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chore(deps): bump tensorflow-gpu from 1.14.0 to 1.15.0 in /deeppavlov/requirements

Closed Andrei Glinskii requested to merge dependabot/pip/deeppavlov/requirements/tensorflow-gpu-1.15.0 into master

Created by: dependabot[bot]

Bumps tensorflow-gpu from 1.14.0 to 1.15.0.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 1.15.0

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.
  • Auto Mixed-Precision graph optimizer simplifies converting models to float16 for acceleration on Volta and Turing Tensor Cores. This feature can be enabled by wrapping an optimizer class with tf.train.experimental.enable_mixed_precision_graph_rewrite().
  • Add environment variable TF_CUDNN_DETERMINISTIC. Setting to "true" or "1" forces the selection of deterministic cuDNN convolution and max-pooling algorithms. When this is enabled, the algorithm selection procedure itself is also deterministic.
  • TensorRT
    • Migrate TensorRT conversion sources from contrib to compiler directory in preparation for TF 2.0.
    • Add additional, user friendly TrtGraphConverter API for TensorRT conversion.
    • Expand support for TensorFlow operators in TensorRT conversion (e.g. Gather, Slice, Pack, Unpack, ArgMin, ArgMax,DepthSpaceShuffle).
    • Support TensorFlow operator CombinedNonMaxSuppression in TensorRT conversion which significantly accelerates object detection models.

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.
... (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
  • 590d6ee Merge pull request #31861 from tensorflow-jenkins/relnotes-1.15.0rc0-16184
  • b27ac43 Update RELEASE.md
  • 07bf663 Merge pull request #33213 from Intel-tensorflow/mkl-dnn-0.20.6
  • 46f50ff Merge pull request #33262 from tensorflow/ggadde-1-15-cp2
  • 49c154e Merge pull request #33263 from tensorflow/ggadde-1-15-final-version
  • a16adeb Update TensorFlow version to 1.15.0 in preparation for final relase.
  • 8d71a87 Add saving of loaded/trained compatibility models in test and fix a compatibi...
  • 8c48aff [Intel Mkl] Upgrading MKL-DNN to 0.20.6 to fix SGEMM regression
  • 38ea9bb Merge pull request #33120 from tensorflow/perf
  • a8ef0f5 Automated rollback of commit db7e43192d405973c6c50f6e60e831a198bb4a49
  • Additional commits viewable in compare view

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