kfx 0.1.0a3

Extensions for kubeflow pipeline sdk.

kfx

kfx is a python package with the namespace kfx. Currently, it provides the following sub-packages

• kfx.lib.dsl - Extensions to yje kubeflow pipeline dsl.

• kfx.lib.vis - Data models and helpers to help generate the mlpipeline-ui-metadata.json required to render visualization in the kubeflow pipeline UI. See Visualize Results in the Pipelines UI

• Documentation: https://kfx.readthedocs.io.
• Repo: https://github.com/e2fyi/kfx

Quick start

Installation

pip install kfx

Usage

Example: Using ArtifactLocationHelper and KfpArtifact to determine the uri of your data artifact generated by the kubeflow pipeline task.

kfx.dsl.ArtifactLocationHelper is a helper to modify the kubeflow pipeline task so that you can use kfx.dsl.KfpArtifact to represent the artifact generated inside the task.

import kfp.components
import kfp.dsl
import kfx.dsl


# creates the helper that has the argo configs (tells you how artifacts will be stored)
# see https://github.com/argoproj/argo/blob/master/docs/workflow-controller-configmap.yaml
helper = kfx.dsl.ArtifactLocationHelper(
    scheme="minio", bucket="mlpipeline", key_prefix="artifacts/"
)

@kfp.components.func_to_container_op
def test_op(
    mlpipeline_ui_metadata: OutputTextFile(str), markdown_data_file: OutputTextFile(str)
):
    "A test kubeflow pipeline task."

    import json

    import kfx.dsl
    import kfx.vis
    import kfx.vis.vega

    data = [
        {"a": "A", "b": 28},
        {"a": "B", "b": 55},
        {"a": "C", "b": 43},
        {"a": "D", "b": 91},
        {"a": "E", "b": 81},
        {"a": "F", "b": 53},
        {"a": "G", "b": 19},
        {"a": "H", "b": 87},
        {"a": "I", "b": 52},
    ]
    vega_data_file.write(json.dumps(data))

    # `KfpArtifact` provides the reference to data artifact created
    # inside this task
    spec = {
        "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
        "description": "A simple bar chart",
        "data": {
            "url": kfx.dsl.KfpArtifact("vega_data_file"),
            "format": {"type": "json"},
        },
        "mark": "bar",
        "encoding": {
            "x": {"field": "a", "type": "ordinal"},
            "y": {"field": "b", "type": "quantitative"},
        },
    }

    # write the markdown to the `markdown-data` artifact
    markdown_data_file.write("### hello world")

    # creates an ui metadata object
    ui_metadata = kfx.vis.kfp_ui_metadata(
        # Describes the vis to generate in the kubeflow pipeline UI.
        [
            # markdown vis from a markdown artifact.
            # `KfpArtifact` provides the reference to data artifact created
            # inside this task
            kfx.vis.markdown(kfx.dsl.KfpArtifact("markdown_data_file")),
            # a vega web app from the vega data artifact.
            kfx.vis.vega.vega_web_app(spec),
        ]
    )

    # writes the ui metadata object as the `mlpipeline-ui-metadata` artifact
    mlpipeline_ui_metadata.write(kfx.vis.asjson(ui_metadata))

    # prints the uri to the markdown artifact
    print(ui_metadata.outputs[0].source)


@kfp.dsl.pipeline()
def test_pipeline():
    "A test kubeflow pipeline"

    op: kfp.dsl.ContainerOp = test_op()

    # modify kfp operator with artifact location metadata through env vars
    op.apply(helper.set_envs())

Example: Using pydantic data models to generate mlpipeline_ui_metadata.

kfx.vis has helper functions (with corresponding hints) to describe and create a mlpipeline_ui_metadata.json file (required by kubeflow pipeline UI to render any visualizations).

import kfp.components
import kfx.vis

from kfx.vis.enums import KfpStorage


@func_to_container_op
def some_op(mlpipeline_ui_metadata: OutputTextFile(str)):
    "kfp operator that provides metadata for visualizations."

    mlpipeline_ui_metadata = kfx.vis.kfp_ui_metadata(
        [
            # creates a confusion matrix vis
            kfx.vis.confusion_matrix(
                source="gs://your_project/your_bucket/your_cm_file",
                labels=["True", "False"],
            ),
            # creates a markdown with inline source
            kfx.vis.markdown(
                "# Inline Markdown: [A link](https://www.kubeflow.org/)",
                storage="inline",
            ),
            # creates a markdown with a remote source
            kfx.vis.markdown(
                "gs://your_project/your_bucket/your_markdown_file",
            ),
            # creates a ROC curve with a remote source
            kfx.vis.roc(
                "gs://your_project/your_bucket/your_roc_file",
            ),
            # creates a Table with a remote source
            kfx.vis.table(
                "gs://your_project/your_bucket/your_csv_file",
                header=["col1", "col2"],
            ),
            # creates a tensorboard viewer
            kfx.vis.tensorboard(
                "gs://your_project/your_bucket/logs/*",
            ),
            # creates a custom web app from a remote html file
            kfx.vis.web_app(
                "gs://your_project/your_bucket/your_html_file",
            ),
        ]
    )

    # write ui metadata so that kubeflow pipelines UI can render visualizations
    mlpipeline_ui_metadata.write(kfx.vis.asjson(mlpipeline_ui_metadata))

Developer guide

This project used:

• isort: to manage import order
• pylint: to manage general coding best practices
• flake8: to manage code complexity and coding best practices
• black: to manage formats and styles
• pydocstyle: to manage docstr style/format
• pytest/coverage: to manage unit tests and code coverage
• bandit: to find common security issues
• pyenv: to manage dev env: python version (3.6)
• pipenv: to manage dev env: python packages

Convention for unit tests are to suffix with _test and colocate with the actual python module - i.e. <module_name>_test.py.

The version of the package is read from version.txt - i.e. please update the appropriate semantic version (major -> breaking changes, minor -> new features, patch -> bug fix, postfix -> pre-release/post-release).

Makefile:

# autoformat codes with docformatter, isort, and black
make format

# check style, formats, and code complexity
make check

# check style, formats, code complexity, and run unit tests
make test

# test everything including building the package and check the sdist
make test-all

# run unit test only
make test-only

# generate and update the requirements.txt and requirements-dev.txt
make requirements

# generate the docs with sphinx and autoapi extension
make docs

# generate distributions
make dists

# publish to pypi with twine (twine must be configured)
make publish