Merge pull request #213088 from Blackmist/sdk-links

v1 -> v2 work

Author: JamesJBarnett

articles/machine-learning/how-to-manage-workspace-terraform.md

@@ -67,7 +67,7 @@ The configuration below creates a workspace in an isolated network environment u
 
 Some resources in Azure require globally unique names. Before deploying your resources using the following templates, set the `resourceprefix` variable to a value that is unique.
 
-When using private link endpoints for both Azure Container Registry and Azure Machine Learning, Azure Container Registry tasks cannot be used for building [environment](/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py&preserve-view=true) images. Instead you can build images using an Azure Machine Learning compute cluster. To configure the cluster name of use, set the [image_build_compute_name](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument. You can configure to [allow public access](./how-to-configure-private-link.md?tabs=python#enable-public-access) to a workspace that has a private link endpoint using the [public_network_access_enabled](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument.
+When using private link endpoints for both Azure Container Registry and Azure Machine Learning, Azure Container Registry tasks cannot be used for building [environment](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) images. Instead you can build images using an Azure Machine Learning compute cluster. To configure the cluster name of use, set the [image_build_compute_name](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument. You can configure to [allow public access](./how-to-configure-private-link.md?tabs=python#enable-public-access) to a workspace that has a private link endpoint using the [public_network_access_enabled](https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/machine_learning_workspace) argument.
 
 **variables.tf**:
 :::code language="terraform" source="~/terraform/quickstart/201-machine-learning-moderately-secure/variables.tf":::

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

@@ -10,11 +10,17 @@ ms.author: jhirono
 author: jhirono
 ms.date: 06/17/2022
 ms.topic: how-to
-ms.custom: contperf-fy20q4, tracking-python, contperf-fy21q1, security, cliv2, sdkv1, event-tier1-build-2022
+ms.custom: contperf-fy20q4, tracking-python, contperf-fy21q1, security, cliv2, sdkv2, event-tier1-build-2022
 ---
 
 # Secure an Azure Machine Learning workspace with virtual networks
 
+[!INCLUDE [sdk/cli v2](../../includes/machine-learning-dev-v2.md)]
+
+> [!div class="op_single_selector" title1="Select the version of Azure Machine Learning SDK/CLI extension you are using:"]
+> * [v1](v1/how-to-secure-workspace-vnet.md)
+> * [v2 (current version)](how-to-secure-workspace-vnet.md)
+
 In this article, you learn how to secure an Azure Machine Learning workspace and its associated resources in a virtual network.
 
 > [!TIP]
@@ -34,7 +40,6 @@ In this article you learn how to enable the following workspaces resources in a
 > [!div class="checklist"]
 > - Azure Machine Learning workspace
 > - Azure Storage accounts
-> - Azure Machine Learning datastores and datasets
 > - Azure Key Vault
 > - Azure Container Registry
 
@@ -66,7 +71,7 @@ In this article you learn how to enable the following workspaces resources in a
 
 ## Limitations
 
-### Azure Storage Account
+### Azure storage account
 
 * If you plan to use Azure Machine Learning studio and the storage account is also in the VNet, there are extra validation requirements:
 
@@ -221,18 +226,26 @@ Azure Container Registry can be configured to use a private endpoint. Use the fo
 
     # [Python SDK](#tab/python)
 
-    [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+    [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
-    The following code snippet demonstrates how to get the container registry information using the [Azure Machine Learning SDK](/python/api/overview/azure/ml/):
+    The following code snippet demonstrates how to get the container registry information using the [Azure Machine Learning SDK](/python/api/overview/azure/ai-ml-readme):
 
-    ```python
-    from azureml.core import Workspace
-    # Load workspace from an existing config file
-    ws = Workspace.from_config()
-    # Get details on the workspace
-    details = ws.get_details()
-    # Print container registry information
-    print(details['containerRegistry'])
+   ```python
+    # import required libraries
+    from azure.ai.ml import MLClient
+    from azure.identity import DefaultAzureCredential
+
+    subscription_id = "<your subscription ID>"
+    resource_group = "<your resource group name>"
+    workspace = "<your workspace name>"
+
+    ml_client = MLClient(
+        DefaultAzureCredential(), subscription_id, resource_group, workspace
+    )
+    
+    # Get workspace info
+    ws=ml_client.workspaces.get(name=workspace)
+    print(ws.container_registry)
     ```
 
     This code returns a value similar to `"/subscriptions/{GUID}/resourceGroups/{resourcegroupname}/providers/Microsoft.ContainerRegistry/registries/{ACRname}"`. The last part of the string is the name of the Azure Container Registry for the workspace.
@@ -268,21 +281,35 @@ Azure Container Registry can be configured to use a private endpoint. Use the fo
 
     # [Python SDK](#tab/python)
 
-    The following code snippet demonstrates how to update the workspace to set a build compute using the [Azure Machine Learning SDK](/python/api/overview/azure/ml/). Replace `mycomputecluster` with the name of the cluster to use:
+    The following code snippet demonstrates how to update the workspace to set a build compute using the [Azure Machine Learning SDK](/python/api/overview/azure/ai-ml-readme). Replace `mycomputecluster` with the name of the cluster to use:
 
-    [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+    [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
     ```python
-    from azureml.core import Workspace
-    # Load workspace from an existing config file
-    ws = Workspace.from_config()
-    # Update the workspace to use an existing compute cluster
-    ws.update(image_build_compute = 'mycomputecluster')
+    # import required libraries
+    from azure.ai.ml import MLClient
+    from azure.identity import DefaultAzureCredential
+
+    subscription_id = "<your subscription ID>"
+    resource_group = "<your resource group name>"
+    workspace = "<your workspace name>"
+
+    ml_client = MLClient(
+        DefaultAzureCredential(), subscription_id, resource_group, workspace
+    )
+    
+    # Get workspace info
+    ws=ml_client.workspaces.get(name=workspace)
+    # Update to use cpu-cluster for image builds
+    ws.image_build_compute="cpu-cluster"
+    ml_client.workspaces.begin_update(ws)
     # To switch back to using ACR to build (if ACR is not in the VNet):
-    # ws.update(image_build_compute = '')
+    # ws.image_build_compute = ''
+    # ml_client.workspaces.begin_update(ws)
     ```
+
     
-    For more information, see the [update()](/python/api/azureml-core/azureml.core.workspace.workspace#update-friendly-name-none--description-none--tags-none--image-build-compute-none--enable-data-actions-none-) method reference.
+    For more information, see the [begin_update](/python/api/azure-ai-ml/azure.ai.ml.operations.workspaceoperations#azure-ai-ml-operations-workspaceoperations-begin-update) method reference.
 
     # [Portal](#tab/portal)
 
@@ -293,54 +320,6 @@ Azure Container Registry can be configured to use a private endpoint. Use the fo
 > [!TIP]
 > When ACR is behind a VNet, you can also [disable public access](../container-registry/container-registry-access-selected-networks.md#disable-public-network-access) to it.
 
-## Datastores and datasets
-The following table lists the services that you need to skip validation for:
-
-| Service | Skip validation required? |
-| ----- |:-----:|
-| Azure Blob storage | Yes |
-| Azure File share | Yes |
-| Azure Data Lake Store Gen1 | No |
-| Azure Data Lake Store Gen2 | No |
-| Azure SQL Database | Yes |
-| PostgreSql | Yes |
-
-> [!NOTE]
-> Azure Data Lake Store Gen1 and Azure Data Lake Store Gen2 skip validation by default, so you don't have to do anything.
-
-The following code sample creates a new Azure Blob datastore and sets `skip_validation=True`.
-
-```python
-blob_datastore = Datastore.register_azure_blob_container(workspace=ws,  
-
-                                                         datastore_name=blob_datastore_name,  
-
-                                                         container_name=container_name,  
-
-                                                         account_name=account_name, 
-
-                                                         account_key=account_key, 
-
-                                                         skip_validation=True ) // Set skip_validation to true
-```
-
-### Use datasets
-
-The syntax to skip dataset validation is similar for the following dataset types:
-- Delimited file
-- JSON 
-- Parquet
-- SQL
-- File
-
-The following code creates a new JSON dataset and sets `validate=False`.
-
-```python
-json_ds = Dataset.Tabular.from_json_lines_files(path=datastore_paths, 
-
-validate=False) 
-```
-
 ## Securely connect to your workspace
 
 [!INCLUDE [machine-learning-connect-secure-workspace](../../includes/machine-learning-connect-secure-workspace.md)]

articles/machine-learning/v1/how-to-create-register-datasets.md

@@ -95,7 +95,7 @@ Create a TabularDataset with [the Python SDK](#create-a-tabulardataset) or [Azur
 
 ## Access datasets in a virtual network
 
-If your workspace is in a virtual network, you must configure the dataset to skip validation. For more information on how to use datastores and datasets in a virtual network, see [Secure a workspace and associated resources](../how-to-secure-workspace-vnet.md#datastores-and-datasets).
+If your workspace is in a virtual network, you must configure the dataset to skip validation. For more information on how to use datastores and datasets in a virtual network, see [Secure a workspace and associated resources](how-to-secure-workspace-vnet.md#datastores-and-datasets).
 
 
 ## Create datasets from datastores
@@ -119,7 +119,7 @@ To create datasets from a datastore with the Python SDK:
 
 Use the [`from_files()`](/python/api/azureml-core/azureml.data.dataset_factory.filedatasetfactory#from-files-path--validate-true-) method on the `FileDatasetFactory` class to load files in any format and to create an unregistered FileDataset. 
 
-If your storage is behind a virtual network or firewall, set the parameter `validate=False` in your `from_files()` method. This bypasses the initial validation step, and ensures that you can create your dataset from these secure files. Learn more about how to [use datastores and datasets in a virtual network](../how-to-secure-workspace-vnet.md#datastores-and-datasets).
+If your storage is behind a virtual network or firewall, set the parameter `validate=False` in your `from_files()` method. This bypasses the initial validation step, and ensures that you can create your dataset from these secure files. Learn more about how to [use datastores and datasets in a virtual network](how-to-secure-workspace-vnet.md#datastores-and-datasets).
 
 ```Python
 from azureml.core import Workspace, Datastore, Dataset
@@ -156,7 +156,7 @@ Use the [`from_delimited_files()`](/python/api/azureml-core/azureml.data.dataset
 
 See the [TabularDatasetFactory reference documentation](/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory) for information about supported file formats, as well as syntax and design patterns such as [multiline support](/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header-true--partition-format-none--support-multi-line-false--empty-as-string-false--encoding--utf8--). 
 
-If your storage is behind a virtual network or firewall, set the parameter `validate=False` in your `from_delimited_files()` method. This bypasses the initial validation step, and ensures that you can create your dataset from these secure files. Learn more about how to use [datastores and datasets in a virtual network](../how-to-secure-workspace-vnet.md#datastores-and-datasets).
+If your storage is behind a virtual network or firewall, set the parameter `validate=False` in your `from_delimited_files()` method. This bypasses the initial validation step, and ensures that you can create your dataset from these secure files. Learn more about how to use [datastores and datasets in a virtual network](how-to-secure-workspace-vnet.md#datastores-and-datasets).
 
 The following code gets the existing workspace and the desired datastore by name. And then passes the datastore and file locations to the `path` parameter to create a new TabularDataset, `weather_ds`.