AzureRunInfo

class health_azure.AzureRunInfo(input_datasets, output_datasets, mount_contexts, run, is_running_in_azure_ml, output_folder, logs_folder)[source]

Bases: object

This class stores all information that a script needs to run inside and outside of AzureML. It is return from submit_to_azure_if_needed, where the return value depends on whether the script is inside or outside AzureML.

Please check the source code for detailed documentation for all fields.

input_datasets: List[Optional[pathlib.Path]]: A list of folders that contain all the datasets that the script uses as inputs. Input datasets must be specified when calling submit_to_azure_if_needed. Here, they are made available as Path objects. If no input datasets are specified, the list is empty.

is_running_in_azure_ml: bool: If True, the present script is executing inside AzureML. If False, outside AzureML.

logs_folder: Optional[pathlib.Path]: The folder into which all log files (for example, tensorboard) should be written. All files written to this folder will be uploaded to blob storage regularly during the script run.

mount_contexts: List[azureml.dataprep.fuse.daemon.MountContext]: A list of mount contexts for input datasets when running outside AzureML. There will be a mount context for each input dataset where there is no local_folder, there is a workspace, and use_mounting is set. This list is maintained only to prevent exit from these contexts until the RunInfo object is deleted.

output_datasets: List[Optional[pathlib.Path]]: A list of folders that contain all the datasets that the script uses as outputs. Output datasets must be specified when calling submit_to_azure_if_needed. Here, they are made available as Path objects. If no output datasets are specified, the list is empty.

output_folder: Optional[pathlib.Path]: The output folder into which all script outputs should be written, if they should be later available in the AzureML portal. Files written to this folder will be uploaded to blob storage at the end of the script run.

run: Optional[azureml.core.run.Run]: An AzureML Run object if the present script is executing inside AzureML, or None if outside of AzureML. The Run object has methods to log metrics, upload files, etc.