How small can a minimum kedro pipeline ready to package be? I made one within 4 files that you can pip install. It's only a total of 35 lines of python, 8 in and 27 in

📝 Note this is only a composable pipeline, not a full project, it does not contain a catalog or runner.

Minimal Kedro Pipeline

I have everything for this post hosted in this gihub repo, you can fork it, clone it, or just follow along.


pip install git+


This repo represents the minimal amount of structure to build a kedro pipeline that can be shared across projects. Its installable, and drops right into your or modules. It is not a runnable pipeline. At this point I think the config loader requires to have a logging config file.

This is a sharable pipeline that can be used across many different projects.



import mini_kedro_project as mkp

class ProjectHooks:
    def register_pipelines(self) -> Dict[str, Pipeline]:
        """Register the project's pipeline.

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


        return {"__default__": Pipeline([]), "mkp": mkp.pipeline}


This builds on another post that I made about creating the minimal python package. I am not sure if it should be called a package, it's a module, but what do you call it after you build it and host it on pypi?


Directory structure

├── .gitignore

This is a very minimal This is enough to get you started with a package that you can share across your team. In practice, there is a bit more that you might want to include as your project grows.

from setuptools import setup


The mini kedro pipeline looks like any set of nodes in your project. Many projects will separate nodes and functions, I prefer to keep them close together. The default recommendation is also to have a create_pipelines function that returns the pipeline.

This pattern creates a singleton, if you were to reference the same pipeline in multiple places within the same running interpreter and modify the one you would run into issues. I don't foresee myself running into this issue, but maybe as more features become available I will change my mind.

An example of a minimal kedro pipeline project
from kedro.pipeline import Pipeline, node

__version__ = "0.1.0"
__author__ = "Waylon S. Walker"

nodes = []

def create_data():
    "creates a dictionary of sample data"
    return {"beans": range(10)}

nodes.append(node(create_data, None, "raw_data", name="create_raw_data"))

def mult_data(data):
    "multiplies each record of each item by 100"
    return {item: [i * 100 for i in data[item]] for item in data}

nodes.append(node(mult_data, "raw_data", "mult_data", name="create_mult_data"))

pipeline = Pipeline(nodes)

Kedro in scripts


If you enjoyed this one check out this companion article where I build a fully runnable kedro project in a single script.

Share your pipelines

Go forth and share your pipelines across projects. Let me know, do you share pipelines or catalogs across projects?