Merge pull request #228282 from jhirono/jhirono-patch-1

Update how-to-secure-training-vnet.md

Author: prmerger-automator[bot]

articles/machine-learning/how-to-secure-training-vnet.md

@@ -165,20 +165,18 @@ az ml compute create --name myci --resource-group rg --workspace-name ws --vnet-
 > The following code snippet assumes that `ml_client` points to an Azure Machine Learning workspace that uses a private endpoint to participate in a VNet. For more information on using `ml_client`, see the tutorial [Azure Machine Learning in a day](tutorial-azure-ml-in-a-day.md).
 
 ```python
-from azure.ai.ml.entities import AmlCompute
-
-# specify aml compute name.
-cpu_compute_target = "cpu-cluster"
-
-try:
-    ml_client.compute.get(cpu_compute_target)
-except Exception:
-    print("Creating a new cpu compute target...")
-    compute = AmlCompute(
-        name=cpu_compute_target, size="STANDARD_D2_V2", min_instances=0, max_instances=4,
-        vnet_name="yourvnet", subnet_name="yoursubnet", enable_node_public_ip=False
-    )
-    ml_client.compute.begin_create_or_update(compute).result()
+from azure.ai.ml.entities import AmlCompute, NetworkSettings
+
+network_settings = NetworkSettings(vnet_name="<vnet-name>", subnet="<subnet-name>")
+compute = AmlCompute(
+    name=cpu_compute_target,
+    size="STANDARD_D2_V2",
+    min_instances=0,
+    max_instances=4,
+    enable_node_public_ip=False,
+    network_settings=network_settings
+)
+ml_client.begin_create_or_update(entity=compute)
 ```
 
 # [Studio](#tab/azure-studio)
@@ -268,21 +266,17 @@ az ml compute create --name myci --resource-group rg --workspace-name ws --vnet-
 > The following code snippet assumes that `ml_client` points to an Azure Machine Learning workspace that uses a private endpoint to participate in a VNet. For more information on using `ml_client`, see the tutorial [Azure Machine Learning in a day](tutorial-azure-ml-in-a-day.md).
 
 ```python
-from azure.ai.ml.entities import AmlCompute
-
-# specify aml compute name.
-cpu_compute_target = "cpu-cluster"
-
-try:
-    ml_client.compute.get(cpu_compute_target)
-except Exception:
-    print("Creating a new cpu compute target...")
-    # Replace "yourvnet" and "yoursubnet" with your VNet and subnet.
-    compute = AmlCompute(
-        name=cpu_compute_target, size="STANDARD_D2_V2", min_instances=0, max_instances=4,
-        vnet_name="yourvnet", subnet_name="yoursubnet"
-    )
-    ml_client.compute.begin_create_or_update(compute).result()
+from azure.ai.ml.entities import AmlCompute, NetworkSettings
+
+network_settings = NetworkSettings(vnet_name="<vnet-name>", subnet="<subnet-name>")
+compute = AmlCompute(
+    name=cpu_compute_target,
+    size="STANDARD_D2_V2",
+    min_instances=0,
+    max_instances=4,
+    network_settings=network_settings
+)
+ml_client.begin_create_or_update(entity=compute)
 ```
 
 # [Studio](#tab/azure-studio)
@@ -365,4 +359,4 @@ This article is part of a series on securing an Azure Machine Learning workflow.
 * [Secure the inference environment](how-to-secure-inferencing-vnet.md)
 * [Enable studio functionality](how-to-enable-studio-virtual-network.md)
 * [Use custom DNS](how-to-custom-dns.md)
-* [Use a firewall](how-to-access-azureml-behind-firewall.md)
+* [Use a firewall](how-to-access-azureml-behind-firewall.md)