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)

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)

• 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
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