Version 0.21.1

Major Features and Improvements

• Pipelines compiled using KubeflowDagRunner now defaults to using the
  gRPC-based MLMD server deployed in Kubeflow Pipelines clusters when
  performing operations on pipeline metadata.
• Added tfx model rewriting and tflite rewriter.
• Added LatestBlessedModelResolver as an experimental feature which gets the
  latest model that was blessed by model validator.
• The specific Artifact subclass that was serialized (if defined in the
  deserializing environment) will be used when deserializing Artifacts and
  when reading Artifacts from ML Metadata (previously, objects of the
  generic tfx.types.artifact.Artifact class were created in some cases).
• Updated Evaluator's executor to support model validation.
• Introduced awareness of chief worker to Trainer's executor, in case running
  in distributed training cluster.
• Added a Chicago Taxi example with native Keras.
• Updated TFLite converter to work with TF2.
• Enabled filtering by artifact producer and output key in ResolverNode.

Bug fixes and other changes

• Added --skaffold_cmd flag when updating a pipeline for kubeflow in CLI.
• Changed python_version to 3.7 when using TF 1.15 and later for Cloud AI Platform Prediction.
• Added 'tfx_runner' label for CAIP, BQML and Dataflow jobs submitted from
  TFX components.
• Fixed the Taxi Colab notebook.
• Adopted the generic trainer executor when using CAIP Training.
• Depends on 'tensorflow-data-validation>=0.21.4,<0.22'.
• Depends on 'tensorflow-model-analysis>=0.21.4,<0.22'.
• Depends on 'tensorflow-transform>=0.21.2,<0.22'.

Deprecations

Breaking changes

• Remove "NOT_BLESSED" artifact.
• Change constants ARTIFACT_PROPERTY_BLESSED_MODEL_* to ARTIFACT_PROPERTY_BASELINE_MODEL_*.

For pipeline authors

For component authors

Documentation updates