Sourced from tensorflow's releases.

TensorFlow 2.5.0-rc0

Release 2.5.0

Major Features and Improvements

• TPU embedding support
  • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
• tf.keras.metrics.AUC now support logit predictions.
• Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
• tf.data:
  • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
  • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
  • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
  • Options returned by tf.data.Dataset.options() are no longer mutable.
  • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
• tf.lite
  • Enabled the new MLIR-based quantization backend by default
    • The new backend is used for 8 bits full integer post-training quantization
    • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
    • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
• tf.keras
  • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
• tf.distribute
  • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
• PluggableDevice
  • Third-party devices can now connect to TensorFlow modularly through StreamExecutor C API and PluggableDevice interface.
    • Add custom ops and kernels through kernel and op registration C API.
    • Register custom graph optimization passes with

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Sourced from tensorflow's changelog.

Release 2.6.0

Breaking Changes

• tf.train.experimental.enable_mixed_precision_graph_rewrite is removed, as the API only works in graph mode and is not customizable. The function is still accessible under tf.compat.v1.mixed_precision.enable_mixed_precision_graph_rewrite, but it is recommended to use the Keras mixed precision API instead.

• tf.lite:

  • Remove experimental.nn.dynamic_rnn, experimental.nn.TfLiteRNNCell and experimental.nn.TfLiteLSTMCell since they're no longer supported. It's recommended to just use keras lstm instead.

* *

Known Caveats

* * *

• TF Core:
  • A longstanding bug in tf.while_loop, which caused it to execute sequentially, even when parallel_iterations>1, has now been fixed. However, the increased parallelism may result in increased memory use. Users who experience unwanted regressions should reset their while_loop's parallel_iterations value to 1, which is consistent with prior behavior.

Major Features and Improvements

* *

• tf.keras:
  • tf.keras.utils.experimental.DatasetCreator now takes an optional tf.distribute.InputOptions for specific options when used with distribution.
  • Updates to Preprocessing layers API for consistency and clarity:
    • StringLookup and IntegerLookup default for mask_token changed to None. This matches the default masking behavior of Hashing and Embedding layers. To keep existing behavior, pass mask_token="" during layer creation.

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• a8b6d5f Merge pull request #48222 from tensorflow/mm-fix-fileystem-on-r2.5
• b9e31e6 Fix typo/logic bug in modular plugins.
• 158505e Switch TF filesystems to keep backwards compatibility.
• 96dfa5c Merge pull request #48107 from tensorflow/mihaimaruseac-patch-1
• 5f7fd89 Fix typo in setup.py
• f8b5b9b Merge pull request #48093 from tensorflow/mihaimaruseac-patch-1
• b84dac5 Update setup.py
• b42047d Merge pull request #48091 from tensorflow-jenkins/version-numbers-2.5.0rc0-30114
• 1d4885b Update version numbers to 2.5.0-rc0
• 6af4297 Merge pull request #48082 from njeffrie:f1_depthwise
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