change snippet names

ignore/doc-qa/how-to-set-up-training-targets/Local.py

@@ -5,7 +5,7 @@ import azureml.core
 
 print("SDK version:", azureml.core.VERSION)
 
-#<local_env>
+#<run_local>
 from azureml.core.runconfig import RunConfiguration
 
 # Edit a run configuration property on the fly.
@@ -15,7 +15,7 @@ run_local.environment.python.user_managed_dependencies = True
 
 # Choose a specific Python environment by pointing to a Python path. For example:
 # run_config.environment.python.interpreter_path = '/home/ninghai/miniconda3/envs/sdk2/bin/python'
-#</local_env>
+#</run_local>
 
 from azureml.core import Workspace
 ws = Workspace.from_config()

ignore/doc-qa/how-to-set-up-training-targets/amlcompute.py

@@ -18,7 +18,7 @@ script_folder= "./"
 exp = Experiment(workspace=ws, name=experiment_name)
 
 
-#<amlcompute_temp>
+#<run_temp_compute>
 from azureml.core.compute import ComputeTarget, AmlCompute
 
 # First, list the supported VM families for Azure Machine Learning Compute
@@ -34,7 +34,7 @@ run_temp_compute.target = "amlcompute"
 # AmlCompute is created in the same region as your workspace
 # Set the VM size for AmlCompute from the list of supported_vmsizes
 run_temp_compute.amlcompute.vm_size = 'STANDARD_D2_V2'
-#</amlcompute_temp>
+#</run_temp_compute>
 
 
 # Submit the experiment using the run configuration

ignore/doc-qa/how-to-set-up-training-targets/amlcompute2.py

@@ -16,7 +16,7 @@ script_folder= "./"
 
 exp = Experiment(workspace=ws, name=experiment_name)
 
-#<cpu_basic>
+#<cpu_cluster>
 from azureml.core.compute import ComputeTarget, AmlCompute
 from azureml.core.compute_target import ComputeTargetException
 
@@ -33,9 +33,9 @@ except ComputeTargetException:
     cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)
 
 cpu_cluster.wait_for_completion(show_output=True)
-#</cpu_basic>
+#</cpu_cluster>
 
-#<aml_runconfig>
+#<run_amlcompute>
 from azureml.core.runconfig import RunConfiguration
 from azureml.core.conda_dependencies import CondaDependencies
 from azureml.core.runconfig import DEFAULT_CPU_IMAGE
@@ -60,7 +60,7 @@ run_amlcompute.auto_prepare_environment = True
 
 # Specify CondaDependencies obj, add necessary packages
 run_amlcompute.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])
-#</aml_runconfig>
+#</run_amlcompute>
 
 # Submit the experiment using the run configuration
 #<amlcompute_submit>