diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 02b0fae5fe..fccd62449b 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -2,6 +2,11 @@
 CHANGELOG
 =========
 
+1.4.1
+=====
+
+* bug-fix: Local Mode: Fix for non Framework containers
+
 1.4.0
 =====
 
diff --git a/README.rst b/README.rst
index 314af97888..8f0a913b82 100644
--- a/README.rst
+++ b/README.rst
@@ -48,7 +48,7 @@ You can install from source by cloning this repository and issuing a pip install
 
     git clone https://github.com/aws/sagemaker-python-sdk.git
     python setup.py sdist
-    pip install dist/sagemaker-1.4.0.tar.gz
+    pip install dist/sagemaker-1.4.1.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/setup.py b/setup.py
index 2d7143793c..09bff11b56 100644
--- a/setup.py
+++ b/setup.py
@@ -23,7 +23,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.4.0",
+      version="1.4.1",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},
diff --git a/src/sagemaker/local/image.py b/src/sagemaker/local/image.py
index d2dc232058..f4a6d74837 100644
--- a/src/sagemaker/local/image.py
+++ b/src/sagemaker/local/image.py
@@ -309,13 +309,15 @@ def _prepare_training_volumes(self, data_dir, input_data_config, hyperparameters
             else:
                 raise ValueError('Unknown URI scheme {}'.format(parsed_uri.scheme))
 
-        # If the training script directory is a local directory, mount it to the container.
-        training_dir = json.loads(hyperparameters[sagemaker.estimator.DIR_PARAM_NAME])
-        parsed_uri = urlparse(training_dir)
-        if parsed_uri.scheme == 'file':
-            volumes.append(_Volume(parsed_uri.path, '/opt/ml/code'))
-            # Also mount a directory that all the containers can access.
-            volumes.append(_Volume(shared_dir, '/opt/ml/shared'))
+        # If there is a training script directory and it is a local directory,
+        #  mount it to the container.
+        if sagemaker.estimator.DIR_PARAM_NAME in hyperparameters:
+            training_dir = json.loads(hyperparameters[sagemaker.estimator.DIR_PARAM_NAME])
+            parsed_uri = urlparse(training_dir)
+            if parsed_uri.scheme == 'file':
+                volumes.append(_Volume(parsed_uri.path, '/opt/ml/code'))
+                # Also mount a directory that all the containers can access.
+                volumes.append(_Volume(shared_dir, '/opt/ml/shared'))
 
         return volumes