DataTransferStep Class

Creates an Azure ML Pipeline step that transfers data between storage options.

DataTransferStep supports common storage types such as Azure Blob Storage and Azure Data Lake as sources and sinks. For more more information, see the Remarks section.

For an example of using DataTransferStep, see the notebook https://aka.ms/pl-data-trans.

Create an Azure ML Pipeline step that transfers data between storage options.

Inheritance

azureml.pipeline.core._data_transfer_step_base._DataTransferStepBase

DataTransferStep

Constructor

DataTransferStep(name, source_data_reference=None, destination_data_reference=None, compute_target=None, source_reference_type=None, destination_reference_type=None, allow_reuse=True)

Parameters

Name	Description
name Required	str [Required] The name of the step.
source_data_reference	Union[InputPortBinding, DataReference, PortDataReference, PipelineData] [Required] An input connection that serves as source of the data transfer operation. Default value: None
destination_data_reference	Union[InputPortBinding, PipelineOutputAbstractDataset, DataReference] [Required] An output connection that serves as destination of the data transfer operation. Default value: None
compute_target	DataFactoryCompute, str [Required] An Azure Data Factory to use for transferring data. Default value: None
source_reference_type	str An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, the type of existing path is used. Use this parameter to differentiate between a file and directory of the same name. Default value: None
destination_reference_type	str An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, Azure ML uses the type of existing path, source reference, or 'directory', in that order. 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 step arguments remain unchanged, the output from the previous run of this step is reused. When reusing the step, instead of transferring data again, 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
name Required	str [Required] The name of the step.
source_data_reference Required	Union[InputPortBinding, DataReference, PortDataReference, PipelineData] [Required] An input connection that serves as source of the data transfer operation.
destination_data_reference Required	Union[InputPortBinding, PipelineOutputAbstractDataset, DataReference] [Required] An output connection that serves as destination of the data transfer operation.
compute_target Required	DataFactoryCompute, str [Required] An Azure Data Factory to use for transferring data.
source_reference_type Required	str An optional string specifying the type of source_data_reference. Possible values include: 'file', 'directory'. When not specified, the type of existing path is used. Use this parameter to differentiate between a file and directory of the same name.
destination_reference_type Required	str An optional string specifying the type of destination_data_reference. Possible values include: 'file', 'directory'. When not specified, Azure ML uses the type of existing path, source reference, or 'directory', in that order.
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 step arguments remain unchanged, the output from the previous run of this step is reused. When reusing the step, instead of transferring data again, 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.

Remarks

This step supports the following storage types as sources and sinks except where noted:

• Azure Blob Storage

• Azure Data Lake Storage Gen1 and Gen2

• Azure SQL Database

• Azure Database for PostgreSQL

• Azure Database for MySQL

For Azure SQL Database, you must use service principal authentication. For more information, see Service Principal Authentication. For an example of using service principal authentication for Azure SQL Database, see https://aka.ms/pl-data-trans.

To establish data dependency between steps, use the get_output method to get a PipelineData object that represents the output of this data transfer step and can be used as input for later steps in the pipeline.

   data_transfer_step = DataTransferStep(name="copy data", ...)

   # Use output of data_transfer_step as input of another step in pipeline
   # This will make training_step wait for data_transfer_step to complete
   training_input = data_transfer_step.get_output()
   training_step = PythonScriptStep(script_name="train.py",
                           arguments=["--model", training_input],
                           inputs=[training_input],
                           compute_target=aml_compute,
                           source_directory=source_directory)

To create an InputPortBinding with specific name, you can combine get_output() output with the output of the as_input or as_mount methods of PipelineData.

   data_transfer_step = DataTransferStep(name="copy data", ...)
   training_input = data_transfer_step.get_output().as_input("my_input_name")

Methods

create_node	Create a node from the DataTransfer step and add it to the given graph. 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.
get_output	Get the output of the step as PipelineData.

create_node

Create a node from the DataTransfer step and add it to the given graph.

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.

get_output

Get the output of the step as PipelineData.

get_output()

Returns

Type	Description
PipelineData	The output of the step.

Remarks

To establish data dependency between steps, use get_output method to get a PipelineData object that represents the output of this data transfer step and can be used as input for later steps in the pipeline.

   data_transfer_step = DataTransferStep(name="copy data", ...)

   # Use output of data_transfer_step as input of another step in pipeline
   # This will make training_step wait for data_transfer_step to complete
   training_input = data_transfer_step.get_output()
   training_step = PythonScriptStep(script_name="train.py",
                           arguments=["--model", training_input],
                           inputs=[training_input],
                           compute_target=aml_compute,
                           source_directory=source_directory)

To create an InputPortBinding with specific name, you can combine get_output() call with as_input or as_mount helper methods.

   data_transfer_step = DataTransferStep(name="copy data", ...)

   training_input = data_transfer_step.get_output().as_input("my_input_name")