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Update tensorflow requirement from <2.12.0 to <2.14.0

Alexander Rodionov requested to merge dependabot/pip/tensorflow-lt-2.14.0 into master

Created by: dependabot[bot]

Updates the requirements on tensorflow to permit the latest version.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.13.0

Release 2.13.0

TensorFlow

Breaking Changes

  • The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.

Major Features and Improvements

  • tf.lite

    • Added 16-bit and 64-bit float type support for built-in op cast.
    • The Python TF Lite Interpreter bindings now have an option experimental_disable_delegate_clustering to turn-off delegate clustering.
    • Added int16x8 support for the built-in op exp
    • Added int16x8 support for the built-in op mirror_pad
    • Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
    • Added 16-bit int type support for built-in op less, greater_than, equal
    • Added 8-bit and 16-bit support for floor_div and floor_mod.
    • Added 16-bit and 32-bit int support for the built-in op bitcast.
    • Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
    • Added int16 indices support for built-in op gather and gather_nd.
    • Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
    • Added reference implementation for 16-bit int unquantized add.
    • Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
    • add_op supports broadcasting up to 6 dimensions.
    • Added 16-bit support for top_k.

  • tf.function

    • ConcreteFunction (tf.types.experimental.ConcreteFunction) as generated through get_concrete_function now performs holistic input validation similar to calling tf.function directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).

  • tf.nn

    • tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse now support ids and weights described by tf.RaggedTensors.
    • Added a new boolean argument allow_fast_lookup to tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse, which enables a simplified and typically faster lookup procedure.

  • tf.data

    • tf.data.Dataset.zip now supports Python-style zipping, i.e. Dataset.zip(a, b, c).
    • tf.data.Dataset.shuffle now supports tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

  • tf.math

    • tf.nn.top_k now supports specifying the output index type via parameter index_type. Supported types are tf.int16, tf.int32 (default), and tf.int64.

  • tf.SavedModel

    • Introduced class method tf.saved_model.experimental.Fingerprint.from_proto(proto), which can be used to construct a Fingerprint object directly from a protobuf.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.13.0

TensorFlow

Breaking Changes

  • The LMDB kernels have been changed to return an error. This is in preparation for completely removing them from TensorFlow. The LMDB dependency that these kernels are bringing to TensorFlow has been dropped, thus making the build slightly faster and more secure.

Major Features and Improvements

  • tf.lite

    • Added 16-bit and 64-bit float type support for built-in op cast.
    • The Python TF Lite Interpreter bindings now have an option experimental_disable_delegate_clustering to turn-off delegate clustering.
    • Added int16x8 support for the built-in op exp
    • Added int16x8 support for the built-in op mirror_pad
    • Added int16x8 support for the built-in ops space_to_batch_nd and batch_to_space_nd
    • Added 16-bit int type support for built-in op less, greater_than, equal
    • Added 8-bit and 16-bit support for floor_div and floor_mod.
    • Added 16-bit and 32-bit int support for the built-in op bitcast.
    • Added 8-bit/16-bit/32-bit int/uint support for the built-in op bitwise_xor
    • Added int16 indices support for built-in op gather and gather_nd.
    • Added 8-bit/16-bit/32-bit int/uint support for the built-in op right_shift
    • Added reference implementation for 16-bit int unquantized add.
    • Added reference implementation for 16-bit int and 32-bit unsigned int unquantized mul.
    • add_op supports broadcasting up to 6 dimensions.
    • Added 16-bit support for top_k.

  • tf.function

    • ConcreteFunction (tf.types.experimental.ConcreteFunction) as generated through get_concrete_function now performs holistic input validation similar to calling tf.function directly. This can cause breakages where existing calls pass Tensors with the wrong shape or omit certain non-Tensor arguments (including default values).

  • tf.nn

    • tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse now support ids and weights described by tf.RaggedTensors.
    • Added a new boolean argument allow_fast_lookup to tf.nn.embedding_lookup_sparse and tf.nn.safe_embedding_lookup_sparse, which enables a simplified and typically faster lookup procedure.

  • tf.data

    • tf.data.Dataset.zip now supports Python-style zipping, i.e. Dataset.zip(a, b, c).
    • tf.data.Dataset.shuffle now supports tf.data.UNKNOWN_CARDINALITY When doing a "full shuffle" using dataset = dataset.shuffle(dataset.cardinality()). But remember, a "full shuffle" will load the full dataset into memory so that it can be shuffled, so make sure to only use this with small datasets or datasets of small objects (like filenames).

  • tf.math

    • tf.nn.top_k now supports specifying the output index type via parameter index_type. Supported types are tf.int16, tf.int32 (default), and tf.int64.

  • tf.SavedModel

    • Introduced class method tf.saved_model.experimental.Fingerprint.from_proto(proto), which can be used to construct a Fingerprint object directly from a protobuf.
    • Introduced member method tf.saved_model.experimental.Fingerprint.singleprint(), which provides a convenient way to uniquely identify a SavedModel.

... (truncated)

Commits
  • 1cb1a03 updating release notes with security fixes (#61119)
  • bd4c381 Merge pull request #61102 from tensorflow/venkat-patch-123
  • 2a17745 update estimator and keras versions
  • 71a2f7f Merge pull request #61097 from tensorflow-jenkins/version-numbers-2.13.0-1179
  • 3e6e3ce Update version numbers to 2.13.0
  • 6657f49 Merge pull request #61075 from elfringham/limit_numpy
  • 90389e9 Fix unit test failure caused by numpy update
  • 5b6abc8 Merge pull request #60904 from tensorflow/venkat-patch-22
  • 5763bc3 Fix TPUExecute for TPU embedding operations. Create temporary device memory
  • 1c27a49 Merge pull request #60888 from tensorflow/venkat-patch-16
  • Additional commits viewable in compare view

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