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:
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
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Bumps tensorflow from 2.14.0 to 2.15.0.
Release notes
Sourced from tensorflow's releases.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
... (truncated)
Commits
6887368Merge pull request #62369 from tensorflow/r2.15-ea45e14c9266f92629Change jaxlib version to the next earliest version for MacOS + Linux CI builds.71b7f97Merge pull request #62350 from rtg0795/r2.15486d1c0Update requirements.in and lock filesd289c2dMerge pull request #62349 from tensorflow-jenkins/version-numbers-2.15.0-209989d77d88Update version numbers to 2.15.09381e7cMerge pull request #62348 from tensorflow/rtg0795-patch-1e554d29Update setup.py with released version of Estimator and Keras2a4ec94Merge pull request #62308 from tensorflow/r2.15-e44f8a08051cca5fdaMerge pull request #62307 from tensorflow/r2.15-a1fd78b23b1Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show