kedro.runner.ParallelRunner¶

class kedro.runner.ParallelRunner(max_workers=None, is_async=False)[source]¶

Bases: kedro.runner.runner.AbstractRunner

ParallelRunner is an AbstractRunner implementation. It can be used to run the Pipeline in parallel groups formed by toposort.

Methods

`ParallelRunner.__init__`([max_workers, is_async])	Instantiates the runner by creating a Manager.
`ParallelRunner.create_default_data_set`(ds_name)	Factory method for creating the default data set for the runner.
`ParallelRunner.run`(pipeline, catalog[, run_id])	Run the `Pipeline` using the DataSet``s provided by ``catalog and save results back to the same objects.
`ParallelRunner.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__(max_workers=None, is_async=False)[source]¶

Instantiates the runner by creating a Manager.

Parameters:	max_workers (`Optional`[`int`]) – Number of worker processes to spawn. If not set, calculated automatically based on the pipeline configuration and CPU core count. is_async (`bool`) – If True, the node inputs and outputs are loaded and saved asynchronously with threads. Defaults to False.
Raises:	`ValueError` – bad parameters passed

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:	`_SharedMemoryDataSet`
Returns:	An instance of an implementation of _SharedMemoryDataSet to be used for all unregistered data sets.

run(pipeline, catalog, run_id=None)¶

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. run_id (`Optional`[`str`]) – The id of the run.
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.