Build	Status
Ubuntu/MacOS/Windows
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 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.

TFA is available on PyPi for Linux/MacOS/Windows. To install the latest version, run the following:

pip install tensorflow-addons

To use addons:

import tensorflow as tf
import tensorflow_addons as tfa

TensorFlow Addons is actively working towards forward compatiblity with TF2.x. However there are still a few private API uses within the respository so at the moment we can only gurentee compatibility with the versions of TF which it was tested against. Warnings will be emitted during TFA import if your TensorFlow version does not match what it was tested against.

While we wait for a TF2.2 stable release if you would like to use TFA with python3.8 run:

pip install tensorflow~=2.2
pip install git+https://github.com/tensorflow/[email protected]

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

Additionally, registering custom-ops does not have an ABI stable interface so it is required that users have a compatible installation of TensorFlow even if the versions match what we had build against. A simplification of this is that TensorFlow Addons custom-ops will work with pip installed TensorFlow but will have issues with TF compiled differently. A typical reason for 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 --enable_runfiles 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="10.1"
export TF_CUDNN_VERSION="7"
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 --enable_runfiles build_pip_pkg
bazel-bin/build_pip_pkg artifacts

pip install artifacts/tensorflow_addons-*.whl

See docs/tutorials/ for end-to-end examples of various addons.

User experience and project maintainability are core concepts in TF-Addons. In order to achieve these we require that our additions conform to established API patterns seen in core TensorFlow.

TensorFlow Addons supports precompiled custom ops for CPU/GPU. Currently however, GPU custom ops only work for Linux distributions. For this reason Windows and MacOS will fallback to pure TensorFlow Python implementations whenever possible.

The order of priority in MacOS/Windows:

1. Pure TensorFlow + Python implementation (work on cpu+gpu)
2. C++ implementation for CPU

The order of priority for Linux:

1. CUDA implementation
2. C++ implementation
3. Pure TensorFlow + Python implementation (work on cpu+gpu)

If you want to change the default priority, "C++ and CUDA" VS "pure TF Python", you can either set the variable TF_ADDONS_PY_OPS from the command line or in your code.

For example, if you're on linux and you have compatibility problems with the compiled ops, and you want to give priority to the Python implementation you can do:

From the command line:

export TF_ADDONS_PY_OPS=1

or in your code:

import tensorflow_addons as tfa
tfa.options.TF_ADDONS_PY_OPS=True

This variable will default to True on Windows and Mac, and False for Linux.

Addons has been designed to compartmentalize subpackages and submodules so that they can be maintained by users who have expertise and a vested interest in that component.

Subpackage maintainership will only be granted after substantial contribution has been made in order to limit the number of users with write permission. Contributions can come in the form of issue closings, bug fixes, documentation, new code, or optimizing existing code. Submodule maintainership can be granted with a lower barrier for entry as this will not include write permissions to the repo.

For more information see the RFC on this topic.

Given the nature of this repository, subpackages and submodules may become less and less useful to the community as time goes on. In order to keep the repository sustainable, we'll be performing bi-annual reviews of our code to ensure everything still belongs within the repo. Contributing factors to this review will be:

1. Number of active maintainers
2. Amount of OSS use
3. Amount of issues or bugs attributed to the code
4. If a better solution is now available

Functionality within TensorFlow Addons can be categorized into three groups:

• Suggested: well-maintained API; use is encouraged.
• Discouraged: a better alternative is available; the API is kept for historic reasons; or the API requires maintenance and is the waiting period to be deprecated.
• Deprecated: use at your own risk; subject to be deleted.

The status change between these three groups is: Suggested <-> Discouraged -> Deprecated.

The period between an API being marked as deprecated and being deleted will be 90 days. The rationale being:

1. In the event that TensorFlow Addons releases monthly, there will be 2-3 releases before an API is deleted. The release notes could give user enough warning.

2. 90 days gives maintainers ample time to fix their code.

TF-Addons is a community led open source project (only a few maintainers work for Google!). As such, the project depends on public contributions, bug-fixes, and documentation. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

Want to contribute but not sure of what? Here are a few suggestions:

1. Add a new tutorial. Located in docs/tutorials/, these are a great way to familiarize yourself and others with TF-Addons. See the guidelines for more information on how to add examples.
2. Improve the docstrings. The docstrings are fetched and then displayed in the documentation. Do a change and hundreds of developers will see it and benefit from it. Maintainers are often focused on making APIs, fixing bugs and other code related changes. The documentation will never be loved enough!
3. Solve an existing issue. These range from low-level software bugs to higher-level design problems. Check out the label help wanted. If you're a new contributor, the label good first issue can be a good place to start.
4. Review a pull request. So you're not a software engineer but you know a lot about a certain field a research? That's awesome and we need your help! Many people are submitting pull requests to add layers/optimizers/functions taken from recent papers. Since TensorFlow Addons maintainers are not specialized in everything, you can imagine how hard it is to review. It takes very long to read the paper, understand it and check the math in the pull request. If you're specialized, look at the list of pull requests. If there is something from a paper you now, please comment on the pull request to check the math is ok. If you see that everything is good, say it! It will help the maintainers to sleep better at night knowing that he/she wasn't the only person to approve the pull request.
5. You have an opinion and want to share it? The docs are not very helpful for a function or a class? You tried to open a pull request but you didn't manage to install or test anything and you think it's too complicated? You made a pull request but you didn't find the process good enough and it made no sense to you? Please say it! We want feedback. Maintainers are too much the head into the code to understand what it's like for someone new to open source to come to this project. If you don't understand something, be aware there are no people who are bad at understanding, there are just bad tutorials and bad guides.

Please see contribution guidelines to get started (and remember, if you don't understand something, open an issue, or even make a pull request to improve the guide!).

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