Bump tensorflow-cpu from 1.15.0 to 2.1.0 in /Code

[dependabot]

Bumps tensorflow-cpu from 1.15.0 to 2.1.0.

Release notes

Sourced from tensorflow-cpu's releases.

TensorFlow 2.1.0

Release 2.1.0

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.
• Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging.
• Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
• The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration.
• tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
• tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

Bug Fixes and Other Changes

• tf.data
  • Fixes concurrency issue with tf.data.experimental.parallel_interleave with sloppy=True.
  • Add tf.data.experimental.dense_to_ragged_batch().
  • Extend tf.data parsing ops to support RaggedTensors.
• tf.distribute
  • Fix issue where GRU would crash or give incorrect output when a tf.distribute.Strategy was used.
• tf.estimator

... (truncated)

Changelog

Sourced from tensorflow-cpu's changelog.

Release 2.1.0

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.
• Removed id from tf.Tensor.__repr__() as id is not useful other than internal debugging.
• Some tf.assert_* methods now raise assertions at operation creation time if the input tensors' values are known at that time, not during the session.run(). This only changes behavior when the graph execution would have resulted in an error. When this happens, a noop is returned and the input tensors are marked non-feedable. In other words, if they are used as keys in feed_dict argument to session.run(), an error will be raised. Also, because some assert ops don't make it into the graph, the graph structure changes. A different graph can result in different per-op random seeds when they are not given explicitly (most often).
• The following APIs are not longer experimental: tf.config.list_logical_devices, tf.config.list_physical_devices, tf.config.get_visible_devices, tf.config.set_visible_devices, tf.config.get_logical_device_configuration, tf.config.set_logical_device_configuration.
• tf.config.experimentalVirtualDeviceConfiguration has been renamed to tf.config.LogicalDeviceConfiguration.
• tf.config.experimental_list_devices has been removed, please use tf.config.list_logical_devices.

Bug Fixes and Other Changes

• tf.data
  • Fixes concurrency issue with tf.data.experimental.parallel_interleave with sloppy=True.
  • Add tf.data.experimental.dense_to_ragged_batch().
  • Extend tf.data parsing ops to support RaggedTensors.
• tf.distribute
  • Fix issue where GRU would crash or give incorrect output when a tf.distribute.Strategy was used.
• tf.estimator
  • Added option in tf.estimator.CheckpointSaverHook to not save the GraphDef.

... (truncated)

Commits

• e5bf8de Merge pull request #35620 from tensorflow-jenkins/version-numbers-2.1.0-23859
• d43e3c7 Update version numbers to 2.1.0
• bd56e04 Merge pull request #35548 from compnerd/r2.1-windows-build
• 8427475 Merge pull request #35567 from Intel-tensorflow/tf-2.1-klocwork-cherry-pick
• 5efe38d Merge pull request #35566 from tensorflow/mm-r21-macos-py3
• 3ed1f02 Cherry-picking 55e20a6 - klockwork fix
• 5b7addf Add Python3.7 testing on MacOS as we drop support for Python2.
• a8adad9 configure.py: add -D_USE_MATH_DEFINES manually
• 9badef2 Define _USE_MATH_DEFINES for windows builds.
• 4c07219 [XLA] Remove use of designated initializers in dynamic dimension inference.
• Additional commits viewable in compare view

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