tfx.v1.orchestration.experimental.KubeflowV2DagRunnerConfig

Runtime configuration specific to execution on Kubeflow V2 pipelines.

tfx.v1.orchestration.experimental.KubeflowV2DagRunnerConfig(
    display_name: Optional[str] = None,
    default_image: Optional[Union[str, MutableMapping[str, str]]] = None,
    default_commands: Optional[List[str]] = None,
    use_pipeline_spec_2_1: bool = False,
    **kwargs
)

Used in the notebooks

Used in the tutorials
Reading data from BigQuery with TFX and Vertex Pipelines Simple TFX Pipeline for Vertex Pipelines Vertex AI Training and Serving with TFX and Vertex Pipelines

Args
display_name	Optional human-readable pipeline name. Defaults to the pipeline name passed into KubeflowV2DagRunner.run().
default_image	The default TFX image to be used if not overriden by per component specification. It can be a map whose key is a component id and value is an image path to set the image by a component level.
default_commands	Optionally specifies the commands of the provided container image. When not provided, the default ENTRYPOINT specified in the docker image is used. Note: the commands here refers to the K8S container command, which maps to Docker entrypoint field. If one supplies command but no args are provided for the container, the container will be invoked with the provided command, ignoring the ENTRYPOINT and CMD defined in the Dockerfile. One can find more details regarding the difference between K8S and Docker conventions at https://kubernetes.io/docs/tasks/inject-data-application/define-command-argument-container/#notes
use_pipeline_spec_2_1	Use the KFP pipeline spec schema 2.1 to support Vertex ML pipeline teamplate gallary.
**kwargs	Additional args passed to base PipelineConfig.