Remove content_type and accept parameters

Author: Balaji Veeramani

src/sagemaker/algorithm.py

@@ -16,7 +16,9 @@
 import sagemaker
 import sagemaker.parameter
 from sagemaker import vpc_utils
+from sagemaker.deserializers import BytesDeserializer
 from sagemaker.estimator import EstimatorBase
+from sagemaker.serializers import IdentitySerializer
 from sagemaker.transformer import Transformer
 from sagemaker.predictor import Predictor
 
@@ -251,37 +253,29 @@ def create_model(
         self,
         role=None,
         predictor_cls=None,
-        serializer=None,
-        deserializer=None,
-        content_type=None,
-        accept=None,
+        serializer=IdentitySerializer(),
+        deserializer=BytesDeserializer(),
         vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
         **kwargs
     ):
         """Create a model to deploy.
 
-        The serializer, deserializer, content_type, and accept arguments are
-        only used to define a default Predictor. They are ignored if an
-        explicit predictor class is passed in. Other arguments are passed
-        through to the Model class.
+        The serializer and deserializer are only used to define a default
+        Predictor. They are ignored if an explicit predictor class is passed in.
+        Other arguments are passed through to the Model class.
 
         Args:
             role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
                 which is also used during transform jobs. If not specified, the
                 role from the Estimator will be used.
             predictor_cls (Predictor): The predictor class to use when
                 deploying the model.
-            serializer (callable): Should accept a single argument, the input
-                data, and return a sequence of bytes. May provide a content_type
-                attribute that defines the endpoint request content type
-            deserializer (callable): Should accept two arguments, the result
-                data and the response content type, and return a sequence of
-                bytes. May provide a content_type attribute that defines the
-                endpoint response Accept content type.
-            content_type (str): The invocation ContentType, overriding any
-                content_type from the serializer
-            accept (str): The invocation Accept, overriding any accept from the
-                deserializer.
+            serializer (sagemaker.serializers.BaseSerializer): A serializer
+                object, used to encode data for an inference endpoint
+                (default: ``IdentitySerializer()``).
+            deserializer (sagemaker.deserializers.BaseDeserializer): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: ``BytesDeserializer()``).
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                 the model. Default: use subnets and security groups from this Estimator.
                 * 'Subnets' (list[str]): List of subnet ids.
@@ -300,7 +294,7 @@ def create_model(
         if predictor_cls is None:
 
             def predict_wrapper(endpoint, session):
-                return Predictor(endpoint, session, serializer, deserializer, content_type, accept)
+                return Predictor(endpoint, session, serializer, deserializer)
 
             predictor_cls = predict_wrapper
 

src/sagemaker/estimator.py

@@ -30,7 +30,9 @@
 from sagemaker.debugger import DebuggerHookConfig
 from sagemaker.debugger import TensorBoardOutputConfig  # noqa: F401 # pylint: disable=unused-import
 from sagemaker.debugger import get_rule_container_image_uri
+from sagemaker.deserializers import BytesDeserializer
 from sagemaker.s3 import S3Uploader
+from sagemaker.serializers import IdentitySerializer
 
 from sagemaker.fw_utils import (
     tar_and_upload_dir,
@@ -1343,16 +1345,14 @@ def create_model(
         role=None,
         image_uri=None,
         predictor_cls=None,
-        serializer=None,
-        deserializer=None,
-        content_type=None,
-        accept=None,
+        serializer=IdentitySerializer(),
+        deserializer=BytesDeserializer(),
         vpc_config_override=vpc_utils.VPC_CONFIG_DEFAULT,
         **kwargs
     ):
         """Create a model to deploy.
 
-        The serializer, deserializer, content_type, and accept arguments are only used to define a
+        The serializer and deserializer arguments are only used to define a
         default Predictor. They are ignored if an explicit predictor class is passed in.
         Other arguments are passed through to the Model class.
 
@@ -1364,17 +1364,12 @@ def create_model(
                 Defaults to the image used for training.
             predictor_cls (Predictor): The predictor class to use when
                 deploying the model.
-            serializer (callable): Should accept a single argument, the input
-                data, and return a sequence of bytes. May provide a content_type
-                attribute that defines the endpoint request content type
-            deserializer (callable): Should accept two arguments, the result
-                data and the response content type, and return a sequence of
-                bytes. May provide a content_type attribute that defines th
-                endpoint response Accept content type.
-            content_type (str): The invocation ContentType, overriding any
-                content_type from the serializer
-            accept (str): The invocation Accept, overriding any accept from the
-                deserializer.
+            serializer (sagemaker.serializers.BaseSerializer): A serializer
+                object, used to encode data for an inference endpoint
+                (default: ``IdentitySerializer()``).
+            deserializer (sagemaker.deserializers.BaseDeserializer): A
+                deserializer object, used to decode data from an inference
+                endpoint (default: ``BytesDeserializer()``).
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                 the model.
                 Default: use subnets and security groups from this Estimator.
@@ -1393,7 +1388,7 @@ def create_model(
         if predictor_cls is None:
 
             def predict_wrapper(endpoint, session):
-                return Predictor(endpoint, session, serializer, deserializer, content_type, accept)
+                return Predictor(endpoint, session, serializer, deserializer)
 
             predictor_cls = predict_wrapper
 

src/sagemaker/predictor.py

@@ -13,9 +13,9 @@
 """Placeholder docstring"""
 from __future__ import print_function, absolute_import
 
-from sagemaker.deserializers import BaseDeserializer
+from sagemaker.deserializers import BaseDeserializer, BytesDeserializer
 from sagemaker.model_monitor import DataCaptureConfig
-from sagemaker.serializers import BaseSerializer
+from sagemaker.serializers import BaseSerializer, IdentitySerializer
 from sagemaker.session import production_variant, Session
 from sagemaker.utils import name_from_base
 
@@ -33,10 +33,8 @@ def __init__(
         self,
         endpoint_name,
         sagemaker_session=None,
-        serializer=None,
-        deserializer=None,
-        content_type=None,
-        accept=None,
+        serializer=IdentitySerializer(),
+        deserializer=BytesDeserializer(),
     ):
         """Initialize a ``Predictor``.
 
@@ -55,14 +53,10 @@ def __init__(
                 chain.
             serializer (sagemaker.serializers.BaseSerializer): A serializer
                 object, used to encode data for an inference endpoint
-                (default: None).
+                (default: ``IdentitySerializer()``).
             deserializer (sagemaker.deserializers.BaseDeserializer): A
                 deserializer object, used to decode data from an inference
-                endpoint (default: None).
-            content_type (str): The invocation's "ContentType", overriding any
-                ``CONTENT_TYPE`` from the serializer (default: None).
-            accept (str): The invocation's "Accept", overriding any accept from
-                the deserializer (default: None).
+                endpoint (default: ``BytesDeserializer()``).
         """
         if serializer is not None and not isinstance(serializer, BaseSerializer):
             serializer = LegacySerializer(serializer)
@@ -73,8 +67,8 @@ def __init__(
         self.sagemaker_session = sagemaker_session or Session()
         self.serializer = serializer
         self.deserializer = deserializer
-        self.content_type = content_type or getattr(serializer, "CONTENT_TYPE", None)
-        self.accept = accept or getattr(deserializer, "ACCEPT", None)
+        self.content_type = serializer.CONTENT_TYPE
+        self.accept = deserializer.ACCEPT
         self._endpoint_config_name = self._get_endpoint_config_name()
         self._model_names = self._get_model_names()
 
@@ -114,14 +108,10 @@ def _handle_response(self, response):
             response:
         """
         response_body = response["Body"]
-        if self.deserializer is not None:
-            if not isinstance(self.deserializer, BaseDeserializer):
-                self.deserializer = LegacyDeserializer(self.deserializer)
-            # It's the deserializer's responsibility to close the stream
-            return self.deserializer.deserialize(response_body, response["ContentType"])
-        data = response_body.read()
-        response_body.close()
-        return data
+        content_type = response.get("ContentType", "application/octet-stream")
+        if not isinstance(self.deserializer, BaseDeserializer):
+            self.deserializer = LegacyDeserializer(self.deserializer)
+        return self.deserializer.deserialize(response_body, content_type)
 
     def _create_request_args(self, data, initial_args=None, target_model=None, target_variant=None):
         """
@@ -136,10 +126,10 @@ def _create_request_args(self, data, initial_args=None, target_model=None, targe
         if "EndpointName" not in args:
             args["EndpointName"] = self.endpoint_name
 
-        if self.content_type and "ContentType" not in args:
+        if "ContentType" not in args:
             args["ContentType"] = self.content_type
 
-        if self.accept and "Accept" not in args:
+        if "Accept" not in args:
             args["Accept"] = self.accept
 
         if target_model:
@@ -148,10 +138,9 @@ def _create_request_args(self, data, initial_args=None, target_model=None, targe
         if target_variant:
             args["TargetVariant"] = target_variant
 
-        if self.serializer is not None:
-            if not isinstance(self.serializer, BaseSerializer):
-                self.serializer = LegacySerializer(self.serializer)
-            data = self.serializer.serialize(data)
+        if not isinstance(self.serializer, BaseSerializer):
+            self.serializer = LegacySerializer(self.serializer)
+        data = self.serializer.serialize(data)
 
         args["Body"] = data
         return args

src/sagemaker/serializers.py

@@ -183,3 +183,20 @@ def serialize(self, data):
             return json.dumps(data.tolist())
 
         return json.dumps(data)
+
+
+class IdentitySerializer(BaseSerializer):
+    """Serialize data by returning data without modification."""
+
+    CONTENT_TYPE = "application/json"
+
+    def serialize(self, data):
+        """Return data without modification.
+
+        Args:
+            data (object): Data to be serialized.
+
+        Returns:
+            object: The unmodified data.
+        """
+        return data

src/sagemaker/sparkml/model.py

@@ -14,7 +14,6 @@
 from __future__ import absolute_import
 
 from sagemaker import Model, Predictor, Session, image_uris
-from sagemaker.content_types import CONTENT_TYPE_CSV
 from sagemaker.serializers import CSVSerializer
 
 framework_name = "sparkml-serving"
@@ -50,7 +49,6 @@ def __init__(self, endpoint_name, sagemaker_session=None):
             endpoint_name=endpoint_name,
             sagemaker_session=sagemaker_session,
             serializer=CSVSerializer(),
-            content_type=CONTENT_TYPE_CSV,
         )
 
 

src/sagemaker/tensorflow/model.py

@@ -34,7 +34,6 @@ def __init__(
         sagemaker_session=None,
         serializer=JSONSerializer(),
         deserializer=JSONDeserializer(),
-        content_type=None,
         model_name=None,
         model_version=None,
     ):
@@ -52,9 +51,6 @@ def __init__(
                 json. Handles dicts, lists, and numpy arrays.
             deserializer (callable): Optional. Default parses the response using
                 ``json.load(...)``.
-            content_type (str): Optional. The "ContentType" for invocation
-                requests. If specified, overrides the ``content_type`` from the
-                serializer (default: None).
             model_name (str): Optional. The name of the SavedModel model that
                 should handle the request. If not specified, the endpoint's
                 default model will handle the request.
@@ -63,7 +59,7 @@ def __init__(
                 version of the model will be used.
         """
         super(TensorFlowPredictor, self).__init__(
-            endpoint_name, sagemaker_session, serializer, deserializer, content_type
+            endpoint_name, sagemaker_session, serializer, deserializer,
         )
 
         attributes = []

tests/unit/sagemaker/tensorflow/test_tfs.py

@@ -20,7 +20,7 @@
 import pytest
 from mock import Mock, patch
 
-from sagemaker.serializers import CSVSerializer
+from sagemaker.serializers import CSVSerializer, IdentitySerializer
 from sagemaker.tensorflow import TensorFlow, TensorFlowModel, TensorFlowPredictor
 
 JSON_CONTENT_TYPE = "application/json"
@@ -318,9 +318,8 @@ def test_predictor(sagemaker_session):
 
 
 def test_predictor_jsons(sagemaker_session):
-    predictor = TensorFlowPredictor(
-        "endpoint", sagemaker_session, serializer=None, content_type="application/jsons"
-    )
+    predictor = TensorFlowPredictor("endpoint", sagemaker_session, serializer=IdentitySerializer())
+    predictor.content_type = "application/jsons"
 
     mock_response(json.dumps(PREDICT_RESPONSE).encode("utf-8"), sagemaker_session)
     result = predictor.predict("[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]")