AutoMLRun Class

Represents an automated ML experiment run in Azure Machine Learning.

The AutoMLRun class can be used to manage a run, check run status, and retrieve run details once an AutoML run is submitted. For more information on working with experiment runs, see the Run class.

Initialize an AutoML run.

Inheritance

Run

AutoMLRun

Constructor

AutoMLRun(experiment, run_id, **kwargs)

Parameters

Name	Description
experiment Required	Experiment The experiment associated with the run.
run_id Required	str The ID of the run.
experiment Required	Experiment The experiment associated with the run.
run_id Required	str The ID of the run.

Remarks

An AutoMLRun object is returned when you use the submit method of an experiment.

To retrieve a run that has already started, use the following code:

   from azureml.train.automl.run import AutoMLRun
   ws = Workspace.from_config()
   experiment = ws.experiments['my-experiment-name']
   automl_run = AutoMLRun(experiment, run_id = 'AutoML_9fe201fe-89fd-41cc-905f-2f41a5a98883')

Methods

cancel	Cancel an AutoML run. Return True if the AutoML run was canceled successfully.
cancel_iteration	Cancel a particular child run.
complete	Complete an AutoML Run.
continue_experiment	Continue an existing AutoML experiment.
fail	Fail an AutoML Run. Optionally set the Error property of the run with a message or exception passed to error_details.
get_best_child	Return the child run with the best score for this AutoML Run.
get_guardrails	Print and return detailed results from running Guardrail verification.
get_output	Return the run with the corresponding best pipeline that has already been tested. If no input parameters are provided, get_output returns the best pipeline according to the primary metric. Alternatively, you can use either the iteration or metric parameter to retrieve a particular iteration or the best run per provided metric, respectively.
get_run_sdk_dependencies	Get the SDK run dependencies for a given run.
pause	Return True if the AutoML run was paused successfully. This method is not implemented.
register_model	Register the model with AzureML ACI service.
resume	Return True if the AutoML run was resumed successfully. This method is not implemented.
retry	Return True if the AutoML run was retried successfully. This method is not implemented.
summary	Get a table containing a summary of algorithms attempted and their scores.
wait_for_completion	Wait for the completion of this run. Returns the status object after the wait.

cancel

Cancel an AutoML run.

Return True if the AutoML run was canceled successfully.

cancel()

Returns

Type	Description
	None

cancel_iteration

Cancel a particular child run.

cancel_iteration(iteration)

Parameters

Name	Description
iteration Required	int The iteration to cancel.

Returns

Type	Description
	None

complete

Complete an AutoML Run.

complete(**kwargs)

Returns

Type	Description
	None

continue_experiment

Continue an existing AutoML experiment.

continue_experiment(X=None, y=None, sample_weight=None, X_valid=None, y_valid=None, sample_weight_valid=None, data=None, label=None, columns=None, cv_splits_indices=None, spark_context=None, experiment_timeout_hours=None, experiment_exit_score=None, iterations=None, show_output=False, training_data=None, validation_data=None, **kwargs)

Parameters

Name	Description
X	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> Training features. Default value: None
y	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> Training labels. Default value: None
sample_weight	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> Sample weights for training data. Default value: None
X_valid	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> Validation features. Default value: None
y_valid	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> Validation labels. Default value: None
sample_weight_valid	DataFrame or ndarray or <xref:azureml.dataprep.Dataflow> validation set sample weights. Default value: None
data	DataFrame Training features and label. Default value: None
label	str Label column in data. Default value: None
columns	list(str) A list of allowed columns in the data to use as features. Default value: None
cv_splits_indices	ndarray Indices where to split training data for cross validation. Each row is a separate cross fold and within each crossfold, provide 2 arrays, the first with the indices for samples to use for training data and the second with the indices to use for validation data. i.e [[t1, v1], [t2, v2], ...] where t1 is the training indices for the first cross fold and v1 is the validation indices for the first cross fold. Default value: None
spark_context	<xref:SparkContext> Spark context, only applicable when used inside azure databricks/spark environment. Default value: None
experiment_timeout_hours	float How many additional hours to run this experiment for. Default value: None
experiment_exit_score	int If specified indicates that the experiment is terminated when this value is reached. Default value: None
iterations	int How many additional iterations to run for this experiment. Default value: None
show_output	bool Flag indicating whether to print output to console. Default value: False
training_data	<xref:azureml.dataprep.Dataflow> or DataFrame Input training data. Default value: None
validation_data	<xref:azureml.dataprep.Dataflow> or DataFrame Validation data. Default value: None

Returns

Type	Description
AutoMLRun	The AutoML parent run.

Exceptions

Type	Description
ConfigException
InvalidRunState

fail

Fail an AutoML Run.

Optionally set the Error property of the run with a message or exception passed to error_details.

fail(error_details=None, error_code=None, _set_status=True, **kwargs)

Parameters

Name	Description
error_details	str or BaseException Optional details of the error. Default value: None
error_code	str Optional error code of the error for the error classification. Default value: None
_set_status	bool Indicates whether to send the status event for tracking. Default value: True

get_best_child

Return the child run with the best score for this AutoML Run.

get_best_child(metric: str | None = None, onnx_compatible: bool = False, **kwargs: Any) -> Run

Parameters

Name	Description
metric	str The metric to use to when selecting the best run to return. Defaults to the primary metric. Default value: None
onnx_compatible	Whether to only return runs that generated onnx models. Default value: False
kwargs Required

Returns

Type	Description
	AutoML Child Run.

get_guardrails

Print and return detailed results from running Guardrail verification.

get_guardrails(to_console: bool = True) -> Dict[str, Any]

Parameters

Name	Description
to_console	bool Indicates whether to write the verification results to the console. Default value: True

Returns

Type	Description
dict	A dictionary of verifier results.

Exceptions

Type	Description
UserErrorException

get_output

Return the run with the corresponding best pipeline that has already been tested.

If no input parameters are provided, get_output returns the best pipeline according to the primary metric. Alternatively, you can use either the iteration or metric parameter to retrieve a particular iteration or the best run per provided metric, respectively.

get_output(iteration: int | None = None, metric: str | None = None, return_onnx_model: bool = False, return_split_onnx_model: SplitOnnxModelName | None = None, **kwargs: Any) -> Tuple[Run, Any]

Parameters

Name	Description
iteration	int The iteration number of the corresponding run and fitted model to return. Default value: None
metric	str The metric to use to when selecting the best run and fitted model to return. Default value: None
return_onnx_model	bool This method will return the converted ONNX model if the enable_onnx_compatible_models parameter was set to True in the AutoMLConfig object. Default value: False
return_split_onnx_model	SplitOnnxModelName The type of the split onnx model to return Default value: None

Returns

Type	Description
Run, <xref:Model>	The run, the corresponding fitted model.

Exceptions

Type	Description
ConfigException
OnnxConvertException
NotFoundException

Remarks

If you'd like to inspect the preprocessor(s) and algorithm (estimator) used, you can do so through Model.steps, similar to sklearn.pipeline.Pipeline.steps. For instance, the code below shows how to retrieve the estimator.

   best_run, model = parent_run.get_output()
   estimator = model.steps[-1]

get_run_sdk_dependencies

Get the SDK run dependencies for a given run.

get_run_sdk_dependencies(iteration=None, check_versions=True, **kwargs)

Parameters

Name	Description
iteration	int The iteration number of the fitted run to be retrieved. If None, retrieve the parent environment. Default value: None
check_versions	bool If True, check the versions with current environment. If False, pass. Default value: True

Returns

Type	Description
dict	The dictionary of dependencies retrieved from RunHistory.

Exceptions

Type	Description
ConfigException

pause

Return True if the AutoML run was paused successfully.

This method is not implemented.

pause()

Exceptions

Type	Description
NotImplementedError

register_model

Register the model with AzureML ACI service.

register_model(model_name=None, description=None, tags=None, iteration=None, metric=None)

Parameters

Name	Description
model_name	str The name of the model being deployed. Default value: None
description	str The description for the model being deployed. Default value: None
tags	dict Tags for the model being deployed. Default value: None
iteration	int Override for which model to deploy. Deploys the model for a given iteration. Default value: None
metric	str Override for which model to deploy. Deploys the best model for a different metric. Default value: None

Returns

Type	Description
<xref:Model>	The registered model object.

resume

Return True if the AutoML run was resumed successfully.

This method is not implemented.

resume()

Exceptions

Type	Description
NotImplementedError:

retry

Return True if the AutoML run was retried successfully.

This method is not implemented.

retry()

Exceptions

Type	Description
NotImplementedError

summary

Get a table containing a summary of algorithms attempted and their scores.

summary()

Returns

Type	Description
DataFrame	Pandas DataFrame containing AutoML model statistics.

wait_for_completion

Wait for the completion of this run.

Returns the status object after the wait.

wait_for_completion(show_output=False, wait_post_processing=False)

Parameters

Name	Description
show_output	bool Indicates whether to show the run output on sys.stdout. Default value: False
wait_post_processing	bool Indicates whether to wait for the post processing to complete after the run completes. Default value: False

Returns

Type	Description
dict	The status object.

Exceptions

Type	Description
ExperimentExecutionException

Attributes

run_id

Return the run ID of the current run.

Returns

Type	Description
str	The run ID of the current run.