class_docstr: Runtime configuration parameters specific to execution on Kubeflow.
class_docstr_tok: ["Runtime","configuration","parameters","specific","to","execution","on","Kubeflow","."]
docstr: Creates a KubeflowDagRunnerConfig object.
  
  The user can use pipeline_operator_funcs to apply modifications to
  ContainerOps used in the pipeline. For example, to ensure the pipeline
  steps mount a GCP secret, and a Persistent Volume, one can create config
  object like so:
  
    from kfp import gcp, onprem
    mount_secret_op = gcp.use_secret('my-secret-name)
    mount_volume_op = onprem.mount_pvc(
      "my-persistent-volume-claim",
      "my-volume-name",
      "/mnt/volume-mount-path")
  
    config = KubeflowDagRunnerConfig(
      pipeline_operator_funcs=[mount_secret_op, mount_volume_op]
    )
  
  Args:
    pipeline_operator_funcs: A list of ContainerOp modifying functions that
      will be applied to every container step in the pipeline.
    tfx_image: The TFX container image to use in the pipeline.
    kubeflow_metadata_config: Runtime configuration to use to connect to
      Kubeflow metadata.
    supported_launcher_classes: A list of component launcher classes that are
      supported by the current pipeline. List sequence determines the order in
      which launchers are chosen for each component being run.
    metadata_ui_path: File location for metadata-ui-metadata.json file.
    **kwargs: keyword args for PipelineConfig.
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code: def __init__(
      self,
      pipeline_operator_funcs: Optional[List[OpFunc]] = None,
      tfx_image: Optional[str] = None,
      kubeflow_metadata_config: Optional[
          kubeflow_pb2.KubeflowMetadataConfig
      ] = None,
      # TODO(b/143883035): Figure out the best practice to put the
      # SUPPORTED_LAUNCHER_CLASSES
      supported_launcher_classes: Optional[
          List[Type[base_component_launcher.BaseComponentLauncher]]
      ] = None,
      metadata_ui_path: str = "/mlpipeline-ui-metadata.json",
      **kwargs,
  ):
      """Creates a KubeflowDagRunnerConfig object.
  
      The user can use pipeline_operator_funcs to apply modifications to
      ContainerOps used in the pipeline. For example, to ensure the pipeline
      steps mount a GCP secret, and a Persistent Volume, one can create config
      object like so:
  
        from kfp import gcp, onprem
        mount_secret_op = gcp.use_secret('my-secret-name)
        mount_volume_op = onprem.mount_pvc(
          "my-persistent-volume-claim",
          "my-volume-name",
          "/mnt/volume-mount-path")
  
        config = KubeflowDagRunnerConfig(
          pipeline_operator_funcs=[mount_secret_op, mount_volume_op]
        )
  
      Args:
        pipeline_operator_funcs: A list of ContainerOp modifying functions that
          will be applied to every container step in the pipeline.
        tfx_image: The TFX container image to use in the pipeline.
        kubeflow_metadata_config: Runtime configuration to use to connect to
          Kubeflow metadata.
        supported_launcher_classes: A list of component launcher classes that are
          supported by the current pipeline. List sequence determines the order in
          which launchers are chosen for each component being run.
        metadata_ui_path: File location for metadata-ui-metadata.json file.
        **kwargs: keyword args for PipelineConfig.
      """
      supported_launcher_classes = supported_launcher_classes or [
          in_process_component_launcher.InProcessComponentLauncher,
          kubernetes_component_launcher.KubernetesComponentLauncher,
      ]
      super().__init__(
          supported_launcher_classes=supported_launcher_classes,
          **kwargs,
      )
      self.pipeline_operator_funcs = (
          pipeline_operator_funcs
          or get_default_pipeline_operator_funcs()
      )
      self.tfx_image = tfx_image or DEFAULT_KUBEFLOW_TFX_IMAGE
      self.kubeflow_metadata_config = (
          kubeflow_metadata_config
          or get_default_kubeflow_metadata_config()
      )
      self.metadata_ui_path = metadata_ui_path
  
imports: import collections
  import copy
  import os
  from typing import Any, Callable, Dict, List, Optional, Type, cast, MutableMapping
  from absl import logging
  from kfp import compiler
  from kfp import dsl
  from kfp import gcp
  from kubernetes import client
  from tfx import version
  from tfx.dsl.compiler import compiler
  from tfx.dsl.components.base import base_component
  from tfx.dsl.components.base import base_node
  from tfx.orchestration import data_types
  from tfx.orchestration import pipeline
  from tfx.orchestration import tfx_runner
  from tfx.orchestration.config import pipeline_config
  from tfx.orchestration.kubeflow import base_component
  from tfx.orchestration.kubeflow import utils
  from tfx.orchestration.kubeflow.proto import kubeflow_pb2
  from tfx.orchestration.launcher import base_component_launcher
  from tfx.orchestration.launcher import in_process_component_launcher
  from tfx.orchestration.launcher import kubernetes_component_launcher
  from tfx.proto.orchestration import pipeline_pb2
  from tfx.utils import telemetry_utils
label_desc: Use image node_id 1 to create a new KubeflowDagRunnerConfig object from inherited base classes: pipeline_config with example: obj = KubeflowDagRunnerConfig(pipeline_operator_funcs, tfx_image, kubeflow_metadata_config, supported_launcher_classes, metadata_ui_path)