tensorflow/tfx v0.25.0-rc0

TFX 0.25.0-rc0 Release

on GitHub

Major Features and Improvements

• Supported multiple artifacts for Transform's input example and output
  transformed example channels.

• Added support for processing specific spans in file-based ExampleGen with
  range configuration.

• Added ContainerExecutableSpec in portable IR to support container components
  portable orchestrator.

• Added Placeholder utility library. Placeholder can be used to represent
  not-yet-available value at pipeline authoring time.

• Added the LocalDagRunner to allow local pipeline execution without using
  Apache Beam.

• Added support for the tfx.dsl.io.fileio pluggable filesystem interface,
  with initial support for local files and the Tensorflow GFile filesystem
  implementation.

• SDK and example code now uses tfx.dsl.io.fileio instead of tf.io.gfile
  when possible for filesystem I/O implementation portability.

• From this release TFX 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 tfx
  

  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 TFX available on PyPI by running the
  command

  pip install tfx
  

• Added CloudTuner KFP e2e example running on Google Cloud Platform with
  distributed tuning.

• Migrated BigQueryExampleGen to the new ReadFromBigQuery on all runners.

• Introduced Kubeflow V2 DAG runner, which is based on
  Kubeflow IR spec.
  Same as KubeflowDagRunner it will compile the DSL pipeline into a payload
  but not trigger the execution locally.

Breaking changes

• N/A

For pipeline authors

• N/A

For component authors

• N/A

Deprecations

• Modules under tfx.components.base have been deprecated and moved to
  tfx.dsl.components.base in preparation for releasing a pipeline authoring
  package without explicit Tensorflow dependency.

Bug fixes and other changes

• Introduced dependency to tensorflow-cloud Python package, with intention
  to separate out Google Cloud Platform specific extensions.
• Depends on mmh>=2.2,<3 in container image for potential performance
  improvement for Beam based hashes.
• New extra dependencies [examples] is required to use codes inside
  tfx/examples.
• Fixed the run_component script.
• Stopped depending on WTForms.
• Fixed an issue with Transform cache and beam 2.24-2.25 in an interactive
  notebook that caused it to fail.
• Scripts - run_component - Added a way to output artifact properties.
• Fixed an issue resulting in incorrect cache miss to ExampleGen when no
  beam_pipeline_args is provided.
• Changed schema as an optional input channel of Trainer as schema can be
  accessed from TFT graph too.
• Depends on apache-beam[gcp]>=2.25,<3.
• Depends on ml-metadata>=0.24,<0.25.
• Depends on tensorflow-cloud>=0.1,<0.2.
• Depends on tensorflow-data-validation>=0.25,<0.26.
• Depends on tensorflow-hub>=0.9.0,<0.10.
• Depends on tensorflow-model-analysis>=0.25,<0.26.
• Depends on tensorflow-transform>=0.25,<0.26.
• Depends on tfx-bsl>=0.25,<0.26.

Documentation updates

• N/A