Bump tensorflow from 1.14 to 2.0.1 in /yugiohbot

[dependabot]

Bumps tensorflow from 1.14 to 2.0.1.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.0.1

Release 2.0.1

Bug Fixes and Other Changes

• Fixes a security vulnerability where converting a Python string to a tf.float16 value produces a segmentation fault (CVE-2020-5215)
• Updates curl to 7.66.0 to handle CVE-2019-5482 and CVE-2019-5481
• Updates sqlite3 to 3.30.01 to handle CVE-2019-19646, CVE-2019-19645 and CVE-2019-16168

TensorFlow 2.0.0

Release 2.0.0

Major Features and Improvements

TensorFlow 2.0 focuses on simplicity and ease of use, featuring updates like:

• Easy model building with Keras and eager execution.
• Robust model deployment in production on any platform.
• Powerful experimentation for research.
• API simplification by reducing duplication and removing deprecated endpoints.

For details on best practices with 2.0, see the Effective 2.0 guide

For information on upgrading your existing TensorFlow 1.x models, please refer to our Upgrade and Migration guides. We have also released a collection of tutorials and getting started guides.

Highlights

• TF 2.0 delivers Keras as the central high level API used to build and train models. Keras provides several model-building APIs such as Sequential, Functional, and Subclassing along with eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines. Checkout guide for additional details.
• Distribution Strategy: TF 2.0 users will be able to use the tf.distribute.Strategy API to distribute training with minimal code changes, yielding great out-of-the-box performance. It supports distributed training with Keras model.fit, as well as with custom training loops. Multi-GPU support is available, along with experimental support for multi worker and Cloud TPUs. Check out the guide for more details.
• Functions, not Sessions. The traditional declarative programming model of building a graph and executing it via a tf.Session is discouraged, and replaced with by writing regular Python functions. Using the tf.function decorator, such functions can be turned into graphs which can be executed remotely, serialized, and optimized for performance.
• Unification of tf.train.Optimizers and tf.keras.Optimizers. Use tf.keras.Optimizers for TF2.0. compute_gradients is removed as public API, use GradientTape to compute gradients.
• 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.
• Unification of exchange formats to SavedModel. All TensorFlow ecosystem projects (TensorFlow Lite, TensorFlow JS, TensorFlow Serving, TensorFlow Hub) accept SavedModels. Model state should be saved to and restored from SavedModels.
• API Changes: Many API symbols have been renamed or removed, and argument names have changed. Many of these changes are motivated by consistency and clarity. The 1.x API remains available in the compat.v1 module. A list of all symbol changes can be found here.
• API clean-up, included removing tf.app, tf.flags, and tf.logging in favor of absl-py.
• No more global variables with helper methods like tf.global_variables_initializer and tf.get_global_step.
• 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.
• Fixes autocomplete for most TensorFlow API references by switching to use relative imports in API __init__.py files.
• 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.

Breaking Changes

• Many backwards incompatible API changes have been made to clean up the APIs and make them more consistent.

• Toolchains:

  • TensorFlow 2.0.0 is built using devtoolset7 (GCC7) on Ubuntu 16. This may lead to ABI incompatibilities with extensions built against earlier versions of TensorFlow.
  • 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.
    Removed the freeze_graph command line tool; SavedModel should be used in place of frozen graphs.

• tf.contrib:

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.0.1

Bug Fixes and Other Changes

• Fixes a security vulnerability where converting a Python string to a tf.float16 value produces a segmentation fault (CVE-2020-5215)
• Updates curl to 7.66.0 to handle CVE-2019-5482 and CVE-2019-5481
• Updates sqlite3 to 3.30.01 to handle CVE-2019-19646, CVE-2019-19645 and CVE-2019-16168

Release 1.15.2

Bug Fixes and Other Changes

• Fixes a security vulnerability where converting a Python string to a tf.float16 value produces a segmentation fault (CVE-2020-5215)
• Updates curl to 7.66.0 to handle CVE-2019-5482 and CVE-2019-5481
• Updates sqlite3 to 3.30.01 to handle CVE-2019-19646, CVE-2019-19645 and CVE-2019-16168

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.

... (truncated)

Commits

• 765ac8d Merge pull request #35913 from tensorflow-jenkins/relnotes-2.0.1-6767
• 0bcb99b Add CVE number for main patch
• a093c7e Merge pull request #36085 from tensorflow/mm-r2.0-fix-release-builds-pt4
• 63aedd7 Disable test that times out on mac non pip builds
• 619c578 Disable the gpu on cpu tests as they were added for 2.1
• 1a617d6 Merge pull request #36047 from tensorflow/mm-r2.0-fix-release-builds-pt3
• 32d9138 Cleanup the windows builds
• dd1ebd7 Cleanup macos builds
• 3b93059 Remove py2 macos scripts
• 606596f Remove builds which are not needed for the release
• Additional commits viewable in compare view

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Superseded by #4.