azure_tracking

class TrackedAzureMLEvaluation(experiment_name: str, workspace: azureml.core.Workspace, evaluator: sensai.evaluation.evaluator.MetricsDictProvider)[source]

Bases: object

Class to automatically track parameters, metrics and artifacts for a single model with azureml-sdk

__init__(experiment_name: str, workspace: azureml.core.Workspace, evaluator: sensai.evaluation.evaluator.MetricsDictProvider)
Parameters

  • experiment_name –

  • workspace –

  • evaluator –

eval_model(model: sensai.vector_model.VectorModel, additional_logging_values_dict: Optional[dict] = None, **start_logging_kwargs)
class TrackedAzureMLExperiment(experiment_name: str, workspace: azureml.core.Workspace, additional_logging_values_dict=None)[source]

Bases: sensai.tracking.tracking_base.TrackedExperiment

__init__(experiment_name: str, workspace: azureml.core.Workspace, additional_logging_values_dict=None)
Parameters

  • experiment_name – name of experiment for tracking in workspace

  • workspace – Azure workspace object

  • additional_logging_values_dict – additional values to be logged for each run