Version 0.21.3

Major Features and Improvements

Added run/pipeline link when creating runs/pipelines on KFP through TFX CLI.
Added support for ValueArtifact, whose attribute value allows users to
access the content of the underlying file directly in the executor. Support
Bytes/Integer/String/Float type. Note: interactive resolution does not
support this for now.
Added InfraValidator component that is used as an early warning layer
before pushing a model into production.

Starting this version, TFX will only release python3 packages.
Replaced relative import with absolute import in generated templates.
Added a native keras model in the taxi template and the template now uses
generic Trainer.
Added support of TF 2.1 runtime configuration for AI Platform Prediction
Pusher.
Added support for using ML Metadata ArtifactType messages as Artifact
classes.
Changed CLI behavior to create new versions of pipelines instead of
delete and create new ones when pipelines are updated for KFP. (Requires
kfp >= 0.3.0)
Added ability to enable quantization in tflite rewriter.
Added k8s pod labels when the pipeline is executed via KubeflowDagRunner for
better usage telemetry.
Parameterized the GCP taxi pipeline sample for easily ramping up to full
taxi dataset.
Added support for hyphens(dash) in addition to underscores in CLI flags.
Underscores will be supported as well.
Fixed ill-formed underscore in the markdown visualization when running on
KFP.