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.
from
kfp
import
dsl
from
kfp.dsl
import
(
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.
@dsl
.
component
def
html_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 information in the Google Cloud console:
Click "View HTML" to open HTML file on a new tab
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.
@dsl
.
component
def
markdown_visualization
(
markdown_artifact
:
Output
[
Markdown
]):
import
urllib.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 information in the Google Cloud 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'
)
def
metrics_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.
