Merge pull request #211247 from Blackmist/dennis-updates

v2 updates

Author: JamesJBarnett

articles/machine-learning/how-to-manage-rest.md

@@ -2,12 +2,13 @@
 title: Use REST to manage ML resources
 titleSuffix: Azure Machine Learning
 description: How to use REST APIs to create, run, and delete Azure Machine Learning resources, such as a workspace, or register models.
-author: blackmist
-ms.author: larryfr
+author: deeikele
+ms.author: deeikele
+ms.reviewer: larryfr
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
-ms.date: 07/28/2022
+ms.date: 09/14/2022
 ms.topic: how-to
 ms.custom: devx-track-python
 ---
@@ -71,7 +72,7 @@ The response should provide an access token good for one hour:
 }
 ```
 
-Make note of the token, as you'll use it to authenticate all additional administrative requests. You'll do so by setting an Authorization header in all requests:
+Make note of the token, as you'll use it to authenticate all administrative requests. You'll do so by setting an Authorization header in all requests:
 
 ```bash
 curl -h "Authorization:Bearer <YOUR-ACCESS-TOKEN>" ...more args...
@@ -340,17 +341,17 @@ providers/Microsoft.Storage/storageAccounts/<YOUR-STORAGE-ACCOUNT-NAME>"
 
 ## Create a workspace using customer-managed encryption keys
 
-By default, metadata for the workspace is stored in an Azure Cosmos DB instance that Microsoft maintains. This data is encrypted using Microsoft-managed keys. Instead of using the Microsoft-managed key, you can also provide your own key. Doing so creates an [additional set of resources](./concept-data-encryption.md#azure-cosmos-db) in your Azure subscription to store your data.
+By default, metadata for the workspace is stored in an Azure Cosmos DB instance that Microsoft maintains. This data is encrypted using Microsoft-managed keys. Instead of using the Microsoft-managed key, you can also provide your own key. Doing so creates an [another set of resources](./concept-data-encryption.md#azure-cosmos-db) in your Azure subscription to store your data.
 
-To create a workspaces that uses your keys for encryption, you need to meet the following prerequisites:
+To create a workspace that uses your keys for encryption, you need to meet the following prerequisites:
 
 * The Azure Machine Learning service principal must have contributor access to your Azure subscription.
 * You must have an existing Azure Key Vault that contains an encryption key.
-* The Azure Key Vault must exist in the same Azure region where you will create the Azure Machine Learning workspace.
-* The Azure Key Vault must have soft delete and purge protection enabled to protect against data loss in case of accidental deletion.
+* The Azure Key Vault must exist in the same Azure region where you'll create the Azure Machine Learning workspace.
+* The Azure Key Vault must have soft delete and purge protection enabled to protect against data loss if there was accidental deletion.
 * You must have an access policy in Azure Key Vault that grants get, wrap, and unwrap access to the Azure Cosmos DB application.
 
-To create a workspaces that uses a user-assigned managed identity and customer-managed keys for encryption, use the below request body. When using an user-assigned managed identity for the workspace, also set the `userAssignedIdentity` property to the resource ID of the managed identity.
+To create a workspace that uses a user-assigned managed identity and customer-managed keys for encryption, use the below request body. When using a user-assigned managed identity for the workspace, also set the `userAssignedIdentity` property to the resource ID of the managed identity.
 
 ```bash
 curl -X PUT \

articles/machine-learning/how-to-workspace-diagnostic-api.md

@@ -8,21 +8,26 @@ ms.subservice: enterprise-readiness
 ms.author: jhirono
 author: jhirono
 ms.reviewer: larryfr
-ms.date: 11/18/2021
+ms.date: 09/14/2022
 ms.topic: how-to
 ms.custom: sdkv1, event-tier1-build-2022
 ---
 
 # How to use workspace diagnostics
 
+[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
+> [!div class="op_single_selector" title1="Select the version of the Azure Machine Learning Python SDK you are using:"]
+> * [v1](v1/how-to-workspace-diagnostic-api.md)
+> * [v2 (current version)](how-to-workspace-diagnostic-api.md)
+
 Azure Machine Learning provides a diagnostic API that can be used to identify problems with your workspace. Errors returned in the diagnostics report include information on how to resolve the problem.
 
 You can use the workspace diagnostics from the Azure Machine Learning studio or Python SDK.
 
 ## Prerequisites
 
-* An Azure Machine learning workspace. If you don't have one, see [Create a workspace](quickstart-create-resources.md).
-* The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml).
+[!INCLUDE [sdk](../../includes/machine-learning-sdk-v2-prereqs.md)]
+
 ## Diagnostics from studio
 
 From [Azure Machine Learning studio](https://ml.azure.com) or the Python SDK, you can run diagnostics on your workspace to check your setup. To run diagnostics, select the '__?__' icon from the upper right corner of the page. Then select __Run workspace diagnostics__.
@@ -35,19 +40,19 @@ After diagnostics run, a list of any detected problems is returned. This list in
 
 The following snippet demonstrates how to use workspace diagnostics from Python
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
 ```python
-from azureml.core import Workspace
+from azure.ai.ml import MLClient
+from azure.ai.ml.entities import Workspace
+from azure.identity import DefaultAzureCredential
 
-ws = Workspace.from_config()
-
-diag_param = {
-      "value": {
-      }
-    }
+subscription_id = '<your-subscription-id>'
+resource_group = '<your-resource-group-name>'
+workspace = '<your-workspace-name>'
 
-resp = ws.diagnose_workspace(diag_param)
+ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
+resp = ml_client.workspaces.begin_diagnose(workspace)
 print(resp)
 ```
 
@@ -75,9 +80,8 @@ The response is a JSON document that contains information on any problems detect
 
 If no problems are detected, an empty JSON document is returned.
 
-For more information, see the [Workspace.diagnose_workspace()](/python/api/azureml-core/azureml.core.workspace(class)#diagnose-workspace-diagnose-parameters-) reference.
+For more information, see the [Workspace](/python/api/azure-ai-ml/azure.ai.ml.entities.workspace) reference.
 
 ## Next steps
 
-* [Workspace.diagnose_workspace()](/python/api/azureml-core/azureml.core.workspace(class)#diagnose-workspace-diagnose-parameters-)
 * [How to manage workspaces in portal or SDK](how-to-manage-workspace.md)

articles/machine-learning/v1/how-to-workspace-diagnostic-api.md

@@ -0,0 +1,81 @@
+---
+title: Workspace diagnostics (v1)
+titleSuffix: Azure Machine Learning
+description: Learn how to use Azure Machine Learning workspace diagnostics with the Python SDK v1.
+services: machine-learning
+ms.service: machine-learning
+ms.subservice: enterprise-readiness
+ms.author: jhirono
+author: jhirono
+ms.reviewer: larryfr
+ms.date: 09/14/2022
+ms.topic: how-to
+ms.custom: sdkv1, event-tier1-build-2022
+---
+
+# How to use workspace diagnostics (SDK v1)
+
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
+> [!div class="op_single_selector" title1="Select the version of the Azure Machine Learning Python SDK you are using:"]
+> * [v1](how-to-workspace-diagnostic-api.md)
+> * [v2 (current version)](../how-to-workspace-diagnostic-api.md)
+
+Azure Machine Learning provides a diagnostic API that can be used to identify problems with your workspace. Errors returned in the diagnostics report include information on how to resolve the problem.
+
+In this article, learn how to use the workspace diagnostics from the Azure Machine Learning Python SDK v1.
+
+## Prerequisites
+
+* An Azure Machine learning workspace. If you don't have one, see [Create a workspace](../quickstart-create-resources.md).
+* The [Azure Machine Learning SDK for Python](/python/api/overview/azure/ml).
+
+## Diagnostics from Python
+
+The following snippet demonstrates how to use workspace diagnostics from Python
+
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
+
+```python
+from azureml.core import Workspace
+
+ws = Workspace.from_config()
+
+diag_param = {
+      "value": {
+      }
+    }
+
+resp = ws.diagnose_workspace(diag_param)
+print(resp)
+```
+
+The response is a JSON document that contains information on any problems detected with the workspace. The following JSON is an example response:
+
+```json
+{
+    'value': {
+        'user_defined_route_results': [], 
+        'network_security_rule_results': [], 
+        'resource_lock_results': [], 
+        'dns_resolution_results': [{
+            'code': 'CustomDnsInUse', 
+            'level': 'Warning', 
+            'message': "It is detected VNet '/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.Network/virtualNetworks/<virtual-network-name>' of private endpoint '/subscriptions/<subscription-id>/resourceGroups/larrygroup0916/providers/Microsoft.Network/privateEndpoints/<workspace-private-endpoint>' is not using Azure default dns. You need to configure your DNS server and check https://docs.microsoft.com/azure/machine-learning/how-to-custom-dns to make sure the custom dns is set up correctly."
+        }], 
+        'storage_account_results': [], 
+        'key_vault_results': [], 
+        'container_registry_results': [], 
+        'application_insights_results': [], 
+        'other_results': []
+    }
+}
+```
+
+If no problems are detected, an empty JSON document is returned.
+
+For more information, see the [Workspace.diagnose_workspace()](/python/api/azureml-core/azureml.core.workspace(class)#diagnose-workspace-diagnose-parameters-) reference.
+
+## Next steps
+
+* [Workspace.diagnose_workspace()](/python/api/azureml-core/azureml.core.workspace(class)#diagnose-workspace-diagnose-parameters-)
+* [How to manage workspaces in portal or SDK](../how-to-manage-workspace.md)

articles/machine-learning/v1/toc.yml

@@ -83,6 +83,8 @@
   - name: Set up software environments CLI (v1)
     href: how-to-use-environments.md
   - name: Create & manage compute resources
+  - name: Workspace Diagnostics
+    href: how-to-workspace-diagnostic-api.md
     items:
     - name: Compute instance
       displayName: compute target