SequentialRunner

kedro.runner.SequentialRunner

SequentialRunner(is_async=False)

Bases: AbstractRunner

SequentialRunner is an AbstractRunner implementation. It can be used to run the Pipeline in a sequential manner using a topological sort of provided nodes.

Parameters:

• is_async (bool, default: False ) –

  If True, the node inputs and outputs are loaded and saved asynchronously with threads. Defaults to False.

Source code in kedro/runner/sequential_runner.py

def __init__(
    self,
    is_async: bool = False,
):
    """Instantiates the runner class.

    Args:
        is_async: If True, the node inputs and outputs are loaded and saved
            asynchronously with threads. Defaults to False.
    """
    super().__init__(is_async=is_async)

_get_executor

_get_executor(max_workers)

Source code in kedro/runner/sequential_runner.py

def _get_executor(self, max_workers: int) -> None:
    return None

_run

_run(pipeline, catalog, hook_manager=None, run_id=None)

The method implementing sequential pipeline running.

Parameters:

• pipeline (Pipeline) –

  The Pipeline to run.

• catalog (CatalogProtocol) –

  An implemented instance of CatalogProtocol from which to fetch data.

• hook_manager (PluginManager | None, default: None ) –

  The PluginManager to activate hooks.

• run_id (str | None, default: None ) –

  The id of the run.

Raises:

• Exception –

  in case of any downstream node failure.

Source code in kedro/runner/sequential_runner.py

def _run(
    self,
    pipeline: Pipeline,
    catalog: CatalogProtocol,
    hook_manager: PluginManager | None = None,
    run_id: str | None = None,
) -> None:
    """The method implementing sequential pipeline running.

    Args:
        pipeline: The ``Pipeline`` to run.
        catalog: An implemented instance of ``CatalogProtocol`` from which to fetch data.
        hook_manager: The ``PluginManager`` to activate hooks.
        run_id: The id of the run.

    Raises:
        Exception: in case of any downstream node failure.
    """
    if not self._is_async:
        self._logger.info(
            "Using synchronous mode for loading and saving data. Use the --async flag "
            "for potential performance gains. https://docs.kedro.org/en/stable/nodes_and_pipelines/run_a_pipeline.html#load-and-save-asynchronously"
        )
    super()._run(
        pipeline=pipeline,
        catalog=catalog,
        hook_manager=hook_manager,
        run_id=run_id,
    )