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TensorFlow Transform 0.25.0
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@dhruvesh09 dhruvesh09 released this 04 Nov 22:48
v0.25.0
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Major Features and Improvements

  • Updated the "Getting Started" guide and examples to demonstrate the support
    for both the "instance dict" and the "TFXIO" format. Users are encouraged to
    start using the "TFXIO" format, expecially in cases where
    pre-canned TFXIO implementations
    is available as it offers better performance.

  • From this release TFT will also be hosting nightly packages on
    https://pypi-nightly.tensorflow.org. To install the nightly package use the
    following command:

    pip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-transform

    Note: These nightly packages are unstable and breakages are likely to
    happen. The fix could often take a week or more depending on the complexity
    involved for the wheels to be available on the PyPI cloud service. You can
    always use the stable version of TFT available on PyPI by running the
    command pip install tensorflow-transform .

Bug Fixes and Other Changes

  • TFTransformOutput.transform_raw_features and TransformFeaturesLayer can
    be used when a transform fn is exported as a TF2 SavedModel and imported in
    graph mode.
  • Utility methods in tft.inspect_preprocessing_fn now take an optional
    parameter force_tf_compat_v1. If this is False, the preprocessing_fn is
    traced using tf.function in TF 2.x when TF 2 behaviors are enabled.
  • Switching to a wrapper for collections.namedtuple to ensure compatibility
    with PySpark which modifies classes produced by the factory.
  • Caching has been disabled for tft.tukey_h_params, tft.tukey_location and
    tft.tukey_scale due to the cached accumulator being non-deterministic.
  • Track variables created within the preprocessing_fn in the native TF 2
    implementation.
  • TFTransformOutput.transform_raw_features returns a wrapped python dict
    that overrides pop to return None instead of raising a KeyError when called
    with a key not found in the dictionary. This is done as preparation for
    switching the default value of drop_unused_features to True.
  • Vocabularies written in tfrecord_gzip format no longer filter out entries
    that are empty or that include a newline character.
  • Depends on apache-beam[gcp]>=2.25,<3.
  • Depends on tensorflow-metadata>=0.25,<0.26.
  • Depends on tfx-bsl>=0.25,<0.26.

Breaking changes

  • N/A

Deprecations

  • The decode method of the available coders (tft.coders.CsvCoder and
    tft.coders.ExampleProtoCoder) has been deprecated and removed.
    Canned TFXIO implementations
    should be used to read and decode data instead.
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