run Module

Reference

Contains functionality for managing experiment metrics and artifacts in Azure Machine Learning.

Classes

InputDatasets	Defines a container for holding a materialized dataset in a run. Initialize the InputDatasets object.
LinkOutput	Defines a container for holding a output path in a run. ... remarks: An LinkOutput object is OutputData that will be linked with dataset in data plane.. Initialize the LinkOutput object.
OutputDatasets	Defines a container for holding a output path in a run. Initialize the OutputDatasets object.
Run	Defines the base class for all Azure Machine Learning experiment runs. A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial. Run objects are created when you submit a script to train a model in many different scenarios in Azure Machine Learning, including HyperDrive runs, Pipeline runs, and AutoML runs. A Run object is also created when you submit or start_logging with the Experiment class. To get started with experiments and runs, see Create and manage environments for training and deployment How to manage runs Initialize the Run object.

Functions

get_run

Get the run for this experiment with its run ID.

get_run(experiment, run_id, rehydrate=True, clean_up=True)

Parameters

experiment: Experiment

Required

The containing experiment.

run_id: string

Required

The run ID.

rehydrate: <xref:boolean>

default value: True

Indicates whether the original run object is returned or just a base run object. If True, this function returns the original run object type. For example, for an AutoML run, an AutoMLRun object is returned, while for a HyperDrive run, a HyperDriveRun object is returned.

If False, the function returns a Run object.

clean_up: bool

default value: True

If true, call _register_kill_handler from run_base

Returns

The submitted run.

Return type

Run