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CLI (v2) OneLake connection YAML schema

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APPLIES TO: Azure CLI ml extension v2 (current)

Note

The YAML syntax detailed in this document is based on the JSON schema for the latest version of the ML CLI v2 extension. This syntax is guaranteed only to work with the latest version of the ML CLI v2 extension. You can find the schemas for older extension versions at https://azuremlschemasprod.azureedge.net/.

YAML syntax

Key Type Description Allowed values Default value
$schema string The YAML schema. If you use the Azure Machine Learning Visual Studio Code extension to author the YAML file, include $schema at the top of your file to invoke schema and resource completions.
name string Required. The connection name.
description string The connection description.
tags object The connection tag dictionary.
type string Required. The connection type. one_lake one_lake
is_shared boolean true if the connection is shared across other projects in the hub; otherwise, false. true
one_lake_artifact object Artifact.
one_lake_artifact.type string The Microsoft OneLake artifact type. lake_house lake_house
one_lake_artifact.workspace_id string Required. The workspace name or GUID.
one_lake_artifact.name string Required. The Microsoft OneLake artifact name or GUID.
one_lake_artifact.endpoint string Required. The endpoint from the artifact.
credentials object Credential-based authentication to access Microsoft OneLake. A service principal can be used. Don't specify credentials when using credential-less authentication, set to None.
credentials.type string The type of credential. service_principal
credentials.client_id string Microsoft Entra ID application ID.
credentials.client_secret string Secret or key.
credentials.tenant_id string Microsoft Entra ID tenant ID.

Remarks

While the az ml connection commands can be used to manage both Azure Machine Learning and Azure AI Studio connections, the Microsoft OneLake connection is specific to Azure AI Studio.

Examples

These examples would be in the form of YAML files and used from the CLI. For example, az ml connection create -f <file-name>.yaml.

YAML: service principal

#MicrosoftOneLakeConnection.yml

name: myonelake_sp
type: one_lake
one_lake_artifact:
    type: lake_house
    name: "XXXXXXXXXX"
    workspace_id: "XXXXXXXXXX"
    endpoint: contoso-onelake.dfs.fabric.microsoft.com

credentials:
    type: service_principal
    tenant_id: "XXXXXXXXXX"
    client_id: "XXXXXXXXXX"
    client_secret: "XXXXXXXXXX"

YAML: credential-less

#MicrosoftOneLakeConnection.yml

name: myonelake_cl
type: one_lake
one_lake_artifact:
    type: lake_house
    name: "XXXXXXXXXX"
    workspace_id: "XXXXXXXXXX"
    endpoint: contoso-onelake.dfs.fabric.microsoft.com

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