Update tensorflow requirement from !=2.6.0,!=2.6.1,<2.8.0,>=2.2.0 to >=2.2.0,!=2.6.0,!=2.6.1,<2.9.0

[dependabot]

Updates the requirements on tensorflow to permit the latest version.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.8.0

Release 2.8.0

Major Features and Improvements

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.raw_ops.Bucketize op on CPU.
    • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
    • tf.random.normal op for output data type tf.float32 on CPU.
    • tf.random.uniform op for output data type tf.float32 on CPU.
    • tf.random.categorical op for output data type tf.int64 on CPU.

• tensorflow.experimental.tensorrt:

  • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and allow_build_at_runtime.
  • Added a new parameter called save_gpu_specific_engines to the .save() function inside TrtGraphConverterV2. When False, the .save() function won't save any TRT engines that have been built. When True (default), the original behavior is preserved.
  • TrtGraphConverterV2 provides a new API called .summary() which outputs a summary of the inference converted by TF-TRT. It namely shows each TRTEngineOp with their input(s)' and output(s)' shape and dtype. A detailed version of the summary is available which prints additionally all the TensorFlow OPs included in each of the TRTEngineOps.

• tf.tpu.experimental.embedding:

  • tf.tpu.experimental.embedding.FeatureConfig now takes an additional argument output_shape which can specify the shape of the output activation for the feature.
  • tf.tpu.experimental.embedding.TPUEmbedding now has the same behavior as tf.tpu.experimental.embedding.serving_embedding_lookup which can take arbitrary rank of dense and sparse tensor. For ragged tensor, though the input tensor remains to be rank 2, the activations now can be rank 2 or above by specifying the output shape in the feature config or via the build method.

• Add tf.config.experimental.enable_op_determinism, which makes TensorFlow ops run deterministically at the cost of performance. Replaces the TF_DETERMINISTIC_OPS environmental variable, which is now deprecated. The "Bug Fixes and Other Changes" section lists more determinism-related changes.

• (Since TF 2.7) Add PluggableDevice support to TensorFlow Profiler.

Bug Fixes and Other Changes

• tf.data:

  • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
  • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.
    • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
    • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.

• tf.lite:

  • Adds GPU Delegation support for serialization to Java API. This boosts initialization time up to 90% when OpenCL is available.
  • Deprecated Interpreter::SetNumThreads, in favor of InterpreterBuilder::SetNumThreads.

• tf.keras:

  • Adds tf.compat.v1.keras.utils.get_or_create_layer to aid migration to TF2 by enabling tracking of nested keras models created in TF1-style, when used with the tf.compat.v1.keras.utils.track_tf1_style_variables decorator.
  • Added a tf.keras.layers.experimental.preprocessing.HashedCrossing layer which applies the hashing trick to the concatenation of crossed scalar inputs. This provides a stateless way to try adding feature crosses of integer or string data to a model.
  • Removed keras.layers.experimental.preprocessing.CategoryCrossing. Users should migrate to the HashedCrossing layer or use tf.sparse.cross/tf.ragged.cross directly.
  • Added additional standardize and split modes to TextVectorization:
    • standardize="lower" will lowercase inputs.
    • standardize="string_punctuation" will remove all puncuation.
    • split="character" will split on every unicode character.
  • Added an output_mode argument to the Discretization and Hashing layers with the same semantics as other preprocessing layers. All categorical preprocessing layers now support output_mode.
  • All preprocessing layer output will follow the compute dtype of a tf.keras.mixed_precision.Policy, unless constructed with output_mode="int" in which case output will be tf.int64. The output type of any preprocessing layer can be controlled individually by passing a dtype argument to the layer.
  • tf.random.Generator for keras initializers and all RNG code.
  • Added 3 new APIs for enable/disable/check the usage of tf.random.Generator in keras backend, which will be the new backend for all the RNG in Keras. We plan to switch on the new code path by default in tf 2.8, and the behavior change will likely to cause some breakage on user side (eg if the test is checking against some golden nubmer). These 3 APIs will allow user to disable and switch back to legacy behavior if they prefer. In future (eg TF 2.10), we expect to totally remove the legacy code path (stateful random Ops), and these 3 APIs will be removed as well.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.8.0

Major Features and Improvements

• tf.lite:

  • Added TFLite builtin op support for the following TF ops:
    • tf.raw_ops.Bucketize op on CPU.
    • tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.
    • tf.random.normal op for output data type tf.float32 on CPU.
    • tf.random.uniform op for output data type tf.float32 on CPU.
    • tf.random.categorical op for output data type tf.int64 on CPU.

• tensorflow.experimental.tensorrt:

  • conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and allow_build_at_runtime.
  • Added a new parameter called save_gpu_specific_engines to the .save() function inside TrtGraphConverterV2. When False, the .save() function won't save any TRT engines that have been built. When True (default), the original behavior is preserved.
  • TrtGraphConverterV2 provides a new API called .summary() which outputs a summary of the inference converted by TF-TRT. It namely shows each TRTEngineOp with their input(s)' and output(s)' shape and dtype. A detailed version of the summary is available which prints additionally all the TensorFlow OPs included in each of the TRTEngineOps.

• tf.tpu.experimental.embedding:

  • tf.tpu.experimental.embedding.FeatureConfig now takes an additional argument output_shape which can specify the shape of the output activation for the feature.
  • tf.tpu.experimental.embedding.TPUEmbedding now has the same behavior as tf.tpu.experimental.embedding.serving_embedding_lookup which can take arbitrary rank of dense and sparse tensor. For ragged tensor, though the input tensor remains to be rank 2, the activations now can be rank 2 or above by specifying the output shape in the feature config or via the build method.

• Add tf.config.experimental.enable_op_determinism, which makes TensorFlow ops run deterministically at the cost of performance. Replaces the TF_DETERMINISTIC_OPS environmental variable, which is now deprecated. The "Bug Fixes and Other Changes" section lists more determinism-related changes.

• (Since TF 2.7) Add PluggableDevice support to TensorFlow Profiler.

Bug Fixes and Other Changes

• tf.data:

  • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
  • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.
    • The optimization parallel_batch now becomes default if not disabled by users, which will parallelize copying of batch elements.
    • Added the ability for TensorSliceDataset to identify and handle inputs that are files. This enables creating hermetic SavedModels when using datasets created from files.

• tf.lite:

  • Adds GPU Delegation support for serialization to Java API. This boosts initialization time up to 90% when OpenCL is available.
  • Deprecated Interpreter::SetNumThreads, in favor of InterpreterBuilder::SetNumThreads.

• tf.keras:

  • Adds tf.compat.v1.keras.utils.get_or_create_layer to aid migration to TF2 by enabling tracking of nested keras models created in TF1-style, when used with the tf.compat.v1.keras.utils.track_tf1_style_variables decorator.
  • Added a tf.keras.layers.experimental.preprocessing.HashedCrossing layer which applies the hashing trick to the concatenation of crossed scalar inputs. This provides a stateless way to try adding feature crosses of integer or string data to a model.
  • Removed keras.layers.experimental.preprocessing.CategoryCrossing. Users should migrate to the HashedCrossing layer or use tf.sparse.cross/tf.ragged.cross directly.
  • Added additional standardize and split modes to TextVectorization:
    • standardize="lower" will lowercase inputs.
    • standardize="string_punctuation" will remove all puncuation.
    • split="character" will split on every unicode character.
  • Added an output_mode argument to the Discretization and Hashing layers with the same semantics as other preprocessing layers. All categorical preprocessing layers now support output_mode.
  • All preprocessing layer output will follow the compute dtype of a tf.keras.mixed_precision.Policy, unless constructed with output_mode="int" in which case output will be tf.int64. The output type of any preprocessing layer can be controlled individually by passing a dtype argument to the layer.
  • tf.random.Generator for keras initializers and all RNG code.
  • Added 3 new APIs for enable/disable/check the usage of tf.random.Generator in keras backend, which will be the new backend for all the RNG in Keras. We plan to switch on the new code path by default in tf 2.8, and the behavior change will likely to cause some breakage on user side (eg if the test is checking against some golden nubmer). These 3 APIs will allow user to disable and switch back to legacy behavior if they prefer. In future (eg TF 2.10), we expect to totally remove the legacy code path (stateful random Ops), and these 3 APIs will be removed as well.
  • tf.keras.callbacks.experimental.BackupAndRestore is now available as tf.keras.callbacks.BackupAndRestore. The experimental endpoint is deprecated and will be removed in a future release.

... (truncated)

Commits

• 3f878cf Merge pull request #54226 from tensorflow-jenkins/version-numbers-2.8.0-22199
• 54307e6 Update version numbers to 2.8.0
• 2f2bdd2 Merge pull request #54193 from tensorflow/update-release-notes
• 97e2f16 Update release notes with security advisories/updates
• 93e224e Merge pull request #54182 from tensorflow/cherrypick-93323537ac0581a88af827af...
• 14defd0 Bump ICU to 69.1 to handle CVE-2020-10531.
• 0a20763 Merge pull request #54159 from tensorflow/cherrypick-b1756cf206fc4db86f05c420...
• b7ecb36 Bump the maximum threshold before erroring
• e542736 Merge pull request #54123 from terryheo/windows-fix-r2.8
• 8dd07bd lite: Update Windows tensorflowlite_flex.dll build
• Additional commits viewable in compare view

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[ascillitoe]

@dependabot rebase

[ascillitoe]

@jklaise coming back to this now. I've not been able to properly verify TF 2.8.0 on our notebook tests, since the pytest timeout appears to exit the entire process when the imdb notebook times out. The pytest-timeout docs seem to suggest an unclean exit is not entirely unexpected.

To check TF 2.8.0 I've run the same notebook tests on my fork with fail-fast=false so that the linux tests still run, and all looks good (only the usual two notebooks are failing).

Perhaps the easiest solution going forward is not to routinely rely on pytest-timeout, and instead, exclude notebooks that are consistently timing out? We could also set fail-fast=false for the test_all_notebooks since we only run it once a week...

[jklaise]

@ascillitoe sounds good to me.