TensorFlow 2.1 will be the last TF release supporting Python 2. Python 2 support officially ends an January 1, 2020. As announced earlier, TensorFlow will also stop supporting Python 2 starting January 1, 2020, and no more releases are expected in 2019.
Major Features and Improvements
The tensorflow pip package now includes GPU support by default (same as
tensorflow-gpu) for both Linux and Windows. This runs on machines with and
without NVIDIA GPUs. tensorflow-gpu is still available, and CPU-only
packages can be downloaded at tensorflow-cpu for users who are concerned
about package size.
Windows users: Officially-released tensorflow Pip packages are now
built with Visual Studio 2019 version 16.4 in order to take advantage of the
new /d2ReducedOptimizeHugeFunctions compiler flag. To use these new
packages, you must install "Microsoft Visual C++ Redistributable for Visual
Studio 2015, 2017 and 2019", available from Microsoft's website
here.
This does not change the minimum required version for building
TensorFlow from source on Windows, but builds enabling
EIGEN_STRONG_INLINE can take over 48 hours to compile without this
flag. Refer to configure.py for more information about
EIGEN_STRONG_INLINE and /d2ReducedOptimizeHugeFunctions.
If either of the required DLLs, msvcp140.dll (old) or msvcp140_1.dll
(new), are missing on your machine, import tensorflow will print a
warning message.
The tensorflow pip package is built with CUDA 10.1 and cuDNN 7.6.
tf.keras
Experimental support for mixed precision is available on GPUs and Cloud
TPUs. See
usage guide.
Introduced the TextVectorization layer, which takes as input raw
strings and takes care of text standardization, tokenization, n-gram
generation, and vocabulary indexing. See this
end-to-end text classification example.
Keras .compile.fit.evaluate and .predict are allowed to be
outside of the DistributionStrategy scope, as long as the model was
constructed inside of a scope.
Experimental support for Keras .compile, .fit, .evaluate, and
.predict is available for Cloud TPUs, Cloud TPU, for all types of
Keras models (sequential, functional and subclassing models).
Automatic outside compilation is now enabled for Cloud TPUs. This allows
tf.summary to be used more conveniently with Cloud TPUs.
Commits
5d80e1e Merge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-8214
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Bumps tensorflow from 1.15.0 to 1.15.2.
Release notes
Sourced from tensorflow's releases.
Changelog
Sourced from tensorflow's changelog.
Commits
5d80e1eMerge pull request #36215 from tensorflow-jenkins/version-numbers-1.15.2-821471e9d8fUpdate version numbers to 1.15.2e50120eMerge pull request #36214 from tensorflow-jenkins/relnotes-1.15.2-22031a7e9fbReleasing 1.15.2 instead of 1.15.185f7aabInsert release notes place-fille75a6d6Merge pull request #36190 from tensorflow/mm-r1.15-fix-v2-builda6d8973Useconfig=v1as this isr1.15branch.fdb8589Merge pull request #35912 from tensorflow-jenkins/relnotes-1.15.1-31298a6051e8Add CVE number for main patch360b2e3Merge pull request #34532 from ROCmSoftwarePlatform/r1.15-rccl-upstream-patchDependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
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