OutputPortBinding Class

Defines a named output of a pipeline step.

OutputPortBinding can be used to specify the type of data which will be produced by a step and how the data will be produced. It can be used with InputPortBinding to specify that the step output is a required input of another step.

Initialize OutputPortBinding.

Inheritance

builtins.object

OutputPortBinding

Constructor

OutputPortBinding(name, datastore=None, output_name=None, bind_mode='mount', path_on_compute=None, is_directory=None, overwrite=None, data_type=None, pipeline_output_name=None, training_output=None, dataset_registration=None, dataset_output=None)

Parameters

Name	Description
name Required	str Name of the OutputPortBinding object, which can contain only letters, digits, and underscores.
datastore	Union[AbstractAzureStorageDatastore, AzureDataLakeDatastore] Datastore the PipelineData will reside on. default value: None
output_name	str Name of the output, if None name is used. Can contain only letters, digits, and underscores. default value: None
bind_mode	str Specifies whether the producing step will use "upload" or "mount" or "hdfs" method to access the data. default value: mount
path_on_compute	str For "upload" mode, the path the module writes the output to. default value: None
is_directory	bool Whether the output is a directory or single file. default value: None
overwrite	bool For "upload" mode, whether to overwrite existing data. default value: None
data_type	str Optional. Data type can be used to specify the expected type of the output and to detail how consuming steps should use the data. Can be any user-defined string. default value: None
pipeline_output_name	str If provided this output will be available by using PipelineRun.get_pipeline_output(). Pipeline output names must be unique in the pipeline. default value: None
training_output	TrainingOutput Defines output for training result. This is needed only for specific trainings which result in different kinds of outputs such as Metrics and Model. For example, AutoMLStep results in metrics and model. You can also define specific training iteration or metric used to get best model. For HyperDriveStep, you can also define the specific model files to be included in the output. default value: None
dataset_registration	DatasetRegistration Optional. This is an internal parameter. You should be using PipelineData.as_dataset instead. default value: None
dataset_output	OutputDatasetConfig Optional. This is an internal parameter. You should be using OutputFileDatasetConfig intead. default value: None
name Required	str Name of the OutputPortBinding object, which can contain only letters, digits, and underscores.
datastore Required	Union[AbstractAzureStorageDatastore, AzureDataLakeDatastore] Datastore the PipelineData will reside on.
output_name Required	str Name of the output, if None name is used. Can only contain letters, digits, and underscores.
bind_mode Required	str Specifies whether the producing step will use "upload" or "mount" or "hdfs" method to access the data.
path_on_compute Required	str For "upload" mode, the path the module writes the output to.
is_directory Required	bool if output is a directory
overwrite Required	bool For "upload" mode, whether to overwrite existing data.
data_type Required	str Optional. Data type can be used to specify the expected type of the output and to detail how consuming steps should use the data. Can be any user-defined string.
pipeline_output_name Required	str If provided this output will be available by using PipelineRun.get_pipeline_output(). Pipeline output names must be unique in the pipeline.
training_output Required	TrainingOutput Defines output for training result. This is needed only for specific trainings which result in different kinds of outputs such as Metrics and Model. For example, AutoMLStep results in metrics and model. You can also define specific training iteration or metric used to get best model. For HyperDriveStep, you can also define the specific model files to be included in the output.
dataset_registration Required	DatasetRegistration Optional. This is an internal parameter. You should be using PipelineData.as_dataset instead.
dataset_output Required	OutputDatasetConfig Optional. This is an internal parameter. You should be using OutputFileDatasetConfig intead.

Remarks

OutputPortBinding can be used in a similar way as PipelineData when building a Pipeline to specify step inputs and outputs. The difference is OutputPortBinding needs to be used with InputPortBinding in order to be consumed as an input to another step.

An example to construct a Pipeline with OutputPortBinding is as follows:

   from azureml.pipeline.core import OutputPortBinding, InputPortBinding, Pipeline
   from azureml.pipeline.steps import PythonScriptStep

   step_1_output = OutputPortBinding("output", datastore=datastore)

   step_1 = PythonScriptStep(
       name='process data',
       script_name="process_data.py",
       compute_target=compute,
       arguments=["--output", step_1_output],
       outputs=[step_1_output]
   )

   step_2_input = InputPortBinding("input", bind_object=step_1_output)

   step_2 = PythonScriptStep(
       name='train',
       script_name="train.py",
       compute_target=compute,
       arguments=["--input", step_2_input],
       inputs=[step_2_input]
   )

   pipeline = Pipeline(workspace=workspace, steps=[step_1, step_2])

This will create a Pipeline with two steps. The process step will be executed first, then after it has completed, the train step will be executed. Azure ML will provide the output produced by the process step, as described by the OutputPortBinding object, to the train step.

Attributes

bind_mode

Get the mode ("upload" or "mount" or "hdfs") the producing step will use to create the data.

Returns

Type	Description
str	The bind mode.

data_type

Get the type of data which will be produced.

Returns

Type	Description
str	The data type name.

dataset_registration

Get the dataset registration information.

Returns

Type	Description
DatasetRegistration	The dataset registration information.

datastore

Datastore the PipelineData will reside on.

Returns

Type	Description
Union[AbstractAzureStorageDatastore, AzureDataLakeDatastore]	The Datastore object.

is_directory

Whether the output is a directory.

Returns

Type	Description
bool	is_directory

name

Name of the OutputPortBinding object.

Returns

Type	Description
str	The name.

overwrite

For "upload" mode, indicate whether to overwrite existing data.

Returns

Type	Description
bool	_overwrite

path_on_compute

For "upload" mode, the path the module writes the output to.

Returns

Type	Description
str	path_on_compute

pipeline_output_name

Get the name of the pipeline output corresponding to this OutputPortBinding.

Returns

Type	Description
str	The pipeline output name.

training_output

Get training output.

Returns

Type	Description
TrainingOutput	Training output