MLClient Class

A client class to interact with Azure ML services.

Use this client to manage Azure ML resources such as workspaces, jobs, models, and so on.

Inheritance

builtins.object

MLClient

Constructor

MLClient(credential: TokenCredential, subscription_id: str | None = None, resource_group_name: str | None = None, workspace_name: str | None = None, registry_name: str | None = None, **kwargs: Any)

Parameters

Name	Description
credential Required	TokenCredential The credential to use for authentication.
subscription_id	Optional[str] The Azure subscription ID. Optional for registry assets only. Defaults to None. default value: None
resource_group_name	Optional[str] The Azure resource group. Optional for registry assets only. Defaults to None. default value: None
workspace_name	Optional[str] The workspace to use in the client. Optional only for operations that are not workspace-dependent. Defaults to None. default value: None
registry_name	Optional[str] The registry to use in the client. Optional only for operations that are not workspace-dependent. Defaults to None. default value: None

Keyword-Only Parameters

Name	Description
show_progress	Optional[bool] Specifies whether or not to display progress bars for long-running operations (e.g. customers may consider setting this to False if not using this SDK in an interactive setup). Defaults to True.
enable_telemetry	Optional[bool] Specifies whether or not to enable telemetry. Will be overridden to False if not in a Jupyter Notebook. Defaults to True if in a Jupyter Notebook.
cloud	Optional[str] The cloud name to use. Defaults to "AzureCloud".

Examples

When using sovereign domains (i.e. any cloud other than AZURE_PUBLIC_CLOUD), you must pass in the cloud name in kwargs and you must use an authority with DefaultAzureCredential.

   from azure.ai.ml import MLClient
   from azure.identity import AzureAuthorityHosts, DefaultAzureCredential

   kwargs = {"cloud": "AzureChinaCloud"}
   ml_client = MLClient(
       subscription_id=subscription_id,
       resource_group_name=resource_group,
       credential=DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_CHINA),
       **kwargs,
   )

Methods

begin_create_or_update	Creates or updates an Azure ML resource asynchronously.
create_or_update	Creates or updates an Azure ML resource.
from_config	Returns a client from an existing Azure Machine Learning Workspace using a file configuration. This method provides a simple way to reuse the same workspace across multiple Python notebooks or projects. You can save a workspace's Azure Resource Manager (ARM) properties in a JSON configuration file using this format: { "subscription_id": "<subscription-id>", "resource_group": "<resource-group>", "workspace_name": "<workspace-name>" } Then, you can use this method to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties.

begin_create_or_update

Creates or updates an Azure ML resource asynchronously.

begin_create_or_update(entity: R, **kwargs) -> LROPoller[R]

Parameters

Name	Description
entity Required	Union[Workspace , Registry, Compute, OnlineDeployment , OnlineEndpoint, BatchDeployment , BatchEndpoint, Schedule] The resource to create or update.

Returns

Type	Description
LROPoller[Union[Workspace , Registry, Compute, OnlineDeployment , OnlineEndpoint, BatchDeployment , BatchEndpoint, Schedule]]	The resource after create/update operation.

create_or_update

Creates or updates an Azure ML resource.

create_or_update(entity: T, **kwargs) -> T

Parameters

Name	Description
entity Required	Union[Job , Model, Environment, Component , Datastore] The resource to create or update.

Returns

Type	Description
Union[Job, Model , Environment, Component, Datastore]	The created or updated resource.

from_config

Returns a client from an existing Azure Machine Learning Workspace using a file configuration.

This method provides a simple way to reuse the same workspace across multiple Python notebooks or projects. You can save a workspace's Azure Resource Manager (ARM) properties in a JSON configuration file using this format:

   {
       "subscription_id": "<subscription-id>",
       "resource_group": "<resource-group>",
       "workspace_name": "<workspace-name>"
   }

Then, you can use this method to load the same workspace in different Python notebooks or projects without retyping the workspace ARM properties.

from_config(credential: TokenCredential, *, path: PathLike | str | None = None, file_name=None, **kwargs) -> MLClient

Parameters

Name	Description
credential Required	TokenCredential The credential object for the workspace.

Keyword-Only Parameters

Name	Description
path	Optional[Union[PathLike, str]] The path to the configuration file or starting directory to search for the configuration file within. Defaults to None, indicating the current directory will be used.
file_name	Optional[str] The configuration file name to search for when path is a directory path. Defaults to "config.json".
cloud	Optional[str] The cloud name to use. Defaults to "AzureCloud".

Returns

Type	Description
MLClient	The client for an existing Azure ML Workspace.

Exceptions

Type	Description
ValidationException	Raised if "config.json", or file_name if overridden, cannot be found in directory. Details will be provided in the error message.

Examples

Creating an MLClient from a file named "config.json" in directory "src".

   from azure.ai.ml import MLClient

   client = MLClient.from_config(credential=DefaultAzureCredential(), path="./sdk/ml/azure-ai-ml/samples/src")

Creating an MLClient from a file named "team_workspace_configuration.json" in the current directory.

   from azure.ai.ml import MLClient

   client = MLClient.from_config(
       credential=DefaultAzureCredential(),
       file_name="./sdk/ml/azure-ai-ml/samples/team_workspace_configuration.json",
   )

Attributes

batch_deployments

A collection of batch deployment related operations.

Returns

Type	Description
BatchDeploymentOperations	Batch Deployment operations.

batch_endpoints

A collection of batch endpoint related operations.

Returns

Type	Description
BatchEndpointOperations	Batch Endpoint operations

components

A collection of component related operations.

Returns

Type	Description
ComponentOperations	Component operations.

compute

A collection of compute related operations.

Returns

Type	Description
ComputeOperations	Compute operations

connections

A collection of workspace connection related operations.

Returns

Type	Description
WorkspaceConnectionsOperations	Workspace Connections operations

data

A collection of data related operations.

Returns

Type	Description
DataOperations	Data operations.

datastores

A collection of datastore related operations.

Returns

Type	Description
DatastoreOperations	Datastore operations.

environments

A collection of environment related operations.

Returns

Type	Description
EnvironmentOperations	Environment operations.

feature_sets

A collection of feature set related operations.

Returns

Type	Description
FeatureSetOperations	FeatureSet operations

feature_store_entities

A collection of feature store entity related operations.

Returns

Type	Description
FeatureStoreEntityOperations	FeatureStoreEntity operations

feature_stores

A collection of feature store related operations.

Returns

Type	Description
FeatureStoreOperations	FeatureStore operations

jobs

A collection of job related operations.

Returns

Type	Description
JobOperations	Job operations

models

A collection of model related operations.

Returns

Type	Description
ModelOperations	Model operations

online_deployments

A collection of online deployment related operations.

Returns

Type	Description
OnlineDeploymentOperations	Online Deployment operations

online_endpoints

A collection of online endpoint related operations.

Returns

Type	Description
OnlineEndpointOperations	Online Endpoint operations

registries

A collection of registry-related operations.

Returns

Type	Description
RegistryOperations	Registry operations

resource_group_name

Get the resource group name of an MLClient object.

Returns

Type	Description
str	An Azure resource group name.

schedules

A collection of schedule related operations.

Returns

Type	Description
ScheduleOperations	Schedule operations.

subscription_id

Get the subscription ID of an MLClient object.

Returns

Type	Description
str	An Azure subscription ID.

workspace_hubs

//aka.ms/azuremlexperimental for more information.

A collection of workspace hub-related operations.

Returns

Type	Description
<xref:HubOperations>	Hub Operations

workspace_name

The name of the workspace where workspace-dependent operations will be executed.

Returns

Type	Description
Optional[str]	The name of the default workspace.

workspace_outbound_rules

A collection of workspace outbound rule related operations.

Returns

Type	Description
WorkspaceOutboundRuleOperations	Workspace outbound rule operations

workspaces

A collection of workspace-related operations.

Returns

Type	Description
WorkspaceOperations	Workspace operations

R

R = ~R

T

T = ~T