Upgrade workspace management to SDK v2

The workspace functionally remains unchanged with the V2 development platform. However, there are network-related changes to be aware of. For details, see Network Isolation Change with Our New API Platform on Azure Resource Manager

This article gives a comparison of scenarios in SDK v1 and SDK v2.

Create a workspace

• SDK v1

  from azureml.core import Workspace
  
  ws = Workspace.create(
      name='my_workspace',
      location='eastus',
      subscription_id = '<SUBSCRIPTION_ID>'
      resource_group = '<RESOURCE_GROUP>'
  )
  

• SDK v2

  from azure.ai.ml import MLClient
  from azure.ai.ml.entities import Workspace
  from azure.identity import DefaultAzureCredential
  
  # specify the details of your subscription
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  
  # get a handle to the subscription
  ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
  
  # specify the workspace details
  ws = Workspace(
      name="my_workspace",
      location="eastus",
      display_name="My workspace",
      description="This example shows how to create a workspace",
      tags=dict(purpose="demo"),
  )
  
  ml_client.workspaces.begin_create(ws).result()
  

Create a workspace for use with Azure Private Link endpoints

• SDK v1

  from azureml.core import Workspace
  from azureml.core import PrivateEndPointConfig
  
  ws = Workspace.create(
      name='my_workspace',
      location='eastus',
      subscription_id = '<SUBSCRIPTION_ID>'
      resource_group = '<RESOURCE_GROUP>'
  )
  
  ple = PrivateEndPointConfig(
      name='my_private_link_endpoint',
      vnet_name='<VNET_NAME>',
      vnet_subnet_name='<VNET_SUBNET_NAME>',
      vnet_subscription_id='<SUBSCRIPTION_ID>', 
      vnet_resource_group='<RESOURCE_GROUP>'
  )
  
  ws.add_private_endpoint(ple, private_endpoint_auto_approval=True)
  

• SDK v2

  from azure.ai.ml import MLClient
  from azure.ai.ml.entities import Workspace
  from azure.identity import DefaultAzureCredential
  
  # specify the details of your subscription
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  
  # get a handle to the subscription
  ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group)
  
  ws = Workspace(
      name="private_link_endpoint_workspace",
      location="eastus",
      display_name="Private Link endpoint workspace",
      description="When using private link, you must set the image_build_compute property to a cluster name to use for Docker image environment building. You can also specify whether the workspace should be accessible over the internet.",
      image_build_compute="cpu-compute",
      public_network_access="Disabled",
      tags=dict(purpose="demonstration"),
  )
  
  ml_client.workspaces.begin_create(ws).result()
  

Load/connect to workspace using parameters

• SDK v1

  from azureml.core import Workspace
  ws = Workspace.from_config()
  
  # specify the details of your subscription
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  
  # get handle on the workspace
  ws = Workspace.get(
      subscription_id='<SUBSCRIPTION_ID>',
      resource_group='<RESOURCE_GROUP>',
      name='my_workspace',
  )
  

• SDK v2

  from azure.ai.ml import MLClient
  from azure.ai.ml.entities import Workspace
  from azure.identity import DefaultAzureCredential
  
  # specify the details of your subscription
  subscription_id = "<SUBSCRIPTION_ID>"
  resource_group = "<RESOURCE_GROUP>"
  
  # get handle on the workspace
  ml_client = MLClient(
      DefaultAzureCredential(),
      subscription_id='<SUBSCRIPTION_ID>',
      resource_group_name='<RESOURCE_GROUP>',
      workspace_name='my_workspace'
  )
  

Load/connect to workspace using config file

• SDK v1

  from azureml.core import Workspace
  
  ws = Workspace.from_config()
  ws.get_details()
  

• SDK v2

  from azure.ai.ml import MLClient
  from azure.ai.ml.entities import Workspace
  from azure.identity import DefaultAzureCredential
  
  ml_client = MLClient.from_config(
      DefaultAzureCredential()
  )
  

Mapping of key functionality in SDK v1 and SDK v2

Related documents

For more information, see:

• What is a workspace?