kedro.runner.SequentialRunner¶

class kedro.runner.SequentialRunner(is_async=False)[source]¶

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

Methods

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

__init__(is_async=False)[source]¶

Instantiates the runner classs.

Parameters: is_async (bool) – If True, the node inputs and outputs are loaded and saved asynchronously with threads. Defaults to False.

create_default_data_set(ds_name)[source]¶

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, hook_manager=None, session_id=None)¶

Run the Pipeline using the datasets 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.
hook_manager (Optional[PluginManager]) – The PluginManager to activate hooks.
session_id (Optional[str]) – The id of the session.

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, hook_manager)¶