Bump tensorflow-gpu from 2.0.0b1 to 2.1.0rc0

[dependabot-preview]

Bumps tensorflow-gpu from 2.0.0b1 to 2.1.0rc0.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.1.0-rc0

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.
• tf.keras
  • Model.fit_generator, Model.evaluate_generator, Model.predict_generator, Model.train_on_batch, Model.test_on_batch, and Model.predict_on_batch methods now respect the run_eagerly property, and will correctly run using tf.function by default.
  • Model.fit_generator, Model.evaluate_generator, and Model.predict_generator are deprecated endpoints. They are subsumed by Model.fit, Model.evaluate, and Model.predict which now support generators and Sequences.
  • 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.
  • Keras model.load_weights now accepts skip_mismatch as an argument. This was available in external Keras, and has now been copied over to tf.keras.
  • 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.
  • Experimental support for Keras .compile, .fit, .evaluate, and .predict is available for Cloud TPU Pods.
  • 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.
  • Experimental support for mixed precision is available on GPUs and Cloud TPUs.
  • Keras reference implementations for many popular models are available in the TensorFlow Model Garden.
• tf.data
  • Changes rebatching for tf.data datasets + distribution strategies 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.
• 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.

Known issues

Because of issues with building on windows, we turned off eigen strong inlining for the Windows builds. Windows binaries are expected to be slightly slower until the build issues are resolved.

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.
• tf.keras
  • Export depthwise_conv2d in tf.keras.backend.
  • In Keras Layers and Models, Variables in trainable_weights, non_trainable_weights, and weights are explicitly deduplicated.
  • Fix the incorrect stateful behavior of Keras convolutional layers.
• tf.lite
  • Legalization for NMS ops in TFLite.

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 1.15.0

This is the last 1.x release for TensorFlow. We do not expect to update the 1.x branch with features, although we will issue patch releases to fix vulnerabilities for at least one year.

Major Features and Improvements

• As announced, tensorflow pip package will by default include GPU support (same as tensorflow-gpu now) for the platforms we currently have GPU support (Linux and Windows). It will work on machines with and without Nvidia GPUs. tensorflow-gpu will still be available, and CPU-only packages can be downloaded at tensorflow-cpu for users who are concerned about package size.
• TensorFlow 1.15 contains a complete implementation of the 2.0 API in its compat.v2 module. It contains a copy of the 1.15 main module (without contrib) in the compat.v1 module. TensorFlow 1.15 is able to emulate 2.0 behavior using the enable_v2_behavior() function.
  This enables writing forward compatible code: by explicitly importing either tensorflow.compat.v1 or tensorflow.compat.v2, you can ensure that your code works without modifications against an installation of 1.15 or 2.0.
• EagerTensor now supports numpy buffer interface for tensors.
• Add toggles tf.enable_control_flow_v2() and tf.disable_control_flow_v2() for enabling/disabling v2 control flow.
• Enable v2 control flow as part of tf.enable_v2_behavior() and TF2_BEHAVIOR=1.
• AutoGraph translates Python control flow into TensorFlow expressions, allowing users to write regular Python inside tf.function-decorated functions. AutoGraph is also applied in functions used with tf.data, tf.distribute and tf.keras APIS.
• Adds enable_tensor_equality(), which switches the behavior such that:
  • Tensors are no longer hashable.
  • Tensors can be compared with == and !=, yielding a Boolean Tensor with element-wise comparison results. This will be the default behavior in 2.0.

Breaking Changes

• Tensorflow code now produces 2 different pip packages: tensorflow_core containing all the code (in the future it will contain only the private implementation) and tensorflow which is a virtual pip package doing forwarding to tensorflow_core (and in the future will contain only the public API of tensorflow). We don't expect this to be breaking, unless you were importing directly from the implementation.
• TensorFlow 1.15 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
• Deprecated the use of constraint= and .constraint with ResourceVariable.
• tf.keras:
  • OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.
  • tf.keras.model.save_model and model.save now defaults to saving a TensorFlow SavedModel.
  • keras.backend.resize_images (and consequently, keras.layers.Upsampling2D) behavior has changed, a bug in the resizing implementation was fixed.
  • Layers now default to float32, and automatically cast their inputs to the layer's dtype. If you had a model that used float64, it will probably silently use float32 in TensorFlow2, and a warning will be issued that starts with Layer "layer-name" is casting an input tensor from dtype float64 to the layer's dtype of float32. To fix, either set the default dtype to float64 with tf.keras.backend.set_floatx('float64'), or pass dtype='float64' to each of the Layer constructors. See tf.keras.layers.Layer for more information.
  • Some tf.assert_* methods now raise assertions at operation creation time (i.e. when this Python line executes) if the input tensors' values are known at that time, not during the session.run(). 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).

Bug Fixes and Other Changes

• tf.estimator:
  • tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
  • Fix tests in canned estimators.
  • Expose Head as public API.
  • Fixes critical bugs that help with DenseFeatures usability in TF2
• tf.data:
  • Promoting unbatch from experimental to core API.
  • Adding support for datasets as inputs to from_tensors and from_tensor_slices and batching and unbatching of nested datasets.
• tf.keras:
  • tf.keras.estimator.model_to_estimator now supports exporting to tf.train.Checkpoint format, which allows the saved checkpoints to be compatible with model.load_weights.
  • Saving a Keras Model using tf.saved_model.save now saves the list of variables, trainable variables, regularization losses, and the call function.
  • Deprecated tf.keras.experimental.export_saved_model and tf.keras.experimental.function. Please use tf.keras.models.save_model(..., save_format='tf') and tf.keras.models.load_model instead.
  • Add an implementation=3 mode for tf.keras.layers.LocallyConnected2D and tf.keras.layers.LocallyConnected1D layers using tf.SparseTensor to store weights, allowing a dramatic speedup for large sparse models.
  • Enable the Keras compile API experimental_run_tf_function flag by default. This flag enables single training/eval/predict execution path. With this 1. All input types are converted to Dataset. 2. When distribution strategy is not specified this goes through the no-op distribution strategy path. 3. Execution is wrapped in tf.function unless run_eagerly=True is set in compile.
  • Raise error if batch_size argument is used when input is dataset/generator/keras sequence.
• tf.lite
  • Add GATHER support to NN API delegate.
  • tflite object detection script has a debug mode.
  • Add delegate support for QUANTIZE.
  • Added evaluation script for COCO minival.
  • Add delegate support for QUANTIZED_16BIT_LSTM.
  • Converts hardswish subgraphs into atomic ops.
• Add support for defaulting the value of cycle_length argument of tf.data.Dataset.interleave to the number of schedulable CPU cores.

... (truncated)

Commits

• c6daad3 Merge pull request #34626 from yifeif/cherrypicks_J8PS0
• fff3b08 Move gpu script update to the correct file.
• 5c43a0a Merge pull request #34623 from yifeif/cherrypicks_ZT8OO
• ae30c1b Fix bug in if -v check.
• 60afa4e Merge pull request #34246 from tensorflow-jenkins/relnotes-2.1.0rc0-20042
• 451387d Merge pull request #34593 from tomerk/cherrypicks_M3RH3
• 1d03f44 Merge pull request #34596 from yifeif/cherrypicks_UUJ02
• 058f148 Update windows release build script to create the correct pip package names.
• 617f788 Allows Keras TensorLikeDataAdapter to handle pandas series and dataframes.
• fee475d Update RELEASE.md
• Additional commits viewable in compare view

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