この YAML ファイルでは、Snowflake DB 接続を作成します。 必ず、該当する値を更新してください。
# my_snowflakedb_connection.yaml
$schema: http://azureml/sdk-2-0/Connection.json
type: snowflake
name: my-sf-db-connection # add your datastore name here
target: jdbc:snowflake://<myaccount>.snowflakecomputing.com/?db=<mydb>&warehouse=<mywarehouse>&role=<myrole>
# add the Snowflake account, database, warehouse name and role name here. If no role name provided it will default to PUBLIC
credentials:
type: username_password
username: <username> # add the Snowflake database user name here or leave this blank and type in CLI command line
password: <password> # add the Snowflake database password here or leave this blank and type in CLI command line
CLI で Azure Machine Learning 接続を作成します。
オプション 1: YAML ファイルでユーザー名とパスワードを使用する
az ml connection create --file my_snowflakedb_connection.yaml
オプション 2: コマンド ラインでユーザー名とパスワードをオーバーライドする
az ml connection create --file my_snowflakedb_connection.yaml --set credentials.
username="<username>" credentials.
password="<password>"
from azure.ai.ml import MLClient
from azure.ai.ml.entities import WorkspaceConnection
from azure.ai.ml.entities import UsernamePasswordConfiguration
# If using username/password, the name/password values should be url-encoded
import urllib.parse
username = urllib.parse.quote(os.environ["SNOWFLAKEDB_USERNAME"], safe="")
password = urllib.parse.quote(os.environ["SNOWFLAKEDB_PASSWORD"], safe="")
target= "jdbc:snowflake://<myaccount>.snowflakecomputing.com/?db=<mydb>&warehouse=<mywarehouse>&role=<myrole>"
# add the Snowflake account, database, warehouse name and role name here. If no role name provided it will default to PUBLIC
name= <my_snowflake_connection> # name of the connection
wps_connection = WorkspaceConnection(name= name,
type="snowflake",
target= target,
credentials= UsernamePasswordConfiguration(username=username, password=password)
)
ml_client.connections.create_or_update(workspace_connection=wps_connection)
この YAML ファイルにより、OAuth を使用する Snowflake DB 接続が作成されます。 必ず、該当する値を更新してください。
# my_snowflakedb_connection.yaml
name: snowflake_service_principal_connection
type: snowflake
# Add the Snowflake account, database, warehouse name, and role name here. If no role name is provided, it will default to PUBLIC.
target: jdbc:snowflake://<myaccount>.snowflakecomputing.com/?db=<mydb>&warehouse=<mywarehouse>&scope=<scopeForServicePrincipal>
credentials:
type: service_principal
client_id: <client-id> # The service principal's client id
client_secret: <client-secret> # The service principal's client secret
tenant_id: <tenant-id> # The Microsoft Entra ID tenant id
CLI で Azure Machine Learning 接続を作成します。
az ml connection create --file my_snowflakedb_connection.yaml
コマンド ラインで YAML ファイル内の情報をオーバーライドすることもできます。
az ml connection create --file my_snowflakedb_connection.yaml --set credentials.client_id="my-client-id" credentials.client_secret="my-client-secret" credentials.tenant_id="my-tenant-id"
from azure.ai.ml import MLClient
from azure.ai.ml.entities import WorkspaceConnection
from azure.ai.ml.entities import ServicePrincipalConfiguration
target= "jdbc:snowflake://<myaccount>.snowflakecomputing.com/?db=<mydb>&warehouse=<mywarehouse>&role=<myrole>"
# add the Snowflake account, database, warehouse name and role name here. If no role name provided it will default to PUBLIC
name= <my_snowflake_connection> # name of the connection
auth = ServicePrincipalConfiguration(client_id="<my-client-id>", client_secret="<my-client-secret>", tenant_id="<my-tenant-id>")
wps_connection = WorkspaceConnection(name= name,
type="snowflake",
target=target,
credentials=auth
)
ml_client.connections.create_or_update(workspace_connection=wps_connection)
この YAML スクリプトでは、Azure SQL DB 接続を作成します。 必ず、該当する値を更新してください。
# my_sqldb_connection.yaml
$schema: http://azureml/sdk-2-0/Connection.json
type: azure_sql_db
name: my-sqldb-connection
target: Server=tcp:<myservername>,<port>;Database=<mydatabase>;Trusted_Connection=False;Encrypt=True;Connection Timeout=30
# add the sql servername, port addresss and database
credentials:
type: sql_auth
username: <username> # add the sql database user name here or leave this blank and type in CLI command line
password: <password> # add the sql database password here or leave this blank and type in CLI command line
CLI で Azure Machine Learning 接続を作成します。
オプション 1: YAML ファイルからのユーザー名/パスワードを使用する
az ml connection create --file my_sqldb_connection.yaml
オプション 2: YAML ファイルでユーザー名とパスワードをオーバーライドする
az ml connection create --file my_sqldb_connection.yaml --set credentials.
username="<username>" credentials.
password="<password>"
from azure.ai.ml import MLClient
from azure.ai.ml.entities import WorkspaceConnection
from azure.ai.ml.entities import UsernamePasswordConfiguration
# If using username/password, the name/password values should be url-encoded
import urllib.parse
username = urllib.parse.quote(os.environ["MYSQL_USERNAME"], safe="")
password = urllib.parse.quote(os.environ["MYSQL_PASSWORD"], safe="")
target= "Server=tcp:<myservername>,<port>;Database=<mydatabase>;Trusted_Connection=False;Encrypt=True;Connection Timeout=30"
# add the sql servername, port address and database
name= <my_sql_connection> # name of the connection
wps_connection = WorkspaceConnection(name= name,
type="azure_sql_db",
target= target,
credentials= UsernamePasswordConfiguration(username=username, password=password)
)
ml_client.connections.create_or_update(workspace_connection=wps_connection)
from azure.ai.ml import MLClient
from azure.ai.ml.entities import WorkspaceConnection
from azure.ai.ml.entities import AccessKeyConfiguration
target=<mybucket> # add the s3 bucket details
name=<my_s3_connection> # name of the connection
wps_connection=WorkspaceConnection(name=name,
type="s3",
target= target,
credentials= AccessKeyConfiguration(access_key_id="XXXJ5kL6mN7oP8qR9sT0uV1wX2yZ3aB4cXXX",acsecret_access_key="C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w")
)
ml_client.connections.create_or_update(workspace_connection=wps_connection)
Azure CLI ML 拡張機能 v2 (現行)
