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Bump tensorflow from 2.8.0 to 2.9.0
Bumps tensorflow from 2.8.0 to 2.9.0.
Release notes
Sourced from tensorflow's releases.
TensorFlow 2.9.0
Release 2.9.0
Breaking Changes
- Due to security issues in TF 2.8, all boosted trees code has now been removed (after being deprecated in TF 2.8). Users should switch to TensorFlow Decision Forests.
- Build, Compilation and Packaging
- TensorFlow is now compiled with
_GLIBCXX_USE_CXX11_ABI=1
. Downstream projects that encounterstd::__cxx11
or[abi:cxx11]
linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.- TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
- Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
- The
tf.keras.mixed_precision.experimental
API has been removed. The non-experimental symbols undertf.keras.mixed_precision
have been available since TensorFlow 2.4 and should be used instead.
- The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
- Remove the word "experimental" from
tf.keras.mixed_precision
symbols. E.g., replacetf.keras.mixed_precision.experimental.global_policy
withtf.keras.mixed_precision.global_policy
.- Replace
tf.keras.mixed_precision.experimental.set_policy
withtf.keras.mixed_precision.set_global_policy
. The experimental symbolset_policy
was renamed toset_global_policy
in the non-experimental API.- Replace
LossScaleOptimizer(opt, "dynamic")
withLossScaleOptimizer(opt)
. If you pass anything other than"dynamic"
to the second argument, see (1) of the next section.- In the following rare cases, you need to make more changes when switching to the non-experimental API:
- If you passed anything other than
"dynamic"
to theloss_scale
argument (the second argument) ofLossScaleOptimizer
:
- The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
- If you passed a value to the
loss_scale
argument (the second argument) ofPolicy
:
- The experimental version of
Policy
optionally took in atf.compat.v1.mixed_precision.LossScale
in the constructor, which defaulted to a dynamic loss scale for the"mixed_float16"
policy and no loss scale for other policies. InModel.compile
, if the model's policy had a loss scale, the optimizer would be wrapped with aLossScaleOptimizer
. With the non-experimentalPolicy
, there is no loss scale associated with thePolicy
, andModel.compile
wraps the optimizer with aLossScaleOptimizer
if and only if the policy is a"mixed_float16"
policy. If you previously passed aLossScale
to the experimentalPolicy
, consider just removing it, as the default loss scaling behavior is usually what you want. If you really want to customize the loss scaling behavior, you can wrap your optimizer with aLossScaleOptimizer
before passing it toModel.compile
.- If you use the very rarely-used function
tf.keras.mixed_precision.experimental.get_layer_policy
:
- Replace
tf.keras.mixed_precision.experimental.get_layer_policy(layer)
withlayer.dtype_policy
.tf.mixed_precision.experimental.LossScale
and its subclasses have been removed from the TF2 namespace. This symbols were very rarely used and were only useful in TF2 for use in the now-removedtf.keras.mixed_precision.experimental
API. The symbols are still available undertf.compat.v1.mixed_precision
.- The
experimental_relax_shapes
heuristic fortf.function
has been deprecated and replaced withreduce_retracing
which encompasses broader heuristics to reduce the number of retraces (see below)Major Features and Improvements
tf.keras
:
- Added
tf.keras.applications.resnet_rs
models. This includes theResNetRS50
,ResNetRS101
,ResNetRS152
,ResNetRS200
,ResNetRS270
,ResNetRS350
andResNetRS420
model architectures. The ResNetRS models are based on the architecture described in Revisiting ResNets: Improved Training and Scaling Strategies- Added
tf.keras.optimizers.experimental.Optimizer
. The reworked optimizer gives more control over different phases of optimizer calls, and is easier to customize. We provide Adam, SGD, Adadelta, AdaGrad and RMSprop optimizers based ontf.keras.optimizers.experimental.Optimizer
. Generally the new optimizers work in the same way as the old ones, but support new constructor arguments. In the future, the symbolstf.keras.optimizers.Optimizer
/Adam
/etc will point to the new optimizers, and the previous generation of optimizers will be moved totf.keras.optimizers.legacy.Optimizer
/Adam
/etc.- Added L2 unit normalization layer
tf.keras.layers.UnitNormalization
.- Added
tf.keras.regularizers.OrthogonalRegularizer
, a new regularizer that encourages orthogonality between the rows (or columns) or a weight matrix.- Added
tf.keras.layers.RandomBrightness
layer for image preprocessing.- Added APIs for switching between interactive logging and absl logging. By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use
tf.keras.utils.disable_interactive_logging()
to write the logs to ABSL logging. You can also usetf.keras.utils.enable_interactive_logging()
to change it back to stdout, ortf.keras.utils.is_interactive_logging_enabled()
to check if interactive logging is enabled.- Changed default value for the
verbose
argument ofModel.evaluate()
andModel.predict()
to"auto"
, which defaults toverbose=1
for most cases and defaults toverbose=2
when used withParameterServerStrategy
or with interactive logging disabled.- Argument
jit_compile
inModel.compile()
now applies toModel.evaluate()
andModel.predict()
. Settingjit_compile=True
incompile()
compiles the model's training, evaluation, and inference steps to XLA. Note thatjit_compile=True
may not necessarily work for all models.- Added DTensor-related Keras APIs under
tf.keras.dtensor
namespace. The APIs are still classified as experimental. You are welcome to try it out. Please check the tutoral and guide on https://www.tensorflow.org/ for more details about DTensor.
tf.lite
:
- Added TFLite builtin op support for the following TF ops:
tf.math.argmin
/tf.math.argmax
for input data typetf.bool
on CPU.tf.nn.gelu
op for output data typetf.float32
and quantization on CPU.- Add nominal support for unsigned 16-bit integer tensor types. Note that very few TFLite kernels support this type natively, so its use in mobile ML authoring is generally discouraged.
- Add support for unsigned 16-bit integer tensor types in cast op.
- Experimental support for lowering
list_ops.tensor_list_set_item
withDynamicUpdateSlice
.- Enabled a new MLIR-based dynamic range quantization backend by default
- The new backend is used for post-training int8 dynamic range quantization and post-training float16 quantization.
- Set
experimental_new_dynamic_range_quantizer
in tf.lite.TFLiteConverter to False to disable this change- Native TF Lite variables are now enabled during conversion by default on all v2 TfLiteConverter entry points.
experimental_enable_resource_variables
on tf.lite.TFLiteConverter is now True by default and will be removed in the future.
... (truncated)
Changelog
Sourced from tensorflow's changelog.
Release 2.9.0
Breaking Changes
Build, Compilation and Packaging
- TensorFlow is now compiled with
_GLIBCXX_USE_CXX11_ABI=1
. Downstream projects that encounterstd::__cxx11
or[abi:cxx11]
linker errors will need to adopt this compiler option. See the GNU C++ Library docs on Dual ABI.- TensorFlow Python wheels now specifically conform to manylinux2014, an upgrade from manylinux2010. The minimum Pip version supporting manylinux2014 is Pip 19.3 (see pypa/manylinux. This change may affect you if you have been using TensorFlow on a very old platform equivalent to CentOS 6, as manylinux2014 targets CentOS 7 as a compatibility base. Note that TensorFlow does not officially support either platform.
- Discussion for these changes can be found on SIG Build's TensorFlow Community Forum thread
The
tf.keras.mixed_precision.experimental
API has been removed. The non-experimental symbols undertf.keras.mixed_precision
have been available since TensorFlow 2.4 and should be used instead.
- The non-experimental API has some minor differences from the experimental API. In most cases, you only need to make three minor changes:
- Remove the word "experimental" from
tf.keras.mixed_precision
symbols. E.g., replacetf.keras.mixed_precision.experimental.global_policy
withtf.keras.mixed_precision.global_policy
.- Replace
tf.keras.mixed_precision.experimental.set_policy
withtf.keras.mixed_precision.set_global_policy
. The experimental symbolset_policy
was renamed toset_global_policy
in the non-experimental API.- Replace
LossScaleOptimizer(opt, "dynamic")
withLossScaleOptimizer(opt)
. If you pass anything other than"dynamic"
to the second argument, see (1) of the next section.- In the following rare cases, you need to make more changes when switching to the non-experimental API:
- If you passed anything other than
"dynamic"
to theloss_scale
argument (the second argument) ofLossScaleOptimizer
:
- The LossScaleOptimizer constructor takes in different arguments. See the TF 2.7 documentation of tf.keras.mixed_precision.experimental.LossScaleOptimizer for details on the differences, which has examples on how to convert to the non-experimental LossScaleOptimizer.
... (truncated)
Commits
8a20d54
Merge pull request #56103 from tensorflow-jenkins/version-numbers-2.9.0-6827d4ee584
Update version numbers to 2.9.031add77
Merge pull request #56087 from tensorflow/fix-relnotes-on-r2.9892677b
Update release notes with security updates186b7e5
Merge pull request #56067 from tensorflow/mm-cp-52488e5072f6fe44411d70c6af09e...8422d91
Merge pull request #56060 from yongtang:curl-7.83.15f2a685
Merge pull request #56039 from tensorflow/penpornk-patch-183b37ad
Merge pull request #56032 from penpornk/cherrypicks_1HAY14f56d50
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