Fix integration tests

Author: Balaji Veeramani

tests/integ/test_byo_estimator.py

@@ -91,6 +91,7 @@ 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 = _FactorizationMachineSerializer()
         predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
+        predictor.content_type = "application/json"
 
         result = predictor.predict(training_set[0][:10])
 
@@ -136,6 +137,7 @@ 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 = _FactorizationMachineSerializer()
         predictor.deserializer = sagemaker.deserializers.JSONDeserializer()
+        predictor.content_type = "application/json"
 
         result = predictor.predict(training_set[0][:10])
 

tests/integ/test_tfs.py

@@ -221,7 +221,7 @@ def test_predict_jsonlines(tfs_predictor):
     predictor = sagemaker.Predictor(
         tfs_predictor.endpoint_name,
         tfs_predictor.sagemaker_session,
-        serializer=JSONLinesSerializer(),
+        serializer=IdentitySerializer(content_type="application/jsonlines"),
         deserializer=JSONLinesDeserializer(),
     )
 

tests/integ/test_tuner.py

@@ -903,6 +903,7 @@ def test_tuning_byo_estimator(sagemaker_session, cpu_instance_type):
         predictor = tuner.deploy(1, cpu_instance_type, endpoint_name=best_training_job)
         predictor.serializer = _FactorizationMachineSerializer()
         predictor.deserializer = JSONDeserializer()
+        predictor.content_type = "application/json"
 
         result = predictor.predict(datasets.one_p_mnist()[0][:10])
 

tests/integ/test_tuner_multi_algo.py

@@ -190,6 +190,7 @@ def _deploy_and_predict(sagemaker_session, tuner, data_set, cpu_instance_type):
             )
         )
         predictor = tuner.deploy(1, cpu_instance_type, endpoint_name=best_training_job)
+        predictor.content_type = "application/json"
 
         print("Making prediction using the deployed model")
         data = data_set[0][:10]