TFX 1.4.0

Major Features and Improvements

• Supported endpoint overwrite for CAIP BulkInferrer.
• Added support for outputting and encoding tf.RaggedTensors in TFX
  Transform component.
• Added conditional for TFX running on KFPv2 (Vertex).
• Supported component level beam pipeline args for Vertex (KFPV2DagRunner).
• Support exit handler for TFX running on KFPv2 (Vertex).
• Added RangeConfig for QueryBasedExampleGen to select date using query
  pattern.
• Added support for union of Channels as input to standard TFX components.
  Users can use channel.union() to combine multiple Channels and use as input
  to these compnents. Artfacts resolved from these channels are expected to
  have the same type, and passed to components in no particular order.

Breaking Changes

• Calling TfxRunner.run(pipeline) with the Pipeline IR proto will no longer
  be supported. Please switch to TfxRunner.run_with_ir(pipeline) instead.
  If you are calling TfxRunner.run(pipeline) with the Pipeline object, this
  change should not affect you.

For Pipeline Authors

• N/A

For Component Authors

• N/A

Deprecations

• Deprecated python3.6 support.

Bug Fixes and Other Changes

• Depends on google-cloud-aiplatform>=1.5.0,<2.
• Depends on tensorflow>=1.15.2,!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,<2.7.
• Depends on pyarrow>=1,<6.
• Fixed FileBasedExampleGen driver for Kubeflow v2 (Vertex). Driver can
  update exec_properties for its executor now, which enables {SPAN} feature.
• example_gen.utils.dict_to_example now accepts Numpy types
• Updated pytest to include v6.x
• Depends on apache-beam[gcp]>=2.33,<3.
• Depends on ml-metadata>=1.4.0,<1.5.0.
• Depends on struct2tensor>=0.35.0,<0.36.0.
• Depends on tensorflow-data-validation>=1.4.0,<1.5.0.
• Depends on tensorflow-model-analysis>=0.35.0,<0.36.0.
• Depends on tensorflow-transform>=1.4.0,<1.5.0.
• Depends on tfx-bsl>=1.4.0,<1.5.0.
• Fixed error where Vertex Endpoints of the same name is not deduped

Documentation Updates

• N/A