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tfx.orchestration.kubeflow.kubeflow_metadata_adapter.KubeflowMetadataAdapter

A Metadata adapter class for pipelines run using KFP.

Inherits From: Metadata

tfx.orchestration.kubeflow.kubeflow_metadata_adapter.KubeflowMetadataAdapter(
    connection_config: tfx.orchestration.metadata.ConnectionConfigType
) -> None

This is used to add properties to artifacts and executions, such as the Argo pod IDs.

store Returns underlying MetadataStore.

Methods

get_artifacts_by_type

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get_artifacts_by_type(
    type_name: Text
) -> List[metadata_store_pb2.Artifact]

Fetches artifacts given artifact type name.

get_artifacts_by_uri

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get_artifacts_by_uri(
    uri: Text
) -> List[metadata_store_pb2.Artifact]

Fetches artifacts given uri.

get_cached_outputs

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get_cached_outputs(
    input_artifacts: Dict[Text, List[Artifact]],
    exec_properties: Dict[Text, Any],
    pipeline_info: tfx.orchestration.data_types.PipelineInfo,
    component_info: tfx.orchestration.data_types.ComponentInfo
) -> Optional[Dict[Text, List[Artifact]]]

Fetches cached output artifacts if any.

Returns the output artifacts of a cached execution if any. An eligible cached execution should take the same input artifacts, execution properties and is associated with the same pipeline context.

Args
input_artifacts inputs used by the run.
exec_properties execution properties used by the run.
pipeline_info info of the current pipeline run.
component_info info of the current component.

Returns
Dict of cached output artifacts if eligible cached execution is found. Otherwise, return None.

get_component_run_context

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get_component_run_context(
    component_info: tfx.orchestration.data_types.ComponentInfo
) -> Optional[metadata_store_pb2.Context]

Gets the context for the component run.

Args
component_info component information for the current component run.

Returns
a matched context or None

get_pipeline_context

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get_pipeline_context(
    pipeline_info: tfx.orchestration.data_types.PipelineInfo
) -> Optional[metadata_store_pb2.Context]

Gets the context for the pipeline run.

Args
pipeline_info pipeline information for the current pipeline run.

Returns
a matched context or None

get_pipeline_run_context

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get_pipeline_run_context(
    pipeline_info: tfx.orchestration.data_types.PipelineInfo
) -> Optional[metadata_store_pb2.Context]

Gets the context for the pipeline run.

Args
pipeline_info pipeline information for the current pipeline run.

Returns
a matched context or None

get_published_artifacts_by_type_within_context

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get_published_artifacts_by_type_within_context(
    type_names: List[Text],
    context_id: int
) -> Dict[Text, List[metadata_store_pb2.Artifact]]

Fetches artifacts given artifact type name and context id.

get_qualified_artifacts

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get_qualified_artifacts(
    contexts: List[metadata_store_pb2.Context],
    type_name: Text,
    producer_component_id: Optional[Text] = None,
    output_key: Optional[Text] = None
) -> List[metadata_store_service_pb2.ArtifactAndType]

Gets qualified artifacts that have the right producer info.

Args
contexts context constraints to filter artifacts
type_name type constraint to filter artifacts
producer_component_id producer constraint to filter artifacts
output_key output key constraint to filter artifacts

Returns
A list of ArtifactAndType, containing qualified artifacts.

publish_artifacts

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publish_artifacts(
    tfx_artifact_list: List[tfx.types.Artifact]
) -> None

Publishes artifacts to MLMD.

This call will also update original tfx artifact list to contain the artifact type info and artifact id.

Args
tfx_artifact_list A list of tfx.types.Artifact which will be updated

publish_execution

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publish_execution(
    component_info: tfx.orchestration.data_types.ComponentInfo,
    output_artifacts: Optional[Dict[Text, List[Artifact]]] = None,
    exec_properties: Optional[Dict[Text, Any]] = None
) -> None

Publishes an execution with input and output artifacts info.

This method will publish any execution with non-final states. It will register unseen artifacts and publish events for them.

Args
component_info component information.
output_artifacts output artifacts produced by the execution.
exec_properties execution properties for the execution to be published.

register_execution

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register_execution(
    pipeline_info: tfx.orchestration.data_types.PipelineInfo,
    component_info: tfx.orchestration.data_types.ComponentInfo,
    contexts: List[metadata_store_pb2.Context],
    exec_properties: Optional[Dict[Text, Any]] = None,
    input_artifacts: Optional[Dict[Text, List[Artifact]]] = None
) -> metadata_store_pb2.Execution

Registers a new execution in metadata.

Args
pipeline_info optional pipeline info of the execution.
component_info optional component info of the execution.
contexts contexts for current run, all contexts will be linked to the execution. In addition, a component run context will be added to the contexts list.
exec_properties the execution properties of the execution.
input_artifacts input artifacts of the execution.

Returns
execution id of the new execution.

register_pipeline_contexts_if_not_exists

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register_pipeline_contexts_if_not_exists(
    pipeline_info: tfx.orchestration.data_types.PipelineInfo
) -> List[metadata_store_pb2.Context]

Creates or fetches the pipeline contexts needed for the run.

There are two potential contexts:

  • Context for the pipeline.
  • Context for the current pipeline run. This is optional, only available when run_id is specified.

Args
pipeline_info pipeline information for current run.

Returns
a list (of size one or two) of context.

search_artifacts

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search_artifacts(
    artifact_name: Text,
    pipeline_info: tfx.orchestration.data_types.PipelineInfo,
    producer_component_id: Text
) -> List[tfx.types.Artifact]

Search artifacts that matches given info.

Args
artifact_name the name of the artifact that set by producer component. The name is logged both in artifacts and the events when the execution being published.
pipeline_info the information of the current pipeline
producer_component_id the id of the component that produces the artifact

Returns
A list of Artifacts that matches the given info

Raises
RuntimeError when no matching execution is found given producer info.

update_artifact_state

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update_artifact_state(
    artifact: metadata_store_pb2.Artifact,
    new_state: Text
) -> None

Update the state of a given artifact.

update_execution

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update_execution(
    execution: metadata_store_pb2.Execution,
    component_info: tfx.orchestration.data_types.ComponentInfo,
    input_artifacts: Optional[Dict[Text, List[Artifact]]] = None,
    output_artifacts: Optional[Dict[Text, List[Artifact]]] = None,
    exec_properties: Optional[Dict[Text, Any]] = None,
    execution_state: Optional[Text] = None,
    artifact_state: Optional[Text] = None,
    contexts: Optional[List[metadata_store_pb2.Context]] = None
) -> None

Updates the given execution in MLMD based on given information.

All artifacts provided will be registered if not already. Registered id will be reflected inline.

Args
execution the execution to be updated. It is required that the execution passed in has an id.
component_info the information of the current running component
input_artifacts artifacts to be declared as inputs of the execution
output_artifacts artifacts to be declared as outputs of the execution
exec_properties execution properties of the execution
execution_state state the execution to be updated to
artifact_state state the artifacts to be updated to
contexts a list of contexts the execution and artifacts to be linked to

Raises
RuntimeError if the execution to be updated has no id.

__enter__

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__enter__() -> "Metadata"

__exit__

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__exit__(
    exc_type: Optional[Type[Exception]],
    exc_value: Optional[Exception],
    exc_tb: Optional[types.TracebackType]
) -> None