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Bump tensorflow from 2.2.0rc2 to 2.15.0rc0 in /python #27

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@dependabot dependabot bot commented on behalf of github Nov 1, 2023

Bumps tensorflow from 2.2.0rc2 to 2.15.0rc0.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.15.0-rc0

Release 2.15.0

TensorFlow

Breaking Changes

  • tf.types.experimental.GenericFunction has been renamed to tf.types.experimental.PolymorphicFunction.

Major Features and Improvements

  • oneDNN CPU performance optimizations Windows x64 & x86.

    • Windows x64 & x86 packages:
      • oneDNN optimizations are enabled by default on X86 CPUs
    • To explicitly enable or disable oneDNN optimizations, set the environment variable TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.
    • oneDNN optimizations can yield slightly different numerical results compared to when oneDNN optimizations are disabled due to floating-point round-off errors from different computation approaches and orders.
    • To verify if oneDNN optimizations are on, look for a message with "oneDNN custom operations are on" in the log. If the exact phrase is not there, it means they are off.
  • Making the tf.function type system fully available:

    • tf.types.experimental.TraceType now allows custom tf.function inputs to declare Tensor decomposition and type casting support.
    • Introducing tf.types.experimental.FunctionType as the comprehensive representation of the signature of tf.function callables. It can be accessed through the function_type property of tf.functions and ConcreteFunctions. See the tf.types.experimental.FunctionType documentation for more details.
  • Introducing tf.types.experimental.AtomicFunction as the fastest way to perform TF computations in Python.

    • Can be accessed through inference_fn property of ConcreteFunctions
    • Does not support gradients.
    • See tf.types.experimental.AtomicFunction documentation for how to call and use it.
  • tf.data:

    • Moved option warm_start from tf.data.experimental.OptimizationOptions to tf.data.Options.
  • tf.lite:

    • sub_op and mul_op support broadcasting up to 6 dimensions.

    • The tflite::SignatureRunner class, which provides support for named parameters and for multiple named computations within a single TF Lite model, is no longer considered experimental. Likewise for the following signature-related methods of tflite::Interpreter:

      • tflite::Interpreter::GetSignatureRunner
      • tflite::Interpreter::signature_keys
      • tflite::Interpreter::signature_inputs
      • tflite::Interpreter::signature_outputs
      • tflite::Interpreter::input_tensor_by_signature
      • tflite::Interpreter::output_tensor_by_signature
    • Similarly, the following signature runner functions in the TF Lite C API are no longer considered experimental:

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.16.0

TensorFlow

Breaking Changes

Known Caveats

Major Features and Improvements

Bug Fixes and Other Changes

  • tf.lite

    • Added support for stablehlo.gather.
    • Added support for stablehlo.add.
    • Added support for stablehlo.multiply.
    • Added support for stablehlo.maximum.
    • Added support for stablehlo.minimum.

Keras

  • keras.layers.experimental.DynamicEmbedding
    • Added DynamicEmbedding Keras layer
    • Added 'UpdateEmbeddingCallback`
    • DynamicEmbedding layer allows for the continuous updating of the vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on the inputs received by the layer and the eviction policy. When this layer is used with an UpdateEmbeddingCallback, which is a time-based callback, the vocabulary lookup tensor is updated at the time interval set in the UpdateEmbeddingCallback based on the most up-to-date vocabulary hash table maintained by the layer. If this layer is not used in conjunction with UpdateEmbeddingCallback the behavior of the layer would be same as keras.layers.Embedding.

... (truncated)

Commits
  • 5b2454e Merge pull request #62179 from rtg0795/r2.15
  • 13ca329 Update BUILD files with released PyPI versions
  • ffd0f7b Merge pull request #62177 from rtg0795/r2.15
  • c1b4d71 Add generated lock files
  • eef3c04 release_updater.sh with stable keras, tensorboard, tensorflow-estimator
  • 8f2cb5a Merge pull request #62176 from tensorflow/r2.15-f37fad4c3ca
  • 5a8bd9a Fix high memory usage when running eager ops as a function.
  • 5691824 Merge pull request #62158 from tensorflow/rtg0795-patch-2
  • 767549c Merge pull request #62156 from tensorflow/rtg0795-patch-1
  • e02b876 Update setup.py on TF release branch with released version of Estimator, Kera...
  • Additional commits viewable in compare view

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Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.2.0rc2 to 2.15.0rc0.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@v2.2.0-rc2...v2.15.0-rc0)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added the dependencies Pull requests that update a dependency file label Nov 1, 2023
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dependabot bot commented on behalf of github Dec 1, 2023

Superseded by #32.

@dependabot dependabot bot closed this Dec 1, 2023
@dependabot dependabot bot deleted the dependabot/pip/python/tensorflow-2.15.0rc0 branch December 1, 2023 23:57
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