⬆️ Bump github.com/tensorflow/tensorflow from 2.1.0+incompatible to 2.2.0+incompatible

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Bumps github.com/tensorflow/tensorflow from 2.1.0+incompatible to 2.2.0+incompatible.

Release notes

Sourced from github.com/tensorflow/tensorflow's releases.

TensorFlow 2.2.0

Release 2.2.0

TensorFlow 2.2 discontinues support for Python 2, previously announced as following Python 2's EOL on January 1, 2020.

Coinciding with this change, new releases of TensorFlow's Docker images provide Python 3 exclusively. Because all images now use Python 3, Docker tags containing -py3 will no longer be provided and existing -py3 tags like latest-py3 will not be updated.

Major Features and Improvements

• Replaced the scalar type for string tensors from std::string to tensorflow::tstring which is now ABI stable.

• A new Profiler for TF 2 for CPU/GPU/TPU. It offers both device and host performance analysis, including input pipeline and TF Ops. Optimization advisory is provided whenever possible. Please see this tutorial and guide for usage guidelines.

• Export C++ functions to Python using pybind11 as opposed to SWIG as a part of our deprecation of swig efforts.

• tf.distribute:

  • Support added for global sync BatchNormalization by using the newly added tf.keras.layers.experimental.SyncBatchNormalization layer. This layer will sync BatchNormalization statistics every step across all replicas taking part in sync training.
  • Performance improvements for GPU multi-worker distributed training using tf.distribute.experimental.MultiWorkerMirroredStrategy
    • Update NVIDIA NCCL to 2.5.7-1 for better performance and performance tuning. Please see nccl developer guide for more information on this.
    • Support gradient allreduce in float16. See this example usage.
    • Experimental support of all reduce gradient packing to allow overlapping gradient aggregation with backward path computation.
    • Deprecated experimental_run_v2 method for distribution strategies and renamed the method run as it is no longer experimental.
    • Add CompositeTensor support for DistributedIterators. This should help prevent unnecessary function retracing and memory leaks.

• tf.keras:

  • Model.fit major improvements:
    • You can now use custom training logic with Model.fit by overriding Model.train_step.
    • Easily write state-of-the-art training loops without worrying about all of the features Model.fit handles for you (distribution strategies, callbacks, data formats, looping logic, etc)
    • See the default Model.train_step for an example of what this function should look like. Same applies for validation and inference via Model.test_step and Model.predict_step.
    • SavedModel uses its own Model._saved_model_inputs_spec attr now instead of relying on Model.inputs and Model.input_names, which are no longer set for subclass Models. This attr is set in eager, tf.function, and graph modes. This gets rid of the need for users to manually call Model._set_inputs when using Custom Training Loops(CTLs).
    • Dynamic shapes are supported for generators by calling the Model on the first batch we "peek" from the generator. This used to happen implicitly in Model._standardize_user_data. Long-term, a solution where the DataAdapter doesn't need to call the Model is probably preferable.
  • The SavedModel format now supports all Keras built-in layers (including metrics, preprocessing layers, and stateful RNN layers)
  • Update Keras batch normalization layer to use the running mean and average computation in the fused_batch_norm. You should see significant performance improvements when using fused_batch_norm in Eager mode.

• tf.lite:

  • Enable TFLite experimental new converter by default.

• XLA

  • XLA now builds and works on windows. All prebuilt packages come with XLA available.
  • XLA can be enabled for a tf.function with “compile or throw exception” semantics on CPU and GPU.

Breaking Changes

• tf.keras:
  • In tf.keras.applications the name of the "top" layer has been standardized to "predictions". This is only a problem if your code relies on the exact name of the layer.
  • Huber loss function has been updated to be consistent with other Keras losses. It now computes mean over the last axis of per-sample losses before applying the reduction function.
• AutoGraph no longer converts functions passed to tf.py_function, tf.py_func and tf.numpy_function.
• Deprecating XLA_CPU and XLA_GPU devices with this release.
• Increasing the minimum bazel version to build TF to 2.0.0 to use Bazel's cc_experimental_shared_library.
• Keras compile/fit behavior for functional and subclassed models have been unified. Model properties such as metrics, metrics_names will now be available only after training/evaluating the model on actual data for functional models. metrics will now include model loss and output losses.loss_functions property has been removed from the model. This was an undocumented property that was accidentally public and has now been removed.

... (truncated)

Commits

• 2b96f36 Update version numbers to 2.2.0
• ab1082b Merge pull request #39202 from tensorflow/goldiegadde-patch-1
• d1c8148 Update RELEASE.md
• b3888df Merge pull request #39194 from tensorflow/mm-cherry-pick-309309347-on-r2.2
• 946cdbf Merge pull request #39193 from tensorflow/mm-cherry-pick-apache-switches-on-r2.2
• ba53f4b Add Python 3.8 to classifiers and remove old ones
• 5f9459d Disable OwnedMultiDeviceIterator on TPU Pod.
• 4352c16 Increase Apache Spark version to 2.4.5 to handle GitHub Security Alert
• 70087ab Merge pull request #39032 from tensorflow/mm-update-estimator-hash
• 285da41 Use Estimator 2.2.0
• Additional commits viewable in compare view

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