update arm template to make workspace sku configurable (#283)

.pipelines/diabetes_regression-variables-template.yml

@@ -1,8 +1,7 @@
 # Pipeline template that defines common runtime environment variables.
 variables:
-
   # Source Config
-    # The directory containing the scripts for training, evaluating, and registering the model
+  # The directory containing the scripts for training, evaluating, and registering the model
   - name: SOURCES_DIR_TRAIN
     value: diabetes_regression
     # The path to the model training script under SOURCES_DIR_TRAIN
@@ -23,7 +22,7 @@ variables:
     value: mlopspython
   - name: DATASET_NAME
     value: diabetes_ds
-  # Uncomment DATASTORE_NAME if you have configured non default datastore to point to your data   
+  # Uncomment DATASTORE_NAME if you have configured non default datastore to point to your data
   # - name: DATASTORE_NAME
   #   value: datablobstore
   - name: DATASET_VERSION
@@ -50,25 +49,23 @@ variables:
   # The name for the (docker/webapp) scoring image
   - name: IMAGE_NAME
     value: "diabetestrained"
- 
+
     # Optional. Used by a training pipeline with R on Databricks
   - name: DB_CLUSTER_ID
     value: ""
 
   # These are the default values set in ml_service\util\env_variables.py. Uncomment and override if desired.
-    # Set to false to disable the evaluation step in the ML pipeline and register the newly trained model unconditionally.
+  # Set to false to disable the evaluation step in the ML pipeline and register the newly trained model unconditionally.
   # - name: RUN_EVALUATION
   #   value: "true"
-    # Set to false to register the model regardless of the outcome of the evaluation step in the ML pipeline.
+  # Set to false to register the model regardless of the outcome of the evaluation step in the ML pipeline.
   # - name: ALLOW_RUN_CANCEL
   #   value: "true"
 
-    # For debugging deployment issues. Specify a build id with the MODEL_BUILD_ID pipeline variable at queue time
-    # to skip training and deploy a model registered by a previous build.
+  # For debugging deployment issues. Specify a build id with the MODEL_BUILD_ID pipeline variable at queue time
+  # to skip training and deploy a model registered by a previous build.
   - name: modelbuildid
     value: $[coalesce(variables['MODEL_BUILD_ID'], variables['Build.BuildId'])]
-
-
   # Flag to allow rebuilding the AML Environment after it was built for the first time. This enables dependency updates from conda_dependencies.yaml.
   # - name: AML_REBUILD_ENVIRONMENT
   #  value: "false"