breaking: refactor Predictor attribute endpoint to endpoint_name

doc/amazon_sagemaker_model_monitoring.rst

@@ -125,7 +125,7 @@ Using ``DefaultMonitor.create_monitoring_schedule()``, you can create a model mo
 
     my_monitor.create_monitoring_schedule(
         monitor_schedule_name='my-monitoring-schedule',
-        endpoint_input=predictor.endpoint,
+        endpoint_input=predictor.endpoint_name,
         statistics=my_monitor.baseline_statistics(),
         constraints=my_monitor.suggested_constraints(),
         schedule_cron_expression=CronExpressionGenerator.hourly(),

doc/frameworks/tensorflow/deploying_tensorflow_serving.rst

@@ -578,7 +578,7 @@ additional ``Predictor`` instances. Here's how:
   # ... continuing from the previous example
 
   # get the endpoint name from the default predictor
-  endpoint = predictor.endpoint
+  endpoint = predictor.endpoint_name
 
   # get a predictor for 'model2'
   model2_predictor = Predictor(endpoint, model_name='model2')

src/sagemaker/amazon/factorization_machines.py

@@ -278,14 +278,18 @@ class FactorizationMachinesPredictor(Predictor):
     https://docs.aws.amazon.com/sagemaker/latest/dg/fm-in-formats.html
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(FactorizationMachinesPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/ipinsights.py

@@ -184,14 +184,21 @@ class IPInsightsPredictor(Predictor):
     second column should contain the IPv4 address in dot notation.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(IPInsightsPredictor, self).__init__(
-            endpoint, sagemaker_session, serializer=csv_serializer, deserializer=json_deserializer
+            endpoint_name,
+            sagemaker_session,
+            serializer=csv_serializer,
+            deserializer=json_deserializer,
         )
 
 

src/sagemaker/amazon/kmeans.py

@@ -209,14 +209,18 @@ class KMeansPredictor(Predictor):
     ``closest_cluster`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(KMeansPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/knn.py

@@ -197,14 +197,18 @@ class KNNPredictor(Predictor):
     key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(KNNPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/lda.py

@@ -181,14 +181,18 @@ class LDAPredictor(Predictor):
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(LDAPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/linear_learner.py

@@ -442,14 +442,18 @@ class LinearLearnerPredictor(Predictor):
     key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(LinearLearnerPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/ntm.py

@@ -211,14 +211,18 @@ class NTMPredictor(Predictor):
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(NTMPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/pca.py

@@ -193,14 +193,18 @@ class PCAPredictor(Predictor):
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(PCAPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/amazon/randomcutforest.py

@@ -172,14 +172,18 @@ class RandomCutForestPredictor(Predictor):
     ``Record.label`` field.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """
         Args:
-            endpoint:
-            sagemaker_session:
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
+                requests are sent.
+            sagemaker_session (sagemaker.session.Session): A SageMaker Session
+                object, used for SageMaker interactions (default: None). If not
+                specified, one is created using the default AWS configuration
+                chain.
         """
         super(RandomCutForestPredictor, self).__init__(
-            endpoint,
+            endpoint_name,
             sagemaker_session,
             serializer=numpy_to_record_serializer(),
             deserializer=record_deserializer(),

src/sagemaker/predictor.py

@@ -37,7 +37,7 @@ class Predictor(object):
 
     def __init__(
         self,
-        endpoint,
+        endpoint_name,
         sagemaker_session=None,
         serializer=None,
         deserializer=None,
@@ -53,7 +53,7 @@ def __init__(
         sequence of bytes from the prediction result without any modifications.
 
         Args:
-            endpoint (str): Name of the Amazon SageMaker endpoint to which
+            endpoint_name (str): Name of the Amazon SageMaker endpoint to which
                 requests are sent.
             sagemaker_session (sagemaker.session.Session): A SageMaker Session
                 object, used for SageMaker interactions (default: None). If not
@@ -74,7 +74,7 @@ def __init__(
             accept (str): The invocation's "Accept", overriding any accept from
                 the deserializer (default: None).
         """
-        self.endpoint = endpoint
+        self.endpoint_name = endpoint_name
         self.sagemaker_session = sagemaker_session or Session()
         self.serializer = serializer
         self.deserializer = deserializer
@@ -137,7 +137,7 @@ def _create_request_args(self, data, initial_args=None, target_model=None, targe
         args = dict(initial_args) if initial_args else {}
 
         if "EndpointName" not in args:
-            args["EndpointName"] = self.endpoint
+            args["EndpointName"] = self.endpoint_name
 
         if self.content_type and "ContentType" not in args:
             args["ContentType"] = self.content_type
@@ -175,7 +175,7 @@ def delete_endpoint(self, delete_endpoint_config=True):
         if delete_endpoint_config:
             self._delete_endpoint_config()
 
-        self.sagemaker_session.delete_endpoint(self.endpoint)
+        self.sagemaker_session.delete_endpoint(self.endpoint_name)
 
     def delete_model(self):
         """Deletes the Amazon SageMaker models backing this predictor."""
@@ -225,10 +225,10 @@ def update_data_capture_config(self, data_capture_config):
                 DataCaptureConfig to update the predictor's endpoint to use.
         """
         endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(
-            EndpointName=self.endpoint
+            EndpointName=self.endpoint_name
         )
 
-        new_config_name = name_from_base(base=self.endpoint)
+        new_config_name = name_from_base(base=self.endpoint_name)
 
         data_capture_config_dict = None
         if data_capture_config is not None:
@@ -241,7 +241,7 @@ def update_data_capture_config(self, data_capture_config):
         )
 
         self.sagemaker_session.update_endpoint(
-            endpoint_name=self.endpoint, endpoint_config_name=new_config_name
+            endpoint_name=self.endpoint_name, endpoint_config_name=new_config_name
         )
 
     def list_monitors(self):
@@ -254,10 +254,10 @@ def list_monitors(self):
 
         """
         monitoring_schedules_dict = self.sagemaker_session.list_monitoring_schedules(
-            endpoint_name=self.endpoint
+            endpoint_name=self.endpoint_name
         )
         if len(monitoring_schedules_dict["MonitoringScheduleSummaries"]) == 0:
-            print("No monitors found for endpoint. endpoint: {}".format(self.endpoint))
+            print("No monitors found for endpoint. endpoint: {}".format(self.endpoint_name))
             return []
 
         monitors = []
@@ -292,7 +292,7 @@ def list_monitors(self):
     def _get_endpoint_config_name(self):
         """Placeholder docstring"""
         endpoint_desc = self.sagemaker_session.sagemaker_client.describe_endpoint(
-            EndpointName=self.endpoint
+            EndpointName=self.endpoint_name
         )
         endpoint_config_name = endpoint_desc["EndpointConfigName"]
         return endpoint_config_name

src/sagemaker/sparkml/model.py

@@ -34,7 +34,7 @@ class SparkMLPredictor(Predictor):
     list.
     """
 
-    def __init__(self, endpoint, sagemaker_session=None):
+    def __init__(self, endpoint_name, sagemaker_session=None):
         """Initializes a SparkMLPredictor which should be used with SparkMLModel
         to perform predictions against SparkML models serialized via MLeap. The
         response is returned in text/csv format which is the default response
@@ -49,7 +49,7 @@ def __init__(self, endpoint, sagemaker_session=None):
         """
         sagemaker_session = sagemaker_session or Session()
         super(SparkMLPredictor, self).__init__(
-            endpoint=endpoint,
+            endpoint_name=endpoint_name,
             sagemaker_session=sagemaker_session,
             serializer=csv_serializer,
             content_type=CONTENT_TYPE_CSV,