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Connect to storage by using identity-based data access with SDK v1

  • Artikkeli

In this article, you'll learn how to connect to storage services on Azure with identity-based data access and Azure Machine Learning datastores, via the Azure Machine Learning SDK for Python.

Typically, datastores use credential-based authentication to verify that you have permission to access the storage service. Datastores keep connection information, like your subscription ID and token authorization, in the key vault associated with the workspace. When you create a datastore that uses identity-based data access, your Azure account (Microsoft Entra token) is used to confirm that you have permission to access the storage service. In the identity-based data access scenario, no authentication credentials are saved. Only the storage account information is stored in the datastore.

To create datastores with identity-based data access via the Azure Machine Learning studio UI, see Connect to data with the Azure Machine Learning studio.

To create datastores that use credential-based authentication, like access keys or service principals, see Connect to storage services on Azure.

Identity-based data access in Azure Machine Learning

You can apply identity-based data access in Azure Machine Learning in two scenarios. These scenarios are a good fit for identity-based access when you work with confidential data, and you need more granular data access management:

Warning

Identity-based data access is not supported for automated ML experiments.

  • Accessing storage services
  • Training machine learning models with private data

Accessing storage services

You can connect to storage services via identity-based data access with Azure Machine Learning datastores or Azure Machine Learning datasets.

Your authentication credentials are kept in a datastore, which ensures that you have permission to access the storage service. When these credentials are registered via datastores, any user with the workspace Reader role can retrieve them. That scale of access can be a security concern for some organizations. Learn more about the workspace Reader role.

When you use identity-based data access, Azure Machine Learning prompts you for your Microsoft Entra token for data access authentication, instead of keeping your credentials in the datastore. That approach allows for data access management at the storage level, and maintains credential security.

The same behavior applies when you:

Note

Credentials stored via credential-based authentication include subscription IDs, shared access signature (SAS) tokens, and storage access key and service principal information, like client IDs and tenant IDs.

Model training on private data

Certain machine learning scenarios involve training models with private data. In such cases, data scientists need to run training workflows without exposure to the confidential input data. In this scenario, a managed identity of the training compute authenticates data access. This approach allows storage admins to grant Storage Blob Data Reader access to the managed identity that the training compute uses to run the training job. The individual data scientists don't need to be granted access. For more information, visit Set up managed identity on a compute cluster.

Prerequisites

Create and register datastores

When you register a storage service on Azure as a datastore, you automatically create and register that datastore to a specific workspace. See Storage access permissions for guidance on required permission types. You can also manually create the storage to which you want to connect without any special permissions, and you just need the name.

See Work with virtual networks for details on how to connect to data storage behind virtual networks.

In the following code, notice the absence of authentication parameters like sas_token, account_key, subscription_id, and the service principal client_id. This omission indicates that Azure Machine Learning uses identity-based data access for authentication. Creation of datastores typically happens interactively in a notebook or via the studio. The data access authentication uses your Microsoft Entra token.

Note

Datastore names should consist only of lowercase letters, numbers, and underscores.

Azure blob container

To register an Azure blob container as a datastore, use register_azure_blob_container().

The following code creates the credentialless_blob datastore, registers it to the ws workspace, and assigns it to the blob_datastore variable. This datastore accesses the my_container_name blob container on the my-account-name storage account.

# Create blob datastore without credentials.
blob_datastore = Datastore.register_azure_blob_container(workspace=ws,
                                                      datastore_name='credentialless_blob',
                                                      container_name='my_container_name',
                                                      account_name='my_account_name')

Azure Data Lake Storage Gen1

Use register_azure_data_lake() to register a datastore that connects to Azure Data Lake Storage Gen1.

The following code creates the credentialless_adls1 datastore, registers it to the workspace workspace, and assigns it to the adls_dstore variable. This datastore accesses the adls_storage Azure Data Lake Storage account.

# Create Azure Data Lake Storage Gen1 datastore without credentials.
adls_dstore = Datastore.register_azure_data_lake(workspace = workspace,
                                                 datastore_name='credentialless_adls1',
                                                 store_name='adls_storage')

Azure Data Lake Storage Gen2

Use register_azure_data_lake_gen2() to register a datastore that connects to Azure Data Lake Storage Gen2.

The following code creates the credentialless_adls2 datastore, registers it to the ws workspace, and assigns it to the adls2_dstore variable. This datastore accesses the file system tabular in the myadls2 storage account.

# Create Azure Data Lake Storage Gen2 datastore without credentials.
adls2_dstore = Datastore.register_azure_data_lake_gen2(workspace=ws, 
                                                       datastore_name='credentialless_adls2', 
                                                       filesystem='tabular', 
                                                       account_name='myadls2')

Azure SQL database

For an Azure SQL database, use register_azure_sql_database() to register a datastore that connects to an Azure SQL database storage.

The following code creates and registers the credentialless_sqldb datastore to the ws workspace and assigns it to the variable, sqldb_dstore. This datastore accesses the database mydb in the myserver SQL DB server.

# Create a sqldatabase datastore without credentials
                                                       
sqldb_dstore = Datastore.register_azure_sql_database(workspace=ws,
                                                       datastore_name='credentialless_sqldb',
                                                       server_name='myserver',
                                                       database_name='mydb')

Storage access permissions

To ensure that you securely connect to your storage service on Azure, Azure Machine Learning requires that you have permission to access the corresponding data storage.

Warning

Cross tenant access to storage accounts is not supported. If cross tenant access is needed for your scenario, please reach out to the Azure Machine Learning Data Support team alias at amldatasupport@microsoft.com for assistance with a custom code solution.

Identity-based data access supports connections to only the following storage services.

  • Azure Blob Storage
  • Azure Data Lake Storage Gen1
  • Azure Data Lake Storage Gen2
  • Azure SQL Database

To access these storage services, you must have at least Storage Blob Data Reader access to the storage account. Only storage account owners can change your access level via the Azure portal.

If you prefer to not use your user identity (Microsoft Entra ID), you can also grant a workspace managed-system identity (MSI) permission to create the datastore. To do so, you must have Owner permissions to the storage account, and you must add the grant_workspace_access= True parameter to your data register method.

If you train a model on a remote compute target and you want to access the data for training, the compute identity must be granted at least the Storage Blob Data Reader role from the storage service. Learn how to set up managed identity on a compute cluster.

Work with virtual networks

By default, Azure Machine Learning can't communicate with a storage account located behind a firewall, or in a virtual network.

You can configure storage accounts to allow access only from within specific virtual networks. This configuration requires more steps, to ensure that data doesn't leak outside of the network. This behavior is the same for credential-based data access. For more information, see How to configure virtual network scenarios.

If your storage account has virtual network settings, they dictate the needed identity type and permissions access. For example for data preview and data profile, the virtual network settings determine what type of identity is used to authenticate data access.

  • In scenarios where only certain IPs and subnets are allowed to access the storage, then Azure Machine Learning uses the workspace MSI to accomplish data previews and profiles.

  • If your storage is ADLS Gen 2 or Blob and has virtual network settings, customers can use either user identity or workspace MSI depending on the datastore settings defined during creation.

  • If the virtual network setting is “Allow Azure services on the trusted services list to access this storage account”, then Workspace MSI is used.

Use data in storage

We recommend that you use Azure Machine Learning datasets when you interact with your data in storage with Azure Machine Learning.

Important

Datasets using identity-based data access are not supported for automated ML experiments.

Datasets package your data into a lazily evaluated consumable object for machine learning tasks like training. Also, with datasets you can download or mount files of any format from Azure storage services like Azure Blob Storage and Azure Data Lake Storage to a compute target.

To create a dataset, you can reference paths from datastores that also use identity-based data access.

  • If your underlying storage account type is Blob or ADLS Gen 2, your user identity needs the Blob Reader role.
  • If your underlying storage is ADLS Gen 1, you can set permissions via the storage's Access Control List (ACL).

In the following example, blob_datastore already exists and uses identity-based data access.

blob_dataset = Dataset.Tabular.from_delimited_files(blob_datastore,'test.csv')

You can also skip datastore creation, and create datasets directly from storage URLs. This functionality currently supports only Azure blobs and Azure Data Lake Storage Gen1 and Gen2. For creation based on storage URL, only the user identity is needed to authenticate.

blob_dset = Dataset.File.from_files('https://myblob.blob.core.windows.net/may/keras-mnist-fashion/')

When you submit a training job that consumes a dataset created with identity-based data access, the training compute managed identity is used for data access authentication. Your Microsoft Entra token isn't used. For this scenario, ensure that the managed identity of the compute is granted at least the Storage Blob Data Reader role from the storage service. For more information, see Set up managed identity on compute clusters.

Next steps