Version: 0.0.7

View in Studio: https://ml.azure.com/registries/azureml/components/automl_hts_inference_setup_step/version/0.0.7

Name	Description	Type	Default	Optional	Enum
raw_data	Folder URI with inference data.	uri_folder
instance_count	Number of nodes in a compute cluster we will run the inference step on.	integer
max_concurrency_per_instance	Number of processes that will be run concurrently on any given node. This number should not be larger than 1/2 of the number of cores in an individual node in the specified cluster	integer
parallel_step_timeout_in_seconds	The PRS step time out setting in seconds.	integer	3600
train_run_id	The train run id used for training models that will be used to generate forecasts.	string		True
train_experiment_name	The train experiment used for inference	string		True
forecast_mode	The forecast mode used for inference. The possible values are recursive and rolling.	string	recursive	True	['recursive', 'rolling']
forecast_step	The forecast step used for rolling forecast. See more details here: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-forecast?view=azureml-api-2#evaluating-model-accuracy-with-a-rolling-forecast	integer		True
allocation_method	Method that allocates forecasts within a hierarchy. Possible values are average_historical_proportions and proportions_of_historical_average	string	average_historical_proportions		['average_historical_proportions', 'proportions_of_historical_average']
forecast_level	Specify the level of the hierarchy for which you are interested in obtaining the forecast for.	string
forecast_quantiles	Space separated list of quantiles for forecasting jobs. It is applicable only when the forecast_mode is recursive.	string		True
optional_train_metadata	Metadata from training run.	uri_folder		True
enable_event_logger	Enable event logger.	boolean		True

Name	Description	Type
metadata	Metadata calculated.	uri_folder
processed_data	Processed input data.	uri_folder

azureml://registries/azureml/environments/automl-gpu/labels/latest