change: use image_uris.retrieve instead of fw_utils.create_image_uri for DLC frameworks

Author: laurenyu

src/sagemaker/chainer/model.py

@@ -16,8 +16,8 @@
 import logging
 
 import sagemaker
+from sagemaker import image_uris
 from sagemaker.fw_utils import (
-    create_image_uri,
     model_code_key_prefix,
     python_deprecation_warning,
     validate_version_or_image_args,
@@ -175,11 +175,12 @@ def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
             str: The appropriate image URI based on the given parameters.
 
         """
-        return create_image_uri(
-            region_name,
+        return image_uris.retrieve(
             self.__framework_name__,
-            instance_type,
-            self.framework_version,
-            self.py_version,
+            region_name,
+            version=self.framework_version,
+            py_version=self.py_version,
+            instance_type=instance_type,
             accelerator_type=accelerator_type,
+            image_scope="inference",
         )

src/sagemaker/cli/compatibility/v2/modifiers/tf_legacy_mode.py

@@ -22,7 +22,7 @@
 
 from sagemaker.cli.compatibility.v2.modifiers import framework_version, matching
 from sagemaker.cli.compatibility.v2.modifiers.modifier import Modifier
-from sagemaker import fw_utils
+from sagemaker import image_uris
 
 TF_NAMESPACES = ("sagemaker.tensorflow", "sagemaker.tensorflow.estimator")
 LEGACY_MODE_PARAMETERS = (
@@ -169,9 +169,14 @@ def _image_uri_from_args(self, keywords):
                 instance_type = kw.value.s if isinstance(kw.value, ast.Str) else None
 
         if tf_version and instance_type:
-            return fw_utils.create_image_uri(
-                self.region, "tensorflow", instance_type, tf_version, "py2"
-            )
+            return image_uris.retrieve(
+                "tensorflow",
+                self.region,
+                version=tf_version,
+                py_version="py2",
+                instance_type=instance_type,
+                image_scope="training",
+            ).replace("-scriptmode", "")
 
         return None
 

src/sagemaker/estimator.py

@@ -25,15 +25,14 @@
 from six import string_types
 from six.moves.urllib.parse import urlparse
 import sagemaker
-from sagemaker import git_utils
+from sagemaker import git_utils, image_uris
 from sagemaker.analytics import TrainingJobAnalytics
 from sagemaker.debugger import DebuggerHookConfig
 from sagemaker.debugger import TensorBoardOutputConfig  # noqa: F401 # pylint: disable=unused-import
 from sagemaker.debugger import get_rule_container_image_uri
 from sagemaker.s3 import S3Uploader
 
 from sagemaker.fw_utils import (
-    create_image_uri,
     tar_and_upload_dir,
     parse_s3_url,
     UploadedCode,
@@ -1822,12 +1821,13 @@ def train_image(self):
         """
         if self.image_uri:
             return self.image_uri
-        return create_image_uri(
-            self.sagemaker_session.boto_region_name,
+        return image_uris.retrieve(
             self.__framework_name__,
-            self.instance_type,
-            self.framework_version,  # pylint: disable=no-member
+            self.sagemaker_session.boto_region_name,
+            instance_type=self.instance_type,
+            version=self.framework_version,  # pylint: disable=no-member
             py_version=self.py_version,  # pylint: disable=no-member
+            image_scope="training",
         )
 
     @classmethod

src/sagemaker/mxnet/model.py

@@ -18,9 +18,9 @@
 import packaging.version
 
 import sagemaker
+from sagemaker import image_uris
 from sagemaker.deserializers import JSONDeserializer
 from sagemaker.fw_utils import (
-    create_image_uri,
     model_code_key_prefix,
     python_deprecation_warning,
     validate_version_or_image_args,
@@ -183,17 +183,14 @@ def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
             str: The appropriate image URI based on the given parameters.
 
         """
-        framework_name = self.__framework_name__
-        if self._is_mms_version():
-            framework_name = "{}-serving".format(framework_name)
-
-        return create_image_uri(
+        return image_uris.retrieve(
+            self.__framework_name__,
             region_name,
-            framework_name,
-            instance_type,
-            self.framework_version,
-            self.py_version,
+            version=self.framework_version,
+            py_version=self.py_version,
+            instance_type=instance_type,
             accelerator_type=accelerator_type,
+            image_scope="inference",
         )
 
     def _is_mms_version(self):

src/sagemaker/pytorch/model.py

@@ -17,9 +17,9 @@
 import packaging.version
 
 import sagemaker
+from sagemaker import image_uris
 from sagemaker.deserializers import NumpyDeserializer
 from sagemaker.fw_utils import (
-    create_image_uri,
     model_code_key_prefix,
     python_deprecation_warning,
     validate_version_or_image_args,
@@ -182,17 +182,14 @@ def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
             str: The appropriate image URI based on the given parameters.
 
         """
-        framework_name = self.__framework_name__
-        if self._is_mms_version():
-            framework_name = "{}-serving".format(framework_name)
-
-        return create_image_uri(
+        return image_uris.retrieve(
+            self.__framework_name__,
             region_name,
-            framework_name,
-            instance_type,
-            self.framework_version,
-            self.py_version,
+            version=self.framework_version,
+            py_version=self.py_version,
+            instance_type=instance_type,
             accelerator_type=accelerator_type,
+            image_scope="inference",
         )
 
     def _is_mms_version(self):

src/sagemaker/tensorflow/estimator.py

@@ -18,7 +18,7 @@
 
 from packaging import version
 
-from sagemaker import utils
+from sagemaker import image_uris, utils
 from sagemaker.debugger import DebuggerHookConfig
 from sagemaker.estimator import Framework
 import sagemaker.fw_utils as fw
@@ -34,7 +34,6 @@ class TensorFlow(Framework):
     """Handle end-to-end training and deployment of user-provided TensorFlow code."""
 
     __framework_name__ = "tensorflow"
-    _ECR_REPO_NAME = "tensorflow-scriptmode"
 
     _HIGHEST_LEGACY_MODE_ONLY_VERSION = version.Version("1.10.0")
     _HIGHEST_PYTHON_2_VERSION = version.Version("2.1.0")
@@ -151,12 +150,13 @@ def _validate_args(self, py_version):
             raise AttributeError(msg)
 
         if self.image_uri is None and self._only_legacy_mode_supported():
-            legacy_image_uri = fw.create_image_uri(
-                self.sagemaker_session.boto_region_name,
+            legacy_image_uri = image_uris.retrieve(
                 "tensorflow",
-                self.instance_type,
-                self.framework_version,
-                self.py_version,
+                self.sagemaker_session.boto_region_name,
+                instance_type=self.instance_type,
+                version=self.framework_version,
+                py_version=self.py_version,
+                image_scope="training",
             )
 
             msg = (
@@ -355,19 +355,6 @@ def _validate_and_set_debugger_configs(self):
             # Set defaults for debugging.
             self.debugger_hook_config = DebuggerHookConfig(s3_output_path=self.output_path)
 
-    def train_image(self):
-        """Placeholder docstring"""
-        if self.image_uri:
-            return self.image_uri
-
-        return fw.create_image_uri(
-            self.sagemaker_session.boto_region_name,
-            self._ECR_REPO_NAME,
-            self.instance_type,
-            self.framework_version,
-            self.py_version,
-        )
-
     def transformer(
         self,
         instance_count,

src/sagemaker/tensorflow/model.py

@@ -16,9 +16,9 @@
 import logging
 
 import sagemaker
+from sagemaker import image_uris
 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 Predictor
 from sagemaker.serializers import JSONSerializer
 
@@ -122,7 +122,7 @@ def predict(self, data, initial_args=None):
 class TensorFlowModel(sagemaker.model.FrameworkModel):
     """A ``FrameworkModel`` implementation for inference with TensorFlow Serving."""
 
-    __framework_name__ = "tensorflow-serving"
+    __framework_name__ = "tensorflow"
     LOG_LEVEL_PARAM_NAME = "SAGEMAKER_TFS_NGINX_LOGLEVEL"
     LOG_LEVEL_MAP = {
         logging.DEBUG: "debug",
@@ -286,13 +286,13 @@ def _get_image_uri(self, instance_type, accelerator_type=None):
         if self.image_uri:
             return self.image_uri
 
-        region_name = self.sagemaker_session.boto_region_name
-        return create_image_uri(
-            region_name,
+        return image_uris.retrieve(
             self.__framework_name__,
-            instance_type,
-            self.framework_version,
+            self.sagemaker_session.boto_region_name,
+            version=self.framework_version,
+            instance_type=instance_type,
             accelerator_type=accelerator_type,
+            image_scope="inference",
         )
 
     def serving_image_uri(

tests/unit/sagemaker/cli/compatibility/v2/modifiers/test_tf_legacy_mode.py

@@ -81,8 +81,8 @@ def test_node_should_be_modified_random_function_call():
 
 
 @patch("boto3.Session")
-@patch("sagemaker.fw_utils.create_image_uri", return_value=IMAGE_URI)
-def test_modify_node_set_model_dir_and_image_name(create_image_uri, boto_session):
+@patch("sagemaker.image_uris.retrieve", return_value=IMAGE_URI)
+def test_modify_node_set_model_dir_and_image_name(retrieve_image_uri, boto_session):
     boto_session.return_value.region_name = REGION_NAME
 
     tf_constructors = (
@@ -97,14 +97,19 @@ def test_modify_node_set_model_dir_and_image_name(create_image_uri, boto_session
         modifier.modify_node(node)
 
         assert "TensorFlow(image_uri='{}', model_dir=False)".format(IMAGE_URI) == pasta.dump(node)
-        create_image_uri.assert_called_with(
-            REGION_NAME, "tensorflow", "ml.m4.xlarge", "1.11.0", "py2"
+        retrieve_image_uri.assert_called_with(
+            "tensorflow",
+            REGION_NAME,
+            instance_type="ml.m4.xlarge",
+            version="1.11.0",
+            py_version="py2",
+            image_scope="training",
         )
 
 
 @patch("boto3.Session")
-@patch("sagemaker.fw_utils.create_image_uri", return_value=IMAGE_URI)
-def test_modify_node_set_image_name_from_args(create_image_uri, boto_session):
+@patch("sagemaker.image_uris.retrieve", return_value=IMAGE_URI)
+def test_modify_node_set_image_name_from_args(retrieve_image_uri, boto_session):
     boto_session.return_value.region_name = REGION_NAME
 
     tf_constructor = "TensorFlow(train_instance_type='ml.p2.xlarge', framework_version='1.4.0')"
@@ -113,7 +118,14 @@ def test_modify_node_set_image_name_from_args(create_image_uri, boto_session):
     modifier = tf_legacy_mode.TensorFlowLegacyModeConstructorUpgrader()
     modifier.modify_node(node)
 
-    create_image_uri.assert_called_with(REGION_NAME, "tensorflow", "ml.p2.xlarge", "1.4.0", "py2")
+    retrieve_image_uri.assert_called_with(
+        "tensorflow",
+        REGION_NAME,
+        instance_type="ml.p2.xlarge",
+        version="1.4.0",
+        py_version="py2",
+        image_scope="training",
+    )
 
     expected_string = (
         "TensorFlow(train_instance_type='ml.p2.xlarge', framework_version='1.4.0', "
@@ -123,8 +135,8 @@ def test_modify_node_set_image_name_from_args(create_image_uri, boto_session):
 
 
 @patch("boto3.Session", MagicMock())
-@patch("sagemaker.fw_utils.create_image_uri", return_value=IMAGE_URI)
-def test_modify_node_set_hyperparameters(create_image_uri):
+@patch("sagemaker.image_uris.retrieve", return_value=IMAGE_URI)
+def test_modify_node_set_hyperparameters(retrieve_image_uri):
     tf_constructor = """TensorFlow(
         checkpoint_path='s3://foo/bar',
         training_steps=100,
@@ -147,8 +159,8 @@ def test_modify_node_set_hyperparameters(create_image_uri):
 
 
 @patch("boto3.Session", MagicMock())
-@patch("sagemaker.fw_utils.create_image_uri", return_value=IMAGE_URI)
-def test_modify_node_preserve_other_hyperparameters(create_image_uri):
+@patch("sagemaker.image_uris.retrieve", return_value=IMAGE_URI)
+def test_modify_node_preserve_other_hyperparameters(retrieve_image_uri):
     tf_constructor = """sagemaker.tensorflow.TensorFlow(
         training_steps=100,
         evaluation_steps=10,
@@ -173,8 +185,8 @@ def test_modify_node_preserve_other_hyperparameters(create_image_uri):
 
 
 @patch("boto3.Session", MagicMock())
-@patch("sagemaker.fw_utils.create_image_uri", return_value=IMAGE_URI)
-def test_modify_node_prefer_param_over_hyperparameter(create_image_uri):
+@patch("sagemaker.image_uris.retrieve", return_value=IMAGE_URI)
+def test_modify_node_prefer_param_over_hyperparameter(retrieve_image_uri):
     tf_constructor = """sagemaker.tensorflow.TensorFlow(
         training_steps=100,
         requirements_file='source/requirements.txt',

tests/unit/sagemaker/tensorflow/test_estimator.py

@@ -187,10 +187,10 @@ def test_create_model(
         container_log_level=container_log_level,
         base_job_name=base_job_name,
         enable_network_isolation=True,
+        output_path="s3://mybucket/output",
     )
 
-    job_name = "doing something"
-    tf.fit(inputs="s3://mybucket/train", job_name=job_name)
+    tf._current_job_name = "doing something"
 
     model_name = "doing something else"
     name_from_base.return_value = model_name
@@ -233,10 +233,10 @@ def test_create_model_with_optional_params(
         base_job_name="job",
         source_dir=source_dir,
         enable_cloudwatch_metrics=enable_cloudwatch_metrics,
+        output_path="s3://mybucket/output",
     )
 
-    job_name = "doing something"
-    tf.fit(inputs="s3://mybucket/train", job_name=job_name)
+    tf._current_job_name = "doing something"
 
     new_role = "role"
     vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}