text: # Copyright 2020 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.
  
  from kfp.v2 import components
  from kfp.v2 import dsl
  from kfp.v2 import compiler
  import pathlib
  
  test_data_dir = pathlib.Path(__file__).parent / 'component_yaml'
  
  ingestion_op = components.load_component_from_file(
      str(test_data_dir / 'ingestion_component.yaml'))
  
  training_op = components.load_component_from_file(
      str(test_data_dir / 'fancy_trainer_component.yaml'))
  
  
  @dsl.pipeline(
      name='two-step-pipeline-with-resource-spec',
      pipeline_root='dummy_root',
      description='A linear two-step pipeline with resource specification.')
  def my_pipeline(input_location: str = 'gs://test-bucket/pipeline_root',
                  optimizer: str = 'sgd',
                  n_epochs: int = 200):
      ingestor = ingestion_op(input_location=input_location)
      _ = (
          training_op(
              examples=ingestor.outputs['examples'],
              optimizer=optimizer,
              n_epochs=n_epochs).set_cpu_limit('4').set_memory_limit(
                  '14Gi').add_node_selector_constraint('tpu-v3').set_gpu_limit('1'))
  
  
  if __name__ == '__main__':
      compiler.Compiler().compile(
          pipeline_func=my_pipeline,
          package_path=__file__.replace('.py', '.json'))
  