diff --git a/src/unity/python/turicreate/test/test_drawing_classifier.py b/src/unity/python/turicreate/test/test_drawing_classifier.py
index c04a1a3d70..4dff9ba832 100644
--- a/src/unity/python/turicreate/test/test_drawing_classifier.py
+++ b/src/unity/python/turicreate/test/test_drawing_classifier.py
@@ -317,3 +317,9 @@ def setUpClass(self):
         super(DrawingClassifierFromScratchTest, self).setUpClass(
             warm_start=None)
 
+class DrawingClassifierUsingQuickdraw245(DrawingClassifierTest):
+    @classmethod
+    def setUpClass(self):
+        super(DrawingClassifierUsingQuickdraw245, self).setUpClass(
+            warm_start="quickdraw_245_v0")
+
diff --git a/src/unity/python/turicreate/toolkits/_pre_trained_models.py b/src/unity/python/turicreate/toolkits/_pre_trained_models.py
index 8eb71a9a2f..087f40868c 100644
--- a/src/unity/python/turicreate/toolkits/_pre_trained_models.py
+++ b/src/unity/python/turicreate/toolkits/_pre_trained_models.py
@@ -245,14 +245,11 @@ def get_model_path(self, format):
 class DrawingClassifierPreTrainedModel(object):
     def __init__(self, warm_start="auto"):
         self.model_to_filename = {
+            "auto": "drawing_classifier_pre_trained_model_245_classes_v0.params",
             "quickdraw_245_v0": "drawing_classifier_pre_trained_model_245_classes_v0.params"
         }
-        self.warm_start = "quickdraw_245_v0" if warm_start == "auto" else warm_start
-        self.source_url = (_urlparse.urljoin(
-            MODELS_URL_ROOT, self.model_to_filename[self.warm_start])
-            if warm_start == 'auto'
-            else warm_start
-            )
+        self.source_url = _urlparse.urljoin(
+            MODELS_URL_ROOT, self.model_to_filename[warm_start])
         # @TODO: Think about how to bypass the md5 checksum if the user wants to
         # provide their own pretrained model.
         self.source_md5 = "71ba78e48a852f35fb22999650f0a655"
diff --git a/src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py b/src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py
index ed6b2866dd..f72ac3357d 100644
--- a/src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py
+++ b/src/unity/python/turicreate/toolkits/drawing_classifier/drawing_classifier.py
@@ -85,11 +85,13 @@ def create(input_dataset, target, feature=None, validation_set='auto',
     warm_start : string optional
         A string to denote which pretrained model to use. Set to "auto"
         by default which uses a model trained on 245 of the 345 classes in the
-        Quick, Draw! dataset. Here is a list of all the pretrained models that
+        Quick, Draw! dataset. To disable warm start, pass in None to this 
+        argument. Here is a list of all the pretrained models that
         can be passed in as this argument:
         "auto": Uses quickdraw_245_v0
         "quickdraw_245_v0": Uses a model trained on 245 of the 345 classes in the
                          Quick, Draw! dataset.
+        None: No Warm Start
 
     batch_size: int optional
         The number of drawings per training step. If not set, a default
@@ -132,6 +134,7 @@ def create(input_dataset, target, feature=None, validation_set='auto',
     from .._mxnet import _mxnet_utils
     
     start_time = _time.time()
+    accepted_values_for_warm_start = ["auto", "quickdraw_245_v0", None]
 
     # @TODO: Should be able to automatically choose number of iterations
     # based on data size: Tracked in Github Issue #1576
@@ -226,6 +229,14 @@ def create(input_dataset, target, feature=None, validation_set='auto',
     model_params.initialize(_mx.init.Xavier(), ctx=ctx)
 
     if warm_start is not None:
+        if type(warm_start) is not str:
+            raise TypeError("'warm_start' must be a string or None. " 
+                + "'warm_start' can take in the following values: " 
+                + str(accepted_values_for_warm_start))
+        if warm_start not in accepted_values_for_warm_start:
+            raise _ToolkitError("Unrecognized value for 'warm_start': " 
+                + warm_start + ". 'warm_start' can take in the following " 
+                + "values: " + str(accepted_values_for_warm_start))
         pretrained_model = _pre_trained_models.DrawingClassifierPreTrainedModel(
             warm_start)
         pretrained_model_params_path = pretrained_model.get_model_path()