Build	Status
Ubuntu/macOS
Ubuntu GPU custom ops

TensorFlow Addons is a repository of contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).

• tfa.activations
• tfa.callbacks
• tfa.image
• tfa.layers
• tfa.losses
• tfa.metrics
• tfa.optimizers
• tfa.rnn
• tfa.seq2seq
• tfa.text

The maintainers of TensorFlow Addons can be found in the CODEOWNERS file of the repo. This file is parsed and pull requests will automatically tag the owners using a bot. If you would like to maintain something, please feel free to submit a PR. We encourage multiple owners for all submodules.

TensorFlow Addons is available on PyPI for Linux & macOS (Windows support was dropped due to inconsistent TF2.15 whl packaging). To install the latest version, run the following:

pip install tensorflow-addons

To ensure you have a version of TensorFlow that is compatible with TensorFlow Addons, you can specify the tensorflow extra requirement during install:

pip install tensorflow-addons[tensorflow]

Similar extras exist for the tensorflow-gpu and tensorflow-cpu packages.

To use TensorFlow Addons:

import tensorflow as tf
import tensorflow_addons as tfa

TensorFlow Addons is actively working towards forward compatibility with TensorFlow 2.x. However, there are still a few private API uses within the repository so at the moment we can only guarantee compatibility with the TensorFlow versions which it was tested against. Warnings will be emitted when importing tensorflow_addons if your TensorFlow version does not match what it was tested against.

TensorFlow C++ APIs are not stable and thus we can only guarantee compatibility with the version TensorFlow Addons was built against. It is possible custom ops will work with multiple versions of TensorFlow, but there is also a chance for segmentation faults or other problematic crashes. Warnings will be emitted when loading a custom op if your TensorFlow version does not match what it was built against.

Additionally, custom ops registration does not have a stable ABI interface so it is required that users have a compatible installation of TensorFlow even if the versions match what we had built against. A simplification of this is that TensorFlow Addons custom ops will work with pip-installed TensorFlow but will have issues when TensorFlow is compiled differently. A typical example of this would be conda-installed TensorFlow. RFC #133 aims to fix this.

There are also nightly builds of TensorFlow Addons under the pip package tfa-nightly, which is built against the latest stable version of TensorFlow. Nightly builds include newer features, but may be less stable than the versioned releases. Contrary to what the name implies, nightly builds are not released every night, but at every commit of the master branch. 0.9.0.dev20200306094440 means that the commit time was 2020/03/06 at 09:44:40 Coordinated Universal Time.

pip install tfa-nightly

You can also install from source. This requires the Bazel build system (version >= 1.0.0).

git clone https://github.com/tensorflow/addons.git
cd addons

# This script links project with TensorFlow dependency
python3 ./configure.py

bazel build build_pip_pkg
bazel-bin/build_pip_pkg artifacts

pip install artifacts/tensorflow_addons-*.whl

git clone https://github.com/tensorflow/addons.git
cd addons

export TF_NEED_CUDA="1"

# Set these if the below defaults are different on your system
export TF_CUDA_VERSION="12"
export TF_CUDNN_VERSION="8"
export CUDA_TOOLKIT_PATH="/usr/local/cuda"
export CUDNN_INSTALL_PATH="/usr/lib/x86_64-linux-gnu"

# This script links project with TensorFlow dependency
python3 ./configure.py

bazel build build_pip_pkg
bazel-bin/build_pip_pkg artifacts

pip install artifacts/tensorflow_addons-*.whl