run Module

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.

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..

OutputDatasets

Defines a container for holding a output path in a run.

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

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