DatastoreOperations Class

Represents a client for performing operations on Datastores.

You should not instantiate this class directly. Instead, you should create MLClient and use this client via the property MLClient.datastores

Inheritance

azure.ai.ml._scope_dependent_operations._ScopeDependentOperations

DatastoreOperations

Constructor

DatastoreOperations(operation_scope: OperationScope, operation_config: OperationConfig, serviceclient_2024_01_01_preview: AzureMachineLearningWorkspaces, serviceclient_2024_07_01_preview: AzureMachineLearningWorkspaces, **kwargs: Dict)

Parameters

Name	Description
operation_scope Required	<xref:azure.ai.ml._scope_dependent_operations.OperationScope> Scope variables for the operations classes of an MLClient object.
operation_config Required	<xref:azure.ai.ml._scope_dependent_operations.OperationConfig> Common configuration for operations classes of an MLClient object.
serviceclient_2024_01_01_preview Required	<xref:<xref:azure.ai.ml._restclient.v2023_01_01_preview. _azure_machine_learning_workspaces.AzureMachineLearningWorkspaces>> Service client to allow end users to operate on Azure Machine Learning Workspace resources.
serviceclient_2024_07_01_preview Required	<xref:<xref:azure.ai.ml._restclient.v2024_07_01_preview. _azure_machine_learning_workspaces.AzureMachineLearningWorkspaces>> Service client to allow end users to operate on Azure Machine Learning Workspace resources.

Methods

create_or_update	Attaches the passed in datastore to the workspace or updates the datastore if it already exists.
delete	Deletes a datastore reference with the given name from the workspace. This method does not delete the actual datastore or underlying data in the datastore.
get	Returns information about the datastore referenced by the given name.
get_default	Returns the workspace's default datastore.
list	Lists all datastores and associated information within a workspace.
mount	Note This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information. Mount a datastore to a local path, so that you can access data inside it under a local path with any tools of your choice.

create_or_update

Attaches the passed in datastore to the workspace or updates the datastore if it already exists.

create_or_update(datastore: Datastore) -> Datastore

Parameters

Name	Description
datastore Required	Datastore The configuration of the datastore to attach.

Returns

Type	Description
Datastore	The attached datastore.

Examples

Create datastore example.

   from azure.ai.ml.entities import AzureBlobDatastore

   datastore_example = AzureBlobDatastore(
       name="azure_blob_datastore",
       account_name="sdkvnextclidcdnrc7zb7xyy",  # cspell:disable-line
       container_name="testblob",
   )
   ml_client.datastores.create_or_update(datastore_example)

delete

Deletes a datastore reference with the given name from the workspace. This method does not delete the actual datastore or underlying data in the datastore.

delete(name: str) -> None

Parameters

Name	Description
name Required	str Name of the datastore

Examples

Delete datastore example.

   ml_client.datastores.delete("azure_blob_datastore")

get

Returns information about the datastore referenced by the given name.

get(name: str, *, include_secrets: bool = False) -> Datastore

Parameters

Name	Description
name Required	str Datastore name

Keyword-Only Parameters

Name	Description
include_secrets	bool Include datastore secrets in the returned datastore, defaults to False

Returns

Type	Description
Datastore	Datastore with the specified name.

Examples

Get datastore example.

   ml_client.datastores.get("azure_blob_datastore")

get_default

Returns the workspace's default datastore.

get_default(*, include_secrets: bool = False) -> Datastore

Keyword-Only Parameters

Name	Description
include_secrets	bool Include datastore secrets in the returned datastore, defaults to False

Returns

Type	Description
Datastore	The default datastore.

Examples

Get default datastore example.

   ml_client.datastores.get_default()

list

Lists all datastores and associated information within a workspace.

list(*, include_secrets: bool = False) -> Iterable[Datastore]

Keyword-Only Parameters

Name	Description
include_secrets	bool Include datastore secrets in returned datastores, defaults to False

Returns

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

Examples

List datastore example.

   ml_client.datastores.list()

mount

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Mount a datastore to a local path, so that you can access data inside it under a local path with any tools of your choice.

mount(path: str, mount_point: str | None = None, mode: str = 'ro_mount', debug: bool = False, persistent: bool = False, **_kwargs) -> None

Parameters

Name	Description
path Required	str The data store path to mount, in the form of or azureml://datastores/.
mount_point Required	str A local path used as mount point.
mode Required	str Mount mode, either ro_mount (read-only) or rw_mount (read-write).
debug Required	bool Whether to enable verbose logging.
persistent Required	bool Whether to persist the mount after reboot. Applies only when running on Compute Instance, where the 'CI_NAME' environment variable is set."

Returns

Type	Description
	None