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TensorFlow Addons is a repository of contributions that conform towell-established API patterns, but implement new functionalitynot available in core TensorFlow. TensorFlow natively supportsa large number of operators, layers, metrics, losses, and optimizers.However, in a fast moving field like ML, there are many interesting newdevelopments 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).
Installation
Stable Builds
To install the latest version, run the following:
To use addons:
Nightly Builds
There are also nightly builds of TensorFlow Addons under the pip packagetfa-nightly, which is built against the latest stable version of TensorFlow. Nightly buildsinclude newer features, but may be less stable than the versioned releases.
Installing from Source
You can also install from source. This requires the Bazel build system.
Core Concepts
Standardized API within Subpackages
User experience and project maintainability are core concepts inTF-Addons. In order to achieve these we require that our additionsconform to established API patterns seen in core TensorFlow. Arcsoft scan n stitch software for mac.
GPU/CPU Custom-Ops
A major benefit of TensorFlow Addons is that there are precompiled ops. Should a CUDA 10 installation not be found then the op will automatically fall back to a CPU implementation.
Proxy Maintainership
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
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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:
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- Number of active maintainers
- Amount of OSS use
- Amount of issues or bugs attributed to the code
- If a better solution is now available
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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:
Dig n rig mac download. 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.
Contributing
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TF-Addons is a community led open source project. As such, the projectdepends 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.
Community
- SIG Monthly Meeting Notes
- Join our mailing list and receive calendar invites to the meeting