playing around

docs/how-to-deploy-to-aci/how-to-deploy-to-aci.py

@@ -6,10 +6,11 @@ print('SDK version' + azureml.core.VERSION)
 
 # PREREQ: load workspace info
 # import azureml.core
-#region load-workspace
+
+# <load>
 from azureml.core import Workspace
 ws = Workspace.from_config()
-#endregion
+# </load>
 
 scorepy_content = "import json\nimport numpy as np\nimport os\nimport pickle\nfrom sklearn.externals import joblib\nfrom sklearn.linear_model import LogisticRegression\n\nfrom azureml.core.model import Model\n\ndef init():\n    global model\n    # retreive the path to the model file using the model name\n    model_path = Model.get_model_path('sklearn_mnist')\n    model = joblib.load(model_path)\n\ndef run(raw_data):\n    data = np.array(json.loads(raw_data)['data'])\n    # make prediction\n    y_hat = model.predict(data)\n    return json.dumps(y_hat.tolist())"
 print(scorepy_content)
@@ -26,7 +27,7 @@ myenv.add_conda_package("scikit-learn")
 with open("myenv.yml","w") as f:
     f.write(myenv.serialize_to_string())
 
-#Region config-image
+#<config-image?
 from azureml.core.image import ContainerImage
 
 image_config = ContainerImage.image_configuration(execution_script = "score.py",
@@ -35,7 +36,7 @@ image_config = ContainerImage.image_configuration(execution_script = "score.py",
                                                   description = "Image with mnist model",
                                                   tags = {"data": "mnist", "type": "classification"}
                                                  )
-#End Region
+#</config-image>
 
 # <config-aci>
 from azureml.core.webservice import AciWebservice