def test_delete_endpoint_only():
sagemaker_session = empty_sagemaker_session()
predictor = RealTimePredictor(ENDPOINT, sagemaker_session=sagemaker_session)
predictor.delete_endpoint(delete_endpoint_config=False)
sagemaker_session.delete_endpoint.assert_called_with(ENDPOINT)
sagemaker_session.delete_endpoint_config.assert_not_called()
def test_delete_endpoint_with_config():
sagemaker_session = empty_sagemaker_session()
sagemaker_session.sagemaker_client.describe_endpoint = Mock(return_value={'EndpointConfigName': 'endpoint-config'})
predictor = RealTimePredictor(ENDPOINT, sagemaker_session=sagemaker_session)
predictor.delete_endpoint()
sagemaker_session.delete_endpoint.assert_called_with(ENDPOINT)
sagemaker_session.delete_endpoint_config.assert_called_with('endpoint-config')
class Predictor(object):
def __init__(self, endpoint_name, sagemaker_session=None):
"""
Args:
endpoint_name (str): name of the Sagemaker endpoint
sagemaker_session (sagemaker.session.Session): Manage interactions
with the Amazon SageMaker APIs and any other AWS services needed.
"""
self.endpoint_name = endpoint_name
self._realtime_predictor = RealTimePredictor(
endpoint_name,
serializer=sagemaker.predictor.json_serializer,
deserializer=sagemaker.predictor.json_deserializer,
sagemaker_session=sagemaker_session)
def get_action(self, obs=None):
"""Get prediction from the endpoint
Args:
obs (list/str): observation of the environment
Returns:
action: action to take from the prediction
event_id: event id of the current prediction
model_id: model id of the hosted model
action_prob: action probability distribution
sample_prob: sample probability distribution used for data split
"""
payload = {}
payload['request_type'] = "observation"
payload['observation'] = obs
response = self._realtime_predictor.predict(payload)
action = response['action']
action_prob = response['action_prob']
event_id = response['event_id']
model_id = response['model_id']
sample_prob = response['sample_prob']
return action, event_id, model_id, action_prob, sample_prob
def get_hosted_model_id(self):
"""Return hostdd model id in the hosting endpoint
Returns:
str: model id of the model being hosted
"""
payload = {}
payload['request_type'] = "model_id"
payload['observation'] = None
response = self._realtime_predictor.predict(payload)
model_id = response['model_id']
return model_id
def delete_endpoint(self):
"""Delete the Sagemaker endpoint
"""
logger.warning(f"Deleting hosting endpoint '{self.endpoint_name}'...")
self._realtime_predictor.delete_endpoint()
from sagemaker.amazon.amazon_estimator import get_image_uri
endpoint_name = 'creditcardfraudlogistic'
Model(
model_data=
's3://creditcardfraud123/logistic/output/linear-learner-191112-2119-002-ac3cc459/output/model.tar.gz',
image=get_image_uri(region_name='us-east-1',
repo_name='linear-learner',
repo_version='latest'),
role='AmazonSageMaker-ExecutionRole-20191005T164168').deploy(
initial_instance_count=1,
instance_type='ml.t2.2xlarge',
endpoint_name=endpoint_name)
predictor = RealTimePredictor(endpoint_name)
predictor.content_type = 'text/csv'
predictor.serializer = csv_serializer
predictor.deserializer = json_deserializer
data = '83916.0,-0.46612620502545604,1.05888696127596,1.6867741713450801,-0.10791713399150099,-0.0534658672545062,-0.67078459643593,0.657296448523877,0.0267747128155009,-0.777065639315537,-0.16451379457928,1.6033857689344901,1.08437897734507,0.621801289885425,0.209210718774203,0.054395914001364995,0.30196805090530604,-0.610384355760504,-0.0111685840197793,0.22161067607904003,0.14522945875429,-0.155193422608588,-0.386047830532794,-0.019162727901044996,0.53588061095157,-0.22766218008636102,0.0387309462886897,0.266651773221212,0.114305983146032,2.58'
print(' ')
if predictor.predict(data)['predictions'][0]['predicted_label'] == 0:
print('Not a Fraudulent Transaction')
else:
print('Fraudulent Transaction')
predictor.delete_endpoint()
predictor.delete_model()
