Upgrade datastore management to SDK v2

Azure Machine Learning Datastores securely keep the connection information to your data storage on Azure, so you don't have to code it in your scripts. V2 Datastore concept remains mostly unchanged compared with V1. The difference is we won't support SQL-like data sources via AzureML Datastores. We'll support SQL-like data sources via AzureML data import&export functionalities.

This article gives a comparison of scenario(s) in SDK v1 and SDK v2.

• SDK v1

  blob_datastore_name='azblobsdk' # Name of the datastore to workspace
  container_name=os.getenv("BLOB_CONTAINER", "<my-container-name>") # Name of Azure blob container
  account_name=os.getenv("BLOB_ACCOUNTNAME", "<my-account-name>") # Storage account name
  account_key=os.getenv("BLOB_ACCOUNT_KEY", "<my-account-key>") # Storage account access key
  
  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)
  

• SDK v2

  from azure.ai.ml.entities import AzureBlobDatastore
  from azure.ai.ml import MLClient
  
  ml_client = MLClient.from_config()
  
  store = AzureBlobDatastore(
      name="blob-protocol-example",
      description="Datastore pointing to a blob container using wasbs protocol.",
      account_name="mytestblobstore",
      container_name="data-container",
      protocol="wasbs",
      credentials={
          "account_key": "XXXxxxXXXxXXXXxxXXXXXxXXXXXxXxxXxXXXxXXXxXXxxxXXxxXXXxXxXXXxxXxxXXXXxxxxxXXxxxxxxXXXxXXX"
      },
  )
  
  ml_client.create_or_update(store)
  

• SDK v1

  blob_datastore_name='azblobsdk' # Name of the datastore to workspace
  container_name=os.getenv("BLOB_CONTAINER", "<my-container-name>") # Name of Azure blob container
  sas_token=os.getenv("BLOB_SAS_TOKEN", "<my-sas-token>") # Sas token
  
  blob_datastore = Datastore.register_azure_blob_container(workspace=ws, 
                                                           datastore_name=blob_datastore_name, 
                                                           container_name=container_name, 
                                                           sas_token=sas_token)
  

• SDK v2

  from azure.ai.ml.entities import AzureBlobDatastore
  from azure.ai.ml import MLClient
  
  ml_client = MLClient.from_config()
  
  store = AzureBlobDatastore(
      name="blob-sas-example",
      description="Datastore pointing to a blob container using SAS token.",
      account_name="mytestblobstore",
      container_name="data-container",
      credentials=SasTokenCredentials(
          sas_token= "?xx=XXXX-XX-XX&xx=xxxx&xxx=xxx&xx=xxxxxxxxxxx&xx=XXXX-XX-XXXXX:XX:XXX&xx=XXXX-XX-XXXXX:XX:XXX&xxx=xxxxx&xxx=XXxXXXxxxxxXXXXXXXxXxxxXXXXXxxXXXXXxXXXXxXXXxXXxXX"
      ),
  )
  
  ml_client.create_or_update(store)
  

• SDK v1

blob_datastore = Datastore.register_azure_blob_container(workspace=ws,
                                                      datastore_name='credentialless_blob',
                                                      container_name='my_container_name',
                                                      account_name='my_account_name')

• SDK v2

  from azure.ai.ml.entities import AzureBlobDatastore
  from azure.ai.ml import MLClient
  
  ml_client = MLClient.from_config()
  
  store = AzureBlobDatastore(
      name="",
      description="",
      account_name="",
      container_name=""
  )
  
  ml_client.create_or_update(store)
  

• SDK v1

  # Get a named datastore from the current workspace
  datastore = Datastore.get(ws, datastore_name='your datastore name')
  

  # List all datastores registered in the current workspace
  datastores = ws.datastores
  for name, datastore in datastores.items():
      print(name, datastore.datastore_type)
  

• SDK v2

  from azure.ai.ml import MLClient
  from azure.identity import DefaultAzureCredential
  
  #Enter details of your AzureML workspace
  subscription_id = '<SUBSCRIPTION_ID>'
  resource_group = '<RESOURCE_GROUP>'
  workspace_name = '<AZUREML_WORKSPACE_NAME>'
  
  ml_client = MLClient(credential=DefaultAzureCredential(),
                       subscription_id=subscription_id, 
                       resource_group_name=resource_group)
  
  datastore = ml_client.datastores.get(datastore_name='your datastore name')
  

For more information, see:

• Create datastores
• Read and write data in a job
• V2 datastore operations