doc: update docstring for Generation two endpoint and validation message (#1402)

Author: bhaoz

src/sagemaker/amazon/kmeans.py

@@ -238,7 +238,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(KMeansPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/knn.py

@@ -229,7 +229,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(KNNPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/lda.py

@@ -213,7 +213,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(LDAPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/linear_learner.py

@@ -482,7 +482,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(LinearLearnerPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/ntm.py

@@ -246,7 +246,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(NTMPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/pca.py

@@ -226,7 +226,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(PCAPredictor, self).__init__(
             endpoint_name,

src/sagemaker/amazon/randomcutforest.py

@@ -199,7 +199,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses responses from x-recordio-protobuf format.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(RandomCutForestPredictor, self).__init__(
             endpoint_name,

src/sagemaker/base_predictor.py

@@ -442,9 +442,9 @@ def delete_endpoint(self, delete_endpoint_config=True):
     def delete_predictor(self, wait: bool = False) -> None:
         """Delete the Amazon SageMaker inference component or endpoint backing this predictor.
 
-        Delete the corresponding inference component if the endpoint is Generation2
+        Delete the corresponding inference component if the endpoint is a Generation2
         endpoint.
-        Otherwise delete the endpoint where this predictor is on.
+        Otherwise delete the endpoint where this predictor is hosted.
         """
 
         if self.component_name:
@@ -462,13 +462,15 @@ def update_predictor(
         container_startup_health_check_timeout: Optional[int] = None,
         resources: Optional[ResourceRequirements] = None,
     ):
-        """Updates the predictor to deploy a new Model specification and apply new configurations.
+        """Updates the predictor.
 
-        This is done by updating the SageMaker InferenceComponent.
+        You can deploy a new Model specification or apply new configurations. The SDK
+        applies your updates by updating the inference component that's associated with
+        the model.
 
         Args:
-            model_name (Optional[str]): The model name to use to update
-                for the predictor. (Default: None).
+            model_name (Optional[str]): The model name to use to update the
+                predictor. (Default: None).
             image_uri (Optional[str]): A Docker image URI. (Default: None).
             model_data (Optional[Union[str, dict]]): Location
                 of SageMaker model data. (Default: None).
@@ -488,8 +490,8 @@ def update_predictor(
         """
         if self.component_name is None:
             raise ValueError(
-                "No existing Inference Component; "
-                "Please ensure you deployed Inference Component first."
+                "No inference component exists for the specified model. "
+                "Ensure that you deployed the inference component, and try again."
             )
         # [TODO]: Move to a module
         request = {
@@ -561,38 +563,38 @@ def list_related_models(
             the predictor.
 
         Args:
-            variant_name_equals (str): Optional. A string that matches the variant
-                name deployed those Inference Components. (Default: None).
-            name_contains (str): Optional. A string that partially matches one or
-                more Inference Components' names. Filters InferenceComponents by name.
+            variant_name_equals (str): Optional. A string that matches the name of the
+                variant that was assigned to the inference component. (Default: None).
+            name_contains (str): Optional. A string that partially matches the names of one or
+                more inference components. Filters inference components by name.
                 (Default: None).
             creation_time_after (datetime.datetime): Optional. Use this parameter to
-                search for InferenceComponents created after a specific date and time.
+                search for inference components created after a specific date and time.
                 (Default: None).
             creation_time_before (datetime.datetime): Optional. Use this parameter to
-                search for InferenceComponents created before a specific date and time.
+                search for inference components created before a specific date and time.
                 (Default: None).
             last_modified_time_after (datetime.datetime): Optional. Use this parameter to
-                search for InferenceComponents last modified after a specific date and time.
-                (Default: None).
+                search for inference components that were last modified after a specific date
+                and time. (Default: None).
             last_modified_time_before (datetime.datetime): Optional. Use this parameter to
-                search for InferenceComponents last modified before a specific date and time.
-                (Default: None).
-            status_equals (str): Optional. The Inference Component status. Filters
-                InferenceComponents by status. (Default: None).
-            sort_order (str): Optional. The order in which InferenceComponents are listed.
+                search for inference components that were last modified before a specific date
+                and time. (Default: None).
+            status_equals (str): Optional. The inference component status. Filters
+                inference components by status. (Default: None).
+            sort_order (str): Optional. The order in which inference components are listed.
                 (Default: None).
-            sort_by (str): Optional. The value on which the InferenceComponent list is
-                sorted. (Default: None).
+            sort_order (str): Optional. The order in which inference components are listed in
+                the response. (Default: None).
             max_results (int): Optional. The maximum number of results returned by
-                ListInferenceComponents. (Default: None).
-            next_token (str): Optional. A token to resume pagination of ListInferenceComponents
+                list_related_models. (Default: None).
+            next_token (str): Optional. A token to resume pagination of list_related_models
                 results. (Default: None).
 
         Returns:
-            Tuple[List[Dict[str, Any]], Optional[str]]: A list of Amazon SageMaker Inference
-                Component objects associated to the Endpoint. If NextToken is returned,
-                there are more results available. The value of nextToken is a unique
+            Tuple[List[Dict[str, Any]], Optional[str]]: A list of Amazon SageMaker inference
+                component objects associated with the endpoint. If a next token is returned,
+                there are more results available. The value of the next token is a unique
                 pagination token.
         """
 

src/sagemaker/chainer/model.py

@@ -67,7 +67,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses the response from .npy format to numpy array.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(ChainerPredictor, self).__init__(
             endpoint_name,

src/sagemaker/compute_resource_requirements/resource_requirements.py

@@ -10,7 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-"""Defines the ResourceRequirements class that configures a compute resources for Model."""
+"""Defines the ResourceRequirements class that configures the compute resources for the Model."""
 
 from __future__ import absolute_import
 
@@ -23,14 +23,14 @@
 
 
 class ResourceRequirements(object):
-    """The class to ResourceRequirements class that configures a compute resources for Model"""
+    """Configures the compute resources for a Model."""
 
     def __init__(
         self,
         requests: Optional[Dict[str, int]] = None,
         limits: Optional[Dict[str, int]] = None,
     ):
-        """It initializes a ``ResourceRequirements`` for Amazon SageMaker Inference Component.
+        """Initializes a ResourceRequirements object for an Amazon SageMaker inference component.
 
         Args:
             requests (dict): Basic resource to be requested, including num_cpus, memory (in MB),
@@ -77,10 +77,7 @@ def __eq__(self, other) -> bool:
         return self.requests == other.requests and self.limits == other.limits
 
     def get_compute_resource_requirements(self) -> dict:
-        """Return a dict of resource requirements
-
-        It is structured as ComputeResourceRequirements in SageMaker InferenceComponent APIs.
-        """
+        """Returns a dict of resource requirements."""
         resource_requirements = {
             "MinMemoryRequiredInMb": self.min_memory,
         }

src/sagemaker/huggingface/model.py

@@ -66,7 +66,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses the response from .npy format to numpy array.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(HuggingFacePredictor, self).__init__(
             endpoint_name,

src/sagemaker/jumpstart/artifacts/resource_requirements.py

@@ -61,7 +61,7 @@ def _retrieve_default_resources(
             specified, one is created using the default AWS configuration
             chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
     Returns:
-        str: the default resource requirements to use for the model or None.
+        str: The default resource requirements to use for the model or None.
 
     Raises:
         ValueError: If the model is not available in the

src/sagemaker/jumpstart/exceptions.py

@@ -23,7 +23,7 @@
 
 NO_AVAILABLE_RESOURCE_REQUIREMENT_RECOMMENDATION_ERROR_MSG = (
     "No available compute resource requirement recommendation for model ID '{model_id}'. "
-    "Please provide the resource requirements in the deploy method."
+    "Provide the resource requirements in the deploy method."
 )
 
 INVALID_MODEL_ID_ERROR_MSG = (

src/sagemaker/jumpstart/factory/model.py

@@ -469,7 +469,7 @@ def _add_deploy_extra_kwargs(kwargs: JumpStartModelInitKwargs) -> Dict[str, Any]
 
 
 def _add_resources_to_kwargs(kwargs: JumpStartModelInitKwargs) -> JumpStartModelInitKwargs:
-    """Sets resource requirements based on default or override, returns full kwargs."""
+    """Sets the resource requirements based on the default or an override. Returns full kwargs."""
 
     kwargs.resources = kwargs.resources or resource_requirements.retrieve_default(
         region=kwargs.region,

src/sagemaker/jumpstart/model.py

@@ -551,8 +551,8 @@ def deploy(
                 this feature. (Default: None).
             managed_instance_scaling (Optional[Dict]): Managed intance scaling options,
                 if configured Amazon SageMaker will manage the instance number behind the
-                Endpoint
-            endpoint_type (EndpointType): The type of an endpoint used to deploy models.
+                endpoint.
+            endpoint_type (EndpointType): The type of endpoint used to deploy models.
                 (Default: EndpointType.GEN1).
         """
 

src/sagemaker/mxnet/model.py

@@ -68,7 +68,7 @@ def __init__(
             deserializer (callable): Optional. Default parses the response using
                 ``json.load(...)``.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(MXNetPredictor, self).__init__(
             endpoint_name,

src/sagemaker/pytorch/model.py

@@ -69,7 +69,7 @@ def __init__(
             deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
                 Default parses the response from .npy format to numpy array.
             component_name (str): Optional. Name of the Amazon SageMaker inference
-                component corresponding the predictor.
+                component corresponding to the predictor.
         """
         super(PyTorchPredictor, self).__init__(
             endpoint_name,