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.
Dynamic batch sizes with DistributionStrategy and Keras are supported on Cloud TPUs.
Support for .fit, .evaluate, .predict on TPU using numpy data, in addition to tf.data.Dataset.
Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
tf.data
Changes rebatching for tf.data datasets + DistributionStrategy for better performance. Note that the dataset also behaves slightly differently, in that the rebatched dataset cardinality will always be a multiple of the number of replicas.
tf.data.Dataset now supports automatic data distribution and sharding in distributed environments, including on TPU pods.
Distribution policies for tf.data.Dataset can now be tuned with 1. tf.data.experimental.AutoShardPolicy(OFF, AUTO, FILE, DATA) 2. tf.data.experimental.ExternalStatePolicy(WARN, IGNORE, FAIL)
tf.debugging
Add tf.debugging.enable_check_numerics() and tf.debugging.disable_check_numerics() to help debugging the root causes of issues involving infinities and NaNs.
tf.distribute
Custom training loop support on TPUs and TPU pods is avaiable through strategy.experimental_distribute_dataset, strategy.experimental_distribute_datasets_from_function, strategy.experimental_run_v2, strategy.reduce.
Support for a global distribution strategy through tf.distribute.experimental_set_strategy(), in addition to strategy.scope().
TensorRT
TensorRT 6.0 is now supported and enabled by default. This adds support for more TensorFlow ops including Conv3D, Conv3DBackpropInputV2, AvgPool3D, MaxPool3D, ResizeBilinear, and ResizeNearestNeighbor. In addition, the TensorFlow-TensorRT python conversion API is exported as tf.experimental.tensorrt.Converter.
Environment variable TF_DETERMINISTIC_OPS has been added. When set to "true" or "1", this environment variable makes tf.nn.bias_add operate deterministically (i.e. reproducibly), but currently only when XLA JIT compilation is not enabled. Setting TF_DETERMINISTIC_OPS to "true" or "1" also makes cuDNN convolution and max-pooling operate deterministically. This makes Keras Conv*D and MaxPool*D layers operate deterministically in both the forward and backward directions when running on a CUDA-enabled GPU.
Breaking Changes
Deletes Operation.traceback_with_start_lines for which we know of no usages.
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Commits
765ac8d Merge pull request #35913 from tensorflow-jenkins/relnotes-2.0.1-6767
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Bumps tensorflow-gpu from 2.0 to 2.0.1.
Release notes
Sourced from tensorflow-gpu's releases.
Changelog
Sourced from tensorflow-gpu's changelog.
Commits
765ac8dMerge pull request #35913 from tensorflow-jenkins/relnotes-2.0.1-67670bcb99bAdd CVE number for main patcha093c7eMerge pull request #36085 from tensorflow/mm-r2.0-fix-release-builds-pt463aedd7Disable test that times out on mac non pip builds619c578Disable the gpu on cpu tests as they were added for 2.11a617d6Merge pull request #36047 from tensorflow/mm-r2.0-fix-release-builds-pt332d9138Cleanup the windows buildsdd1ebd7Cleanup macos builds3b93059Remove py2 macos scripts606596fRemove builds which are not needed for the releaseDependabot 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
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