]
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(