old_contents: # Copyright 2021 The Kubeflow Authors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Pipeline using dsl.importer."""
  
  from typing import NamedTuple
  from kfp import components
  from kfp.v2 import compiler
  from kfp.v2 import dsl
  from kfp.v2.dsl import component, importer, Dataset, Model, Input
  
  
  @component
  def train(
          dataset: Input[Dataset]
  ) -> NamedTuple('Outputs', [
      ('scalar', str),
      ('model', Model),
  ]):
      """Dummy Training step."""
      with open(dataset.path, 'r') as f:
          data = f.read()
      print('Dataset:', data)
  
      scalar = '123'
      model = 'My model trained using data: {}'.format(data)
  
      from collections import namedtuple
      output = namedtuple('Outputs', ['scalar', 'model'])
      return output(scalar, model)
  
  
  
  
  @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)
  
  
  
  if __name__ == "__main__":
      # execute only if run as a script
      compiler.Compiler().compile(
          pipeline_func=pipeline_with_importer,
          package_path='pipeline_with_importer.json'
      )
  
  
  
new_contents: # Copyright 2021 The Kubeflow Authors
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Pipeline using dsl.importer."""
  import os
  from typing import NamedTuple
  
  from kfp import components
  from kfp.v2 import compiler, dsl
  from kfp.v2.dsl import Dataset, Input, Model, component, importer
  
  # In tests, we install a KFP package from the PR under test. Users should not
  # normally need to specify `kfp_package_path` in their component definitions.
  _KFP_PACKAGE_PATH = os.getenv('KFP_PACKAGE_PATH')
  
  
  @component(kfp_package_path=_KFP_PACKAGE_PATH)
  def train(
      dataset: Input[Dataset]
  ) -> NamedTuple('Outputs', [
      ('scalar', str),
      ('model', Model),
  ]):
      """Dummy Training step."""
      with open(dataset.path, 'r') as f:
          data = f.read()
      print('Dataset:', data)
  
      scalar = '123'
      model = 'My model trained using data: {}'.format(data)
  
      from collections import namedtuple
      output = namedtuple('Outputs', ['scalar', 'model'])
      return output(scalar, model)
  
  
  @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)
  
  
  if __name__ == "__main__":
      # execute only if run as a script
      compiler.Compiler().compile(
          pipeline_func=pipeline_with_importer,
          package_path='pipeline_with_importer.json')
  
subject: Add `kfp_package_path` to importer sample test. (#6507)