Merge pull request #107628 from Blackmist/hdinsight-vnet-updates

updating with info about attaching hdinsight and vms

Author: PRMerger16

articles/machine-learning/how-to-set-up-training-targets.md

@@ -9,7 +9,7 @@ ms.reviewer: sgilley
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 01/16/2020
+ms.date: 03/13/2020
 ms.custom: seodec18
 ---
 # Set up and use compute targets for model training 
@@ -20,7 +20,7 @@ With Azure Machine Learning, you can train your model on a variety of resources
 You can create and manage a compute target using the Azure Machine Learning SDK, Azure Machine Learning studio, Azure CLI or Azure Machine Learning VS Code extension. If you have compute targets that were created through another service (for example, an HDInsight cluster), you can use them by attaching them to your Azure Machine Learning workspace.
 
 In this article, you learn how to use various compute targets for model training.  The steps for all compute targets follow the same workflow:
-1. __Create__ a compute target if you don’t already have one.
+1. __Create__ a compute target if you don't already have one.
 2. __Attach__ the compute target to your workspace.
 3. __Configure__ the compute target so that it contains the Python environment and package dependencies needed by your script.
 
@@ -83,7 +83,7 @@ Use the sections below to configure these compute targets:
 
  [!code-python[](~/aml-sdk-samples/ignore/doc-qa/how-to-set-up-training-targets/local.py?name=run_local)]
 
-Now that you’ve attached the compute and configured your run, the next step is to [submit the training run](#submit).
+Now that you've attached the compute and configured your run, the next step is to [submit the training run](#submit).
 
 ### <a id="amlcompute"></a>Azure Machine Learning Compute
 
@@ -108,7 +108,7 @@ You can create Azure Machine Learning Compute as a compute target at run time. T
   [!code-python[](~/aml-sdk-samples/ignore/doc-qa/how-to-set-up-training-targets/amlcompute.py?name=run_temp_compute)]
 
 
-Now that you’ve attached the compute and configured your run, the next step is to [submit the training run](#submit).
+Now that you've attached the compute and configured your run, the next step is to [submit the training run](#submit).
 
 #### <a id="persistent"></a>Persistent compute
 
@@ -130,7 +130,7 @@ A persistent Azure Machine Learning Compute can be reused across jobs. The compu
 
    [!code-python[](~/aml-sdk-samples/ignore/doc-qa/how-to-set-up-training-targets/amlcompute2.py?name=run_amlcompute)]
 
-Now that you’ve attached the compute and configured your run, the next step is to [submit the training run](#submit).
+Now that you've attached the compute and configured your run, the next step is to [submit the training run](#submit).
 
 
 ### <a id="vm"></a>Remote virtual machines
@@ -148,15 +148,30 @@ Use the Azure Data Science Virtual Machine (DSVM)  as the Azure VM of choice for
 
 1. **Attach**: To attach an existing virtual machine as a compute target, you must provide the fully qualified domain name (FQDN), user name, and password for the virtual machine. In the example, replace \<fqdn> with the public FQDN of the VM, or the public IP address. Replace \<username> and \<password> with the SSH user name and password for the VM.
 
+    > [!IMPORTANT]
+    > The following Azure regions do not support attaching a virtual machine using the public IP address of the VM. Instead, use the Azure Resource Manager ID of the VM with the `resource_id` parameter:
+    >
+    > * US East
+    > * US West 2
+    > * US South Central
+    >
+    > The resource ID of the VM can be constructed using the subscription ID, resource group name, and VM name using the following string format: `/subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.Compute/virtualMachines/<vm_name>`.
+
+
    ```python
    from azureml.core.compute import RemoteCompute, ComputeTarget
 
    # Create the compute config 
    compute_target_name = "attach-dsvm"
-   attach_config = RemoteCompute.attach_configuration(address = "<fqdn>",
+   attach_config = RemoteCompute.attach_configuration(address='<fqdn>',
                                                     ssh_port=22,
                                                     username='<username>',
                                                     password="<password>")
+   # If in US East, US West 2, or US South Central, use the following instead:
+   # attach_config = RemoteCompute.attach_configuration(resource_id='<resource_id>',
+   #                                                 ssh_port=22,
+   #                                                 username='<username>',
+   #                                                 password="<password>")
 
    # If you authenticate with SSH keys instead, use this code:
    #                                                  ssh_port=22,
@@ -178,7 +193,7 @@ Use the Azure Data Science Virtual Machine (DSVM)  as the Azure VM of choice for
    [!code-python[](~/aml-sdk-samples/ignore/doc-qa/how-to-set-up-training-targets/dsvm.py?name=run_dsvm)]
 
 
-Now that you’ve attached the compute and configured your run, the next step is to [submit the training run](#submit).
+Now that you've attached the compute and configured your run, the next step is to [submit the training run](#submit).
 
 ### <a id="hdinsight"></a>Azure HDInsight 
 
@@ -192,6 +207,15 @@ Azure HDInsight is a popular platform for big-data analytics. The platform provi
 
 1. **Attach**: To attach an HDInsight cluster as a compute target, you must provide the hostname, user name, and password for the HDInsight cluster. The following example uses the SDK to attach a cluster to your workspace. In the example, replace \<clustername> with the name of your cluster. Replace \<username> and \<password> with the SSH user name and password for the cluster.
 
+    > [!IMPORTANT]
+    > The following Azure regions do not support attaching an HDInsight cluster using the public IP address of the cluster. Instead, use the Azure Resource Manager ID of the cluster with the `resource_id` parameter:
+    >
+    > * US East
+    > * US West 2
+    > * US South Central
+    >
+    > The resource ID of the cluster can be constructed using the subscription ID, resource group name, and cluster name using the following string format: `/subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.HDInsight/clusters/<cluster_name>`.
+
    ```python
    from azureml.core.compute import ComputeTarget, HDInsightCompute
    from azureml.exceptions import ComputeTargetException
@@ -202,6 +226,11 @@ Azure HDInsight is a popular platform for big-data analytics. The platform provi
                                                           ssh_port=22, 
                                                           username='<ssh-username>', 
                                                           password='<ssh-pwd>')
+    # If you are in US East, US West 2, or US South Central, use the following instead:
+    # attach_config = HDInsightCompute.attach_configuration(resource_id='<resource_id>',
+    #                                                      ssh_port=22, 
+    #                                                      username='<ssh-username>', 
+    #                                                      password='<ssh-pwd>')
     hdi_compute = ComputeTarget.attach(workspace=ws, 
                                        name='myhdi', 
                                        attach_configuration=attach_config)
@@ -219,7 +248,7 @@ Azure HDInsight is a popular platform for big-data analytics. The platform provi
    [!code-python[](~/aml-sdk-samples/ignore/doc-qa/how-to-set-up-training-targets/hdi.py?name=run_hdi)]
 
 
-Now that you’ve attached the compute and configured your run, the next step is to [submit the training run](#submit).
+Now that you've attached the compute and configured your run, the next step is to [submit the training run](#submit).
 
 
 ### <a id="azbatch"></a>Azure Batch 
@@ -228,9 +257,9 @@ Azure Batch is used to run large-scale parallel and high-performance computing (
 
 To attach Azure Batch as a compute target, you must use the Azure Machine Learning SDK and provide the following information:
 
--	**Azure Batch compute name**: A friendly name to be used for the compute within the workspace
--	**Azure Batch account name**: The name of the Azure Batch account
--	**Resource Group**: The resource group that contains the Azure Batch account.
+-    **Azure Batch compute name**: A friendly name to be used for the compute within the workspace
+-    **Azure Batch account name**: The name of the Azure Batch account
+-    **Resource Group**: The resource group that contains the Azure Batch account.
 
 The following code demonstrates how to attach Azure Batch as a compute target: