Update EI section in regards to model loading within MXNet README

doc/using_mxnet.rst

@@ -1,8 +1,11 @@
 Using MXNet Estimators and Models
 ---------------------------------
 
+-----------------
+Table of Contents
+-----------------
 .. contents::
-
+    :local:
 
 
 With MXNet estimators, you can train and host MXNet models on Amazon SageMaker.
@@ -530,11 +533,31 @@ The following code-snippet shows an example custom ``model_fn`` implementation.
         """
         Load the gluon model. Called once when hosting service starts.
         :param: model_dir The directory where model files are stored.
-            :return: a model (in this case a Gluon network)
-            """
-            net = models.get_model('resnet34_v2', ctx=mx.cpu(), pretrained=False, classes=10)
-            net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
-            return net
+        :return: a model (in this case a Gluon network)
+        """
+        net = models.get_model('resnet34_v2', ctx=mx.cpu(), pretrained=False, classes=10)
+        net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
+        return net
+
+MXNet on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`__, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to load and serve your MXNet model through Amazon Elastic Inference, the MXNet context passed to your MXNet Symbol or Module object within your ``model_fn`` needs to be set to ``eia``, as shown `here <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html#ei-mxnet>`__.
+
+Based on the example above, the following code-snippet shows an example custom ``model_fn`` implementation, which enables loading and serving our MXNet model through Amazon Elastic Inference.
+
+.. code:: python
+
+    def model_fn(model_dir):
+        """
+        Load the gluon model in an Elastic Inference context. Called once when hosting service starts.
+        :param: model_dir The directory where model files are stored.
+        :return: a model (in this case a Gluon network)
+        """
+        net = models.get_model('resnet34_v2', ctx=mx.eia(), pretrained=False, classes=10)
+        net.load_params('%s/model.params' % model_dir, ctx=mx.eia())
+        return net
+
+The `default_model_fn <https://github.com/aws/sagemaker-mxnet-container/pull/55/files#diff-aabf018d906ed282a3c738377d19a8deR71>`__ will load and serve your model through Elastic Inference, if applicable, within the SageMaker MXNet containers.
+
+For more information on how to enable MXNet to interact with Amazon Elastic Inference, see `Use Elastic Inference with MXNet <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html>`__.
 
 Model serving
 ^^^^^^^^^^^^^

src/sagemaker/mxnet/README.rst

@@ -487,7 +487,7 @@ After calling ``fit``, you can call ``deploy`` on an ``MXNet`` Estimator to crea
 
 You use the SageMaker MXNet model server to host your MXNet model when you call ``deploy`` on an ``MXNet`` Estimator. The model server runs inside a SageMaker Endpoint, which your call to ``deploy`` creates. You can access the name of the Endpoint by the ``name`` property on the returned ``Predictor``.
 
-MXNet on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`_, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to ``accelerator_type`` to your ``deploy`` call.
+MXNet on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`__, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to ``accelerator_type`` to your ``deploy`` call.
 
 .. code:: python
 
@@ -525,11 +525,31 @@ The following code-snippet shows an example custom ``model_fn`` implementation.
         """
         Load the gluon model. Called once when hosting service starts.
         :param: model_dir The directory where model files are stored.
-            :return: a model (in this case a Gluon network)
-            """
-            net = models.get_model('resnet34_v2', ctx=mx.cpu(), pretrained=False, classes=10)
-            net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
-            return net
+        :return: a model (in this case a Gluon network)
+        """
+        net = models.get_model('resnet34_v2', ctx=mx.cpu(), pretrained=False, classes=10)
+        net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
+        return net
+
+MXNet on SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`__, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance. In order to load and serve your MXNet model through Amazon Elastic Inference, the MXNet context passed to your MXNet Symbol or Module object within your ``model_fn`` needs to be set to ``eia``, as shown `here <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html#ei-mxnet>`__.
+
+Based on the example above, the following code-snippet shows an example custom ``model_fn`` implementation, which enables loading and serving our MXNet model through Amazon Elastic Inference.
+
+.. code:: python
+
+    def model_fn(model_dir):
+        """
+        Load the gluon model in an Elastic Inference context. Called once when hosting service starts.
+        :param: model_dir The directory where model files are stored.
+        :return: a model (in this case a Gluon network)
+        """
+        net = models.get_model('resnet34_v2', ctx=mx.eia(), pretrained=False, classes=10)
+        net.load_params('%s/model.params' % model_dir, ctx=mx.eia())
+        return net
+
+The `default_model_fn <https://github.com/aws/sagemaker-mxnet-container/pull/55/files#diff-aabf018d906ed282a3c738377d19a8deR71>`__ will load and serve your model through Elastic Inference, if applicable, within the SageMaker MXNet containers.
+
+For more information on how to enable MXNet to interact with Amazon Elastic Inference, see `Use Elastic Inference with MXNet <https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-mxnet-elastic-inference.html>`__.
 
 Model serving
 ^^^^^^^^^^^^^