Output HTML and Markdown

Vertex AI Pipelines provides a set of predefined visualization typesfor evaluating the result of a pipeline job (for example,Metrics,ClassificationMetrics). However, there are many cases where customvisualization is needed. Vertex AI Pipelines provides two mainapproaches to output custom visualization artifacts: Markdown and HTML files.

Import required dependencies

In your development environment import the required dependencies.

fromkfpimportdslfromkfp.dslimport(Output,HTML,Markdown)

Output HTML

To export an HTML file, define a component with theOutput[HTML] artifact.You also must write HTML content to the artifact's path. In this example youuse a string variable to represent HTML content.

Note: HTML and Markdown files are stored under thepipeline_root path in Cloud Storage. You must have sufficient permission to the Cloud Storage bucket to view visualization content.

@dsl.componentdefhtml_visualization(html_artifact:Output[HTML]):public_url='https://user-images.githubusercontent.com/37026441/140434086-d9e1099b-82c7-4df8-ae25-83fda2929088.png'html_content= \'<html><head></head><body><h1>Global Feature Importance</h1>\n<img src="{}" width="97%"/></body></html>'.format(public_url)withopen(html_artifact.path,'w')asf:f.write(html_content)

HTML artifact in the Google Cloud console:

HTML artifact in the console

HTML artifact information in the Google Cloud console:

HTML artifact info in the console

Click "View HTML" to open HTML file on a new tab

HTML artifact info in the console

Output Markdown

To export a Markdown file, define a component with theOutput[Markdown]artifact. You also must write Markdown content to the artifact's path. In thisexample you use a string variable to represent Markdown content.

Note: HTML and Markdown files are stored under thepipeline_root path in Cloud Storage. You must have sufficient permission to the Cloud Storage bucket to view visualization content.

@dsl.componentdefmarkdown_visualization(markdown_artifact:Output[Markdown]):importurllib.requestwithurllib.request.urlopen('https://gist.githubusercontent.com/zijianjoy/a288d582e477f8021a1fcffcfd9a1803/raw/68519f72abb59152d92cf891b4719cd95c40e4b6/table_visualization.md')astable:markdown_content=table.read().decode('utf-8')withopen(markdown_artifact.path,'w')asf:f.write(markdown_content)

Markdown artifact in the Google Cloud console:

Markdown artifact in the console

Markdown artifact information in the Google Cloud console:

Markdown artifact info in the console

Create your pipeline

After you have defined your component with the HTML or Markdown artifact createand run a pipeline that use the component.

@dsl.pipeline(name=f'metrics-visualization-pipeline')defmetrics_visualization_pipeline():html_visualization_op=html_visualization()markdown_visualization_op=markdown_visualization()

After submitting the pipeline run, you can view the graph for this run inthe Google Cloud console. This graph includes the HTML and Markdown artifactsyou declared in corresponding components. You can select these artifactsto view detailed visualization.

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Last updated 2025-12-15 UTC.