TensorFlow 2.1.0 will include breaking changes: First release candidate available
The machine learning platform TensorFlow, currently in version 2.0, is making its way toward the minor release 2.1.0: TensorFlow 2.1.0-rc0 is the first release candidate and includes some breaking changes. The upcoming version will be the last to support Python 2.7.
TensorFlow 2.1.0-rc0 is now available, the first release candidate for the machine learning platform’s next minor version. As Python 2.7 will reach end of life on January 1, 2020, TensorFlow 2.1 will be the last version to support it.
TensorFlow is an open source software library for ML that was originally developed by the Google Brain team in 2015. It has since become very popular within the open source community and was found to be the 5th most popular open source project on GitHub in the latest State of the Octoverse report.
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In the release candidate for TensorFlow 2.1.0, new features have been added for the tensorflow pip package, Keras and more. Among the breaking changes are API renamings as well as removals, and six APIs are now stable.
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Breaking Changes
The release candidate for 2.1.0 removes id from tf.Tensor.__repr__() as well as Operation.traceback_with_star. These six APIs have graduated from the experimental stage:
tf.config.list_logical_devicestf.config.list_physical_devicestf.config.get_visible_devicestf.config.set_visible_devicestf.config.get_logical_device_configurationtf.config.set_logical_device_configuration
tf.config.experimentalVirtualDeviceConfiguration has been renamed tf.config.LogicalDeviceConfiguration
and tf.config.experimental_list_devices has been removed in favor of tf.config.list_logical_devices.
Features
The tensorflow pip package has received an update: GPU support is now included by default for Linux and Windows on machines with and without NVIDIA GPUs.
In TensorFlow 2.1.0-rc0, the deep learning platform NVIDIA TensorRT 6 is supported and enabled by default. According to NVIDIA, TensorRT 6 accelerates use cases like conversational AI and can be used within different frameworks like TensorFlow and ONNX Runtime.
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Further new features were added for tf.keras and tf.data. Until a known issue with building on Windows has been resolved, eigen strong lining has been turned off for Windows builds, which is expected to cause a slight reduction in speed.
See the release notes for more information on the changes in TensorFlow 2.1.0-rc0.
