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ml_datastore

docs/integrations/prefect-azure/api-ref/prefect_azure-ml_datastore.mdx

3.6.30.dev33.6 KB
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prefect_azure.ml_datastore

Tasks for interacting with Azure ML Datastore

Functions

ml_list_datastores <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L25" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

python
ml_list_datastores(ml_credentials: 'AzureMlCredentials') -> Dict

Lists the Datastores in the Workspace.

Args:

  • ml_credentials: Credentials to use for authentication with Azure.

ml_get_datastore <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L79" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

python
ml_get_datastore(ml_credentials: 'AzureMlCredentials', datastore_name: Optional[str] = None) -> Datastore

Gets the Datastore within the Workspace.

Args:

  • ml_credentials: Credentials to use for authentication with Azure.
  • datastore_name: The name of the Datastore. If None, then the default Datastore of the Workspace is returned.

ml_upload_datastore <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L119" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

python
ml_upload_datastore(path: Union[str, Path, List[Union[str, Path]]], ml_credentials: 'AzureMlCredentials', target_path: Union[str, Path, None] = None, relative_root: Union[str, Path, None] = None, datastore_name: Optional[str] = None, overwrite: bool = False) -> 'DataReference'

Uploads local files to a Datastore.

Args:

  • path: The path to a single file, single directory, or a list of path to files to be uploaded.
  • ml_credentials: Credentials to use for authentication with Azure.
  • target_path: The location in the blob container to upload to. If None, then upload to root.
  • relative_root: The root from which is used to determine the path of the files in the blob. For example, if we upload /path/to/file.txt, and we define base path to be /path, when file.txt is uploaded to the blob storage, it will have the path of /to/file.txt.
  • datastore_name: The name of the Datastore. If None, then the default Datastore of the Workspace is returned.
  • overwrite: Overwrite existing file(s).

ml_register_datastore_blob_container <sup><a href="https://github.com/PrefectHQ/prefect/blob/main/src/integrations/prefect-azure/prefect_azure/ml_datastore.py#L208" target="_blank"><Icon icon="github" style="width: 14px; height: 14px;" /></a></sup>

python
ml_register_datastore_blob_container(container_name: str, ml_credentials: 'AzureMlCredentials', blob_storage_credentials: 'AzureBlobStorageCredentials', datastore_name: Optional[str] = None, create_container_if_not_exists: bool = False, overwrite: bool = False, set_as_default: bool = False) -> 'AzureBlobDatastore'

Registers a Azure Blob Storage container as a Datastore in a Azure ML service Workspace.

Args:

  • container_name: The name of the container.
  • ml_credentials: Credentials to use for authentication with Azure ML.
  • blob_storage_credentials: Credentials to use for authentication with Azure Blob Storage.
  • datastore_name: The name of the datastore. If not defined, the container name will be used.
  • create_container_if_not_exists: Create a container, if one does not exist with the given name.
  • overwrite: Overwrite an existing datastore. If the datastore does not exist, it will be created.
  • set_as_default: Set the created Datastore as the default datastore for the Workspace.