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 setup.py and 27 in mini_kedro_pipeline.py.
📝 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.
Installation
pip install git+https://github.com/WaylonWalker/mini-kedro-pipeline
Caveats
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 hooks.py or run.py 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.
Usage
# hooks.py
import mini_kedro_project as mkp
class ProjectHooks:
@hook_impl
def register_pipelines(self) -> Dict[str, Pipeline]:
"""Register the project's pipeline.
Returns:
A mapping from a pipeline name to a ``Pipeline`` object.
"""
return {"__default__": Pipeline([]), "mkp": mkp.pipeline}
Implemantation
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?
https://waylonwalker.com/minimal-python-package/
Directory structure
.
├── .gitignore
├── README.md
├── setup.py
└── my_pipeline.py
setup.py
This is a very minimal setup.py. 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
setup(
name="MiniKedroPipeline",
version="0.1.0",
py_modules=["mini_kedro_pipeline"],
install_requires=["kedro"],
)
mini_kedro_pipeline.py
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
https://waylonwalker.com/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?