change: allow specifying model name in create_model() for TensorFlow, SKLearn, and XGBoost

src/sagemaker/sklearn/estimator.py

@@ -165,25 +165,27 @@ def create_model(
         # remove unwanted entry_point kwarg
         if "entry_point" in kwargs:
             logger.debug("removing unused entry_point argument: %s", str(kwargs["entry_point"]))
-            kwargs = {k: v for k, v in kwargs.items() if k != "entry_point"}
+            del kwargs["entry_point"]
 
         # remove image kwarg
         if "image" in kwargs:
             image = kwargs["image"]
-            kwargs = {k: v for k, v in kwargs.items() if k != "image"}
+            del kwargs["image"]
         else:
             image = None
 
         if "enable_network_isolation" not in kwargs:
             kwargs["enable_network_isolation"] = self.enable_network_isolation()
 
+        if "name" not in kwargs:
+            kwargs["name"] = self._current_job_name
+
         return SKLearnModel(
             self.model_data,
             role,
             self.entry_point,
             source_dir=self._model_source_dir(),
             enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
-            name=self._current_job_name,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

src/sagemaker/tensorflow/estimator.py

@@ -584,6 +584,12 @@ def create_model(
         """
         role = role or self.role
 
+        if "image" not in kwargs:
+            kwargs["image"] = self.image_name
+
+        if "name" not in kwargs:
+            kwargs["name"] = self._current_job_name
+
         if endpoint_type == "tensorflow-serving" or self._script_mode_enabled():
             return self._create_tfs_model(
                 role=role,
@@ -614,18 +620,9 @@ def _create_tfs_model(
         **kwargs
     ):
         """Placeholder docstring"""
-        # remove image kwarg
-        if "image" in kwargs:
-            image = kwargs["image"]
-            kwargs = {k: v for k, v in kwargs.items() if k != "image"}
-        else:
-            image = None
-
         return Model(
             model_data=self.model_data,
             role=role,
-            image=(image or self.image_name),
-            name=self._current_job_name,
             container_log_level=self.container_log_level,
             framework_version=utils.get_short_version(self.framework_version),
             sagemaker_session=self.sagemaker_session,
@@ -648,22 +645,13 @@ def _create_default_model(
         **kwargs
     ):
         """Placeholder docstring"""
-        # remove image kwarg
-        if "image" in kwargs:
-            image = kwargs["image"]
-            kwargs = {k: v for k, v in kwargs.items() if k != "image"}
-        else:
-            image = None
-
         return TensorFlowModel(
             self.model_data,
             role,
             entry_point or self.entry_point,
             source_dir=source_dir or self._model_source_dir(),
             enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             env={"SAGEMAKER_REQUIREMENTS": self.requirements_file},
-            image=(image or self.image_name),
-            name=self._current_job_name,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

src/sagemaker/xgboost/estimator.py

@@ -15,22 +15,16 @@
 
 import logging
 
-from sagemaker.estimator import Framework
+from sagemaker.estimator import Framework, _TrainingJob
 from sagemaker.fw_registry import default_framework_uri
 from sagemaker.fw_utils import (
     framework_name_from_image,
     framework_version_from_tag,
     get_unsupported_framework_version_error,
     UploadedCode,
 )
-
-
 from sagemaker.session import Session
-
-from sagemaker.estimator import _TrainingJob
-
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
-
 from sagemaker.xgboost import defaults
 from sagemaker.xgboost.model import XGBoostModel
 
@@ -154,7 +148,10 @@ def create_model(
         # Remove unwanted entry_point kwarg
         if "entry_point" in kwargs:
             logger.debug("Removing unused entry_point argument: %s", str(kwargs["entry_point"]))
-            kwargs = {k: v for k, v in kwargs.items() if k != "entry_point"}
+            del kwargs["entry_point"]
+
+        if "name" not in kwargs:
+            kwargs["name"] = self._current_job_name
 
         return XGBoostModel(
             self.model_data,
@@ -163,7 +160,6 @@ def create_model(
             framework_version=self.framework_version,
             source_dir=self._model_source_dir(),
             enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
-            name=self._current_job_name,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

tests/unit/test_sklearn.py

@@ -20,9 +20,7 @@
 from mock import Mock
 from mock import patch
 
-from sagemaker.sklearn import defaults
-from sagemaker.sklearn import SKLearn
-from sagemaker.sklearn import SKLearnPredictor, SKLearnModel
+from sagemaker.sklearn import defaults, SKLearn, SKLearnModel, SKLearnPredictor
 from sagemaker.fw_utils import UploadedCode
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
@@ -254,13 +252,18 @@ def test_create_model_with_optional_params(sagemaker_session):
     new_role = "role"
     model_server_workers = 2
     vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
+    model_name = "model-name"
     model = sklearn.create_model(
-        role=new_role, model_server_workers=model_server_workers, vpc_config_override=vpc_config
+        role=new_role,
+        model_server_workers=model_server_workers,
+        vpc_config_override=vpc_config,
+        name=model_name,
     )
 
     assert model.role == new_role
     assert model.model_server_workers == model_server_workers
     assert model.vpc_config == vpc_config
+    assert model.name == model_name
 
 
 def test_create_model_with_custom_image(sagemaker_session):

tests/unit/test_tf_estimator.py

@@ -322,17 +322,20 @@ def test_create_model_with_optional_params(sagemaker_session):
     new_role = "role"
     model_server_workers = 2
     vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
+    model_name = "model-name"
     model = tf.create_model(
         role=new_role,
         model_server_workers=model_server_workers,
         vpc_config_override=vpc_config,
         entry_point=SERVING_SCRIPT_FILE,
+        name=model_name,
     )
 
     assert model.role == new_role
     assert model.model_server_workers == model_server_workers
     assert model.vpc_config == vpc_config
     assert model.entry_point == SERVING_SCRIPT_FILE
+    assert model.name == model_name
 
 
 @patch("sagemaker.tensorflow.estimator.TensorFlow.create_model")

tests/unit/test_xgboost.py

@@ -22,8 +22,7 @@
 
 
 from sagemaker.xgboost.defaults import XGBOOST_LATEST_VERSION
-from sagemaker.xgboost import XGBoost
-from sagemaker.xgboost import XGBoostPredictor, XGBoostModel
+from sagemaker.xgboost import XGBoost, XGBoostModel, XGBoostPredictor
 
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
@@ -242,13 +241,18 @@ def test_create_model_with_optional_params(sagemaker_session):
     new_role = "role"
     model_server_workers = 2
     vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
+    model_name = "model-name"
     model = xgboost.create_model(
-        role=new_role, model_server_workers=model_server_workers, vpc_config_override=vpc_config
+        role=new_role,
+        model_server_workers=model_server_workers,
+        vpc_config_override=vpc_config,
+        name=model_name,
     )
 
     assert model.role == new_role
     assert model.model_server_workers == model_server_workers
     assert model.vpc_config == vpc_config
+    assert model.name == model_name
 
 
 def test_create_model_with_custom_image(sagemaker_session):