CommandStep Class

Create an Azure ML Pipeline step that runs a command.

Inheritance
azureml.pipeline.core._python_script_step_base._PythonScriptStepBase
CommandStep

Constructor

CommandStep(command=None, name=None, compute_target=None, runconfig=None, runconfig_pipeline_params=None, inputs=None, outputs=None, params=None, source_directory=None, allow_reuse=True, version=None)

Parameters

command
list or str
default value: None

The command to run or path of the executable/script relative to source_directory. It is required unless it is provided with runconfig. It can be specified with string arguments in a single string or with input/output/PipelineParameter in a list.

name
str
default value: None

The name of the step. If unspecified, the first word in the command is used.

compute_target
DsvmCompute or AmlCompute or RemoteCompute or HDInsightCompute or str or tuple
default value: None

The compute target to use. If unspecified, the target from the runconfig is used. This parameter may be specified as 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 RemoteCompute type is 'VirtualMachine').

runconfig
ScriptRunConfig or RunConfiguration
default value: None

The optional configuration object which encapsulates the information necessary to submit a training run in an experiment.

runconfig_pipeline_params
<xref:<xref:{str: PipelineParameter}>>
default value: None

Overrides 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
list[InputPortBinding or DataReference or PortDataReference or PipelineData or <xref:azureml.pipeline.core.pipeline_output_dataset.PipelineOutputDataset> or DatasetConsumptionConfig]
default value: None

A list of input port bindings.

outputs
list[PipelineData or OutputDatasetConfig or PipelineOutputAbstractDataset or OutputPortBinding]
default value: None

A list of output port bindings.

params
dict
default value: None

A dictionary of name-value pairs registered as environment variables with "AML_PARAMETER_".

source_directory
str
default value: None

A folder that contains scripts, conda env, and other resources used in the step.

allow_reuse
bool
default value: True

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
str
default value: None

An optional version tag to denote a change in functionality for the step.

Remarks

An CommandStep is a basic, built-in step to run a command on the given compute target. It takes a command as a parameter or from other parameters like runconfig. It also takes other optional parameters like compute target, inputs and outputs. You should use a ScriptRunConfig or RunConfiguration to specify requirements for the CommandStep, such as custom docker image.

The best practice for working with CommandStep is to use a separate folder for the executable or script to run any dependent files associated with the step, and specify that folder with the source_directory parameter. Following this best practice 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 snapshot.

For the system-known commands source_directory is not required but you can still provide it with any dependent files associated with the step.

The following code example shows how to use a CommandStep in a machine learning training scenario. To list files in linux:


   from azureml.pipeline.steps import CommandStep

   trainStep = CommandStep(name='list step',
                           command='ls -lrt',
                           compute_target=compute_target)

To run a python script:


   from azureml.pipeline.steps import CommandStep

   trainStep = CommandStep(name='train step',
                           command='python train.py arg1 arg2',
                           source_directory=project_folder,
                           compute_target=compute_target)

To run a python script via ScriptRunConfig:


   from azureml.core import ScriptRunConfig
   from azureml.pipeline.steps import CommandStep

   train_src = ScriptRunConfig(source_directory=script_folder,
                               command='python train.py arg1 arg2',
                               environment=my_env)
   trainStep = CommandStep(name='train step',
                           runconfig=train_src)

See https://aka.ms/pl-first-pipeline for more details on creating pipelines in general.

Methods

create_node

Create a node for CommandStep and add it to the specified 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

Create a node for CommandStep and add it to the specified 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

graph
Graph
Required

The graph object to add the node to.

default_datastore
AbstractAzureStorageDatastore or AzureDataLakeDatastore
Required

The default datastore.

context
<xref:_GraphContext>
Required

The graph context.

Returns

The created node.

Return type