prefect_azure.ml_datastore
¶
Tasks for interacting with Azure ML Datastore
ml_get_datastore
async
¶
Gets the Datastore within the Workspace.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ml_credentials |
AzureMlCredentials
|
Credentials to use for authentication with Azure. |
required |
datastore_name |
str
|
The name of the Datastore. If |
None
|
Example
Get Datastore object
from prefect import flow
from prefect_azure import AzureMlCredentials
from prefect_azure.ml_datastore import ml_get_datastore
@flow
def example_ml_get_datastore_flow():
ml_credentials = AzureMlCredentials(
tenant_id="tenant_id",
service_principal_id="service_principal_id",
service_principal_password="service_principal_password",
subscription_id="subscription_id",
resource_group="resource_group",
workspace_name="workspace_name",
)
results = ml_get_datastore(ml_credentials, datastore_name="datastore_name")
return results
Source code in prefect_azure/ml_datastore.py
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ml_list_datastores
¶
Lists the Datastores in the Workspace.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
ml_credentials |
AzureMlCredentials
|
Credentials to use for authentication with Azure. |
required |
Example
List Datastore objects
from prefect import flow
from prefect_azure import AzureMlCredentials
from prefect_azure.ml_datastore import ml_list_datastores
@flow
def example_ml_list_datastores_flow():
ml_credentials = AzureMlCredentials(
tenant_id="tenant_id",
service_principal_id="service_principal_id",
service_principal_password="service_principal_password",
subscription_id="subscription_id",
resource_group="resource_group",
workspace_name="workspace_name",
)
results = ml_list_datastores(ml_credentials)
return results
Source code in prefect_azure/ml_datastore.py
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ml_register_datastore_blob_container
async
¶
Registers a Azure Blob Storage container as a Datastore in a Azure ML service Workspace.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
container_name |
str
|
The name of the container. |
required |
ml_credentials |
AzureMlCredentials
|
Credentials to use for authentication with Azure ML. |
required |
blob_storage_credentials |
AzureBlobStorageCredentials
|
Credentials to use for authentication with Azure Blob Storage. |
required |
datastore_name |
str
|
The name of the datastore. If not defined, the container name will be used. |
None
|
create_container_if_not_exists |
bool
|
Create a container, if one does not exist with the given name. |
False
|
overwrite |
bool
|
Overwrite an existing datastore. If the datastore does not exist, it will be created. |
False
|
set_as_default |
bool
|
Set the created Datastore as the default datastore for the Workspace. |
False
|
Example
Upload Datastore object
from prefect import flow
from prefect_azure import AzureMlCredentials
from prefect_azure.ml_datastore import ml_register_datastore_blob_container
@flow
def example_ml_register_datastore_blob_container_flow():
ml_credentials = AzureMlCredentials(
tenant_id="tenant_id",
service_principal_id="service_principal_id",
service_principal_password="service_principal_password",
subscription_id="subscription_id",
resource_group="resource_group",
workspace_name="workspace_name",
)
blob_storage_credentials = AzureBlobStorageCredentials("connection_string")
result = ml_register_datastore_blob_container(
"container",
ml_credentials,
blob_storage_credentials,
datastore_name="datastore_name"
)
return result
Source code in prefect_azure/ml_datastore.py
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|
ml_upload_datastore
async
¶
Uploads local files to a Datastore.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
path |
Union[str, Path, List[Union[str, Path]]]
|
The path to a single file, single directory, or a list of path to files to be uploaded. |
required |
ml_credentials |
AzureMlCredentials
|
Credentials to use for authentication with Azure. |
required |
target_path |
Union[str, Path]
|
The location in the blob container to upload to. If None, then upload to root. |
None
|
relative_root |
Union[str, Path]
|
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. |
None
|
datastore_name |
str
|
The name of the Datastore. If |
None
|
overwrite |
bool
|
Overwrite existing file(s). |
False
|
Example
Upload Datastore object
from prefect import flow
from prefect_azure import AzureMlCredentials
from prefect_azure.ml_datastore import ml_upload_datastore
@flow
def example_ml_upload_datastore_flow():
ml_credentials = AzureMlCredentials(
tenant_id="tenant_id",
service_principal_id="service_principal_id",
service_principal_password="service_principal_password",
subscription_id="subscription_id",
resource_group="resource_group",
workspace_name="workspace_name",
)
result = ml_upload_datastore(
"path/to/dir/or/file",
ml_credentials,
datastore_name="datastore_name"
)
return result
Source code in prefect_azure/ml_datastore.py
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|