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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.optimizers||SIG-Addons||@facaiy @windqaq @squadrick|
To install the latest version, run the following:
pip install tensorflow-addons
To use addons:
import tensorflow as tf import tensorflow_addons as tfa
There are also nightly builds of TensorFlow Addons under the pip package
tfa-nightly, which is built against
tf-nightly. Nightly builds
include newer features, but may be less stable than the versioned releases.
pip install tfa-nightly
Installing from Source
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 # If building GPU Ops (Requires CUDA 10.1 and CuDNN 7) export TF_NEED_CUDA=1 export CUDA_HOME="/path/to/cuda10.1" (default: /usr/local/cuda) export CUDNN_INSTALL_PATH="/path/to/cudnn" (default: /usr/lib/x86_64-linux-gnu) # This script links project with TensorFlow dependency ./configure.sh bazel build build_pip_pkg bazel-bin/build_pip_pkg artifacts --nightly pip install artifacts/tensorflow_addons-*.whl
for end-to-end examples of various addons.
Standardized API within Subpackages
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.
A major benefit of TensorFlow Addons is that there are precompiled ops. Should a CUDA 10.1 installation not be found then the op will automatically fall back to a CPU implementation.
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.
Periodic Evaluation of Subpackages
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:
- Number of active maintainers
- Amount of OSS use
- Amount of issues or bugs attributed to the code
- 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:
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.
90 days gives maintainers ample time to fix their code.
TF-Addons is a community led open source project. As such, the project depends on public contributions, bug-fixes, and documentation. Please see contribution guidelines for a guide on how to contribute. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
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