EstimatorStep Class

DEPRECATED. Creates a pipeline step to run Estimator for Azure ML model training.

Create an Azure ML Pipeline step to run Estimator for Machine Learning model training.

DEPRECATED. Use the CommandStep instead. For an example see How to run ML training in pipelines with CommandStep.

Inheritance

PythonScriptStep

EstimatorStep

Constructor

EstimatorStep(name=None, estimator=None, estimator_entry_script_arguments=None, runconfig_pipeline_params=None, inputs=None, outputs=None, compute_target=None, allow_reuse=True, version=None)

Parameters

Name	Description
name	str The name of the step. Default value: None
estimator	Estimator The associated estimator object for this step. Can be a pre-configured estimator such as Chainer, PyTorch, TensorFlow, or SKLearn. Default value: None
estimator_entry_script_arguments	list[str] [Required] A list of command-line arguments. If the Estimator's entry script does not accept commandline arguments, set this parameter value to an empty list. Default value: None
runconfig_pipeline_params	dict[str, PipelineParameter] An override of runconfig properties at runtime using key-value pairs, each with name of the runconfig property and PipelineParameter for that property. Supported values: 'NodeCount', 'MpiProcessCountPerNode', 'TensorflowWorkerCount', 'TensorflowParameterServerCount' Default value: None
inputs	list[Union[PipelineData, PipelineOutputAbstractDataset, DataReference, DatasetConsumptionConfig]] A list of inputs to use. Default value: None
outputs	list[Union[PipelineData, OutputDatasetConfig, PipelineOutputAbstractDataset]] A list of PipelineData objects. Default value: None
compute_target	Union[DsvmCompute, AmlCompute, RemoteCompute, str] [Required] The compute target to use. Default value: None
allow_reuse	bool Indicates whether the step should reuse previous results when re-run with the same settings. Reuse is enabled by default. If the step contents (scripts/dependencies) as well as inputs and parameters remain unchanged, the output from the previous run of this step is reused. When reusing the step, instead of submitting the job to compute, the results from the previous run are immediately made available to any subsequent steps. If you use Azure Machine Learning datasets as inputs, reuse is determined by whether the dataset's definition has changed, not by whether the underlying data has changed. Default value: True
version	str An optional version tag to denote a change in functionality for the module. Default value: None
name Required	str The name of the step.
estimator Required	<xref:Estimator> The associated estimator object for this step. Can be a pre-configured estimator such as Chainer, PyTorch, TensorFlow, or SKLearn.
estimator_entry_script_arguments Required	[str] [Required] A list of command-line arguments. If the Estimator's entry script does not accept commandline arguments, set this parameter value to an empty list.
runconfig_pipeline_params Required	dict[str, PipelineParameter] An override of runconfig properties at runtime using key-value pairs, each with name of the runconfig property and PipelineParameter for that property. Supported values: 'NodeCount', 'MpiProcessCountPerNode', 'TensorflowWorkerCount', 'TensorflowParameterServerCount'
inputs Required	list[Union[PipelineData, PipelineOutputAbstractDataset, DataReference, DatasetConsumptionConfig, PipelineOutputTabularDataset, PipelineOutputFileDataset]] A list of inputs to use.
outputs Required	list[Union[PipelineData, PipelineOutputAbstractDataset] A list of PipelineData objects.
compute_target Required	Union[DsvmCompute, AmlCompute, RemoteCompute, str] [Required] The compute target to use.
allow_reuse Required	bool Indicates whether the step should reuse previous results when re-run with the same settings. Reuse is enabled by default. If the step contents (scripts/dependencies) as well as inputs and parameters remain unchanged, the output from the previous run of this step is reused. When reusing the step, instead of submitting the job to compute, the results from the previous run are immediately made available to any subsequent steps. If you use Azure Machine Learning datasets as inputs, reuse is determined by whether the dataset's definition has changed, not by whether the underlying data has changed.
version Required	str version

Remarks

Note that the arguments to the entry script used in the Estimator object must be specified as list using the estimator_entry_script_arguments parameter when instantiating an EstimatorStep. The Estimator parameter script_params accepts a dictionary. However, estimator_entry_script_argument parameter expects arguments as a list.

The EstimatorStep initialization involves specifying a list of inputs with the inputs parameter and you do not need to specify the inputs with the Estimator, an exception will be thrown if you do. Please refer to the inputs parameter for the types of inputs that are allowed. You can also optionally specify any outputs for the step. Please refer to the outputs parameter for the types of outputs that are allowed.

The best practice for working with EstimatorStep is to use a separate folder for scripts and any dependent files associated with the step, and specify that folder as the Estimator object's source_directory. Doing so has two benefits. First, it helps reduce the size of the snapshot created for the step because only what is needed for the step is snapshotted. Second, the step's output from a previous run can be reused if there are no changes to the source_directory that would trigger a re-upload of the snaphot.

Methods

create_node

Create a node from the Estimator step and add it to the specified graph. DEPRECATED. Use the CommandStep instead. For an example see How to run ML training in pipelines with CommandStep. This method is not intended to be used directly. When a pipeline is instantiated with this step, Azure ML automatically passes the parameters required through this method so that step can be added to a pipeline graph that represents the workflow.

create_node

Create a node from the Estimator step and add it to the specified graph.

DEPRECATED. Use the CommandStep instead. For an example see How to run ML training in pipelines with CommandStep.

This method is not intended to be used directly. When a pipeline is instantiated with this step, Azure ML automatically passes the parameters required through this method so that step can be added to a pipeline graph that represents the workflow.

create_node(graph, default_datastore, context)

Parameters

Name	Description
graph Required	Graph The graph object to add the node to.
default_datastore Required	Union[AbstractAzureStorageDatastore, AzureDataLakeDatastore] The default datastore.
context Required	<xref:azureml.pipeline.core._GraphContext> The graph context.

Returns

Type	Description
Node	The created node.