Add host commandline argument for UI and sklearn server (#27)

* Add host argument.

* Add host argument.

* Clarify the help text.

mlflow/cli.py

@@ -124,12 +124,16 @@ def run(uri, entry_point, version, param_list, experiment_id, mode, cluster_spec
 @click.option("--file-store-path", default=None,
               help="The root of the backing file store for experiment and run data. Defaults to %s."
                    % file_store._default_root_dir())
-def ui(file_store_path):
+@click.option("--host", default="127.0.0.1",
+              help="The networking interface on which the UI server listens. Defaults to "
+                   "127.0.0.1.  Use 0.0.0.0 to bind to all addresses, which is useful for running "
+                   "inside of docker.")
+def ui(file_store_path, host):
     """
     Run the MLflow tracking UI. The UI is served at http://localhost:5000.
     """
     server.handlers.store = FileStore(file_store_path)
-    server.app.run("0.0.0.0")
+    server.app.run(host)
 
 
 cli.add_command(mlflow.sklearn.commands)

mlflow/sklearn.py

@@ -76,7 +76,11 @@ def commands():
 @click.argument("model_path")
 @click.option("--run_id", "-r", metavar="RUN_ID", help="Run ID to look for the model in.")
 @click.option("--port", "-p", default=5000, help="Server port. [default: 5000]")
-def serve_model(model_path, run_id=None, port=None):
+@click.option("--host", default="127.0.0.1",
+              help="The networking interface on which the prediction server listens. Defaults to "
+                   "127.0.0.1.  Use 0.0.0.0 to bind to all addresses, which is useful for running "
+                   "inside of docker.")
+def serve_model(model_path, run_id=None, port=None, host="127.0.0.1"):
     """
     Serve a SciKit-Learn model saved with MLflow.
 
@@ -96,4 +100,4 @@ def predict():  # noqa
         result = json.dumps({"predictions": predictions.tolist()})
         return flask.Response(status=200, response=result + "\n", mimetype='application/json')
 
-    app.run(port=port)
+    app.run(host, port=port)