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Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support.
tf.Tensor
The class hierarchy for tf.Tensor has changed, and there are now explicit EagerTensor and SymbolicTensor classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. type(t) == tf.Tensor) will need to update their code to use isinstance(t, tf.Tensor). The tf.is_symbolic_tensor helper added in 2.13 may be used when it is necessary to determine if a value is specifically a symbolic tensor.
tf.compat.v1.Session
tf.compat.v1.Session.partial_run and tf.compat.v1.Session.partial_run_setup will be deprecated in the next release.
Known Caveats
tf.lite
when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set
Major Features and Improvements
The tensorflow pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.
Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more than 2**32 elements.
Disabling TensorFloat-32 execution now causes TPUs to use float32 precision for float32 matmuls and other ops. TPUs have always used bfloat16 precision for certain ops, like matmul, when such ops had float32 inputs. Now, disabling TensorFloat-32 by calling tf.config.experimental.enable_tensor_float_32_execution(False) will cause TPUs to use float32 precision for such ops instead of bfloat16.
tf.experimental.dtensor
API changes for Relayout. Added a new API, dtensor.relayout_like, for relayouting a tensor according to the layout of another tensor.
Support for Python 3.8 has been removed starting with TF 2.14. The TensorFlow 2.13.1 patch release will still have Python 3.8 support.
tf.Tensor
The class hierarchy for tf.Tensor has changed, and there are now explicit EagerTensor and SymbolicTensor classes for eager and tf.function respectively. Users who relied on the exact type of Tensor (e.g. type(t) == tf.Tensor) will need to update their code to use isinstance(t, tf.Tensor). The tf.is_symbolic_tensor helper added in 2.13 may be used when it is necessary to determine if a value is specifically a symbolic tensor.
tf.compat.v1.Session
tf.compat.v1.Session.partial_run and tf.compat.v1.Session.partial_run_setup will be deprecated in the next release.
Known Caveats
tf.lite
when converter flag "_experimenal_use_buffer_offset" is enabled, additional metadata is automatically excluded from the generated model. The behaviour is the same as "exclude_conversion_metadata" is set
If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set
Major Features and Improvements
The tensorflow pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run pip install tensorflow[and-cuda] to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.
Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more than 2**32 elements.
Disabling TensorFloat-32 execution now causes TPUs to use float32 precision for float32 matmuls and other ops. TPUs have always used bfloat16 precision for certain ops, like matmul, when such ops had float32 inputs. Now, disabling TensorFloat-32 by calling tf.config.experimental.enable_tensor_float_32_execution(False) will cause TPUs to use float32 precision for such ops instead of bfloat16.
tf.experimental.dtensor
API changes for Relayout. Added a new API, dtensor.relayout_like, for relayouting a tensor according to the layout of another tensor.
Added dtensor.get_default_mesh, for retrieving the current default mesh under the dtensor context.
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Commits
4dacf3f Merge pull request #61943 from georgiyekkert/r2.14
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Bumps tensorflow from 2.11.1 to 2.14.0.
Release notes
Sourced from tensorflow's releases.
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Changelog
Sourced from tensorflow's changelog.
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Commits
4dacf3f
Merge pull request #61943 from georgiyekkert/r2.140025df9
Pin ml_dtypes25ffb73
Merge pull request #61930 from tensorflow/r2.14-0e3480236ced9f5428
Update RELEASE.md to remove estimator deprecation notice (#61931)656737b
include THIRD_PARTY_NOTICES.txt in the wheel.30d843d
Merge pull request #61929 from tensorflow/r2.14-d03c477d7274e2744b
Add licenses and notices for third party libraries9b87467
Merge pull request #61838 from rtg0795/r2.14d5e6de1
Update RELEASE.md for 2.14.0 releasee9a1d03
Merge pull request #61837 from rtg0795/r2.14Dependabot 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