AdlaStep Class
Creates an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics.
For an example of using this AdlaStep, see the notebook https://aka.ms/pl-adla.
Create an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics.
- Inheritance
-
azureml.pipeline.core._adla_step_base._AdlaStepBaseAdlaStep
Constructor
AdlaStep(script_name, name=None, inputs=None, outputs=None, params=None, degree_of_parallelism=None, priority=None, runtime_version=None, compute_target=None, source_directory=None, allow_reuse=True, version=None, hash_paths=None)Parameters
- inputs
- list[Union[InputPortBinding, DataReference, PortDataReference, PipelineData]]
A list of input port bindings.
A list of output port bindings.
- degree_of_parallelism
- int
The degree of parallelism to use for this job. This must be greater than 0. If set to less than 0, defaults to 1.
- priority
- int
The priority value to use for the current job. Lower numbers have a higher priority. By default, a job has a priority of 1000. The value you specify must be greater than 0.
- compute_target
- AdlaCompute, str
[Required] The ADLA compute to use for this job.
- 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.
- version
- str
Optional version tag to denote a change in functionality for the step.
- hash_paths
- list
DEPRECATED: no longer needed.
A list of paths to hash when checking for changes to the step contents. If there
are no changes detected, the pipeline will reuse the step contents from a previous run. By default,
the contents of source_directory is hashed except for files listed in .amlignore or .gitignore.
- inputs
- list[Union[InputPortBinding, DataReference, PortDataReference, PipelineData]]
List of input port bindings
- outputs
- list[Union[PipelineData, <xref:azureml.pipeline.core.pipeline_output_dataset.PipelineAbstractOutputDataset>, OutputPortBinding]]
A list of output port bindings.
- degree_of_parallelism
- int
The degree of parallelism to use for this job. This must be greater than 0. If set to less than 0, defaults to 1.
- priority
- int
The priority value to use for the current job. Lower numbers have a higher priority. By default, a job has a priority of 1000. The value you specify must be greater than 0.
- 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.
- hash_paths
- list
DEPRECATED: no longer needed.
A list of paths to hash when checking for changes to the step contents. If there
are no changes detected, the pipeline will reuse the step contents from a previous run. By default,
the contents of source_directory is hashed except for files listed in .amlignore or .gitignore.
Remarks
You can use @@name@@ syntax in your script to refer to inputs, outputs, and params.
if name is the name of an input or output port binding, any occurrences of @@name@@ in the script are replaced with the actual data path of a corresponding port binding.
if name matches any key in params dict, any occurrences of @@name@@ will be replaced with corresponding value in dict.
AdlaStep works only with data stored in the default Data Lake Storage of the Data Lake Analytics account. If the data is in a non-default storage, use a DataTransferStep to copy the data to the default storage. You can find the default storage by opening your Data Lake Analytics account in the Azure portal and then navigating to 'Data sources' item under Settings in the left pane.
The following example shows how to use AdlaStep in an Azure Machine Learning Pipeline.
adla_step = AdlaStep(
name='extract_employee_names',
script_name='sample_script.usql',
source_directory=sample_folder,
inputs=[sample_input],
outputs=[sample_output],
compute_target=adla_compute)
Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-use-adla-as-compute-target.ipynb
Methods
| create_node |
Create a node from the AdlaStep step 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 from the AdlaStep step 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
- default_datastore
- Union[AbstractAzureStorageDatastore, AzureDataLakeDatastore]
The default datastore.
- context
- <xref:azureml.pipeline.core._GraphContext>
The graph context.
Returns
The node object.
Return type
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