Sourced from tensorflow's releases.

TensorFlow 2.14.0

Release 2.14.0

Tensorflow

Breaking Changes

• 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.

  • Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
  • Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

• tf.lite

  • Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

Bug Fixes and Other Changes

• tf.py_function and tf.numpy_function can now be used as function decorators for clearer code:

  @tf.py_function(Tout=tf.float32)
  def my_fun(x):
    print("This always executes eagerly.")
    return x+1
  

• tf.lite

  • Strided_Slice now supports UINT32.

• tf.config.experimental.enable_tensor_float_32_execution

  • 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.

... (truncated)

Sourced from tensorflow's changelog.

Release 2.14.0

Breaking Changes

• 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.

• tf.estimator

  • tf.estimator API will be removed in the next release. TF Estimator Python package will no longer be released.

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.

... (truncated)

• 4dacf3f Merge pull request #61943 from georgiyekkert/r2.14
• 0025df9 Pin ml_dtypes
• 25ffb73 Merge pull request #61930 from tensorflow/r2.14-0e3480236ce
• d9f5428 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-d03c477d727
• 4e2744b Add licenses and notices for third party libraries
• 9b87467 Merge pull request #61838 from rtg0795/r2.14
• d5e6de1 Update RELEASE.md for 2.14.0 release
• e9a1d03 Merge pull request #61837 from rtg0795/r2.14
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