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
-
AutoMLRun
Constructor
AutoMLRun(experiment, run_id, **kwargs)Parameters
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 |
| 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_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
None
cancel_iteration
Cancel a particular child run.
cancel_iteration(iteration)Parameters
Returns
None
complete
Complete an AutoML Run.
complete(**kwargs)Returns
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
Sample weights for training data.
Validation features.
Validation labels.
validation set sample weights.
- 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.
- spark_context
- <xref:SparkContext>
Spark context, only applicable when used inside azure databricks/spark environment.
- experiment_timeout_hours
- float
How many additional hours to run this experiment for.
- experiment_exit_score
- int
If specified indicates that the experiment is terminated when this value is reached.
- training_data
- <xref:azureml.dataprep.Dataflow> or DataFrame
Input training data.
Returns
The AutoML parent run.
Return type
Exceptions
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
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) -> RunParameters
- metric
- str
The metric to use to when selecting the best run to return. Defaults to the primary metric.
- onnx_compatible
Whether to only return runs that generated onnx models.
- kwargs
Returns
AutoML Child Run.
get_guardrails
Print and return detailed results from running Guardrail verification.
get_guardrails(to_console: bool = True) -> Dict[str, Any]Parameters
- to_console
- bool
Indicates whether to write the verification results to the console.
Returns
A dictionary of verifier results.
Return type
Exceptions
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
- iteration
- int
The iteration number of the corresponding run and fitted model to return.
- metric
- str
The metric to use to when selecting the best run and fitted model to return.
- 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.
- return_split_onnx_model
- SplitOnnxModelName
The type of the split onnx model to return
Returns
The run, the corresponding fitted model.
Return type
Exceptions
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
- iteration
- int
The iteration number of the fitted run to be retrieved. If None, retrieve the parent environment.
- check_versions
- bool
If True, check the versions with current environment. If False, pass.
Returns
The dictionary of dependencies retrieved from RunHistory.
Return type
Exceptions
pause
Return True if the AutoML run was paused successfully.
This method is not implemented.
pause()Exceptions
register_model
Register the model with AzureML ACI service.
register_model(model_name=None, description=None, tags=None, iteration=None, metric=None)Parameters
- iteration
- int
Override for which model to deploy. Deploys the model for a given iteration.
- metric
- str
Override for which model to deploy. Deploys the best model for a different metric.
Returns
The registered model object.
Return type
resume
Return True if the AutoML run was resumed successfully.
This method is not implemented.
resume()Exceptions
retry
Return True if the AutoML run was retried successfully.
This method is not implemented.
retry()Exceptions
summary
Get a table containing a summary of algorithms attempted and their scores.
summary()Returns
Pandas DataFrame containing AutoML model statistics.
Return type
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
- wait_post_processing
- bool
Indicates whether to wait for the post processing to complete after the run completes.
Returns
The status object.
Return type
Exceptions
Attributes
run_id
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