ModuleStepBase Class

Adds a step to a pipeline that uses a specific module.

A ModuleStep derives from ModuleStepBase and is a node in a pipeline that uses an existing Module, and specifically, one of its versions. In order to define which ModuleVersion would eventually be used in the submitted pipeline, you can define one of the following when creating the ModuleStep:

• ModuleVersion object
• Module object and a version value
• Only Module without a version value; in this case, the version resolution used may vary across submissions.

You also need to define the mapping between the step's inputs and outputs to the ModuleVersion object's inputs and outputs.

Initialize ModuleStepBase.

Inheritance

PipelineStep

ModuleStepBase

Constructor

ModuleStepBase(module=None, version=None, module_version=None, inputs_map=None, outputs_map=None, compute_target=None, runconfig=None, runconfig_pipeline_params=None, arguments=None, params=None, name=None, _workflow_provider=None)

Parameters

Name	Description
module	Module The Module of the step. default value: None
version	str The version of the Module. default value: None
module_version	ModuleVersion The ModuleVersion of the step. Either Module of ModuleVersion must be provided. default value: None
inputs_map	Dict[str, Union[InputPortBinding, DataReference, PortDataReference, PipelineData, Dataset, DatasetDefinition, PipelineDataset]] A dictionary where keys are names of inputs on the module_version and values are input port bindings. default value: None
outputs_map	Dict[str, Union[OutputPortBinding, DataReference, PortDataReference, PipelineData, Dataset, DatasetDefinition, PipelineDataset]] A dictionary where keys are names of inputs on the module_version and values are output port bindings. default value: None
runconfig_pipeline_params	Dict[str, PipelineParameter] Override 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
arguments	[str] Command line arguments for the script file. The arguments will be delivered to compute via arguments in RunConfiguration. For more details on how to handle arguments such as special symbols, please refer arguments in RunConfiguration. default value: None
params	Dict[str, str] A dictionary of name-value parameter pairs. default value: None
_workflow_provider	<xref:azureml.pipeline.core._aeva_provider._AevaWorkflowProvider> (Internal use only.) The workflow provider. default value: None
module Required	Module The Module of the step
version Required	str The version of the Module
module_version Required	ModuleVersion The ModuleVersion of the step. Either Module of ModuleVersion must be provided
inputs_map Required	Dict[str, Union[InputPortBinding, DataReference, PortDataReference, PipelineData, Dataset, DatasetDefinition, PipelineDataset]] A dictionary where keys are names of inputs on the module_version and values are input port bindings.
outputs_map Required	Dict[str, Union[OutputPortBinding, DataReference, PortDataReference, PipelineData, Dataset, DatasetDefinition, PipelineDataset]] A dictionary where keys are names of inputs on the module_version and values are output port bindings.
compute_target	<xref:DsvmCompute>, <xref:AmlCompute>, <xref:ComputeInstance>, <xref:RemoteTarget>, <xref:HDIClusterTarget>, str, tuple Compute target to use. If unspecified, the target from the runconfig will be used. compute_target may be a compute target object or the string name of a compute target on the workspace. Optionally if the compute target is not available at pipeline creation time, you may specify a tuple of ('compute target name', 'compute target type') to avoid fetching the compute target object (AmlCompute type is 'AmlCompute' and RemoteTarget type is 'VirtualMachine') default value: None
runconfig	RunConfiguration The RunConfiguration to use, optional. A RunConfiguration can be used to specify additional requirements for the run, such as conda dependencies and a docker image. default value: None
runconfig_pipeline_params Required	Dict[str, PipelineParameter] Override 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'
arguments Required	[str] Command line arguments for the script file. The arguments will be delivered to compute via arguments in RunConfiguration. For more details how to handle arguments such as special symbols, please refer arguments in RunConfiguration
params Required	Dict[str, str] A dictionary of name-value pairs.
_workflow_provider Required	<xref:azureml.pipeline.core._aeva_provider._AevaWorkflowProvider> (Internal use only.) The workflow provider.
name	default value: None

Methods

create_node

Create a pipeline graph node.

create_node

Create a pipeline graph node.

create_node(graph, default_datastore, context)

Parameters

Name	Description
graph Required	Graph The graph to add the node to.
default_datastore Required	AbstractAzureStorageDatastore or AzureDataLakeDatastore The default datastore to use for this step.
context Required	<xref:azureml.pipeline.core._GraphContext> (Internal use only.) The graph context object.

Returns

Type	Description
Node	The node object.