Source code for kfp.components.importer_node

# Copyright 2020 The Kubeflow Authors
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# Licensed under the Apache License, Version 2.0 (the "License");
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"""Utility function for building Importer Node spec."""

from typing import Any, Mapping, Optional, Type, Union

from kfp.components import importer_component
from kfp.components import pipeline_channel
from kfp.components import pipeline_task
from kfp.components import placeholders
from kfp.components import structures
from kfp.components.types import artifact_types

INPUT_KEY = 'uri'
OUTPUT_KEY = 'artifact'


[docs]def importer( artifact_uri: Union[pipeline_channel.PipelineParameterChannel, str], artifact_class: Type[artifact_types.Artifact], reimport: bool = False, metadata: Optional[Mapping[str, Any]] = None, ) -> pipeline_task.PipelineTask: """Imports an existing artifact for use in a downstream component. Args: artifact_uri: The URI of the artifact to import. artifact_class: The artifact class being imported. reimport: Whether to reimport the artifact. metadata: Properties of the artifact. Returns: A task with the artifact accessible via its ``.output`` attribute. Examples:: @dsl.pipeline(name='pipeline-with-importer') def pipeline_with_importer(): importer1 = importer( artifact_uri='gs://ml-pipeline-playground/shakespeare1.txt', artifact_class=Dataset, reimport=False) train(dataset=importer1.output) """ component_spec = structures.ComponentSpec( name='importer', implementation=structures.Implementation( importer=structures.ImporterSpec( artifact_uri=placeholders.InputValuePlaceholder( INPUT_KEY).to_placeholder_string(), type_schema=artifact_class.TYPE_NAME, reimport=reimport, metadata=metadata)), inputs={INPUT_KEY: structures.InputSpec(type='String')}, outputs={ OUTPUT_KEY: structures.OutputSpec(type=artifact_class.__name__) }, ) importer = importer_component.ImporterComponent( component_spec=component_spec) return importer(uri=artifact_uri)