Workspace

Workspaces are a foundational object used throughout Azure ML and are used in the constructors of many other classes. Throughout this documentation we frequently omit the workspace object instantiation and simply refer to ws.

See Installation for instructions on creating a new workspace.

Get workspace#

Instantiate Workspace object used to connect to your AML assets.

run.py

from azureml.core import Workspace
ws = Workspace(
    subscription_id="<subscription_id>",
    resource_group="<resource_group>",
    workspace_name="<workspace_name>",
)

For convenience store your workspace metadata in a config.json.

.azureml/config.json

{
    "subscription_id": <subscription-id>,
    "resource_group": <resource-group>,
    "workspace_name": <workspace-name>
}

Helpful methods#

ws.write_config(path, file_name) : Write the config.json on your behalf. The path defaults to '.azureml/' in the current working directory and file_name defaults to 'config.json'.
Workspace.from_config(path, _file_name): Read the workspace configuration from config. The parameter defaults to starting the search in the current directory.

info

It is recommended to store these in a directory .azureml/ as this path is searched by default in the Workspace.from_config method.

Get Workspace Assets#

The workspace provides a handle to your Azure ML assets:

Compute Targets#

Get all compute targets attached to the workspace.

ws.compute_targets: Dict[str, ComputeTarget]

Datastores#

Get all datastores registered to the workspace.

ws.datastores: Dict[str, Datastore]

Get the workspace's default datastore.

ws.get_default_datastore(): Datastore

Keyvault#

Get workspace's default Keyvault.

ws.get_default_keyvault(): Keyvault

Environments#

Get environments registered to the workspace.

ws.environments: Dict[str, Environment]

MLFlow#

Get MLFlow tracking uri.

ws.get_mlflow_tracking_uri(): str