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diff --git a/doc/source/python/python_wrapping_docker.md b/doc/source/python/python_wrapping_docker.md
@@ -1,19 +1,19 @@
 # Packaging a Python model for Seldon Core using Docker
 
+
 In this guide, we illustrate the steps needed to wrap your own python model in a docker image ready for deployment with Seldon Core using Docker.
 
 ## Step 1 - Create your source code
 
 You will need:
 
-- A python file with a class that runs your model
-- A requirements.txt with a seldon-core entry
+ * A python file with a class that runs your model
+ * A requirements.txt with a seldon-core entry
 
 We will go into detail for each of these steps:
 
 ### Python file
-
-Your source code should contain a python file which defines a class of the same name as the file. For example, looking at our skeleton python model file at `wrappers/s2i/python/test/model-template-app/MyModel.py`:
+Your source code should contain a python file which defines a class of the same name as the file. For example, looking at our skeleton python model file at ```wrappers/s2i/python/test/model-template-app/MyModel.py```:
 
 ```python
 class MyModel(object):
@@ -40,13 +40,12 @@ class MyModel(object):
         return X
 ```
 
-- The file is called MyModel.py and it defines a class MyModel
-- The class contains a predict method that takes an array (numpy) X and feature_names and returns an array of predictions.
-- You can add any required initialization inside the class init method.
-- Your return array should be at least 2-dimensional.
+ * The file is called MyModel.py and it defines a class MyModel
+ * The class contains a predict method that takes an array (numpy) X and feature_names and returns an array of predictions.
+ * You can add any required initialization inside the class init method.
+ * Your return array should be at least 2-dimensional.
 
 ### requirements.txt
-
 Populate a requirements.txt with any software dependencies your code requires. At a minimum the file should contain:
 
 ```
@@ -73,20 +72,19 @@ ENV PERSISTENCE 0
 CMD exec seldon-core-microservice $MODEL_NAME $API_TYPE --service-type $SERVICE_TYPE --persistence $PERSISTENCE
 ```
 
-## Step 3 - Build your image
 
-Use `docker build . -t $ORG/$MODEL_NAME:$TAG` to create your Docker image from source code. A simple name can be used but convention is to use the ORG/IMAGE:TAG format.
+## Step 3 - Build your image
+Use ```docker build . -t $ORG/$MODEL_NAME:$TAG``` to create your Docker image from source code. A simple name can be used but convention is to use the ORG/IMAGE:TAG format.
 
 ## Using with Keras/Tensorflow Models
 
 To ensure Keras models with the Tensorflow backend work correctly you may need to call `_make_predict_function()` on your model after it is loaded. This is because Flask may call the prediction request in a separate thread from the one that initialised your model. See the [keras issue](https://github.com/keras-team/keras/issues/6462) for further discussion.
 
 ## Environment Variables
-
 The required environment variables understood by the builder image are explained below. You can provide them in the Dockerfile or as `-e` parameters to `docker run`.
 
-### MODEL_NAME
 
+### MODEL_NAME
 The name of the class containing the model. Also the name of the python file which will be imported.
 
 ### API_TYPE
@@ -97,11 +95,11 @@ API type to create. Can be REST or GRPC
 
 The service type being created. Available options are:
 
-- MODEL
-- ROUTER
-- TRANSFORMER
-- COMBINER
-- OUTLIER_DETECTOR
+ * MODEL
+ * ROUTER
+ * TRANSFORMER
+ * COMBINER
+ * OUTLIER_DETECTOR
 
 ### PERSISTENCE
 
@@ -111,51 +109,49 @@ Set either to 0 or 1. Default is 0. If set to 1 then your model will be saved pe
 
 See [Flask - Builtin Configuration Values](https://flask.palletsprojects.com/config/#builtin-configuration-values) for possible configurations; the following are configurable when prefixed with the `FLASK_` string (e.g. `FLASK_JSON_SORT_KEYS` translates to `JSON_SORT_KEYS` in Flask):
 
-- DEBUG
-- EXPLAIN_TEMPLATE_LOADING
-- JSONIFY_PRETTYPRINT_REGULAR
-- JSON_SORT_KEYS
-- PROPAGATE_EXCEPTIONS
-- PRESERVE_CONTEXT_ON_EXCEPTION
-- SESSION_COOKIE_HTTPONLY
-- SESSION_COOKIE_SECURE
-- SESSION_REFRESH_EACH_REQUEST
-- TEMPLATES_AUTO_RELOAD
-- TESTING
-- TRAP_HTTP_EXCEPTIONS
-- TRAP_BAD_REQUEST_ERRORS
-- USE_X_SENDFILE
+ * DEBUG
+ * EXPLAIN_TEMPLATE_LOADING
+ * JSONIFY_PRETTYPRINT_REGULAR
+ * JSON_SORT_KEYS
+ * PROPAGATE_EXCEPTIONS
+ * PRESERVE_CONTEXT_ON_EXCEPTION
+ * SESSION_COOKIE_HTTPONLY
+ * SESSION_COOKIE_SECURE
+ * SESSION_REFRESH_EACH_REQUEST
+ * TEMPLATES_AUTO_RELOAD
+ * TESTING
+ * TRAP_HTTP_EXCEPTIONS
+ * TRAP_BAD_REQUEST_ERRORS
 
 ## Creating different service types
 
 ### MODEL
 
-- [A minimal skeleton for model source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/model-template-app)
-- [Example model notebooks](../examples/notebooks.html)
+ * [A minimal skeleton for model source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/model-template-app)
+ * [Example model notebooks](../examples/notebooks.html)
 
 ### ROUTER
-
-- [Description of routers in Seldon Core](../analytics/routers.html)
-- [A minimal skeleton for router source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/router-template-app)
+ * [Description of routers in Seldon Core](../analytics/routers.html)
+ * [A minimal skeleton for router source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/router-template-app)
 
 ### TRANSFORMER
 
-- [A minimal skeleton for transformer source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/transformer-template-app)
-- [Example transformers](https://github.com/SeldonIO/seldon-core/tree/master/examples/transformers)
+ * [A minimal skeleton for transformer source code](https://github.com/SeldonIO/seldon-core/tree/master/wrappers/s2i/python/test/transformer-template-app)
+ * [Example transformers](https://github.com/SeldonIO/seldon-core/tree/master/examples/transformers)
+
 
 ## Advanced Usage
 
 ### Model Class Arguments
-
-You can add arguments to your component which will be populated from the `parameters` defined in the SeldonDeloyment when you deploy your image on Kubernetes. For example, our [Python TFServing proxy](https://github.com/SeldonIO/seldon-core/tree/master/integrations/tfserving) has the class init method signature defined as below:
+You can add arguments to your component which will be populated from the ```parameters``` defined in the SeldonDeloyment when you deploy your image on Kubernetes. For example, our [Python TFServing proxy](https://github.com/SeldonIO/seldon-core/tree/master/integrations/tfserving) has the class init method signature defined as below:
 
 ```python
 class TfServingProxy(object):
 
     def __init__(self,rest_endpoint=None,grpc_endpoint=None,model_name=None,signature_name=None,model_input=None,model_output=None):
 ```
 
-These arguments can be set when deploying in a Seldon Deployment. An example can be found in the [MNIST TFServing example](https://github.com/SeldonIO/seldon-core/blob/master/examples/models/tfserving-mnist/tfserving-mnist.ipynb) where the arguments are defined in the [SeldonDeployment](https://github.com/SeldonIO/seldon-core/blob/master/examples/models/tfserving-mnist/mnist_tfserving_deployment.json.template) which is partly show below:
+These arguments can be set when deploying in a Seldon Deployment. An example can be found in the [MNIST TFServing example](https://github.com/SeldonIO/seldon-core/blob/master/examples/models/tfserving-mnist/tfserving-mnist.ipynb) where the arguments are defined in the [SeldonDeployment](https://github.com/SeldonIO/seldon-core/blob/master/examples/models/tfserving-mnist/mnist_tfserving_deployment.json.template)  which is partly show below:
 
 ```
  "graph": {
@@ -193,13 +189,14 @@ These arguments can be set when deploying in a Seldon Deployment. An example can
 },
 ```
 
-The allowable `type` values for the parameters are defined in the [proto buffer definition](https://github.com/SeldonIO/seldon-core/blob/44f7048efd0f6be80a857875058d23efc4221205/proto/seldon_deployment.proto#L117-L131).
 
-### Custom Metrics
+The allowable ```type``` values for the parameters are defined in the [proto buffer definition](https://github.com/SeldonIO/seldon-core/blob/44f7048efd0f6be80a857875058d23efc4221205/proto/seldon_deployment.proto#L117-L131).
 
-`from version 0.3`
 
-To add custom metrics to your response you can define an optional method `metrics` in your class that returns a list of metric dicts. An example is shown below:
+### Custom Metrics
+```from version 0.3```
+
+To add custom metrics to your response you can define an optional method ```metrics``` in your class that returns a list of metric dicts. An example is shown below:
 
 ```python
 class MyModel(object):
@@ -215,11 +212,10 @@ For more details on custom metrics and the format of the metric dict see [here](
 
 There is an [example notebook illustrating a model with custom metrics in python](../examples/custom_metrics.html).
 
-### Custom Request Tags
-
-`from version 0.3`
+### Custom Meta Data
+```from version 0.3```
 
-To add custom request tags data you can add an optional method `tags` which can return a dict of custom meta tags as shown in the example below:
+To add custom meta data you can add an optional method ```tags``` which can return a dict of custom meta tags as shown in the example below:
 
 ```python
 class MyModel(object):

diff --git a/python/seldon_core/wrapper.py b/python/seldon_core/wrapper.py
@@ -21,9 +21,7 @@
 logger = logging.getLogger(__name__)
 
 PRED_UNIT_ID = os.environ.get("PREDICTIVE_UNIT_ID", "0")
-METRICS_ENDPOINT = os.environ.get(
-    "PREDICTIVE_UNIT_METRICS_ENDPOINT", "/metrics"
-)
+METRICS_ENDPOINT = os.environ.get("PREDICTIVE_UNIT_METRICS_ENDPOINT", "/metrics")
 
 
 def get_rest_microservice(user_model, seldon_metrics):
@@ -131,9 +129,7 @@ def HealthPing():
     @app.route("/health/status", methods=["GET"])
     def HealthStatus():
         logger.debug("REST Health Status Request")
-        response = seldon_core.seldon_methods.health_status(
-            user_model, seldon_metrics
-        )
+        response = seldon_core.seldon_methods.health_status(user_model, seldon_metrics)
         logger.debug("REST Health Status Response: %s", response)
         return jsonify(response)
 
@@ -216,9 +212,7 @@ def __init__(self, user_model, seldon_metrics):
         self.user_model = user_model
         self.seldon_metrics = seldon_metrics
 
-        self.metadata_data = seldon_core.seldon_methods.init_metadata(
-            user_model
-        )
+        self.metadata_data = seldon_core.seldon_methods.init_metadata(user_model)
 
     def Predict(self, request_grpc, context):
         return seldon_core.seldon_methods.predict(
@@ -260,28 +254,20 @@ def ModelMetadata(self, request_grpc, context):
 
     def GraphMetadata(self, request_grpc, context):
         """GraphMetadata method of rpc Seldon service"""
-        raise NotImplementedError(
-            "GraphMetadata not available on the Model level."
-        )
+        raise NotImplementedError("GraphMetadata not available on the Model level.")
 
 
-def get_grpc_server(
-    user_model, seldon_metrics, annotations={}, trace_interceptor=None
-):
+def get_grpc_server(user_model, seldon_metrics, annotations={}, trace_interceptor=None):
     seldon_model = SeldonModelGRPC(user_model, seldon_metrics)
     options = []
     if ANNOTATION_GRPC_MAX_MSG_SIZE in annotations:
         max_msg = int(annotations[ANNOTATION_GRPC_MAX_MSG_SIZE])
-        logger.info(
-            "Setting grpc max message and receive length to %d", max_msg
-        )
+        logger.info("Setting grpc max message and receive length to %d", max_msg)
         options.append(("grpc.max_message_length", max_msg))
         options.append(("grpc.max_send_message_length", max_msg))
         options.append(("grpc.max_receive_message_length", max_msg))
 
-    server = grpc.server(
-        futures.ThreadPoolExecutor(max_workers=10), options=options
-    )
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10), options=options)
 
     if trace_interceptor:
         from grpc_opentracing.grpcext import intercept_server
@@ -291,9 +277,7 @@ def get_grpc_server(
     prediction_pb2_grpc.add_GenericServicer_to_server(seldon_model, server)
     prediction_pb2_grpc.add_ModelServicer_to_server(seldon_model, server)
     prediction_pb2_grpc.add_TransformerServicer_to_server(seldon_model, server)
-    prediction_pb2_grpc.add_OutputTransformerServicer_to_server(
-        seldon_model, server
-    )
+    prediction_pb2_grpc.add_OutputTransformerServicer_to_server(seldon_model, server)
     prediction_pb2_grpc.add_CombinerServicer_to_server(seldon_model, server)
     prediction_pb2_grpc.add_RouterServicer_to_server(seldon_model, server)
     prediction_pb2_grpc.add_SeldonServicer_to_server(seldon_model, server)