API

health.azure Package

Functions

fetch_run(workspace, run_recovery_id)	Finds an existing run in an experiment, based on a recovery ID that contains the experiment ID and the actual RunId.
set_environment_variables_for_multi_node()	Sets the environment variables that PyTorch Lightning needs for multi-node training.
split_recovery_id(id)	Splits a run ID into the experiment name and the actual run.
create_run_configuration(workspace, …[, …])	Creates an AzureML run configuration, that contains information about environment, multi node execution, and Docker.
create_script_run([snapshot_root_directory, …])	Creates an AzureML ScriptRunConfig object, that holds the information about the snapshot, the entry script, and its arguments.
get_workspace(aml_workspace, …)	Obtain the AzureML workspace from either the passed in value or the passed in path.
submit_run(workspace, experiment_name, …)	Starts an AzureML run on a given workspace, via the script_run_config.
submit_to_azure_if_needed([…])	Submit a folder to Azure, if needed and run it.

Classes

DatasetConfig(name[, datastore, version, …])	Contains information to use AzureML datasets as inputs or outputs.
AzureRunInfo(input_datasets, …)	This class stores all information that a script needs to run inside and outside of AzureML.