DataOperations Class

Reference

DataOperations.

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._scope_dependent_operations._ScopeDependentOperations

DataOperations

Constructor

DataOperations(operation_scope: OperationScope, operation_config: OperationConfig, service_client: AzureMachineLearningWorkspaces | AzureMachineLearningWorkspaces, service_client_012024_preview: AzureMachineLearningWorkspaces, datastore_operations: DatastoreOperations, **kwargs: Any)

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.
service_client Required	Union[ <xref:azure.ai.ml._restclient.v2023_04_01_preview._azure_machine_learning_workspaces.AzureMachineLearningWorkspaces>, <xref:azure.ai.ml._restclient.v2021_10_01_dataplanepreview._azure_machine_learning_workspaces. AzureMachineLearningWorkspaces>] Service client to allow end users to operate on Azure Machine Learning Workspace resources (ServiceClient042023Preview or ServiceClient102021Dataplane).
datastore_operations Required	DatastoreOperations Represents a client for performing operations on Datastores.
service_client_012024_preview Required

Methods

archive	Archive a data asset.
create_or_update	Returns created or updated data asset. If not already in storage, asset will be uploaded to the workspace's blob storage.
get	Get the specified data asset.
import_data	Note This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information. Returns the data import job that is creating the data asset.
list	List the data assets of the workspace.
list_materialization_status	List materialization jobs of the asset.
mount	Note This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information. Mount a data asset to a local path, so that you can access data inside it under a local path with any tools of your choice.
restore	Restore an archived data asset.
share	Note This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information. Share a data asset from workspace to registry.

create_or_update

Returns created or updated data asset.

If not already in storage, asset will be uploaded to the workspace's blob storage.

create_or_update(data: Data) -> Data

Parameters

Name	Description
data Required	Data Data asset object.

Returns

Type	Description
Data	Data asset object.

Exceptions

Type	Description
AssetPathException	Raised when the Data artifact path is already linked to another asset
ValidationException	Raised if Data cannot be successfully validated. Details will be provided in the error message.
EmptyDirectoryError	Raised if local path provided points to an empty directory.

Examples

Create data assets example.


   from azure.ai.ml.entities import Data

   data_asset_example = Data(name=data_asset_name, version="2.0", path="./sdk/ml/azure-ai-ml/samples/src")
   ml_client.data.create_or_update(data_asset_example)

get

Get the specified data asset.

get(name: str, version: str | None = None, label: str | None = None) -> Data

Parameters

Name	Description
name Required	str Name of data asset.
version Required	str Version of data asset.
label Required	str Label of the data asset. (mutually exclusive with version)

Returns

Type	Description
Data	Data asset object.

Exceptions

Type	Description
ValidationException	Raised if Data cannot be successfully identified and retrieved. Details will be provided in the error message.

Examples

Get data assets example.


   ml_client.data.get(name="data_asset_name", version="2.0")

import_data

Note

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

Returns the data import job that is creating the data asset.

import_data(data_import: DataImport, **kwargs: Any) -> PipelineJob

Parameters

Name	Description
data_import Required	DataImport DataImport object.

Returns

Type	Description
PipelineJob	data import job object.

Examples

Import data assets example.


   from azure.ai.ml.entities._data_import.data_import import DataImport
   from azure.ai.ml.entities._inputs_outputs.external_data import Database

   database_example = Database(query="SELECT ID FROM DataTable", connection="azureml:my_azuresqldb_connection")
   data_import_example = DataImport(
       name="data_asset_name", path="azureml://datastores/workspaceblobstore/paths/", source=database_example
   )
   ml_client.data.import_data(data_import_example)

list

List the data assets of the workspace.

list(name: str | None = None, *, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY) -> ItemPaged[Data]

Parameters

Name	Description
name Required	Optional[str] Name of a specific data asset, optional.

Keyword-Only Parameters

Name	Description
list_view_type	View type for including/excluding (for example) archived data assets. Default: ACTIVE_ONLY.

Returns

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

Examples

List data assets example.


   ml_client.data.list(name="data_asset_name")

list_materialization_status

List materialization jobs of the asset.

list_materialization_status(name: str, *, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY, **kwargs: Any) -> Iterable[PipelineJob]

Parameters

Name	Description
name Required	str name of asset being created by the materialization jobs.

Keyword-Only Parameters

Name	Description
list_view_type	Optional[ListViewType] View type for including/excluding (for example) archived jobs. Default: ACTIVE_ONLY.

Returns

Type	Description
ItemPaged[PipelineJob]	An iterator like instance of Job objects.

Examples

List materialization jobs example.


   ml_client.data.list_materialization_status("data_asset_name")

mount

Note

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

Mount a data asset 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 asset path to mount, in the form of azureml: or azureml::.
mount_point Required	str A local path used as mount point.
mode Required	str Mount mode. Only ro_mount (read-only) is supported for data asset mount.
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

restore

Restore an archived data asset.

restore(name: str, version: str | None = None, label: str | None = None, **kwargs: Any) -> None

Parameters

Name	Description
name Required	str Name of data asset.
version Required	str Version of data asset.
label Required	str Label of the data asset. (mutually exclusive with version)

Returns

Type	Description
	None

Examples

Restore data asset example.


   ml_client.data.restore("data_asset_name")

Note

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

Share a data asset from workspace to registry.

share(name: str, version: str, *, share_with_name: str, share_with_version: str, registry_name: str, **kwargs: Any) -> Data

Parameters

Name	Description
name Required	str Name of data asset.
version Required	str Version of data asset.

Keyword-Only Parameters

Name	Description
share_with_name	str Name of data asset to share with.
share_with_version	str Version of data asset to share with.
registry_name	str Name of the destination registry.

Returns

Type	Description
Data	Data asset object.

Examples

Share data asset example.


       ml_client.data.share(
           name="data_asset_name",
           version="2.0",
           registry_name="my-registry",
           share_with_name="transformed-nyc-taxi-data-shared-from-ws",
           share_with_version="2.0",
       )

Share via

DataOperations Class

Constructor

Parameters

Methods

archive

Parameters

Returns

Examples

create_or_update

Parameters

Returns

Exceptions

Examples

get

Parameters

Returns

Exceptions

Examples

import_data

Parameters

Returns

Examples

list

Parameters

Keyword-Only Parameters

Returns

Examples

list_materialization_status

Parameters

Keyword-Only Parameters

Returns

Examples

mount

Parameters

Returns

restore

Parameters

Returns

Examples

share

Parameters

Keyword-Only Parameters

Returns

Examples

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