Table of Contents
How to install the Azure ML SDK
The latest info is always here.
How to retrieve the current workspace from a remote Run context
ws = Run.get_context().experiment.workspace print(ws)
Useful in pipelines, see this article for a complete example.
How to download a model from the models repository
model_ws = Model(ws, '<your-model-name>') pickled_model_path = model_ws.download(exist_ok = True) model = joblib.load(pickled_model_path) print(model)
Testing pipeline steps locally
This mostly works with the current version of the SDK, however when trying to access stuff like the current experiment or its workspace we get a nice
AttributeError: '_OfflineRun' object has no attribute 'experiment'. You can prevent this by checking if the
experiment property exists and initialize the workspace from a local config.json if it doesn’t.
aml_context = Run.get_context() # assumes a config.json file exists in the current or the parent directory ws = Workspace.from_config() if not hasattr(aml_context, 'experiment') else aml_context.experiment.workspace
Alternate way which looks better to me as a C# dev, but I can’t stop wondering if it’s pythonic enough. 🤨
from azureml.core.run import _OfflineRun aml_context = Run.get_context() ws = Workspace.from_config() if type(aml_context) == _OfflineRun else aml_context.experiment.workspace
Requesting a compute quota increase
By default you get 10 cores, which may or may not be enough. You can increase them by contacting support, see this and this for details.
Authenticating to a different tenant than usual
from azureml.core import Workspace from azureml.core.authentication import InteractiveLoginAuthentication forced_interactive_auth = InteractiveLoginAuthentication(tenant_id='your-tenant-id', force=True) ws = Workspace.from_config(auth=forced_interactive_auth)
– via Azure Machine Learning Notebooks