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將工作區管理升級至 SDK v2

  • 發行項

在 V2 開發平臺中，工作區的功能會保持不變。 不過，有與網路相關的變更需要注意。 如需詳細資訊，請參閱 Azure Resource Manager 上的新 API 平台的網路隔離變更

本文提供 SDK v1 和 SDK v2 中案例的比較。

建立工作區

  • 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)

  • SDK v1

    from azureml.core import Workspace

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)

使用參數載入/連線到工作區

  • 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
ws = MLClient(
    DefaultAzureCredential(),
    subscription_id='<SUBSCRIPTION_ID>',
    resource_group_name='<RESOURCE_GROUP>',
    workspace_name='my_workspace'
)

使用組態檔載入/連線到工作區

  • 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

ws = MLClient.from_config(
    DefaultAzureCredential()
)

SDK v1 和 SDK v2 中的主要功能對應

SDK v1 中的功能 SDK v2 中的粗略對應
SDK v1 中的方法/API（使用參考文件的連結） SDK v2 中的方法/API（使用參考文件的連結）

如需詳細資訊，請參閱