Output HTML and Markdown

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

Import required dependencies

In your development environment import the required dependencies.

fromkfpimport dsl
fromkfp.dslimport (
 Output,
 HTML,
 Markdown
)

Output HTML

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

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

@dsl.component
defhtml_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)
 with open(html_artifact.path, 'w') as f:
 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 the Output[Markdown] artifact. You also must write Markdown content to the artifact's path. In this example you use a string variable to represent Markdown content.

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

@dsl.component
defmarkdown_visualization(markdown_artifact: Output[Markdown]):
 importurllib.request
 with urllib.request.urlopen('https://gist.githubusercontent.com/zijianjoy/a288d582e477f8021a1fcffcfd9a1803/raw/68519f72abb59152d92cf891b4719cd95c40e4b6/table_visualization.md') as table:
 markdown_content = table.read().decode('utf-8')
 with open(markdown_artifact.path, 'w') as f:
 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 create and 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 in the Google Cloud console. This graph includes the HTML and Markdown artifacts you declared in corresponding components. You can select these artifacts to view detailed visualization.