def setUp(self):
self.sagemaker = SageMakerTransformOperator(
task_id='test_sagemaker_operator',
aws_conn_id='sagemaker_test_id',
config=config,
wait_for_completion=False,
check_interval=5)
class TestSageMakerTransformOperator(unittest.TestCase):
def setUp(self):
self.sagemaker = SageMakerTransformOperator(
task_id='test_sagemaker_operator',
aws_conn_id='sagemaker_test_id',
config=config,
wait_for_completion=False,
check_interval=5,
)
def test_parse_config_integers(self):
self.sagemaker.parse_config_integers()
test_config = self.sagemaker.config['Transform']
self.assertEqual(
test_config['TransformResources']['InstanceCount'],
int(test_config['TransformResources']['InstanceCount']),
)
self.assertEqual(test_config['MaxConcurrentTransforms'],
int(test_config['MaxConcurrentTransforms']))
self.assertEqual(test_config['MaxPayloadInMB'],
int(test_config['MaxPayloadInMB']))
@mock.patch.object(SageMakerHook, 'get_conn')
@mock.patch.object(SageMakerHook, 'create_model')
@mock.patch.object(SageMakerHook, 'create_transform_job')
def test_execute(self, mock_transform, mock_model, mock_client):
mock_transform.return_value = {
'TransformJobArn': 'testarn',
'ResponseMetadata': {
'HTTPStatusCode': 200
},
}
self.sagemaker.execute(None)
mock_model.assert_called_once_with(create_model_params)
mock_transform.assert_called_once_with(create_transform_params,
wait_for_completion=False,
check_interval=5,
max_ingestion_time=None)
@mock.patch.object(SageMakerHook, 'get_conn')
@mock.patch.object(SageMakerHook, 'create_model')
@mock.patch.object(SageMakerHook, 'create_transform_job')
def test_execute_with_failure(self, mock_transform, mock_model,
mock_client):
mock_transform.return_value = {
'TransformJobArn': 'testarn',
'ResponseMetadata': {
'HTTPStatusCode': 404
},
}
self.assertRaises(AirflowException, self.sagemaker.execute, None)
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)
cleanup_task = DummyOperator(task_id='cleaning_up', dag=dag)
# set the dependencies between tasks
init.set_downstream(preprocess_task)
preprocess_task.set_downstream(prepare_task)
prepare_task.set_downstream(branching)
branching.set_downstream(tune_model_task)
branching.set_downstream(train_model_task)
tune_model_task.set_downstream(batch_transform_task)
train_model_task.set_downstream(batch_transform_task)
