Manage Azure Machine Learning workspaces using Azure CLI extension v1

APPLIES TO: Azure CLI ml extension v1

In this article, you learn how to create and manage Azure Machine Learning workspaces using the Azure CLI. The Azure CLI provides commands for managing Azure resources and is designed to get you working quickly with Azure, with an emphasis on automation. The machine learning extension for the CLI provides commands for working with Azure Machine Learning resources.

Prerequisites

• An Azure subscription. If you don't have one, try the trial subscription.

• To use the CLI commands in this document from your local environment, you need the Azure CLI.

Limitations

• When you create a new workspace, you can either automatically create services needed by the workspace or use existing services. If you want to use existing services from a different Azure subscription than the workspace, you must register the Azure Machine Learning namespace in the subscription that contains those services. For example, if you create a workspace in subscription A that uses a storage account in subscription B, the Azure Machine Learning namespace must be registered in subscription B before the workspace can use the storage account.

  The resource provider for Azure Machine Learning is Microsoft.MachineLearningServices. For information on seeing whether it's registered or registering it, see Azure resource providers and types.

  Important

  This information applies only to resources provided during workspace creation: Azure Storage Accounts, Azure Container Registry, Azure Key Vault, and Application Insights.

Secure CLI communications

Some of the Azure CLI commands communicate with Azure Resource Manager over the internet. This communication is secured using HTTPS/TLS 1.2.

With the Azure Machine Learning CLI extension v1 (azure-cli-ml), only some of the commands communicate with the Azure Resource Manager. Specifically, commands that create, update, delete, list, or show Azure resources. Operations such as submitting a training job communicate directly with the Azure Machine Learning workspace. If your workspace is secured with a private endpoint, that is enough to secure commands provided by the azure-cli-ml extension.

Connect the CLI to your Azure subscription

There are several ways that you can authenticate to your Azure subscription from the CLI. The most simple is to interactively authenticate using a browser. To authenticate interactively, open a command line or terminal and use the following command:

az login

If the CLI can open your default browser, it will do so and load a sign-in page. Otherwise, you need to open a browser and follow the instructions on the command line. The instructions involve browsing to https://aka.ms/deviceloginchina and entering an authorization code.

For other methods of authenticating, see Sign in with Azure CLI.

Create a resource group

The Azure Machine Learning workspace must be created inside a resource group. You can use an existing resource group or create a new one. To create a new resource group, use the following command. Replace <resource-group-name> with the name to use for this resource group. Replace <location> with the Azure region to use for this resource group:

az group create --name <resource-group-name> --location <location>

The response from this command is similar to the following JSON. You can use the output values to locate the created resources or parse them as input to subsequent CLI steps for automation.

{
  "id": "/subscriptions/<subscription-GUID>/resourceGroups/<resourcegroupname>",
  "location": "<location>",
  "managedBy": null,
  "name": "<resource-group-name>",
  "properties": {
    "provisioningState": "Succeeded"
  },
  "tags": null,
  "type": null
}

For more information on working with resource groups, see az group.

Create a workspace

When you deploy an Azure Machine Learning workspace, various other services are required as dependent associated resources. When you use the CLI to create the workspace, the CLI can either create new associated resources on your behalf or you could attach existing resources.

az ml workspace create -w <workspace-name> -g <resource-group-name>

az ml workspace create -w <workspace-name>
                       -g <resource-group-name>
                       --container-registry "/subscriptions/<service-GUID>/resourceGroups/<resource-group-name>/providers/Microsoft.ContainerRegistry/registries/<acr-name>"

The output of the workspace creation command is similar to the following JSON. You can use the output values to locate the created resources or parse them as input to subsequent CLI steps.

{
  "applicationInsights": "/subscriptions/<service-GUID>/resourcegroups/<resource-group-name>/providers/microsoft.insights/components/<application-insight-name>",
  "containerRegistry": "/subscriptions/<service-GUID>/resourcegroups/<resource-group-name>/providers/microsoft.containerregistry/registries/<acr-name>",
  "creationTime": "2019-08-30T20:24:19.6984254+00:00",
  "description": "",
  "friendlyName": "<workspace-name>",
  "id": "/subscriptions/<service-GUID>/resourceGroups/<resource-group-name>/providers/Microsoft.MachineLearningServices/workspaces/<workspace-name>",
  "identityPrincipalId": "<GUID>",
  "identityTenantId": "<GUID>",
  "identityType": "SystemAssigned",
  "keyVault": "/subscriptions/<service-GUID>/resourcegroups/<resource-group-name>/providers/microsoft.keyvault/vaults/<key-vault-name>",
  "location": "<location>",
  "name": "<workspace-name>",
  "resourceGroup": "<resource-group-name>",
  "storageAccount": "/subscriptions/<service-GUID>/resourcegroups/<resource-group-name>/providers/microsoft.storage/storageaccounts/<storage-account-name>",
  "type": "Microsoft.MachineLearningServices/workspaces",
  "workspaceid": "<GUID>"
}

Advanced configurations

Configure workspace for private network connectivity

Dependent on your use case and organizational requirements, you can choose to configure Azure Machine Learning using private network connectivity. You can use the Azure CLI to deploy a workspace and a Private link endpoint for the workspace resource. For more information on using a private endpoint and virtual network (VNet) with your workspace, see Virtual network isolation and privacy overview. For complex resource configurations, also refer to template based deployment options including Azure Resource Manager.

If you want to restrict workspace access to a virtual network, you can use the following parameters as part of the az ml workspace create command or use the az ml workspace private-endpoint commands.

az ml workspace create -w <workspace-name>
                       -g <resource-group-name>
                       --pe-name "<pe name>"
                       --pe-auto-approval "<pe-autoapproval>"
                       --pe-resource-group "<pe name>"
                       --pe-vnet-name "<pe name>"
                       --pe-subnet-name "<pe name>"

• --pe-name: The name of the private endpoint that is created.
• --pe-auto-approval: Whether private endpoint connections to the workspace should be automatically approved.
• --pe-resource-group: The resource group to create the private endpoint in. Must be the same group that contains the virtual network.
• --pe-vnet-name: The existing virtual network to create the private endpoint in.
• --pe-subnet-name: The name of the subnet to create the private endpoint in. The default value is default.

For more information on how to use these commands, see the CLI reference pages.

Customer-managed key and high business impact workspace

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 extra set of resources in your Azure subscription to store your data.

To learn more about the resources that are created when you bring your own key for encryption, see Data encryption with Azure Machine Learning.

Use the --cmk-keyvault parameter to specify the Azure Key Vault that contains the key, and --resource-cmk-uri to specify the resource ID and uri of the key within the vault.

To limit the data that Microsoft collects on your workspace, you can additionally specify the --hbi-workspace parameter.

az ml workspace create -w <workspace-name>
                       -g <resource-group-name>
                       --cmk-keyvault "<cmk keyvault name>"
                       --resource-cmk-uri "<resource cmk uri>"
                       --hbi-workspace

For more information on customer-managed keys and high business impact workspace, see Enterprise security for Azure Machine Learning.

Using the CLI to manage workspaces

Get workspace information

To get information about a workspace, use the following command:

az ml workspace show -w <workspace-name> -g <resource-group-name>

Update a workspace

To update a workspace, use the following command:

az ml workspace update -n <workspace-name> -g <resource-group-name>

Sync keys for dependent resources

If you change access keys for one of the resources used by your workspace, it takes around an hour for the workspace to synchronize to the new key. To force the workspace to sync the new keys immediately, use the following command:

az ml workspace sync-keys -w <workspace-name> -g <resource-group-name>

For more information on changing keys, see Regenerate storage access keys.

Delete a workspace

To delete a workspace after it's no longer needed, use the following command:

az ml workspace delete -w <workspace-name> -g <resource-group-name>

You can also delete the resource group, which deletes the workspace and all other Azure resources in the resource group. To delete the resource group, use the following command:

az group delete -g <resource-group-name>

Troubleshooting

Resource provider errors

When creating an Azure Machine Learning workspace, or a resource used by the workspace, you may receive an error similar to the following messages:

• No registered resource provider found for location {location}
• The subscription is not registered to use namespace {resource-provider-namespace}

Most resource providers are automatically registered, but not all. If you receive this message, you need to register the provider mentioned.

The following table contains a list of the resource providers required by Azure Machine Learning:

If you plan on using a customer-managed key with Azure Machine Learning, then the following service providers must be registered:

If you plan on using a managed virtual network with Azure Machine Learning, then the Microsoft.Network resource provider must be registered. This resource provider is used by the workspace when creating private endpoints for the managed virtual network.

For information on registering resource providers, see Resolve errors for resource provider registration.

Moving the workspace

Deleting the Azure Container Registry

The Azure Machine Learning workspace uses Azure Container Registry (ACR) for some operations. It will automatically create an ACR instance when it first needs one.

Next steps

For more information on the Azure CLI extension for machine learning, see the az ml (v1) documentation.

To check for problems with your workspace, see How to use workspace diagnostics.

To learn how to move a workspace to a new Azure subscription, see How to move a workspace.

For information on how to keep your Azure Machine Learning up to date with the latest security updates, see Vulnerability management.