Release 2.16.1

TensorFlow

• TensorFlow Windows Build:
  • Clang is now the default compiler to build TensorFlow CPU wheels on the Windows Platform starting with this release. The currently supported version is LLVM/clang 17. The official Wheels-published on PyPI will be based on Clang; however, users retain the option to build wheels using the MSVC compiler following the steps mentioned in https://www.tensorflow.org/install/source_windows as has been the case before
  • TensorFlow 2.16 will be released as TF 2.16.1 (instead of 2.16.0). The patch release will be done as 2.16.2 during the next release cycle.

Breaking Changes

• 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

  • The tf.estimator API is removed.
  • To continue using tf.estimator, you will need to use TF 2.15 or an earlier version.

• 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.

• 1. Install tf-keras via pip install tf-keras~=2.16

1.  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

1.  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.

Known Caveats

• Full aarch64 Linux and Arm64 macOS wheels are now published to the tensorflow pypi repository and no longer redirect to a separate package.

Major Features and Improvements

• Support for Python 3.12 has been added.
• tensorflow-tpu package is now available for easier TPU based installs.
• TensorFlow pip packages are now built with CUDA 12.3 and cuDNN 8.9.7
• Added experimental support for float16 auto-mixed precision using the new AMX-FP16 instruction set on X86 CPUs.

Bug Fixes and Other Changes

• tf.lite

  • Added support for stablehlo.gather.
  • Added support for stablehlo.add.
  • Added support for stablehlo.multiply.
  • Added support for stablehlo.maximum.
  • Added support for stablehlo.minimum.
  • Added boolean parameter support for tfl.gather_nd.
  • C API:
    • New API functions:
      • tensorflow/lite/c/c_api_experimental.h:
        • TfLiteInterpreterGetVariableTensorCount
        • TfLiteInterpreterGetVariableTensor
        • TfLiteInterpreterGetBufferHandle
        • TfLiteInterpreterSetBufferHandle
      • tensorflow/lite/c/c_api_opaque.h:
        • TfLiteOpaqueTensorSetAllocationTypeToDynamic
    • API functions promoted from experimental to stable:
      • tensorflow/lite/c/c_api.h:
        • TfLiteInterpreterOptionsEnableCancellation
        • TfLiteInterpreterCancel
  • C++ API:
    • New virtual methods in the 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

  • These now take in a new argument called 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

  • Added 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

• keras.layers.experimental.DynamicEmbedding
  • Added DynamicEmbedding Keras layer
  • Added 'UpdateEmbeddingCallback`
  • DynamicEmbedding 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
  • Added the option to set adaptive epsilon to match implementations with Jax and PyTorch equivalents.

Thanks to our Contributors

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