use gpu base image

pipelines_master.py

@@ -10,6 +10,8 @@ from azureml.core.runconfig import (
     CondaDependencies,
     RunConfiguration
 )
+from azureml.core.runconfig import DEFAULT_GPU_IMAGE
+
 from azureml.pipeline.core import Pipeline
 from azureml.pipeline.steps import PythonScriptStep
 from azureml.pipeline.core.schedule import Schedule
@@ -138,6 +140,7 @@ conda_dependencies = CondaDependencies.create(
 env = Environment("prednet")
 env.python.conda_dependencies = conda_dependencies
 env.docker.enabled = True
+env.docker.base_image = DEFAULT_GPU_IMAGE
 env.register(ws)
 
 # Runconfigs

pipelines_slave.py

@@ -35,7 +35,7 @@ def build_prednet_pipeline(dataset, ws):
 
     # folder for scripts that need to be uploaded to Aml compute target
     script_folder = "./scripts"
-    os.makedirs(script_folder)
+    os.makedirs(script_folder, exist_ok=True)
 
     shutil.copytree(
         os.path.join(base_dir, "models"),