doc fix and remove framework mode tests

Author: icywang86rui

doc/using_tf.rst

@@ -443,20 +443,10 @@ After a TensorFlow estimator has been fit, it saves a TensorFlow SavedModel in
 the S3 location defined by ``output_path``. You can call ``deploy`` on a TensorFlow
 estimator to create a SageMaker Endpoint.
 
-SageMaker provides two different options for deploying TensorFlow models to a SageMaker
-Endpoint:
+Your model will be deployed to a TensorFlow Serving-based server. The server provides a super-set of the
+`TensorFlow Serving REST API <https://www.tensorflow.org/serving/api_rest>`_.
 
-- The first option uses a Python-based server that allows you to specify your own custom
-  input and output handling functions in a Python script. This is the default option.
-
-  See `Deploying to Python-based Endpoints <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/deploying_python.rst>`_ to learn how to use this option.
-
-
-- The second option uses a TensorFlow Serving-based server to provide a super-set of the
-  `TensorFlow Serving REST API <https://www.tensorflow.org/serving/api_rest>`_. This option
-  does not require (or allow) a custom python script.
-
-  See `Deploying to TensorFlow Serving Endpoints <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/deploying_tensorflow_serving.rst>`_ to learn how to use this option.
+See `Deploying to TensorFlow Serving Endpoints <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/deploying_tensorflow_serving.rst>`_ to learn how to deploy your model and make inference requests.
 
 
 SageMaker TensorFlow Docker containers

src/sagemaker/tensorflow/deploying_python.rst

@@ -26,7 +26,7 @@
 from sagemaker.tensorflow.defaults import TF_VERSION
 from sagemaker.tensorflow.model import TensorFlowModel
 from sagemaker.tensorflow.serving import Model
-from sagemaker.utils import get_config_value, get_short_version
+from sagemaker import utils
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
 logger = logging.getLogger("sagemaker")
@@ -502,7 +502,7 @@ def _create_tfs_model(self, role=None, vpc_config_override=VPC_CONFIG_DEFAULT):
             image=self.image_name,
             name=self._current_job_name,
             container_log_level=self.container_log_level,
-            framework_version=get_short_version(self.framework_version),
+            framework_version=utils.get_short_version(self.framework_version),
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
         )
@@ -566,7 +566,7 @@ def hyperparameters(self):
         return hyperparameters
 
     def _default_s3_path(self, directory, mpi=False):
-        local_code = get_config_value("local.local_code", self.sagemaker_session.config)
+        local_code = utils.get_config_value("local.local_code", self.sagemaker_session.config)
         if self.sagemaker_session.local_mode and local_code:
             return "/opt/ml/shared/{}".format(directory)
         elif mpi:

src/sagemaker/tensorflow/estimator.py

@@ -85,14 +85,14 @@ def _create_model(output_path):
 
 @pytest.mark.local_mode
 @pytest.mark.skipif(PYTHON_VERSION != "py2", reason="TensorFlow image supports only python 2.")
-def test_tf_local_mode(tf_full_version, sagemaker_local_session):
+def test_tf_local_mode(sagemaker_local_session):
     with timeout(minutes=5):
         script_path = os.path.join(DATA_DIR, "iris", "iris-dnn-classifier.py")
 
         estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
-            framework_version=tf_full_version,
+            framework_version='1.12',
             training_steps=1,
             evaluation_steps=1,
             hyperparameters={"input_tensor_name": "inputs"},
@@ -135,6 +135,7 @@ def test_tf_distributed_local_mode(sagemaker_local_session):
         estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
+            framework_version='1.12',
             training_steps=1,
             evaluation_steps=1,
             hyperparameters={"input_tensor_name": "inputs"},
@@ -176,6 +177,7 @@ def test_tf_local_data(sagemaker_local_session):
         estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
+            framework_version='1.12',
             training_steps=1,
             evaluation_steps=1,
             hyperparameters={"input_tensor_name": "inputs"},
@@ -216,6 +218,7 @@ def test_tf_local_data_local_script():
         estimator = TensorFlow(
             entry_point=script_path,
             role="SageMakerRole",
+            framework_version='1.12',
             training_steps=1,
             evaluation_steps=1,
             hyperparameters={"input_tensor_name": "inputs"},