added unit test

Author: Basil Beirouti

tests/unit/sagemaker/model/test_model.py

@@ -687,3 +687,95 @@ def test_script_mode_model_uses_proper_sagemaker_submit_dir(repack_model, sagema
         ]
         == "/opt/ml/model/code"
     )
+
+
+model_name = model_package_name = "my-flower-detection-model"
+model_description = "This model accepts petal length, petal width, sepal length, sepal width and predicts whether flower is of type setosa, versicolor, or virginica"
+
+supported_realtime_inference_instance_types = ["ml.m4.xlarge"]
+supported_batch_transform_instance_types = ["ml.m4.xlarge"]
+
+supported_content_types = ["text/csv", "application/json", "application/jsonlines"]
+supported_response_MIME_types = ["application/json", "text/csv", "application/jsonlines"]
+
+validation_file_name = "input.csv"
+validation_input_path = "s3://" + BUCKET_NAME + "/validation-input-csv/"
+validation_output_path = "s3://" + BUCKET_NAME + "/validation-output-csv/"
+
+ValidationSpecification = {
+    "ValidationRole": "some_role",
+    "ValidationProfiles": [
+        {
+            "ProfileName": "Validation-test",
+            "TransformJobDefinition": {
+                "BatchStrategy": "SingleRecord",
+                "TransformInput": {
+                    "DataSource": {
+                        "S3DataSource": {
+                            "S3DataType": "S3Prefix",
+                            "S3Uri": validation_input_path,
+                        }
+                    },
+                    "ContentType": supported_content_types[0],
+                },
+                "TransformOutput": {
+                    "S3OutputPath": validation_output_path,
+                },
+                "TransformResources": {
+                    "InstanceType": supported_batch_transform_instance_types[0],
+                    "InstanceCount": 1,
+                },
+            },
+        },
+    ],
+}
+
+import pdb
+
+@patch("sagemaker.get_model_package_args")
+def test_call_to_get_model_package_args(get_model_package_args, sagemaker_session):
+
+    source_dir = "s3://blah/blah/blah"
+    t = Model(
+        entry_point=ENTRY_POINT_INFERENCE,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        source_dir=source_dir,
+        image_uri=IMAGE_URI,
+        model_data=MODEL_DATA,
+    )
+
+    t.register(
+        supported_content_types,
+        supported_response_MIME_types,
+        supported_realtime_inference_instance_types,
+        supported_batch_transform_instance_types,
+        marketplace_cert=True,
+        description=model_description,
+        model_package_name=model_package_name,
+        validation_specification=ValidationSpecification,
+
+    )
+
+    # check that the kwarg validation_specification was passed to the internal method 'get_model_package_args'
+    assert(
+        "validation_specification" in get_model_package_args.call_args_list[0][1],
+        "validation_specification kwarg was not passed to get_model_package_args"
+    )
+
+    # check that the kwarg validation_specification is identical to the one passed into the method 'register'
+    assert(
+        ValidationSpecification == get_model_package_args.call_args_list[0][1]["validation_specification"],
+        """ValidationSpecification from model.register method is not identical to validation_spec from 
+        get_model_package_args"""
+    )
+
+
+
+
+
+
+
+
+
+