breaking: remove legacy TensorFlowModel and TensorFlowPredictor classes

doc/sagemaker.tensorflow.rst

@@ -10,22 +10,6 @@ TensorFlow Estimator
     :undoc-members:
     :show-inheritance:
 
-TensorFlow Model
-----------------
-
-.. autoclass:: sagemaker.tensorflow.model.TensorFlowModel
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-TensorFlow Predictor
---------------------
-
-.. autoclass:: sagemaker.tensorflow.model.TensorFlowPredictor
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 TensorFlow Serving Model
 ------------------------
 

src/sagemaker/cli/tensorflow.py

@@ -68,13 +68,12 @@ def create_model(self, model_url):
         Args:
             model_url:
         """
-        from sagemaker.tensorflow.model import TensorFlowModel
+        from sagemaker.tensorflow.serving import Model
 
-        return TensorFlowModel(
+        return Model(
             model_data=model_url,
             role=self.role_name,
             entry_point=self.script,
-            py_version=self.python,
             name=self.endpoint_name,
             env=self.environment,
         )

src/sagemaker/tensorflow/__init__.py

@@ -10,21 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-"""Placeholder docstring"""
+"""Classes for using TensorFlow and TensorFlow Serving with Amazon SageMaker."""
 from __future__ import absolute_import
 
-import sys
-import os
-
-# Hack to use our local copy of tensorflow_serving.apis, which contains the protobuf-generated
-# classes for tensorflow serving. Currently tensorflow_serving_api can only be pip-installed for
-# python 2.
-sys.path.append(os.path.dirname(__file__))
-
-from sagemaker.tensorflow.estimator import (  # noqa: E402, F401 # pylint: disable=wrong-import-position
-    TensorFlow,
-)
-from sagemaker.tensorflow.model import (  # noqa: E402, F401 # pylint: disable=wrong-import-position
-    TensorFlowModel,
-    TensorFlowPredictor,
-)
+from sagemaker.tensorflow.estimator import TensorFlow  # noqa: F401 (imported but unused)

src/sagemaker/tensorflow/estimator.py

@@ -23,7 +23,6 @@
 from sagemaker.estimator import Framework
 import sagemaker.fw_utils as fw
 from sagemaker.tensorflow import defaults
-from sagemaker.tensorflow.model import TensorFlowModel
 from sagemaker.tensorflow.serving import Model
 from sagemaker.transformer import Transformer
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -252,10 +251,8 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
 
     def create_model(
         self,
-        model_server_workers=None,
         role=None,
         vpc_config_override=VPC_CONFIG_DEFAULT,
-        endpoint_type=None,
         entry_point=None,
         source_dir=None,
         dependencies=None,
@@ -266,43 +263,25 @@ def create_model(
 
         Args:
             role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also
-                used during transform jobs. If not specified, the role from the Estimator will be
-                used.
-            model_server_workers (int): Optional. The number of worker processes used by the
-                inference server. If None, server will use one worker per vCPU.
+                used during transform jobs. If not specified, the role from the Estimator is used.
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the
-                model.
-                Default: use subnets and security groups from this Estimator.
+                model. Default: use subnets and security groups from this Estimator.
+
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
-            endpoint_type (str): Optional. Selects the software stack used by the inference server.
-                If  not specified, the model will be configured to use the default
-                SageMaker model server. If 'tensorflow-serving', the model will be configured to
-                use the SageMaker Tensorflow Serving container.
+
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified and
-                ``endpoint_type`` is 'tensorflow-serving', no entry point is used. If
-                ``endpoint_type`` is also ``None``, then the training entry point is used.
+                should be executed as the entry point to training (default: None).
             source_dir (str): Path (absolute or relative) to a directory with any other serving
-                source code dependencies aside from the entry point file. If not specified and
-                ``endpoint_type`` is 'tensorflow-serving', no source_dir is used. If
-                ``endpoint_type`` is also ``None``, then the model source directory from training
-                is used.
+                source code dependencies aside from the entry point file (default: None).
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
-                any additional libraries that will be exported to the container.
-                If not specified and ``endpoint_type`` is 'tensorflow-serving', ``dependencies`` is
-                set to ``None``.
-                If ``endpoint_type`` is also ``None``, then the dependencies from training are used.
-            **kwargs: Additional kwargs passed to :class:`~sagemaker.tensorflow.serving.Model`
-                and :class:`~sagemaker.tensorflow.model.TensorFlowModel` constructors.
+                any additional libraries that will be exported to the container (default: None).
+            **kwargs: Additional kwargs passed to :class:`~sagemaker.tensorflow.serving.Model`.
 
         Returns:
-            sagemaker.tensorflow.model.TensorFlowModel or sagemaker.tensorflow.serving.Model: A
-                ``Model`` object. See :class:`~sagemaker.tensorflow.serving.Model` or
-                :class:`~sagemaker.tensorflow.model.TensorFlowModel` for full details.
+            sagemaker.tensorflow.serving.Model: A ``Model`` object.
+                See :class:`~sagemaker.tensorflow.serving.Model` for full details.
         """
-        role = role or self.role
-
         if "image" not in kwargs:
             kwargs["image"] = self.image_name
 
@@ -312,41 +291,11 @@ def create_model(
         if "enable_network_isolation" not in kwargs:
             kwargs["enable_network_isolation"] = self.enable_network_isolation()
 
-        if endpoint_type == "tensorflow-serving" or self._script_mode_enabled:
-            return self._create_tfs_model(
-                role=role,
-                vpc_config_override=vpc_config_override,
-                entry_point=entry_point,
-                source_dir=source_dir,
-                dependencies=dependencies,
-                **kwargs
-            )
-
-        return self._create_default_model(
-            model_server_workers=model_server_workers,
-            role=role,
-            vpc_config_override=vpc_config_override,
-            entry_point=entry_point,
-            source_dir=source_dir,
-            dependencies=dependencies,
-            **kwargs
-        )
-
-    def _create_tfs_model(
-        self,
-        role=None,
-        vpc_config_override=VPC_CONFIG_DEFAULT,
-        entry_point=None,
-        source_dir=None,
-        dependencies=None,
-        **kwargs
-    ):
-        """Placeholder docstring"""
         return Model(
             model_data=self.model_data,
-            role=role,
+            role=role or self.role,
             container_log_level=self.container_log_level,
-            framework_version=utils.get_short_version(self.framework_version),
+            framework_version=self.framework_version,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
             entry_point=entry_point,
@@ -355,34 +304,6 @@ def _create_tfs_model(
             **kwargs
         )
 
-    def _create_default_model(
-        self,
-        model_server_workers,
-        role,
-        vpc_config_override,
-        entry_point=None,
-        source_dir=None,
-        dependencies=None,
-        **kwargs
-    ):
-        """Placeholder docstring"""
-        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,
-            container_log_level=self.container_log_level,
-            code_location=self.code_location,
-            py_version=self.py_version,
-            framework_version=self.framework_version,
-            model_server_workers=model_server_workers,
-            sagemaker_session=self.sagemaker_session,
-            vpc_config=self.get_vpc_config(vpc_config_override),
-            dependencies=dependencies or self.dependencies,
-            **kwargs
-        )
-
     def hyperparameters(self):
         """Return hyperparameters used by your custom TensorFlow code during model training."""
         hyperparameters = super(TensorFlow, self).hyperparameters()
@@ -479,9 +400,7 @@ def transformer(
         max_payload=None,
         tags=None,
         role=None,
-        model_server_workers=None,
         volume_kms_key=None,
-        endpoint_type=None,
         entry_point=None,
         vpc_config_override=VPC_CONFIG_DEFAULT,
         enable_network_isolation=None,
@@ -515,15 +434,8 @@ def transformer(
             role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also
                 used during transform jobs. If not specified, the role from the Estimator will be
                 used.
-            model_server_workers (int): Optional. The number of worker processes used by the
-                inference server. If None, server will use one worker per vCPU.
             volume_kms_key (str): Optional. KMS key ID for encrypting the volume attached to the ML
                 compute instance (default: None).
-            endpoint_type (str): Optional. Selects the software stack used by the inference server.
-                If not specified, the model will be configured to use the default
-                SageMaker model server.
-                If 'tensorflow-serving', the model will be configured to
-                use the SageMaker Tensorflow Serving container.
             entry_point (str): Path (absolute or relative) to the local Python source file which
                 should be executed as the entry point to training. If not specified and
                 ``endpoint_type`` is 'tensorflow-serving', no entry point is used. If
@@ -575,10 +487,8 @@ def transformer(
             enable_network_isolation = self.enable_network_isolation()
 
         model = self.create_model(
-            model_server_workers=model_server_workers,
             role=role,
             vpc_config_override=vpc_config_override,
-            endpoint_type=endpoint_type,
             entry_point=entry_point,
             enable_network_isolation=enable_network_isolation,
             name=model_name,