Merge branch 'zwei' into tf-image-uri

Author: laurenyu

doc/frameworks/tensorflow/upgrade_from_legacy.rst

@@ -245,11 +245,10 @@ For example, if you want to use JSON serialization and deserialization:
 
 .. code:: python
 
-    from sagemaker.predictor import json_deserializer
+    from sagemaker.deserializers import JSONDeserializer
     from sagemaker.serializers import JSONSerializer
 
     predictor.serializer = JSONSerializer()
-    predictor.accept = "application/json"
-    predictor.deserializer = json_deserializer
+    predictor.deserializer = JSONDeserializer()
 
     predictor.predict(data)

src/sagemaker/amazon/ipinsights.py

@@ -16,7 +16,8 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le
-from sagemaker.predictor import Predictor, csv_serializer, json_deserializer
+from sagemaker.deserializers import JSONDeserializer
+from sagemaker.predictor import Predictor, csv_serializer
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -198,7 +199,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
             endpoint_name,
             sagemaker_session,
             serializer=csv_serializer,
-            deserializer=json_deserializer,
+            deserializer=JSONDeserializer(),
         )
 
 

src/sagemaker/deserializers.py

@@ -187,3 +187,24 @@ def deserialize(self, data, content_type):
             data.close()
 
         raise ValueError("%s cannot read content type %s." % (__class__.__name__, content_type))
+
+
+class JSONDeserializer(BaseDeserializer):
+    """Deserialize JSON data from an inference endpoint into a Python object."""
+
+    ACCEPT = "application/json"
+
+    def deserialize(self, data, content_type):
+        """Deserialize JSON data from an inference endpoint into a Python object.
+
+        Args:
+            data (botocore.response.StreamingBody): Data to be deserialized.
+            content_type (str): The MIME type of the data.
+
+        Returns:
+            object: The JSON-formatted data deserialized into a Python object.
+        """
+        try:
+            return json.load(codecs.getreader("utf-8")(data))
+        finally:
+            data.close()

src/sagemaker/mxnet/model.py

@@ -18,6 +18,7 @@
 import packaging.version
 
 import sagemaker
+from sagemaker.deserializers import JSONDeserializer
 from sagemaker.fw_utils import (
     create_image_uri,
     model_code_key_prefix,
@@ -26,7 +27,7 @@
 )
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.mxnet import defaults
-from sagemaker.predictor import Predictor, json_deserializer
+from sagemaker.predictor import Predictor
 from sagemaker.serializers import JSONSerializer
 
 logger = logging.getLogger("sagemaker")
@@ -51,7 +52,7 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 using the default AWS configuration chain.
         """
         super(MXNetPredictor, self).__init__(
-            endpoint_name, sagemaker_session, JSONSerializer(), json_deserializer
+            endpoint_name, sagemaker_session, JSONSerializer(), JSONDeserializer()
         )
 
 

src/sagemaker/predictor.py

@@ -13,13 +13,11 @@
 """Placeholder docstring"""
 from __future__ import print_function, absolute_import
 
-import codecs
 import csv
-import json
 from six import StringIO
 import numpy as np
 
-from sagemaker.content_types import CONTENT_TYPE_JSON, CONTENT_TYPE_CSV
+from sagemaker.content_types import CONTENT_TYPE_CSV
 from sagemaker.deserializers import BaseDeserializer
 from sagemaker.model_monitor import DataCaptureConfig
 from sagemaker.serializers import BaseSerializer
@@ -594,29 +592,3 @@ def _row_to_csv(obj):
     if isinstance(obj, str):
         return obj
     return ",".join(obj)
-
-
-class _JsonDeserializer(object):
-    """Placeholder docstring"""
-
-    def __init__(self):
-        """Placeholder docstring"""
-        self.accept = CONTENT_TYPE_JSON
-
-    def __call__(self, stream, content_type):
-        """Decode a JSON object into the corresponding Python object.
-
-        Args:
-            stream (stream): The response stream to be deserialized.
-            content_type (str): The content type of the response.
-
-        Returns:
-            object: Body of the response deserialized into a JSON object.
-        """
-        try:
-            return json.load(codecs.getreader("utf-8")(stream))
-        finally:
-            stream.close()
-
-
-json_deserializer = _JsonDeserializer()

src/sagemaker/tensorflow/model.py

@@ -17,8 +17,9 @@
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
+from sagemaker.deserializers import JSONDeserializer
 from sagemaker.fw_utils import create_image_uri
-from sagemaker.predictor import json_deserializer, Predictor
+from sagemaker.predictor import Predictor
 from sagemaker.serializers import JSONSerializer
 
 
@@ -32,7 +33,7 @@ def __init__(
         endpoint_name,
         sagemaker_session=None,
         serializer=JSONSerializer(),
-        deserializer=json_deserializer,
+        deserializer=JSONDeserializer(),
         content_type=None,
         model_name=None,
         model_version=None,

tests/integ/test_byo_estimator.py

@@ -86,7 +86,7 @@ def test_byo_estimator(sagemaker_session, region, cpu_instance_type, training_se
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
         predictor.serializer = fm_serializer
         predictor.content_type = "application/json"
-        predictor.deserializer = sagemaker.predictor.json_deserializer
+        predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
 
         result = predictor.predict(training_set[0][:10])
 
@@ -132,7 +132,7 @@ def test_async_byo_estimator(sagemaker_session, region, cpu_instance_type, train
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
         predictor.serializer = fm_serializer
         predictor.content_type = "application/json"
-        predictor.deserializer = sagemaker.predictor.json_deserializer
+        predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
 
         result = predictor.predict(training_set[0][:10])
 

tests/integ/test_tfs.py

@@ -188,7 +188,7 @@ def test_predict_jsons_json_content_type(tfs_predictor):
         tfs_predictor.endpoint_name,
         tfs_predictor.sagemaker_session,
         serializer=None,
-        deserializer=sagemaker.predictor.json_deserializer,
+        deserializer=sagemaker.deserializers.JSONDeserializer(),
         content_type="application/json",
         accept="application/json",
     )
@@ -205,7 +205,7 @@ def test_predict_jsons(tfs_predictor):
         tfs_predictor.endpoint_name,
         tfs_predictor.sagemaker_session,
         serializer=None,
-        deserializer=sagemaker.predictor.json_deserializer,
+        deserializer=sagemaker.deserializers.JSONDeserializer(),
         content_type="application/jsons",
         accept="application/jsons",
     )
@@ -222,7 +222,7 @@ def test_predict_jsonlines(tfs_predictor):
         tfs_predictor.endpoint_name,
         tfs_predictor.sagemaker_session,
         serializer=None,
-        deserializer=sagemaker.predictor.json_deserializer,
+        deserializer=sagemaker.deserializers.JSONDeserializer(),
         content_type="application/jsonlines",
         accept="application/jsonlines",
     )

tests/integ/test_tuner.py

@@ -25,9 +25,9 @@
 from sagemaker.amazon.amazon_estimator import get_image_uri
 from sagemaker.amazon.common import read_records
 from sagemaker.chainer import Chainer
+from sagemaker.deserializers import JSONDeserializer
 from sagemaker.estimator import Estimator
 from sagemaker.mxnet.estimator import MXNet
-from sagemaker.predictor import json_deserializer
 from sagemaker.pytorch import PyTorch
 from sagemaker.tensorflow import TensorFlow
 from sagemaker.tuner import (
@@ -891,7 +891,7 @@ def test_tuning_byo_estimator(sagemaker_session, cpu_instance_type):
         predictor = tuner.deploy(1, cpu_instance_type, endpoint_name=best_training_job)
         predictor.serializer = _fm_serializer
         predictor.content_type = "application/json"
-        predictor.deserializer = json_deserializer
+        predictor.deserializer = JSONDeserializer()
 
         result = predictor.predict(datasets.one_p_mnist()[0][:10])
 

tests/integ/test_tuner_multi_algo.py

@@ -21,8 +21,8 @@
 from sagemaker.amazon.amazon_estimator import get_image_uri
 from sagemaker.analytics import HyperparameterTuningJobAnalytics
 from sagemaker.content_types import CONTENT_TYPE_JSON
+from sagemaker.deserializers import JSONDeserializer
 from sagemaker.estimator import Estimator
-from sagemaker.predictor import json_deserializer
 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
@@ -219,7 +219,7 @@ def _create_training_inputs(sagemaker_session):
 def _make_prediction(predictor, data):
     predictor.serializer = _prediction_data_serializer
     predictor.content_type = CONTENT_TYPE_JSON
-    predictor.deserializer = json_deserializer
+    predictor.deserializer = JSONDeserializer()
     return predictor.predict(data)
 
 

tests/unit/sagemaker/test_deserializers.py

@@ -23,6 +23,7 @@
     CSVDeserializer,
     StreamDeserializer,
     NumpyDeserializer,
+    JSONDeserializer,
 )
 
 
@@ -145,3 +146,28 @@ def test_numpy_deserializer_from_npy_object_array(numpy_deserializer):
     result = numpy_deserializer.deserialize(stream, "application/x-npy")
 
     assert np.array_equal(array, result)
+
+
+@pytest.fixture
+def json_deserializer():
+    return JSONDeserializer()
+
+
+def test_json_deserializer_array(json_deserializer):
+    result = json_deserializer.deserialize(io.BytesIO(b"[1, 2, 3]"), "application/json")
+
+    assert result == [1, 2, 3]
+
+
+def test_json_deserializer_2dimensional(json_deserializer):
+    result = json_deserializer.deserialize(
+        io.BytesIO(b"[[1, 2, 3], [3, 4, 5]]"), "application/json"
+    )
+
+    assert result == [[1, 2, 3], [3, 4, 5]]
+
+
+def test_json_deserializer_invalid_data(json_deserializer):
+    with pytest.raises(ValueError) as error:
+        json_deserializer.deserialize(io.BytesIO(b"[[1]"), "application/json")
+    assert "column" in str(error)

tests/unit/test_predictor.py

@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-import io
 import json
 import os
 
@@ -21,10 +20,7 @@
 from mock import Mock, call, patch
 
 from sagemaker.predictor import Predictor
-from sagemaker.predictor import (
-    json_deserializer,
-    csv_serializer,
-)
+from sagemaker.predictor import csv_serializer
 from sagemaker.serializers import JSONSerializer
 from tests.unit import DATA_DIR
 
@@ -97,24 +93,6 @@ def test_csv_serializer_csv_reader():
         assert result == validation_data
 
 
-def test_json_deserializer_array():
-    result = json_deserializer(io.BytesIO(b"[1, 2, 3]"), "application/json")
-
-    assert result == [1, 2, 3]
-
-
-def test_json_deserializer_2dimensional():
-    result = json_deserializer(io.BytesIO(b"[[1, 2, 3], [3, 4, 5]]"), "application/json")
-
-    assert result == [[1, 2, 3], [3, 4, 5]]
-
-
-def test_json_deserializer_invalid_data():
-    with pytest.raises(ValueError) as error:
-        json_deserializer(io.BytesIO(b"[[1]"), "application/json")
-    assert "column" in str(error)
-
-
 # testing 'predict' invocations