def setUp(self):
self.sagemaker = SageMakerTuningOperator(
task_id='test_sagemaker_operator',
aws_conn_id='sagemaker_test_conn',
config=create_tuning_params,
wait_for_completion=False,
check_interval=5)
dag=dag,
python_callable=lambda: "model_tuning"
if hpo_enabled else "model_training")
# launch sagemaker training job and wait until it completes
train_model_task = SageMakerTrainingOperator(task_id='model_training',
dag=dag,
config=train_config,
aws_conn_id='airflow-sagemaker',
wait_for_completion=True,
check_interval=30)
# launch sagemaker hyperparameter job and wait until it completes
tune_model_task = SageMakerTuningOperator(task_id='model_tuning',
dag=dag,
config=tuner_config,
aws_conn_id='airflow-sagemaker',
wait_for_completion=True,
check_interval=30)
# launch sagemaker batch transform job and wait until it completes
batch_transform_task = SageMakerTransformOperator(
task_id='predicting',
dag=dag,
config=transform_config,
aws_conn_id='airflow-sagemaker',
wait_for_completion=True,
check_interval=30,
trigger_rule=TriggerRule.ONE_SUCCESS)
basher_task = BashOperator(task_id='sleep', bash_command='sleep 5', dag=dag)