Add docs on TensorFlowModel class usage + requirements file for serving

src/sagemaker/tensorflow/README.rst

@@ -647,6 +647,39 @@ When the ``deploy`` call finishes, the created SageMaker Endpoint is ready for p
 how to make predictions against the Endpoint, how to use different content-types in your requests, and how to extend the Web server
 functionality.
 
+Deploying directly from model artifacts
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you already have existing model artifacts, you can skip training and deploy them directly to an endpoint:
+
+.. code:: python
+
+  from sagemaker.tensorflow import TensorFlowModel
+
+  tf_model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz',
+                             role='MySageMakerRole',
+                             entry_point='entry.py',
+                             name='model_name')
+
+  predictor = tf_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')
+
+You can also optionally specify a pip requirements if you need to install additional packages into the deployed
+runtime environment by including it in your source_dir and specifying it in the 'SAGEMAKER_REQUIREMENTS' env variable:
+
+.. code:: python
+
+  from sagemaker.tensorflow import TensorFlowModel
+
+  tf_model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz',
+                             role='MySageMakerRole',
+                             entry_point='entry.py',
+                             source_dir='my_src', # directory which contains entry_point script and requirements file
+                             name='model_name',
+                             env={'SAGEMAKER_REQUIREMENTS': 'requirements.txt'} # path relative to source_dir
+                             )
+
+  predictor = tf_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')
+
 
 Making predictions against a SageMaker Endpoint
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~