporting 3.8 for linux (windows soon) and we'll want to support py38 come TF2.2 release.

Maintainership:

As was originally mentioned in our sustainability RFC, we want to start sending out the status of our maintainership so submodules and subpackages can more readily be adopted. To be come a maintainer with write-access on tensorflow/addons we ask that you sign up to support some submodules first then we can grant as defined on our landing page. If you would like to be a submodule maintainer please feel free to submit a PR adding yourself to the CODEOWNERS file.

Below we list submodules without ownership... though we also encourage becoming a submodule maintainer for an already covered submodule!

• /tensorflow_addons/image/interpolate_spline*.py
• /tensorflow_addons/image/sparse_image_warp*.py
• /tensorflow_addons/losses/metric_learning*.py
• /tensorflow_addons/losses/triplet*.py

Best Regards,

SIG-Addons