tfx.components.ImporterNode

Definition for TFX ImporterNode.

Inherits From: BaseNode

tfx.components.ImporterNode(
    instance_name: Text,
    source_uri: Text,
    artifact_type: Type[tfx.types.Artifact],
    reimport: Optional[bool] = False,
    properties: Optional[Dict[Text, Union[Text, int]]] = None,
    custom_properties: Optional[Dict[Text, Union[Text, int]]] = None
)

ImporterNode is a special TFX node which registers an external resource into MLMD so that downstream nodes can use the registered artifact as input.

Here is an example to use ImporterNode:

... importer = ImporterNode( instance_name='import_schema', source_uri='uri/to/schema' artifact_type=standard_artifacts.Schema, reimport=False) schema_gen = SchemaGen( fixed_schema=importer.outputs['result'], examples=...) ...

instance_name the name of the ImporterNode instance.
source_uri the URI of the resource that needs to be registered.
artifact_type the type of the artifact to import.
reimport whether or not to re-import as a new artifact if the URI has been imported in before.
properties Dictionary of properties for the imported Artifact. These properties should be ones declared for the given artifact_type (see the PROPERTIES attribute of the definition of the type for details).
custom_properties Dictionary of custom properties for the imported Artifact. These properties should be of type Text or int.

_source_uri the source uri to import.
_reimport whether or not to re-import the URI even if it already exists in MLMD.
component_id DEPRECATED FUNCTION
component_type DEPRECATED FUNCTION
downstream_nodes

exec_properties

id Node id, unique across all TFX nodes in a pipeline.

If instance name is available, node_id will be: . otherwise, node_id will be:

inputs

outputs

type

upstream_nodes

Methods

add_downstream_node

View source

add_downstream_node(
    downstream_node
)

Experimental: Add another component that must run after this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_upstream_node.

Args
downstream_node a component that must run after this node.

add_upstream_node

View source

add_upstream_node(
    upstream_node
)

Experimental: Add another component that must run before this one.

This method enables task-based dependencies by enforcing execution order for synchronous pipelines on supported platforms. Currently, the supported platforms are Airflow, Beam, and Kubeflow Pipelines.

Note that this API call should be considered experimental, and may not work with asynchronous pipelines, sub-pipelines and pipelines with conditional nodes. We also recommend relying on data for capturing dependencies where possible to ensure data lineage is fully captured within MLMD.

It is symmetric with add_downstream_node.

Args
upstream_node a component that must run before this node.

from_json_dict

View source

@classmethod
from_json_dict(
    dict_data: Dict[Text, Any]
) -> Any

Convert from dictionary data to an object.

get_id

View source

@classmethod
get_id(
    instance_name: Optional[Text] = None
)

Gets the id of a node.

This can be used during pipeline authoring time. For example: from tfx.components import Trainer

resolver = ResolverNode(..., model=Channel( type=Model, producer_component_id=Trainer.get_id('my_trainer')))

Args
instance_name (Optional) instance name of a node. If given, the instance name will be taken into consideration when generating the id.

Returns
an id for the node.

to_json_dict

View source

to_json_dict() -> Dict[Text, Any]

Convert from an object to a JSON serializable dictionary.