Version 0.25.0

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 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.

• Added 'penguin' example. Penguin example uses Palmer Penguins dataset and
  classify penguin species using four numeric features.

• Iris e2e examples are replaced by penguin examples.

• TFX BeamDagRunner is migrated to use the tech stack built on top of IR.
  While this is no-op to users, it is a major step towards supporting more
  flexible TFX DSL semetic.
  Please refer to the RFC
  of IR to learn more details.

• Supports forward compatibility when evolving TFX artifact types, which
  allows jobs of old release and new release run with the same MLMD instance.

Breaking changes

• Moved the directory that CLI stores pipeline information from
  ${HOME}/${ORCHESTRATOR} to ${HOME}/tfx/${ORCHESTRATOR}. For example,
  "/kubeflow" was changed to "/tfx/kubeflow". This directory is used to
  store pipeline information including pipeline ids in the Kubeflow Pipelines
  cluster which are needed to create runs or update pipelines.
  These files will be moved automatically when it is first used and no
  separate action is needed.
  See https://github.com/tensorflow/tfx/blob/master/docs/guide/cli.md for the
  detail.

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.
• Deprecated setting instance_name at pipeline node level. Instead, users
  are encouraged to set id explicitly of any pipeline node through newly
  added APIs.

Bug fixes and other changes

• Added the LocalDagRunner to allow local pipeline execution without using
  Apache Beam. This functionality is in development.
• 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.
• Fixed an issue during recording of a component's execution where
  "missing or modified key in exec_properties" was raised from MLMD when
  exec_properties both omitted an existing property and added a new
  property.
• Supported users to set id of pipeline nodes directly.
• Added a new template, 'penguin' which is simple subset of
  penguin examples,
  and uses the same
  Palmer Penguins
  dataset. The new template focused on easy ingestion of user's own data.
• Changed default data path for the taxi template from tfx-template/data
  to tfx-template/data/taxi.
• Depends on apache-beam[gcp]>=2.25,<3.
• Depends on attrs>=19.3.0,<21.
• Depends on kfp-pipeline-spec>=0.1.0,<0.2.
• Depends on kfp>=1.1.0,<2.
• Depends on ml-metadata>=0.25,<0.26.
• 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