Bump tensorflow-gpu from 2.10.1 to 2.12.0

[dependabot]

Bumps tensorflow-gpu from 2.10.1 to 2.12.0.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.11.0

Release 2.11.0

Breaking Changes

• The tf.keras.optimizers.Optimizer base class now points to the new Keras optimizer, while the old optimizers have been moved to the tf.keras.optimizers.legacy namespace.

  If you find your workflow failing due to this change, you may be facing one of the following issues:

  • Checkpoint loading failure. The new optimizer handles optimizer state differently from the old optimizer, which simplifies the logic of checkpoint saving/loading, but at the cost of breaking checkpoint backward compatibility in some cases. If you want to keep using an old checkpoint, please change your optimizer to tf.keras.optimizer.legacy.XXX (e.g. tf.keras.optimizer.legacy.Adam).
  • TF1 compatibility. The new optimizer, tf.keras.optimizers.Optimizer, does not support TF1 any more, so please use the legacy optimizer tf.keras.optimizer.legacy.XXX. We highly recommend migrating your workflow to TF2 for stable support and new features.
  • Old optimizer API not found. The new optimizer, tf.keras.optimizers.Optimizer, has a different set of public APIs from the old optimizer. These API changes are mostly related to getting rid of slot variables and TF1 support. Please check the API documentation to find alternatives to the missing API. If you must call the deprecated API, please change your optimizer to the legacy optimizer.
  • Learning rate schedule access. When using a tf.keras.optimizers.schedules.LearningRateSchedule, the new optimizer's learning_rate property returns the current learning rate value instead of a LearningRateSchedule object as before. If you need to access the LearningRateSchedule object, please use optimizer._learning_rate.
  • If you implemented a custom optimizer based on the old optimizer. Please set your optimizer to subclass tf.keras.optimizer.legacy.XXX. If you want to migrate to the new optimizer and find it does not support your optimizer, please file an issue in the Keras GitHub repo.
  • Errors, such as Cannot recognize variable.... The new optimizer requires all optimizer variables to be created at the first apply_gradients() or minimize() call. If your workflow calls the optimizer to update different parts of the model in multiple stages, please call optimizer.build(model.trainable_variables) before the training loop.
  • Timeout or performance loss. We don't anticipate this to happen, but if you see such issues, please use the legacy optimizer, and file an issue in the Keras GitHub repo.

  The old Keras optimizer will never be deleted, but will not see any new feature additions. New optimizers (for example, tf.keras.optimizers.Adafactor) will only be implemented based on the new tf.keras.optimizers.Optimizer base class.

• tensorflow/python/keras code is a legacy copy of Keras since the TensorFlow v2.7 release, and will be deleted in the v2.12 release. Please remove any import of tensorflow.python.keras and use the public API with from tensorflow import keras or import tensorflow as tf; tf.keras.

Major Features and Improvements

• tf.lite:

  • New operations supported: tf.math.unsorted_segment_sum, tf.atan2 and tf.sign.
  • Updates to existing operations:
    • tfl.mul now supports complex32 inputs.

• tf.experimental.StructuredTensor:

  • Introduced tf.experimental.StructuredTensor, which provides a flexible and TensorFlow-native way to encode structured data such as protocol buffers or pandas dataframes.

• tf.keras:

  • Added a new get_metrics_result() method to tf.keras.models.Model.
    • Returns the current metrics values of the model as a dict.
  • Added a new group normalization layer - tf.keras.layers.GroupNormalization.
  • Added weight decay support for all Keras optimizers via the weight_decay argument.
  • Added the Adafactor optimizer - tf.keras.optimizers.Adafactor.
  • Added warmstart_embedding_matrix to tf.keras.utils.
    • This utility can be used to warmstart an embedding matrix, so you reuse previously-learned word embeddings when working with a new set of words which may include previously unseen words (the embedding vectors for unseen words will be randomly initialized).

• tf.Variable:

  • Added CompositeTensor as a base class to ResourceVariable.
    • This allows tf.Variables to be nested in tf.experimental.ExtensionTypes.
  • Added a new constructor argument experimental_enable_variable_lifting to tf.Variable, defaulting to True.
    • When it's set to False, the variable won't be lifted out of tf.function; thus it can be used as a tf.function-local variable: during each execution of the tf.function, the variable will be created and then disposed, similar to a local (that is, stack-allocated) variable in C/C++. Currently, experimental_enable_variable_lifting=False only works on non-XLA devices (for example, under @tf.function(jit_compile=False)).

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Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.12.0

Breaking Changes

• Build, Compilation and Packaging

  • Removal of redundant packages: the tensorflow-gpu and tf-nightly-gpu packages have been effectively removed and replaced with packages that direct users to switch to tensorflow or tf-nightly respectively. The naming difference was the only difference between the two sets of packages ever since TensorFlow 2.1, so there is no loss of functionality or GPU support. See https://pypi.org/project/tensorflow-gpu for more details.

• tf.function:

  • tf.function now uses the Python inspect library directly for parsing the signature of the Python function it is decorated on.
  • This can break certain cases that were previously ignored where the signature is malformed, e.g. * Using functools.wraps on a function with different signature * Using functools.partial with an invalid tf.function input
  • tf.function now enforces input parameter names to be valid Python identifiers. Incompatible names are automatically sanitized similarly to existing SavedModel behavior.
  • tf.types.experimental.TraceType now requires an additional placeholder_value method to be defined.
  • tf.function now traces with placeholder values generated by TraceType instead of the value itself.

• tf.config.experimental.enable_mlir_graph_optimization:

  • Experimental API removed.

• tf.config.experimental.disable_mlir_graph_optimization:

  • Experimental API removed.

• tf.keras

  • Added a model.export(filepath) API to create a lightweight SavedModel artifact that can be used for inference (e.g. with TF-Serving).
  • Improvements and fixes in Keras loss masking:
    • Whether you represent a ragged tensor as a tf.RaggedTensor or using keras masking, the returned loss values should be the identical to each other. In previous versions Keras may have silently ignored the mask.

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