kfp-py-func-local 0.0.1

kfp-py-func-local

kfp local

Utils to run 'kfp-pipeline' with only py-function components in local without kubernetes & docker build.

Main purpose is to debug faster in early stages.

Getting Started

Install:

pip install kfp-py-func-local

Assume you have

1. a custom library with a custom function

def add_op(a: float, b: float) -> float:
    '''Calculates sum of two arguments.'''
    return a + b

2. a kfp component based on the function

from kfp.v2.dsl import component
@component(base_image=custom_image_with_my_library_installed)
def add_op(a: float, b: float) -> float:
    kwargs = locals()
    from my_library import add_op
    return add_op(**kwargs)

3. a kfp pipeline using the kfp component

@pipeline
def add_pipeline(
            a: float = 1,
            b: float = 7
    ):
    first_add_task = add_op(a, 4)
    second_add_task = add_op(first_add_task.output, b)

Then you would push your image custom_image_with_my_library_installed to a container registry. And compile it to a yaml to then run it in top of a kubernetes.

With kfp-local you can avoid this, and run your code locally on your computer doing

from kfp_local import create_run_from_pipeline_func_locally
arguments = dict(a=1,b=2)
func_imports = ["from my_library import add_op"]
create_run_from_pipeline_func_locally(add_pipeline, arguments=arguments, func_imports=func_imports)

Why run locally a kubernetes orchestrator?

Here we're assuming that all components are based on custom-made functions. To debug those,

1. debugging them separately is not practical nor accurate (cause are build to interact with each other)
2. create (and mantain) 2 pipelines, one for local and other for kubeflow is painful and error pruning

Typical usage of kfp-local would be to debug your custom code locally with very few data samples. Check that code works fine, and then run it in kubernetes with all samples.