User experience and project maintainability are core concepts in Pip install artifacts/tensorflow_addons-*.whlĬore Concepts Standardized API within Subpackages # This script links project with TensorFlow dependency # If building GPU Ops (Requires CUDA 10.0 and CuDNN 7)Įxport CUDA_TOOLKIT_PATH="/path/to/cuda10" (default: /usr/local/cuda)Įxport CUDNN_INSTALL_PATH="/path/to/cudnn" (default: /usr/lib/x86_64-linux-gnu) Include newer features, but may be less stable than the versioned releases. Tfa-nightly, which is built against the latest stable version of TensorFlow. There are also nightly builds of TensorFlow Addons under the pip package To install the latest version, run the following: pip install tensorflow-addons Used by a smaller subset of the community). (because their broad applicability is not yet clear, or it is mostly However, in a fast moving field like ML, there are many interesting newĭevelopments that cannot be integrated into core TensorFlow TensorFlow natively supportsĪ large number of operators, layers, metrics, losses, and optimizers. Well-established API patterns, but implement new functionality TensorFlow Addons is a repository of contributions that conform to
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