breaking: change Model parameter order to make model_data optional

src/sagemaker/amazon/factorization_machines.py

@@ -310,8 +310,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         repo = "{}:{}".format(FactorizationMachines.repo_name, FactorizationMachines.repo_version)
         image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(FactorizationMachinesModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=FactorizationMachinesPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/ipinsights.py

@@ -216,8 +216,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         )
 
         super(IPInsightsModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=IPInsightsPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/kmeans.py

@@ -241,8 +241,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         repo = "{}:{}".format(KMeans.repo_name, KMeans.repo_version)
         image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(KMeansModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=KMeansPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/knn.py

@@ -231,8 +231,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             registry(sagemaker_session.boto_session.region_name, KNN.repo_name), repo
         )
         super(KNNModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=KNNPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/lda.py

@@ -215,8 +215,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             registry(sagemaker_session.boto_session.region_name, LDA.repo_name), repo
         )
         super(LDAModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=LDAPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/linear_learner.py

@@ -474,8 +474,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         repo = "{}:{}".format(LinearLearner.repo_name, LinearLearner.repo_version)
         image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(LinearLearnerModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=LinearLearnerPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/ntm.py

@@ -245,8 +245,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             registry(sagemaker_session.boto_session.region_name, NTM.repo_name), repo
         )
         super(NTMModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=NTMPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/object2vec.py

@@ -355,8 +355,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             registry(sagemaker_session.boto_session.region_name, Object2Vec.repo_name), repo
         )
         super(Object2VecModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=RealTimePredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/pca.py

@@ -225,8 +225,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
         repo = "{}:{}".format(PCA.repo_name, PCA.repo_version)
         image = "{}/{}".format(registry(sagemaker_session.boto_session.region_name), repo)
         super(PCAModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=PCAPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/amazon/randomcutforest.py

@@ -206,8 +206,8 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             registry(sagemaker_session.boto_session.region_name, RandomCutForest.repo_name), repo
         )
         super(RandomCutForestModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=RandomCutForestPredictor,
             sagemaker_session=sagemaker_session,

src/sagemaker/estimator.py

@@ -1398,8 +1398,8 @@ def predict_wrapper(endpoint, session):
             kwargs["enable_network_isolation"] = self.enable_network_isolation()
 
         return Model(
-            self.model_data,
             image or self.train_image(),
+            self.model_data,
             role,
             vpc_config=self.get_vpc_config(vpc_config_override),
             sagemaker_session=self.sagemaker_session,

src/sagemaker/model.py

@@ -60,8 +60,8 @@ class Model(object):
 
     def __init__(
         self,
-        model_data,
         image,
+        model_data=None,
         role=None,
         predictor_cls=None,
         env=None,
@@ -74,9 +74,9 @@ def __init__(
         """Initialize an SageMaker ``Model``.
 
         Args:
-            model_data (str): The S3 location of a SageMaker model data
-                ``.tar.gz`` file.
             image (str): A Docker image URI.
+            model_data (str): The S3 location of a SageMaker model data
+                ``.tar.gz`` file (default: None).
             role (str): An AWS IAM role (either name or full ARN). The Amazon
                 SageMaker training jobs and APIs that create Amazon SageMaker
                 endpoints use this role to access training data and model
@@ -361,6 +361,8 @@ def compile(
             )
         if job_name is None:
             raise ValueError("You must provide a compilation job name")
+        if self.model_data is None:
+            raise ValueError("You must provide an S3 path to the compressed model artifacts.")
 
         framework = framework.upper()
         framework_version = self._get_framework_version() or framework_version
@@ -778,8 +780,8 @@ def __init__(
             :class:`~sagemaker.model.Model`.
         """
         super(FrameworkModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=predictor_cls,
             env=env,

src/sagemaker/multidatamodel.py

@@ -103,8 +103,8 @@ def __init__(
         # Set the ``Model`` parameters if the model parameter is not specified
         if not self.model:
             super(MultiDataModel, self).__init__(
-                self.model_data_prefix,
                 image,
+                self.model_data_prefix,
                 role,
                 name=self.name,
                 sagemaker_session=self.sagemaker_session,

src/sagemaker/sparkml/model.py

@@ -96,8 +96,8 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
         region_name = (sagemaker_session or Session()).boto_region_name
         image = "{}/{}:{}".format(registry(region_name, framework_name), repo_name, spark_version)
         super(SparkMLModel, self).__init__(
-            model_data,
             image,
+            model_data,
             role,
             predictor_cls=SparkMLPredictor,
             sagemaker_session=sagemaker_session,

tests/unit/sagemaker/model/test_deploy.py

@@ -54,7 +54,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
     container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
     prepare_container_def.return_value = container_def
 
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
     model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
 
     name_from_image.assert_called_with(MODEL_IMAGE)
@@ -81,7 +81,7 @@ def test_deploy(name_from_image, prepare_container_def, production_variant, sage
 @patch("sagemaker.production_variant")
 def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
     model = Model(
-        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+        MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
     )
 
     production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
@@ -113,7 +113,7 @@ def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sag
 @patch("sagemaker.model.Model._create_sagemaker_model", Mock())
 @patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
 def test_deploy_endpoint_name(sagemaker_session):
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
 
     endpoint_name = "blah"
     model.deploy(
@@ -136,7 +136,7 @@ def test_deploy_endpoint_name(sagemaker_session):
 @patch("sagemaker.model.Model._create_sagemaker_model")
 def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
     model = Model(
-        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+        MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
     )
 
     tags = [{"Key": "ModelName", "Value": "TestModel"}]
@@ -157,7 +157,7 @@ def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_sessi
 @patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
 def test_deploy_kms_key(production_variant, sagemaker_session):
     model = Model(
-        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+        MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
     )
 
     key = "some-key-arn"
@@ -177,7 +177,7 @@ def test_deploy_kms_key(production_variant, sagemaker_session):
 @patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
 def test_deploy_async(production_variant, sagemaker_session):
     model = Model(
-        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+        MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
     )
 
     model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
@@ -196,7 +196,7 @@ def test_deploy_async(production_variant, sagemaker_session):
 @patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
 def test_deploy_data_capture_config(production_variant, sagemaker_session):
     model = Model(
-        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+        MODEL_IMAGE, MODEL_DATA, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
     )
 
     data_capture_config = Mock()
@@ -223,20 +223,20 @@ def test_deploy_data_capture_config(production_variant, sagemaker_session):
 @patch("sagemaker.local.LocalSession")
 def test_deploy_creates_correct_session(local_session, session):
     # We expect a LocalSession when deploying to instance_type = 'local'
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
     model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
     assert model.sagemaker_session == local_session.return_value
 
     # We expect a real Session when deploying to instance_type != local/local_gpu
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE)
     model.deploy(
         endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
     )
     assert model.sagemaker_session == session.return_value
 
 
 def test_deploy_no_role(sagemaker_session):
-    model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
+    model = Model(MODEL_IMAGE, MODEL_DATA, sagemaker_session=sagemaker_session)
 
     with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
         model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
@@ -248,8 +248,8 @@ def test_deploy_no_role(sagemaker_session):
 @patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
 def test_deploy_predictor_cls(production_variant, sagemaker_session):
     model = Model(
-        MODEL_DATA,
         MODEL_IMAGE,
+        MODEL_DATA,
         role=ROLE,
         name=MODEL_NAME,
         predictor_cls=sagemaker.predictor.RealTimePredictor,
@@ -269,7 +269,7 @@ def test_deploy_predictor_cls(production_variant, sagemaker_session):
 
 
 def test_deploy_update_endpoint(sagemaker_session):
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
     model.deploy(
         instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, update_endpoint=True
     )
@@ -300,7 +300,7 @@ def test_deploy_update_endpoint_optional_args(sagemaker_session):
     kms_key = "foo"
     data_capture_config = Mock()
 
-    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+    model = Model(MODEL_IMAGE, MODEL_DATA, role=ROLE, sagemaker_session=sagemaker_session)
     model.deploy(
         instance_type=INSTANCE_TYPE,
         initial_instance_count=INSTANCE_COUNT,