Run a pipeline


Runners are the execution mechanisms used to run pipelines. They all inherit from AbstractRunner.


Use SequentialRunner to execute pipeline nodes one-by-one based on their dependencies.

We recommend using SequentialRunner in cases where:

  • the pipeline has limited branching

  • the pipeline is fast

  • the resource-consuming steps require most of a scarce resource (e.g., significant RAM, disk memory or CPU)

Kedro uses SequentialRunner by default, so to execute the pipeline sequentially:

kedro run

You can also explicitly use SequentialRunner as follows:

kedro run --runner=SequentialRunner



You can alternatively run the nodes within the pipeline concurrently, using a ParallelRunner. To do so, add a flag as follows:

kedro run --parallel


kedro run --runner=ParallelRunner

Note: You cannot use both --parallel and --runner flags at the same time (e.g. kedro run --parallel --runner=SequentialRunner raises an exception).


While ParallelRunner uses multiprocessing, you can also run the pipeline with multithreading for concurrent execution by specifying ThreadRunner as follows:

kedro run --runner=ThreadRunner

Note: SparkDataSet doesn’t work correctly with ParallelRunner. To add concurrency to the pipeline with SparkDataSet, you must use ThreadRunner.

For more information on how to maximise concurrency when using Kedro with PySpark, please visit our guide on how to build a Kedro pipeline with PySpark.

Custom runners

If the built-in Kedro runners do not meet your requirements, you can also define your own runner within your project. For example, you may want to add a dry runner, which lists which nodes would be run without executing them:

Click to expand
# in <project-name>/src/<python_package>/
from import AbstractDataSet, DataCatalog, MemoryDataSet
from kedro.pipeline import Pipeline
from kedro.runner.runner import AbstractRunner

class DryRunner(AbstractRunner):
    """``DryRunner`` is an ``AbstractRunner`` implementation. It can be used to list which
    nodes would be run without actually executing anything.

    def create_default_data_set(self, ds_name: str) -> AbstractDataSet:
        """Factory method for creating the default data set for the runner.

            ds_name: Name of the missing data set
            An instance of an implementation of AbstractDataSet to be used
            for all unregistered data sets.

        return MemoryDataSet()

    def _run(self, pipeline: Pipeline, catalog: DataCatalog) -> None:
        """The method implementing dry pipeline running.
        Example logs output using this implementation:

            kedro.runner.dry_runner - INFO - Actual run would execute 3 nodes:
            node3: identity([A]) -> [B]
            node2: identity([C]) -> [D]
            node1: identity([D]) -> [E]

            pipeline: The ``Pipeline`` to run.
            catalog: The ``DataCatalog`` from which to fetch data.

        nodes = pipeline.nodes
            "Actual run would execute %d nodes:\n%s",
            "\n".join(map(str, nodes)),

And use it with kedro run through the --runner flag:

$ kedro run --runner=src.<python_package>.runner.DryRunner

Load and save asynchronously

Note: ThreadRunner doesn’t support asynchronous load-input or save-output operations.

When processing a node, both SequentialRunner and ParallelRunner perform the following steps in order:

  1. Load data based on node input(s)

  2. Execute node function with the input(s)

  3. Save the output(s)

If a node has multiple inputs or outputs (e.g., node(func, ["a", "b", "c"], ["d", "e", "f"])), you can reduce load and save time by using asynchronous mode. You can enable it by passing an --async flag to the run command as follows:

$ kedro run --async
2020-03-24 09:20:01,482 - kedro.runner.sequential_runner - INFO - Asynchronous mode is enabled for loading and saving data
2020-03-24 09:20:01,483 - - INFO - Loading data from `example_iris_data` (CSVDataSet)...

Note: All the datasets used in the run have to be thread-safe in order for asynchronous loading/saving to work properly.

Run a pipeline by name

To run the pipeline by its name, you need to add your new pipeline to register_pipelines() function src/<python_package>/ as below:

Click to expand
from kedro.framework.hooks import hook_impl

class ProjectHooks:
    def register_pipelines(self):
        """Register the project's pipelines.

            A mapping from a pipeline name to a ``Pipeline`` object.


        data_engineering_pipeline = de.create_pipeline()
        data_science_pipeline = ds.create_pipeline()
        my_pipeline = Pipeline(
                # your definition goes here

        return {
            "de": data_engineering_pipeline,
            "my_pipeline": my_pipeline,
            "__default__": data_engineering_pipeline + data_science_pipeline,

project_hooks = ProjectHooks()

Then from the command line, execute the following:

kedro run --pipeline my_pipeline

Note: If you specify kedro run without the --pipeline option, it runs the __default__ pipeline from the dictionary returned by register_pipelines().

Further information about kedro run can be found in the Kedro CLI documentation.

Run pipelines with IO

The above definition of pipelines only applies for non-stateful or “pure” pipelines that do not interact with the outside world. In practice, we would like to interact with APIs, databases, files and other sources of data. By combining IO and pipelines, we can tackle these more complex use cases.

By using DataCatalog from the IO module we are still able to write pure functions that work with our data and outsource file saving and loading to DataCatalog.

Through DataCatalog, we can control where inputs are loaded from, where intermediate variables get persisted and ultimately the location to which output variables are written.

In a simple example, we define a MemoryDataSet called xs to store our inputs, save our input list [1, 2, 3] into xs, then instantiate SequentialRunner and call its run method with the pipeline and data catalog instances:

Click to expand
io = DataCatalog(dict(xs=MemoryDataSet()))


Out[10]: ['xs']"xs", [1, 2, 3])
SequentialRunner().run(pipeline, catalog=io)


Out[11]: {'v': 0.666666666666667}

Output to a file

We can also use IO to save outputs to a file. In this example, we define a custom LambdaDataSet that would serialise the output to a file locally:

Click to expand
def save(value):
    with open("./data/07_model_output/variance.pickle", "wb") as f:
        pickle.dump(value, f)

def load():
    with open("./data/07_model_output/variance.pickle", "rb") as f:
        return pickle.load(f)

pickler = LambdaDataSet(load=load, save=save)
io.add("v", pickler)

It is important to make sure that the data catalog variable name v matches the name v in the pipeline definition.

Next we can confirm that this LambdaDataSet behaves correctly:

Click to expand"v", 5)


Out[12]: 5

Finally, let’s run the pipeline again now and serialise the output:

Click to expand
SequentialRunner().run(pipeline, catalog=io)


Out[13]: {}

The output has been persisted to a local file so we don’t see it directly, but it can be retrieved from the catalog:

Click to expand


Out[14]: 0.666666666666667
except FileNotFoundError: