diff --git a/src/sagemaker/local/image.py b/src/sagemaker/local/image.py
index 7aecdf3dd6..a2a61d0b3c 100644
--- a/src/sagemaker/local/image.py
+++ b/src/sagemaker/local/image.py
@@ -43,7 +43,6 @@
 DOCKER_COMPOSE_HTTP_TIMEOUT_ENV = "COMPOSE_HTTP_TIMEOUT"
 DOCKER_COMPOSE_HTTP_TIMEOUT = "120"
 
-
 # Environment variables to be set during training
 REGION_ENV_NAME = "AWS_REGION"
 TRAINING_JOB_NAME_ENV_NAME = "TRAINING_JOB_NAME"
@@ -256,7 +255,11 @@ def retrieve_artifacts(self, compose_data, output_data_config, job_name):
         for host in self.hosts:
             volumes = compose_data["services"][str(host)]["volumes"]
             for volume in volumes:
-                host_dir, container_dir = volume.split(":")
+                if re.search(r"^[A-Za-z]:", volume):
+                    unit, host_dir, container_dir = volume.split(":")
+                    host_dir = unit + ":" + host_dir
+                else:
+                    host_dir, container_dir = volume.split(":")
                 if container_dir == "/opt/ml/model":
                     sagemaker.local.utils.recursive_copy(host_dir, model_artifacts)
                 elif container_dir == "/opt/ml/output":
@@ -639,9 +642,7 @@ def __init__(self, host_dir, container_dir=None, channel=None):
         if container_dir and channel:
             raise ValueError("container_dir and channel cannot be declared together.")
 
-        self.container_dir = (
-            container_dir if container_dir else os.path.join("/opt/ml/input/data", channel)
-        )
+        self.container_dir = container_dir if container_dir else "/opt/ml/input/data/" + channel
         self.host_dir = host_dir
         if platform.system() == "Darwin" and host_dir.startswith("/var"):
             self.host_dir = os.path.join("/private", host_dir)