Bump tensorflow from 2.4.1 to 2.15.0.post1

[dependabot]

Bumps tensorflow from 2.4.1 to 2.15.0.post1.

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.post1

TensorFlow

Bug Fixes and Other Changes

• Hot-fix was needed for an issue affecting the TensorFlow installation process.
  • TensorFlow 2.15.0 Python package was requesting tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags.
  • This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda].
  • Depending on the installation method, TensorFlow 2.14 would be installed instead of 2.15, or users could receive an installation error due to those missing dependencies.
• TensorFlow 2.15.0.post1 is being released for Linux x86_64 to resolve this issue as quickly as possible.
  • This version removes the tensorrt Python package dependencies from the tensorflow[and-cuda] installation method to ensure pip install tensorflow[and-cuda] works as originally intended for TensorFlow 2.15.
  • Support for TensorRT is otherwise unaffected as long as TensorRT is already installed on the system.
• Using .post1 instead of a full minor release allowed us to push this release out quickly. However, please note the following caveat:
  • For users wishing to pin their Python dependency in a requirements file or other situation, under Python's version specification rules, tensorflow[and-cuda]==2.15.0 will not install this fixed version. Please use ==2.15.0.post1 to specify this exact version on Linux platforms, or a fuzzy version specification, such as ==2.15.*, to specify the most recent compatible version of TensorFlow 2.15 on all platforms.

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

... (truncated)

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[dependabot]

Looks like tensorflow is no longer being updated by Dependabot, so this is no longer needed.