data-prep-toolkit-kfp-shared 0.2.0

Data Preparation Kit Library. KFP support

Shared Workflow Support

This provides support for implementing KFP pipelines automating transform's execution. This library is not dependent on KFP version. KFP dependent modules are in kfp_v1_workflow_support and kfp_v2_workflow_support

this module combines 2 inner modules

• python apiserver client, which is a copy of Kuberay API server-client python APIs We added it into the project, because these APIs are not exposed by any PyPi.
• runtime_utils

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 [e-mail]:IBM/data-prep-kit.git
cd kfp/kfp_support_lib/shared_workflow_support
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 the package involves the following steps:

• cd to the package directory.
• update the DPK_LIB_KFP_SHARED version in .make.versions file.
• run make set-versions and 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-kit-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