kedro.runner.AbstractRunner

class kedro.runner.AbstractRunner

Bases: abc.ABC

AbstractRunner is the base class for all Pipeline runner implementations.

Methods

AbstractRunner.create_default_data_set(ds_name)	Factory method for creating the default data set for the runner.
AbstractRunner.run(pipeline, catalog)	Run the Pipeline using the DataSet``s provided by ``catalog and save results back to the same objects.
AbstractRunner.run_only_missing(pipeline, …)	Run only the missing outputs from the Pipeline using the DataSet``s provided by ``catalog and save results back to the same objects.

__init__

Initialize self. See help(type(self)) for accurate signature.

create_default_data_set(ds_name)

Factory method for creating the default data set for the runner.

Parameters:ds_name (str) – Name of the missing data set Return type:AbstractDataSet Returns:An instance of an implementation of AbstractDataSet to be used for all unregistered data sets.

run(pipeline, catalog)

Run the Pipeline using the DataSet``s provided by ``catalog and save results back to the same objects.

Parameters:

• pipeline (Pipeline) – The Pipeline to run.
• catalog (DataCatalog) – The DataCatalog from which to fetch data.

Raises:

ValueError – Raised when Pipeline inputs cannot be satisfied.

Return type:

Dict[str, Any]

Returns:

Any node outputs that cannot be processed by the DataCatalog. These are returned in a dictionary, where the keys are defined by the node outputs.

run_only_missing(pipeline, catalog)

Run only the missing outputs from the Pipeline using the DataSet``s provided by ``catalog and save results back to the same objects.

Parameters:

• pipeline (Pipeline) – The Pipeline to run.
• catalog (DataCatalog) – The DataCatalog from which to fetch data.

Raises:

ValueError – Raised when Pipeline inputs cannot be satisfied.

Return type:

Dict[str, Any]

Returns:

Any node outputs that cannot be processed by the DataCatalog. These are returned in a dictionary, where the keys are defined by the node outputs.