WorkspaceOperations Class

WorkspaceOperations.

You should not instantiate this class directly. Instead, you should create an MLClient instance that instantiates it for you and attaches it as an attribute.

Inheritance

azure.ai.ml.operations._workspace_operations_base.WorkspaceOperationsBase

WorkspaceOperations

Constructor

WorkspaceOperations(operation_scope: OperationScope, service_client: AzureMachineLearningWorkspaces, all_operations: OperationsContainer, credentials: TokenCredential | None = None, **kwargs: Any)

Parameters

Name	Description
operation_scope Required
service_client Required
all_operations Required
credentials	default value: None

Methods

begin_create	Create a new Azure Machine Learning Workspace. Returns the workspace if already exists.
begin_delete	Delete a workspace.
begin_diagnose	Diagnose workspace setup problems. If your workspace is not working as expected, you can run this diagnosis to check if the workspace has been broken. For private endpoint workspace, it will also help check if the network setup to this workspace and its dependent resource has problems or not.
begin_provision_network	Triggers the workspace to provision the managed network. Specifying spark enabled as true prepares the workspace managed network for supporting Spark.
begin_sync_keys	Triggers the workspace to immediately synchronize keys. If keys for any resource in the workspace are changed, it can take around an hour for them to automatically be updated. This function enables keys to be updated upon request. An example scenario is needing immediate access to storage after regenerating storage keys.
begin_update	Updates a Azure Machine Learning Workspace.
get	Get a Workspace by name.
get_keys	Get WorkspaceKeys by workspace name.
list	List all Workspaces that the user has access to in the current resource group or subscription.

begin_create

Create a new Azure Machine Learning Workspace.

Returns the workspace if already exists.

begin_create(workspace: Workspace, update_dependent_resources: bool = False, **kwargs: Any) -> LROPoller[Workspace]

Parameters

Name	Description
workspace Required	Workspace Workspace definition.
update_dependent_resources Required	<xref:boolean> Whether to update dependent resources, defaults to False.

Returns

Type	Description
LROPoller[Workspace]	An instance of LROPoller that returns a Workspace.

Examples

Begin create for a workspace.

   from azure.ai.ml.entities import Workspace

   ws = Workspace(
       name="test-ws1",
       description="a test workspace",
       tags={"purpose": "demo"},
       location="eastus",
       resource_group=resource_group,
   )
   ws = ml_client.workspaces.begin_create(workspace=ws).result()

begin_delete

Delete a workspace.

begin_delete(name: str, *, delete_dependent_resources: bool, permanently_delete: bool = False, **kwargs: Dict) -> LROPoller[None]

Parameters

Name	Description
name Required	str Name of the workspace

Keyword-Only Parameters

Name	Description
delete_dependent_resources	bool Whether to delete resources associated with the workspace, i.e., container registry, storage account, key vault, application insights, log analytics. The default is False. Set to True to delete these resources.
permanently_delete	bool Workspaces are soft-deleted by default to allow recovery of workspace data. Set this flag to true to override the soft-delete behavior and permanently delete your workspace.

Returns

Type	Description
LROPoller[None]	A poller to track the operation status.

Examples

Begin permanent (force) deletion for a workspace and delete dependent resources.

   ml_client.workspaces.begin_delete(name="test-ws", delete_dependent_resources=True, permanently_delete=True)

begin_diagnose

Diagnose workspace setup problems.

If your workspace is not working as expected, you can run this diagnosis to check if the workspace has been broken. For private endpoint workspace, it will also help check if the network setup to this workspace and its dependent resource has problems or not.

begin_diagnose(name: str, **kwargs: Dict) -> LROPoller[DiagnoseResponseResultValue]

Parameters

Name	Description
name Required	str Name of the workspace

Returns

Type	Description
LROPoller[DiagnoseResponseResultValue]	A poller to track the operation status.

Examples

Begin diagnose operation for a workspace.

   diagnose_result = ml_client.workspaces.begin_diagnose(name="test-ws1").result()

begin_provision_network

Triggers the workspace to provision the managed network. Specifying spark enabled as true prepares the workspace managed network for supporting Spark.

begin_provision_network(*, workspace_name: str | None = None, include_spark: bool = False, **kwargs: Any) -> LROPoller[ManagedNetworkProvisionStatus]

Keyword-Only Parameters

Name	Description
workspace_name	str Name of the workspace.
include_spark	Whether the workspace managed network should prepare to support Spark.

Returns

Type	Description
LROPoller[ManagedNetworkProvisionStatus]	An instance of LROPoller.

Examples

Begin provision network for a workspace with managed network.

   ml_client.workspaces.begin_provision_network(workspace_name="test-ws1", include_spark=False)

begin_sync_keys

Triggers the workspace to immediately synchronize keys. If keys for any resource in the workspace are changed, it can take around an hour for them to automatically be updated. This function enables keys to be updated upon request. An example scenario is needing immediate access to storage after regenerating storage keys.

begin_sync_keys(name: str | None = None) -> LROPoller[None]

Parameters

Name	Description
name Required	str Name of the workspace.

Returns

Type	Description
LROPoller[None]	An instance of LROPoller that returns either None or the sync keys result.

Examples

Begin sync keys for the workspace with the given name.

   ml_client.workspaces.begin_sync_keys(name="test-ws1")

begin_update

Updates a Azure Machine Learning Workspace.

begin_update(workspace: Workspace, *, update_dependent_resources: bool = False, **kwargs: Any) -> LROPoller[Workspace]

Parameters

Name	Description
workspace Required	Workspace Workspace definition.

Keyword-Only Parameters

Name	Description
update_dependent_resources	<xref:boolean> Whether to update dependent resources, defaults to False.

Returns

Type	Description
LROPoller[Workspace]	An instance of LROPoller that returns a Workspace.

Examples

Begin update for a workspace.

   ws = ml_client.workspaces.get(name="test-ws1")
   ws.description = "a different description"
   ws = ml_client.workspaces.begin_update(workspace=ws).result()

get

Get a Workspace by name.

get(name: str | None = None, **kwargs: Dict) -> Workspace | None

Parameters

Name	Description
name Required	str Name of the workspace.

Returns

Type	Description
Workspace	The workspace with the provided name.

Examples

Get the workspace with the given name.

   workspace = ml_client.workspaces.get(name="test-ws1")

get_keys

Get WorkspaceKeys by workspace name.

get_keys(name: str | None = None) -> WorkspaceKeys | None

Parameters

Name	Description
name Required	str Name of the workspace.

Returns

Type	Description
WorkspaceKeys	Keys of workspace dependent resources.

Examples

Get the workspace keys for the workspace with the given name.

   ws_keys = ml_client.workspaces.get_keys(name="test-ws1")

list

List all Workspaces that the user has access to in the current resource group or subscription.

list(*, scope: str = 'resource_group') -> Iterable[Workspace]

Keyword-Only Parameters

Name	Description
scope	str scope of the listing, "resource_group" or "subscription", defaults to "resource_group"

Returns

Type	Description
ItemPaged[Workspace]	An iterator like instance of Workspace objects

Examples

List the workspaces by resource group or subscription.

   from azure.ai.ml.constants import Scope

   # list workspaces in the resource group set in ml_client
   workspaces = ml_client.workspaces.list()
   workspaces = ml_client.workspaces.list(scope=Scope.RESOURCE_GROUP)

   # list workspaces in the subscription set in ml_client
   workspaces = ml_client.workspaces.list(scope=Scope.SUBSCRIPTION)