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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.

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. Note that from_config accepts the same kwargs as the main ~azure.ai.ml.MLClient constructor such as cloud.

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. Note that from_config accepts the same kwargs as the main ~azure.ai.ml.MLClient constructor such as cloud.

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.

Default value: None
file_name
Optional[str]

The configuration file name to search for when path is a directory path. Defaults to "config.json".

Default value: None
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

azure_openai_deployments

//aka.ms/azuremlexperimental for more information.

A collection of Azure OpenAI deployment related operations.

Returns

Type Description
AzureOpenAIDeploymentOperations

Azure OpenAI deployment operations.

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

capability_hosts

//aka.ms/azuremlexperimental for more information.

A collection of capability hosts related operations.

Returns

Type Description
CapabilityHostsOperations

Capability hosts 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 connection related operations.

Returns

Type Description
WorkspaceConnectionsOperations

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.

evaluators

//aka.ms/azuremlexperimental for more information.

A collection of model related operations.

Returns

Type Description
ModelOperations

Model 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

indexes

//aka.ms/azuremlexperimental for more information.

A collection of index related operations.

Returns

Type Description
IndexOperations

Index operations.

jobs

A collection of job related operations.

Returns

Type Description
JobOperations

Job operations

marketplace_subscriptions

//aka.ms/azuremlexperimental for more information.

A collection of marketplace subscription related operations.

Returns

Type Description
MarketplaceSubscriptionOperations

Marketplace subscription 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.

serverless_endpoints

//aka.ms/azuremlexperimental for more information.

A collection of serverless endpoint related operations.

Returns

Type Description
ServerlessEndpointOperations

Serverless endpoint operations.

subscription_id

Get the subscription ID of an MLClient object.

Returns

Type Description
str

An Azure subscription ID.

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. Also manages workspace sub-classes like projects and hubs.

Returns

Type Description
WorkspaceOperations

Workspace operations

R 

R = ~R

T 

T = ~T