docstr: End-to-end test for pipeline with RuntimeParameter.
docstr_tok: ["End-to-end","test","for","pipeline","with","RuntimeParameter","."]
code: def testEndToEndPipelineRun(self):
      """End-to-end test for pipeline with RuntimeParameter."""
      pipeline_name = "kubeflow-v1-e2e-test-{}".format(self._test_id)
      kubeflow_pipeline = penguin_pipeline_kubeflow.create_pipeline(
          pipeline_name=pipeline_name,
          pipeline_root=self._pipeline_root(pipeline_name),
          data_root=self._penguin_data_root,
          module_file=self._penguin_module_file,
          enable_tuning=False,
          enable_cache=True,
          user_provided_schema_path=self._penguin_schema_file,
          ai_platform_training_args=penguin_pipeline_kubeflow._ai_platform_training_args,
          ai_platform_serving_args=penguin_pipeline_kubeflow._ai_platform_serving_args,
          beam_pipeline_args=penguin_pipeline_kubeflow._beam_pipeline_args_by_runner[
              "DirectRunner"
          ],
          use_cloud_component=False,
          use_aip=False,
          use_vertex=False,
          serving_model_dir=self._serving_model_dir,
      )
  
      parameters = {
          "train-args": '{"num_steps": 100}',
          "eval-args": '{"num_steps": 50}',
      }
      self._compile_and_run_pipeline(
          pipeline=kubeflow_pipeline, parameters=parameters
      )
      self.assertTrue(fileio.exists(self._serving_model_dir))
  
file_path: tfx/tfx/examples/penguin/penguin_pipeline_kubeflow_e2e_test.py
imports: import os
  import tensorflow
  from tfx.dsl.io import fileio
  from tfx.examples.penguin import penguin_pipeline_kubeflow
  from tfx.orchestration.kubeflow import test_utils
  from tfx.orchestration.kubeflow.v2.e2e_tests import base_test_case
  from tfx.utils import io_utils
label_desc: Use image node_id 2 for calling the PenguinPipelineKubeflowTest obj's underlying member method code with example usage: obj.testEndToEndPipelineRun() without return types