feature: Remove LegacySerializer and LegacyDeserializer

src/sagemaker/predictor.py

@@ -13,9 +13,7 @@
 """Placeholder docstring"""
 from __future__ import print_function, absolute_import
 
-from sagemaker.deserializers import BaseDeserializer
 from sagemaker.model_monitor import DataCaptureConfig
-from sagemaker.serializers import BaseSerializer
 from sagemaker.session import production_variant, Session
 from sagemaker.utils import name_from_base
 
@@ -64,11 +62,6 @@ def __init__(
             accept (str): The invocation's "Accept", overriding any accept from
                 the deserializer (default: None).
         """
-        if serializer is not None and not isinstance(serializer, BaseSerializer):
-            serializer = LegacySerializer(serializer)
-        if deserializer is not None and not isinstance(deserializer, BaseDeserializer):
-            deserializer = LegacyDeserializer(deserializer)
-
         self.endpoint_name = endpoint_name
         self.sagemaker_session = sagemaker_session or Session()
         self.serializer = serializer
@@ -115,8 +108,6 @@ def _handle_response(self, 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()
@@ -149,8 +140,6 @@ def _create_request_args(self, data, initial_args=None, target_model=None, targe
             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)
 
         args["Body"] = data
@@ -403,85 +392,3 @@ def _get_model_names(self):
         )
         production_variants = endpoint_config["ProductionVariants"]
         return [d["ModelName"] for d in production_variants]
-
-
-class LegacySerializer(BaseSerializer):
-    """Wrapper that makes legacy serializers forward compatibile."""
-
-    def __init__(self, serializer):
-        """Initialize a ``LegacySerializer``.
-
-        Args:
-            serializer (callable): A legacy serializer.
-        """
-        self.serializer = serializer
-        self.content_type = getattr(serializer, "content_type", None)
-
-    def __call__(self, *args, **kwargs):
-        """Wraps the call method of the legacy serializer.
-
-        Args:
-            data (object): Data to be serialized.
-
-        Returns:
-            object: Serialized data used for a request.
-        """
-        return self.serializer(*args, **kwargs)
-
-    def serialize(self, data):
-        """Wraps the call method of the legacy serializer.
-
-        Args:
-            data (object): Data to be serialized.
-
-        Returns:
-            object: Serialized data used for a request.
-        """
-        return self.serializer(data)
-
-    @property
-    def CONTENT_TYPE(self):
-        """The MIME type of the data sent to the inference endpoint."""
-        return self.content_type
-
-
-class LegacyDeserializer(BaseDeserializer):
-    """Wrapper that makes legacy deserializers forward compatibile."""
-
-    def __init__(self, deserializer):
-        """Initialize a ``LegacyDeserializer``.
-
-        Args:
-            deserializer (callable): A legacy deserializer.
-        """
-        self.deserializer = deserializer
-        self.accept = getattr(deserializer, "accept", None)
-
-    def __call__(self, *args, **kwargs):
-        """Wraps the call method of the legacy deserializer.
-
-        Args:
-            data (object): Data to be deserialized.
-            content_type (str): The MIME type of the data.
-
-        Returns:
-            object: The data deserialized into an object.
-        """
-        return self.deserializer(*args, **kwargs)
-
-    def deserialize(self, data, content_type):
-        """Wraps the call method of the legacy deserializer.
-
-        Args:
-            data (object): Data to be deserialized.
-            content_type (str): The MIME type of the data.
-
-        Returns:
-            object: The data deserialized into an object.
-        """
-        return self.deserializer(data, content_type)
-
-    @property
-    def ACCEPT(self):
-        """The content type that is expected from the inference endpoint."""
-        return self.accept

tests/integ/test_byo_estimator.py

@@ -20,6 +20,7 @@
 import sagemaker
 from sagemaker import image_uris
 from sagemaker.estimator import Estimator
+from sagemaker.serializers import BaseSerializer
 from sagemaker.utils import unique_name_from_base
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, datasets
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
@@ -35,11 +36,15 @@ def training_set():
     return datasets.one_p_mnist()
 
 
-def fm_serializer(data):
-    js = {"instances": []}
-    for row in data:
-        js["instances"].append({"features": row.tolist()})
-    return json.dumps(js)
+class _FactorizationMachineSerializer(BaseSerializer):
+
+    CONTENT_TYPE = "application/json"
+
+    def serialize(self, data):
+        js = {"instances": []}
+        for row in data:
+            js["instances"].append({"features": row.tolist()})
+        return json.dumps(js)
 
 
 @pytest.mark.canary_quick
@@ -84,8 +89,7 @@ def test_byo_estimator(sagemaker_session, region, cpu_instance_type, training_se
     with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
         model = estimator.create_model()
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
-        predictor.serializer = fm_serializer
-        predictor.content_type = "application/json"
+        predictor.serializer = _FactorizationMachineSerializer()
         predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
 
         result = predictor.predict(training_set[0][:10])
@@ -130,8 +134,7 @@ def test_async_byo_estimator(sagemaker_session, region, cpu_instance_type, train
         )
         model = estimator.create_model()
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
-        predictor.serializer = fm_serializer
-        predictor.content_type = "application/json"
+        predictor.serializer = _FactorizationMachineSerializer()
         predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
 
         result = predictor.predict(training_set[0][:10])

tests/integ/test_tuner.py

@@ -28,6 +28,7 @@
 from sagemaker.estimator import Estimator
 from sagemaker.mxnet.estimator import MXNet
 from sagemaker.pytorch import PyTorch
+from sagemaker.serializers import BaseSerializer
 from sagemaker.tensorflow import TensorFlow
 from sagemaker.tuner import (
     IntegerParameter,
@@ -900,8 +901,7 @@ def test_tuning_byo_estimator(sagemaker_session, cpu_instance_type):
     best_training_job = tuner.best_training_job()
     with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
         predictor = tuner.deploy(1, cpu_instance_type, endpoint_name=best_training_job)
-        predictor.serializer = _fm_serializer
-        predictor.content_type = "application/json"
+        predictor.serializer = _FactorizationMachineSerializer()
         predictor.deserializer = JSONDeserializer()
 
         result = predictor.predict(datasets.one_p_mnist()[0][:10])
@@ -912,11 +912,15 @@ def test_tuning_byo_estimator(sagemaker_session, cpu_instance_type):
 
 
 # Serializer for the Factorization Machines predictor (for BYO example)
-def _fm_serializer(data):
-    js = {"instances": []}
-    for row in data:
-        js["instances"].append({"features": row.tolist()})
-    return json.dumps(js)
+class _FactorizationMachineSerializer(BaseSerializer):
+
+    CONTENT_TYPE = "application/json"
+
+    def serialize(self, data):
+        js = {"instances": []}
+        for row in data:
+            js["instances"].append({"features": row.tolist()})
+        return json.dumps(js)
 
 
 def _assert_model_name_match(sagemaker_client, endpoint_config_name, model_name):

tests/integ/test_tuner_multi_algo.py

@@ -21,6 +21,7 @@
 from sagemaker.analytics import HyperparameterTuningJobAnalytics
 from sagemaker.deserializers import JSONDeserializer
 from sagemaker.estimator import Estimator
+from sagemaker.serializers import BaseSerializer
 from sagemaker.tuner import ContinuousParameter, IntegerParameter, HyperparameterTuner
 from tests.integ import datasets, DATA_DIR, TUNING_DEFAULT_TIMEOUT_MINUTES
 from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
@@ -213,14 +214,17 @@ def _create_training_inputs(sagemaker_session):
 
 
 def _make_prediction(predictor, data):
-    predictor.serializer = _prediction_data_serializer
-    predictor.content_type = "application/json"
+    predictor.serializer = PredictionDataSerializer()
     predictor.deserializer = JSONDeserializer()
     return predictor.predict(data)
 
 
-def _prediction_data_serializer(data):
-    js = {"instances": []}
-    for row in data:
-        js["instances"].append({"features": row.tolist()})
-    return json.dumps(js)
+class PredictionDataSerializer(BaseSerializer):
+
+    CONTENT_TYPE = "application/json"
+
+    def serialize(self, data):
+        js = {"instances": []}
+        for row in data:
+            js["instances"].append({"features": row.tolist()})
+        return json.dumps(js)