Visualize Results in the Pipelines UI

This page shows you how to use the Kubeflow Pipelines UI to visualize output from a Kubeflow Pipelines component. For details about how to build a component, see the guide to building your own component.

Introduction

The Kubeflow Pipelines UI offers built-in support for several types of visualizations, which you can use to provide rich performance evaluation and comparison data. To make use of this programmable UI, your pipeline component must write a JSON file to the component’s local filesystem. You can do this at any point during the pipeline execution.

You can view the output visualizations in the following places on the Kubeflow Pipelines UI:

• The Run output tab shows the visualizations for all pipeline steps in the selected run. To open the tab in the Kubeflow Pipelines UI:

  1. Click Experiments to see your current pipeline experiments.
  2. Click the experiment name of the experiment that you want to view.
  3. Click the run name of the run that you want to view.
  4. Click the Run output tab.

  

• The Artifacts tab shows the visualization for the selected pipeline step. To open the tab in the Kubeflow Pipelines UI:

  1. Click Experiments to see your current pipeline experiments.
  2. Click the experiment name of the experiment that you want to view.
  3. Click the run name of the run that you want to view.
  4. On the Graph tab, click the step representing the pipeline component that you want to view. The step details slide into view, showing the Artifacts tab.

  

All screenshots and code snippets on this page come from a sample pipeline that you can run directly from the Kubeflow Pipelines UI. See the sample description and links below.

Writing out metadata for the output viewers

The pipeline component must write a JSON file specifying metadata for the output viewer(s) that you want to use for visualizing the results. The file name must be /mlpipeline-ui-metadata.json, and the component must write the file to the root level of the container filesystem.

The JSON specifies an array of outputs. Each outputs entry describes the metadata for an output viewer. The JSON structure looks like this:

{
  "version": 1,
  "outputs": [
    {
      "type": "confusion_matrix",
      "format": "csv",
      "source": "my-dir/my-matrix.csv",
      "schema": [
        {"name": "target", "type": "CATEGORY"},
        {"name": "predicted", "type": "CATEGORY"},
        {"name": "count", "type": "NUMBER"},
      ],
      "labels": "vocab"
    },
    {
      ...
    }
  ]
}

If the component writes such a file to its container filesystem, the Kubeflow Pipelines system extracts the file, and the Kubeflow Pipelines UI uses the file to generate the specified viewer(s). The metadata specifies where to load the artifact data from. The Kubeflow Pipelines UI loads the data into memory and renders it. Note: You should keep this data at a volume that’s manageable by the UI, for example by running a sampling step before exporting the file as an artifact.

The table below shows the available metadata fields that you can specify in the outputs array. Each outputs entry must have a type. Depending on value of type, other fields may also be required as described in the list of output viewers later on the page.

Available output viewers

The sections below describe the available viewer types and the required metadata fields for each type.

Confusion matrix

Type: confusion_matrix

Required metadata fields:

• format
• labels
• schema
• source

The confusion_matrix viewer plots a confusion matrix visualization of the data from the given source path, using the schema to parse the data. The labels provide the names of the classes to be plotted on the x and y axes.

Example:

  metadata = {
    'outputs' : [{
      'type': 'confusion_matrix',
      'format': 'csv',
      'schema': [
        {'name': 'target', 'type': 'CATEGORY'},
        {'name': 'predicted', 'type': 'CATEGORY'},
        {'name': 'count', 'type': 'NUMBER'},
      ],
      'source': cm_file,
      # Convert vocab to string because for bealean values we want "True|False" to match csv data.
      'labels': list(map(str, vocab)),
    }]
  }
  with file_io.FileIO('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

Markdown

Type: markdown

Required metadata fields:

• source
• storage

The markdown viewer renders Markdown strings on the Kubeflow Pipelines UI. The viewer can read the Markdown data from the following locations:

• A Markdown-formatted string embedded in the source field. The value of the storage field must be inline.
• Markdown code in a remote file, at a path specified in the source field. The storage field can contain any value except inline.

Example:

  metadata = {
    'outputs' : [
    # Markdown that is hardcoded inline
    {
      'storage': 'inline',
      'source': '# Inline Markdown\n[A link](https://www.kubeflow.org/)',
      'type': 'markdown',
    },
    # Markdown that is read from a file
    {
      'source': 'gs://your_project/your_bucket/your_markdown_file',
      'type': 'markdown',
    }]
  }
  with file_io.FileIO('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

ROC curve

Type: roc

Required metadata fields:

• format
• schema
• source

The roc viewer plots a receiver operating characteristic (ROC) curve using the data from the given source path. The Kubeflow Pipelines UI assumes that the schema includes three columns with the following names:

• fpr (false positive rate)
• tpr (true positive rate)
• thresholds

When viewing the ROC curve, you can hover your cursor over the ROC curve to see the threshold value used for the cursor’s closest fpr and tpr values.

Example:

  df_roc = pd.DataFrame({'fpr': fpr, 'tpr': tpr, 'thresholds': thresholds})
  roc_file = os.path.join(args.output, 'roc.csv')
  with file_io.FileIO(roc_file, 'w') as f:
    df_roc.to_csv(f, columns=['fpr', 'tpr', 'thresholds'], header=False, index=False)

  metadata = {
    'outputs': [{
      'type': 'roc',
      'format': 'csv',
      'schema': [
        {'name': 'fpr', 'type': 'NUMBER'},
        {'name': 'tpr', 'type': 'NUMBER'},
        {'name': 'thresholds', 'type': 'NUMBER'},
      ],
      'source': roc_file
    }]
  }
  with file_io.FileIO('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

Table

Type: table

Required metadata fields:

• format
• header
• source

The table viewer builds an HTML table out of the data at the given source path, where the header field specifies the values to be shown in the first row of the table. The table supports pagination.

Example:

  metadata = {
    'outputs' : [{
      'type': 'table',
      'storage': 'gcs',
      'format': 'csv',
      'header': [x['name'] for x in schema],
      'source': prediction_results
    }]
  }
  with open('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

TensorBoard

Type: tensorboard

Required metadata Fields:

• source

The tensorboard viewer adds a Start Tensorboard button to the output page.

When viewing the output page, you can:

• Click Start Tensorboard to start a TensorBoard Pod in your Kubeflow cluster. The button text switches to Open Tensorboard.
• Click Open Tensorboard to open the TensorBoard interface in a new tab, pointing to the logdir data specified in the source field.

Note: The Kubeflow Pipelines UI doesn’t fully manage your TensorBoard instances. The “Start Tensorboard” button is a convenience feature so that you don’t have to interrupt your workflow when looking at pipeline runs. You’re responsible for recycling or deleting the TensorBoard Pods using your Kubernetes management tools.

Example:

  metadata = {
    'outputs' : [{
      'type': 'tensorboard',
      'source': args.job_dir,
    }]
  }
  with open('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

Web app

Type: web-app

Required metadata fields:

• source

The web-app viewer provides flexibility for rendering custom output. You can specify an HTML file that your component creates, and the Kubeflow Pipelines UI renders that HTML in the output page. The HTML file must be self-contained, with no references to other files in the filesystem. The HTML file can contain absolute references to files on the web. Content running inside the web app is isolated in an iframe and cannot communicate with the Kubeflow Pipelines UI.

Example:

  static_html_path = os.path.join(output_dir, _OUTPUT_HTML_FILE)
  file_io.write_string_to_file(static_html_path, rendered_template)

  metadata = {
    'outputs' : [{
      'type': 'web-app',
      'storage': 'gcs',
      'source': static_html_path,
    }]
  }
  with file_io.FileIO('/mlpipeline-ui-metadata.json', 'w') as f:
    json.dump(metadata, f)

Visualization on the Kubeflow Pipelines UI:

Source of examples on this page

The above examples come from the tax tip prediction sample that is pre-installed when you deploy Kubeflow.

You can run the sample by selecting [Sample] ML - TFX - Taxi Tip Prediction Model Trainer from the Kubeflow Pipelines UI. For help getting started with the UI, follow the Kubeflow Pipelines quickstart.

The sample code is available in the Kubeflow Pipelines samples repo. The pipeline uses a number of prebuilt, reusable components, including:

• The Confusion Matrix component which writes out the data for the confusion_matrix viewer.
• The ROC component which writes out the data for the roc viewer.
• The dnntrainer component which writes out the data for the tensorboard viewer.
• The tfma component which writes out the data for the web-app viewer.
• The dataflow predict component which writes out the data for the table viewer.

Next step

See how to export metrics from your pipeline.

Feedback

Was this page helpful?

Glad to hear it! Please tell us how we can improve.

Sorry to hear that. Please tell us how we can improve.