] if __name__ == "__main__": absl.logging.set_verbosity(absl.logging.INFO) module_path = os.getcwd() if module_path not in sys.path: sys.path.append(module_path) from pipelines.base_pipeline import init_components components = init_components( data_dir, module_file, ai_platform_training_args=ai_platform_training_args, serving_model_dir=serving_model_dir, # ai_platform_serving_args=ai_platform_serving_args ) p = pipeline.Pipeline( pipeline_name=pipeline_name, pipeline_root=pipeline_root, components=components, beam_pipeline_args=beam_pipeline_args, ) # Metadata config. The defaults works work with the installation of # KF Pipelines using Kubeflow. If installing KF Pipelines using the # lightweight deployment option, you may need to override the defaults. metadata_config = kubeflow_dag_runner.get_default_kubeflow_metadata_config(
] if __name__ == "__main__": logging.set_verbosity(logging.INFO) module_path = os.getcwd() if module_path not in sys.path: sys.path.append(module_path) from pipelines.base_pipeline import init_components components = init_components( data_dir, module_file, vertex_training_custom_config=vertex_training_custom_config, serving_model_dir=serving_model_dir, # ai_platform_serving_args=ai_platform_serving_args ) p = pipeline.Pipeline( pipeline_name=pipeline_name, pipeline_root=pipeline_root, components=components, beam_pipeline_args=beam_pipeline_args, ) # Metadata config. The defaults works work with the installation of # KF Pipelines using Kubeflow. If installing KF Pipelines using the # lightweight deployment option, you may need to override the defaults. metadata_config = kubeflow_dag_runner.get_default_kubeflow_metadata_config(