An Open Source Machine Learning Framework for Everyone
tf.summary.trace_on now takes a profiler_outdir argument. This must be set if profiler arg is set to True.
tf.summary.trace_export's profiler_outdir arg is now a no-op. Enabling the profiler now requires setting profiler_outdir in trace_on.tf.estimator
Keras 3.0 will be the default Keras version. You may need to update your script to use Keras 3.0.
Please refer to the new Keras documentation for Keras 3.0 (https://keras.io/keras_3).
To continue using Keras 2.0, do the following.
Install tf-keras via pip install tf-keras~=2.16
To switch tf.keras to use Keras 2 (tf-keras), set the environment variable TF_USE_LEGACY_KERAS=1 directly or in your python program with import os;os.environ["TF_USE_LEGACY_KERAS"]="1". Please note that this will set it for all packages in your Python runtime program
Change the keras import: replace import tensorflow.keras as keras or import keras with import tf_keras as keras. Update any tf.keras references to keras.
Apple Silicon users: If you previously installed TensorFlow using pip install tensorflow-macos, please update your installation method. Use pip install tensorflow from now on.
Mac x86 users: Mac x86 builds are being deprecated and will no longer be released as a Pip package from TF 2.17 onwards.
tensorflow pypi repository and no longer redirect to a separate package.tf.lite
stablehlo.gather.stablehlo.add.stablehlo.multiply.stablehlo.maximum.stablehlo.minimum.tfl.gather_nd.tensorflow/lite/c/c_api_experimental.h:
TfLiteInterpreterGetVariableTensorCount
TfLiteInterpreterGetVariableTensor
TfLiteInterpreterGetBufferHandle
TfLiteInterpreterSetBufferHandle
tensorflow/lite/c/c_api_opaque.h:
TfLiteOpaqueTensorSetAllocationTypeToDynamic
tensorflow/lite/c/c_api.h:
TfLiteInterpreterOptionsEnableCancellation
TfLiteInterpreterCancel
tflite::SimpleDelegateInterface class in tensorflow/lite/delegates/utils/simple_delegate.h,
and likewise in the tflite::SimpleOpaqueDelegateInterface class in tensorflow/lite/delegates/utils/simple_opaque_delegate.h:
CopyFromBufferHandle
CopyToBufferHandle
FreeBufferHandle
tf.train.CheckpointOptions and tf.saved_model.SaveOptions
experimental_sharding_callback. This is a callback function wrapper that will be executed to determine how tensors will be split into shards when the saver writes the checkpoint shards to disk. tf.train.experimental.ShardByTaskPolicy is the default sharding behavior, but tf.train.experimental.MaxShardSizePolicy can be used to shard the checkpoint with a maximum shard file size. Users with advanced use cases can also write their own custom tf.train.experimental.ShardingCallbacks.tf.train.CheckpointOptions
experimental_skip_slot_variables (a boolean option) to skip restoring of optimizer slot variables in a checkpoint.tf.saved_model.SaveOptions
SaveOptions now takes a new argument called experimental_debug_stripper. When enabled, this strips the debug nodes from both the node defs and the function defs of the graph. Note that this currently only strips the Assert nodes from the graph and converts them into NoOps instead.keras.layers.experimental.DynamicEmbedding
DynamicEmbedding Keras layerDynamicEmbedding layer allows for the continuous updating of the vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on the inputs received by the layer and the eviction policy. When this layer is used with an UpdateEmbeddingCallback, which is a time-based callback, the vocabulary lookup tensor is updated at the time interval set in the UpdateEmbeddingCallback based on the most up-to-date vocabulary hash table maintained by the layer. If this layer is not used in conjunction with UpdateEmbeddingCallback the behavior of the layer would be same as keras.layers.Embedding.keras.optimizers.Adam
This release contains contributions from many people at Google, as well as:
Aakar Dwivedi, Akhil Goel, Alexander Grund, Alexander Pivovarov, Andrew Goodbody, Andrey Portnoy, Aneta Kaczyńska, AnetaKaczynska, ArkadebMisra, Ashiq Imran, Ayan Moitra, Ben Barsdell, Ben Creech, Benedikt Lorch, Bhavani Subramanian, Bianca Van Schaik, Chao, Chase Riley Roberts, Connor Flanagan, David Hall, David Svantesson, David Svantesson-Yeung, dependabot[bot], Dr. Christoph Mittendorf, Dragan Mladjenovic, ekuznetsov139, Eli Kobrin, Eugene Kuznetsov, Faijul Amin, Frédéric Bastien, fsx950223, gaoyiyeah, Gauri1 Deshpande, Gautam, Giulio C.N, guozhong.zhuang, Harshit Monish, James Hilliard, Jane Liu, Jaroslav Sevcik, jeffhataws, Jerome Massot, Jerry Ge, jglaser, jmaksymc, Kaixi Hou, kamaljeeti, Kamil Magierski, Koan-Sin Tan, lingzhi98, looi, Mahmoud Abuzaina, Malik Shahzad Muzaffar, Meekail Zain, mraunak, Neil Girdhar, Olli Lupton, Om Thakkar, Paul Strawder, Pavel Emeliyanenko, Pearu Peterson, pemeliya, Philipp Hack, Pierluigi Urru, Pratik Joshi, radekzc, Rafik Saliev, Ragu, Rahul Batra, rahulbatra85, Raunak, redwrasse, Rodrigo Gomes, ronaghy, Sachin Muradi, Shanbin Ke, shawnwang18, Sheng Yang, Shivam Mishra, Shu Wang, Strawder, Paul, Surya, sushreebarsa, Tai Ly, talyz, Thibaut Goetghebuer-Planchon, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, weihanmines, wenchenvincent, Wenjie Zheng, Who Who Who, Yasir Ashfaq, yasiribmcon, Yoshio Soma, Yuanqiang Liu, Yuriy Chernyshov
tf.summary.trace_on now takes a profiler_outdir argument. This must be set if profiler arg is set to True.
tf.summary.trace_export's profiler_outdir arg is now a no-op. Enabling the profiler now requires setting profiler_outdir in trace_on.tf.estimator
Keras 3 will be the default Keras version. You may need to update your script to use Keras 3. Please refer to the new Keras documentation for Keras 3 (https://keras.io/keras_3). To continue using Keras 2, do the following:
tf-keras via pip install tf-keras~=2.16
TF_USE_LEGACY_KERAS=1 directly or in your Python program by doing import os;os.environ["TF_USE_LEGACY_KERAS"]=1. Please note that this will set it for all packages in your Python runtime program.pip install tensorflow-macos, please update your installation method. Use pip install tensorflow from now on. Starting with TF 2.17, the tensorflow-macos package will no longer receive updates.tensorflow pypi repository and no longer redirect to a separate package.tf.lite
stablehlo.gather.stablehlo.add.stablehlo.multiply.stablehlo.maximum.stablehlo.minimum.tfl.gather_nd.tf.train.CheckpointOptions and tf.saved_model.SaveOptions
experimental_sharding_callback. This is a callback function wrapper that will be executed to determine how tensors will be split into shards when the saver writes the checkpoint shards to disk. tf.train.experimental.ShardByTaskPolicy is the default sharding behavior, but tf.train.experimental.MaxShardSizePolicy can be used to shard the checkpoint with a maximum shard file size. Users with advanced use cases can also write their own custom tf.train.experimental.ShardingCallbacks.tf.train.CheckpointOptions
experimental_skip_slot_variables (a boolean option) to skip restoring of optimizer slot variables in a checkpoint.tf.saved_model.SaveOptions
SaveOptions now takes a new argument called experimental_debug_stripper. When enabled, this strips the debug nodes from both the node defs and the function defs of the graph. Note that this currently only strips the Assert nodes from the graph and converts them into NoOps instead.keras.layers.experimental.DynamicEmbedding
DynamicEmbedding Keras layerDynamicEmbedding layer allows for the continuous updating of the vocabulary and embeddings during the training process. This layer maintains a hash table to track the most up-to-date vocabulary based on the inputs received by the layer and the eviction policy. When this layer is used with an UpdateEmbeddingCallback, which is a time-based callback, the vocabulary lookup tensor is updated at the time interval set in the UpdateEmbeddingCallback based on the most up-to-date vocabulary hash table maintained by the layer. If this layer is not used in conjunction with UpdateEmbeddingCallback the behavior of the layer would be same as keras.layers.Embedding.keras.optimizers.Adam
This release contains contributions from many people at Google, as well as:
Aakar Dwivedi, Akhil Goel, Alexander Grund, Alexander Pivovarov, Andrew Goodbody, Andrey Portnoy, Aneta Kaczyńska, AnetaKaczynska, ArkadebMisra, Ashiq Imran, Ayan Moitra, Ben Barsdell, Ben Creech, Benedikt Lorch, Bhavani Subramanian, Bianca Van Schaik, Chao, Chase Riley Roberts, Connor Flanagan, David Hall, David Svantesson, David Svantesson-Yeung, dependabot[bot], Dr. Christoph Mittendorf, Dragan Mladjenovic, ekuznetsov139, Eli Kobrin, Eugene Kuznetsov, Faijul Amin, Frédéric Bastien, fsx950223, gaoyiyeah, Gauri1 Deshpande, Gautam, Giulio C.N, guozhong.zhuang, Harshit Monish, James Hilliard, Jane Liu, Jaroslav Sevcik, jeffhataws, Jerome Massot, Jerry Ge, jglaser, jmaksymc, Kaixi Hou, kamaljeeti, Kamil Magierski, Koan-Sin Tan, lingzhi98, looi, Mahmoud Abuzaina, Malik Shahzad Muzaffar, Meekail Zain, mraunak, Neil Girdhar, Olli Lupton, Om Thakkar, Paul Strawder, Pavel Emeliyanenko, Pearu Peterson, pemeliya, Philipp Hack, Pierluigi Urru, Pratik Joshi, radekzc, Rafik Saliev, Ragu, Rahul Batra, rahulbatra85, Raunak, redwrasse, Rodrigo Gomes, ronaghy, Sachin Muradi, Shanbin Ke, shawnwang18, Sheng Yang, Shivam Mishra, Shu Wang, Strawder, Paul, Surya, sushreebarsa, Tai Ly, talyz, Thibaut Goetghebuer-Planchon, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, weihanmines, wenchenvincent, Wenjie Zheng, Who Who Who, Yasir Ashfaq, yasiribmcon, Yoshio Soma, Yuanqiang Liu, Yuriy Chernyshov
tf.types.experimental.GenericFunction has been renamed to tf.types.experimental.PolymorphicFunction.oneDNN CPU performance optimizations Windows x64 & x86.
TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.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.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.
inference_fn property of ConcreteFunctionstf.types.experimental.AtomicFunction documentation for how to call and use it.tf.data:
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:
TfLiteInterpreterGetSignatureCount
TfLiteInterpreterGetSignatureKey
TfLiteInterpreterGetSignatureRunner
TfLiteSignatureRunnerAllocateTensors
TfLiteSignatureRunnerGetInputCount
TfLiteSignatureRunnerGetInputName
TfLiteSignatureRunnerGetInputTensor
TfLiteSignatureRunnerGetOutputCount
TfLiteSignatureRunnerGetOutputName
TfLiteSignatureRunnerGetOutputTensor
TfLiteSignatureRunnerInvoke
TfLiteSignatureRunnerResizeInputTensor
New C API function TfLiteExtensionApisVersion added to tensorflow/lite/c/c_api.h.
Add int8 and int16x8 support for RSQRT operator
Android NDK r25 is supported.
Add TensorFlow Quantizer to TensorFlow pip package.
tf.sparse.segment_sum tf.sparse.segment_mean tf.sparse.segment_sqrt_n SparseSegmentSum/Mean/SqrtN[WithNumSegments]
sparse_gradient option (default=false) that makes the gradient of these functions/ops sparse (IndexedSlices) instead of dense (Tensor), using new SparseSegmentSum/Mean/SqrtNGradV2 ops.tf.nn.embedding_lookup_sparse
tf.saved_model.SaveOptions
experimental_skip_saver argument which, if specified, will suppress the addition of SavedModel-native save and restore ops to the SavedModel, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.Add ops to tensorflow.raw_ops that were missing.
tf.CheckpointOptions
experimental_write_callbacks. These are callbacks that will be executed after a saving event finishes writing the checkpoint file.Add an option disable_eager_executer_streaming_enqueue to tensorflow.ConfigProto.Experimental to control the eager runtime's behavior around parallel remote function invocations; when set to True, the eager runtime will be allowed to execute multiple function invocations in parallel.
tf.constant_initializer
support_partition. If True, constant_initializers can create sharded variables. This is disabled by default, similar to existing behavior.tf.lite
stablehlo.scatter.tf.estimator
This release contains contributions from many people at Google, as well as:
Aiden Grossman, Akash Patel, Akhil Goel, Alexander Pivovarov, Andrew Goodbody, Ayan Moitra, Ben Barsdell, Ben Olson, Bhavani Subramanian, Boian Petkantchin, Bruce Lai, Chao Chen, Christian Steinmeyer, cjflan, David Korczynski, Donghak Park, Dragan Mladjenovic, Eli Kobrin, Fadi Arafeh, Feiyue Chen, Frédéric Bastien, guozhong.zhuang, halseycamilla, Harshavardhan Bellamkonda, James Ward, jameshollyer, Jane Liu, johnnkp, jswag180, justkw, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, Kun-Lu, kushanam, Lu Teng, mdfaijul, Mehdi Drissi, mgokulkrish, mraunak, Mustafa Uzun, Namrata Bhave, Pavel Emeliyanenko, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, Rahul Batra, Raunak, redwrasse, Saoirse Stewart, SaoirseARM, seanshpark, Shanbin Ke, Spenser Bauman, Surya, sushreebarsa, Tai Ly, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, Tj Xu, Vladislav, weihanmines, Wen Chen, wenchenvincent, wenscarl, William Muir, Zhoulong, Jiang
tf.types.experimental.GenericFunction has been renamed to tf.types.experimental.PolymorphicFunction.oneDNN CPU performance optimizations Windows x64 & x86.
TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.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.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.
inference_fn property of ConcreteFunctionstf.types.experimental.AtomicFunction documentation for how to call and use it.tf.data:
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:
TfLiteInterpreterGetSignatureCount
TfLiteInterpreterGetSignatureKey
TfLiteInterpreterGetSignatureRunner
TfLiteSignatureRunnerAllocateTensors
TfLiteSignatureRunnerGetInputCount
TfLiteSignatureRunnerGetInputName
TfLiteSignatureRunnerGetInputTensor
TfLiteSignatureRunnerGetOutputCount
TfLiteSignatureRunnerGetOutputName
TfLiteSignatureRunnerGetOutputTensor
TfLiteSignatureRunnerInvoke
TfLiteSignatureRunnerResizeInputTensor
New C API function TfLiteExtensionApisVersion added to tensorflow/lite/c/c_api.h.
Add int8 and int16x8 support for RSQRT operator
Android NDK r25 is supported.
Add TensorFlow Quantizer to TensorFlow pip package.
tf.sparse.segment_sum tf.sparse.segment_mean tf.sparse.segment_sqrt_n SparseSegmentSum/Mean/SqrtN[WithNumSegments]
sparse_gradient option (default=false) that makes the gradient of these functions/ops sparse (IndexedSlices) instead of dense (Tensor), using new SparseSegmentSum/Mean/SqrtNGradV2 ops.tf.nn.embedding_lookup_sparse
tf.saved_model.SaveOptions
experimental_skip_saver argument which, if specified, will suppress the addition of SavedModel-native save and restore ops to the SavedModel, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.Add ops to tensorflow.raw_ops that were missing.
tf.CheckpointOptions
experimental_write_callbacks. These are callbacks that will be executed after a saving event finishes writing the checkpoint file.Add an option disable_eager_executer_streaming_enqueue to tensorflow.ConfigProto.Experimental to control the eager runtime's behavior around parallel remote function invocations; when set to True, the eager runtime will be allowed to execute multiple function invocations in parallel.
tf.constant_initializer
support_partition. If True, constant_initializers can create sharded variables. This is disabled by default, similar to existing behavior.tf.lite
stablehlo.scatter.tf.estimator
This release contains contributions from many people at Google, as well as:
Aiden Grossman, Akash Patel, Akhil Goel, Alexander Pivovarov, Andrew Goodbody, Ayan Moitra, Ben Barsdell, Ben Olson, Bhavani Subramanian, Boian Petkantchin, Bruce Lai, Chao Chen, Christian Steinmeyer, cjflan, David Korczynski, Donghak Park, Dragan Mladjenovic, Eli Kobrin, Fadi Arafeh, Feiyue Chen, Frédéric Bastien, guozhong.zhuang, halseycamilla, Harshavardhan Bellamkonda, James Ward, jameshollyer, Jane Liu, johnnkp, jswag180, justkw, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, Kun-Lu, kushanam, Lu Teng, mdfaijul, Mehdi Drissi, mgokulkrish, mraunak, Mustafa Uzun, Namrata Bhave, Pavel Emeliyanenko, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, Rahul Batra, Raunak, redwrasse, Saoirse Stewart, SaoirseARM, seanshpark, Shanbin Ke, Spenser Bauman, Surya, sushreebarsa, Tai Ly, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, Tj Xu, Vladislav, weihanmines, Wen Chen, wenchenvincent, wenscarl, William Muir, Zhoulong, Jiang
tf.types.experimental.GenericFunction has been renamed to tf.types.experimental.PolymorphicFunction.oneDNN CPU performance optimizations Windows x64 & x86.
TF_ENABLE_ONEDNN_OPTS to 1 (enable) or 0 (disable) before running TensorFlow. To fall back to default settings, unset the environment variable.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.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.
inference_fn property of ConcreteFunctionstf.types.experimental.AtomicFunction documentation for how to call and use it.tf.data:
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:
TfLiteInterpreterGetSignatureCount
TfLiteInterpreterGetSignatureKey
TfLiteInterpreterGetSignatureRunner
TfLiteSignatureRunnerAllocateTensors
TfLiteSignatureRunnerGetInputCount
TfLiteSignatureRunnerGetInputName
TfLiteSignatureRunnerGetInputTensor
TfLiteSignatureRunnerGetOutputCount
TfLiteSignatureRunnerGetOutputName
TfLiteSignatureRunnerGetOutputTensor
TfLiteSignatureRunnerInvoke
TfLiteSignatureRunnerResizeInputTensor
New C API function TfLiteExtensionApisVersion added to tensorflow/lite/c/c_api.h.
Add int8 and int16x8 support for RSQRT operator
Android NDK r25 is supported.
Add TensorFlow Quantizer to TensorFlow pip package.
tf.sparse.segment_sum tf.sparse.segment_mean tf.sparse.segment_sqrt_n SparseSegmentSum/Mean/SqrtN[WithNumSegments]
sparse_gradient option (default=false) that makes the gradient of these functions/ops sparse (IndexedSlices) instead of dense (Tensor), using new SparseSegmentSum/Mean/SqrtNGradV2 ops.tf.nn.embedding_lookup_sparse
tf.saved_model.SaveOptions
experimental_skip_saver argument which, if specified, will suppress the addition of SavedModel-native save and restore ops to the SavedModel, for cases where users already build custom save/restore ops and checkpoint formats for the model being saved, and the creation of the SavedModel-native save/restore ops simply cause longer model serialization times.Add ops to tensorflow.raw_ops that were missing.
tf.CheckpointOptions
experimental_write_callbacks. These are callbacks that will be executed after a saving event finishes writing the checkpoint file.Add an option disable_eager_executer_streaming_enqueue to tensorflow.ConfigProto.Experimental to control the eager runtime's behavior around parallel remote function invocations; when set to True, the eager runtime will be allowed to execute multiple function invocations in parallel.
tf.constant_initializer
support_partition. If True, constant_initializers can create sharded variables. This is disabled by default, similar to existing behavior.tf.lite
stablehlo.scatter.This release contains contributions from many people at Google, as well as:
Aiden Grossman, Akash Patel, Akhil Goel, Alexander Pivovarov, Andrew Goodbody, Ayan Moitra, Ben Barsdell, Ben Olson, Bhavani Subramanian, Boian Petkantchin, Bruce Lai, Chao Chen, Christian Steinmeyer, cjflan, David Korczynski, Donghak Park, Dragan Mladjenovic, Eli Kobrin, Fadi Arafeh, Feiyue Chen, Frédéric Bastien, guozhong.zhuang, halseycamilla, Harshavardhan Bellamkonda, James Ward, jameshollyer, Jane Liu, johnnkp, jswag180, justkw, Kanvi Khanna, Keith Smiley, Koan-Sin Tan, Kulin Seth, Kun-Lu, kushanam, Lu Teng, mdfaijul, Mehdi Drissi, mgokulkrish, mraunak, Mustafa Uzun, Namrata Bhave, Pavel Emeliyanenko, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, Rahul Batra, Raunak, redwrasse, Saoirse Stewart, SaoirseARM, seanshpark, Shanbin Ke, Spenser Bauman, Surya, sushreebarsa, Tai Ly, Thibaut Goetghebuer-Planchon, tilakrayal, Tirumalesh, Tj Xu, Vladislav, weihanmines, Wen Chen, wenchenvincent, wenscarl, William Muir, Zhoulong, Jiang
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
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.lite
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.
tf.lite
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
UINT32.tf.config.experimental.enable_tensor_float_32_execution
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
dtensor.relayout_like, for relayouting a tensor according to the layout of another tensor.dtensor.get_default_mesh, for retrieving the current default mesh under the dtensor context.tf.experimental.strict_mode
strict_mode, which converts all deprecation warnings into runtime errors with instructions on switching to a recommended substitute.TensorFlow Debugger (tfdbg) CLI: ncurses-based CLI for tfdbg v1 was removed.
TensorFlow now supports C++ RTTI on mobile and Android. To enable this feature, pass the flag --define=tf_force_rtti=true to Bazel when building TensorFlow. This may be needed when linking TensorFlow into RTTI-enabled programs since mixing RTTI and non-RTTI code can cause ABI issues.
tf.ones, tf.zeros, tf.fill, tf.ones_like, tf.zeros_like now take an additional Layout argument that controls the output layout of their results.
tf.nest and tf.data now support user defined classes implementing __tf_flatten__ and __tf_unflatten__ methods. See nest_util code examples
for an example.
TensorFlow IO support is now available for Apple Silicon packages.
Refactor CpuExecutable to propagate LLVM errors.
Keras is a framework built on top of the TensorFlow. See more details on the Keras website.
tf.keras
Model.compile now support steps_per_execution='auto' as a parameter, allowing automatic tuning of steps per execution during Model.fit,
Model.predict, and Model.evaluate for a significant performance boost.This release contains contributions from many people at Google, as well as:
Aakar Dwivedi, Adrian Popescu, ag.ramesh, Akhil Goel, Albert Zeyer, Alex Rosen, Alexey Vishnyakov, Andrew Goodbody, angerson, Ashiq Imran, Ayan Moitra, Ben Barsdell, Bhavani Subramanian, Boian Petkantchin, BrianWieder, Chris Mc, cloudhan, Connor Flanagan, Daniel Lang, Daniel Yudelevich, Darya Parygina, David Korczynski, David Svantesson, dingyuqing05, Dragan Mladjenovic, dskkato, Eli Kobrin, Erick Ochoa, Erik Schultheis, Frédéric Bastien, gaikwadrahul8, Gauri1 Deshpande, guozhong.zhuang, H. Vetinari, Isaac Cilia Attard, Jake Hall, Jason Furmanek, Jerry Ge, Jinzhe Zeng, JJ, johnnkp, Jonathan Albrecht, jongkweh, justkw, Kanvi Khanna, kikoxia, Koan-Sin Tan, Kun-Lu, ltsai1, Lu Teng, luliyucoordinate, Mahmoud Abuzaina, mdfaijul, Milos Puzovic, Nathan Luehr, Om Thakkar, pateldeev, Peng Sun, Philipp Hack, pjpratik, Poliorcetics, rahulbatra85, rangjiaheng, Renato Arantes, Robert Kalmar, roho, Rylan Justice, Sachin Muradi, samypr100, Saoirse Stewart, Shanbin Ke, Shivam Mishra, shuw, Song Ziming, Stephan Hartmann, Sulav, sushreebarsa, T Coxon, Tai Ly, talyz, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tirumalesh, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, Wen Chen, Yaohui Liu, Yimei Sun, Zhoulong Jiang, Zhoulong, Jiang
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
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.tf.lite
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.
tf.lite
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
UINT32.tf.config.experimental.enable_tensor_float_32_execution
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
dtensor.relayout_like, for relayouting a tensor according to the layout of another tensor.dtensor.get_default_mesh, for retrieving the current default mesh under the dtensor context.tf.experimental.strict_mode
strict_mode, which converts all deprecation warnings into runtime errors with instructions on switching to recommended substitute.TensorFlow Debugger (tfdbg) CLI: ncurses-based CLI for tfdbg v1 was removed.
TensorFlow now supports C++ RTTI on mobile and Android. To enable this feature, pass the flag --define=tf_force_rtti=true to Bazel when building TensorFlow. This may be needed when linking TensorFlow into RTTI-enabled programs since mixing RTTI and non-RTTI code can cause ABI issues.
tf.ones, tf.zeros, tf.fill, tf.ones_like, tf.zeros_like now take an additional Layout argument that controls the output layout of their results.
tf.nest and tf.data now support user defined classes implementing __tf_flatten__ and __tf_unflatten__ methods. See nest_util code examples for an example.
Keras is a framework built on top of the TensorFlow. See more details on the Keras website.
tf.keras
Model.compile now support steps_per_execution='auto' as a parameter, allowing automatic tuning of steps per execution during Model fit, Model.predict, and Model.evaluate for a significant performance boost.This release contains contributions from many people at Google, as well as:
Aakar Dwivedi, Adrian Popescu, ag.ramesh, Akhil Goel, Albert Zeyer, Alex Rosen, Alexey Vishnyakov, Andrew Goodbody, angerson, Ashiq Imran, Ayan Moitra, Ben Barsdell, Bhavani Subramanian, Boian Petkantchin, BrianWieder, Chris Mc, cloudhan, Connor Flanagan, Daniel Lang, Daniel Yudelevich, Darya Parygina, David Korczynski, David Svantesson, dingyuqing05, Dragan Mladjenovic, dskkato, Eli Kobrin, Erick Ochoa, Erik Schultheis, Frédéric Bastien, gaikwadrahul8, Gauri1 Deshpande, georgiie, guozhong.zhuang, H. Vetinari, Isaac Cilia Attard, Jake Hall, Jason Furmanek, Jerry Ge, Jinzhe Zeng, JJ, johnnkp, Jonathan Albrecht, jongkweh, justkw, Kanvi Khanna, kikoxia, Koan-Sin Tan, Kun-Lu, Learning-To-Play, ltsai1, Lu Teng, luliyucoordinate, Mahmoud Abuzaina, mdfaijul, Milos Puzovic, Nathan Luehr, Om Thakkar, pateldeev, Peng Sun, Philipp Hack, pjpratik, Poliorcetics, rahulbatra85, rangjiaheng, Renato Arantes, Robert Kalmar, roho, Rylan Justice, Sachin Muradi, samypr100, Saoirse Stewart, Shanbin Ke, Shivam Mishra, shuw, Song Ziming, Stephan Hartmann, Sulav, sushreebarsa, T Coxon, Tai Ly, talyz, Tensorflow Jenkins, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tirumalesh, Tj Xu, Tom Allsop, Trevor Morris, Varghese, Jojimon, Wen Chen, Yaohui Liu, Yimei Sun, Zhoulong Jiang, Zhoulong, Jiang