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Update tensorflow requirement from <=2.13.1 to <=2.15.0 #3

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Update tensorflow requirement from <=2.13.1 to <=2.15.0 #3

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@dependabot dependabot bot commented on behalf of github Nov 20, 2023 •
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Updates the requirements on tensorflow to permit the latest version.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.15.0

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 2.15.0

TensorFlow

Breaking Changes

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

Known Caveats

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

... (truncated)

Commits
  • 6887368 Merge pull request #62369 from tensorflow/r2.15-ea45e14c926
  • 6f92629 Change jaxlib version to the next earliest version for MacOS + Linux CI builds.
  • 71b7f97 Merge pull request #62350 from rtg0795/r2.15
  • 486d1c0 Update requirements.in and lock files
  • d289c2d Merge pull request #62349 from tensorflow-jenkins/version-numbers-2.15.0-20998
  • 9d77d88 Update version numbers to 2.15.0
  • 9381e7c Merge pull request #62348 from tensorflow/rtg0795-patch-1
  • e554d29 Update setup.py with released version of Estimator and Keras
  • 2a4ec94 Merge pull request #62308 from tensorflow/r2.15-e44f8a08051
  • cca5fda Merge pull request #62307 from tensorflow/r2.15-a1fd78b23b1
  • Additional commits viewable in compare view

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@dependabot
Update tensorflow requirement from <=2.13.1 to <=2.15.0
b5d6165 
Updates the requirements on [tensorflow](https://github.com/tensorflow/tensorflow) to permit the latest version.
- [Release notes](https://github.com/tensorflow/tensorflow/releases)
- [Changelog](https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md)
- [Commits](tensorflow/tensorflow@tflite-v0.1.7...v2.15.0)

---
updated-dependencies:
- dependency-name: tensorflow
  dependency-type: direct:development
...

Signed-off-by: dependabot[bot] <[email protected]>
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dependabot bot commented on behalf of github Nov 20, 2023

Dependabot tried to add @glenn-jocher as a reviewer to this PR, but received the following error from GitHub:

POST https://api.github.com/repos/vvng7190/slk/pulls/3/requested_reviewers: 422 - Reviews may only be requested from collaborators. One or more of the users or teams you specified is not a collaborator of the vvng7190/slk repository. // See: https://docs.github.com/rest/pulls/review-requests#request-reviewers-for-a-pull-request

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dependabot bot commented on behalf of github Nov 20, 2023

The following labels could not be found: dependencies.

@dependabot dependabot bot mentioned this pull request Nov 20, 2023
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dependabot bot commented on behalf of github Nov 22, 2023

Looks like tensorflow is up-to-date now, so this is no longer needed.

@dependabot dependabot bot closed this Nov 22, 2023
@dependabot dependabot bot deleted the dependabot/pip/tensorflow-lte-2.15.0 branch November 22, 2023 09:16
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