KFP support library

This provides support for implementing KFP pipelines automating transform's execution. It comprises 2 main modules

• api server client
• workflow support

Development

Requirements

1. python 3.10 or later
2. git command line tools
3. pre-commit
4. twine (pip install twine)
   • but on Mac you may have to include a dir in your PATH, such as export PATH=$PATH:/Library/Frameworks/Python.framework/Versions/3.10/bin

Git

Simple clone the repo and set up the pre-commit hooks.

git clone git@github.com:IBM/data-prep-lab.git
cd kfp/kfp_support_lib
pre-commit install

If you don't have pre-commit, you can install from here

Library Artifact Build and Publish

The process of creating a release for fm_data_processing_kfp package involves the following steps:

cd to the package directory.

update the version in requirements.env file.

run make build and make publish.

Testing

To run the package tests perform the following:

To begin with, establish a Kind cluster and deploy all required components by executing the makfefile command in the main directory of this repository. As an alternative, you can manually execute the instructions provided in the README.md file.

make setup

The next step is to deploy the data-prep-lab-kfp package locally within a Python virtual environment.

make  build

lastly, execute the tests:

make test

Cleanup

It is advisable to execute the following command prior to running make test once more. This will ensure that any previous test runs resources are removed before starting new tests.

kubectl delete workflows -n kubeflow --all