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Pipeline Metrics

Export and visualize pipeline metrics

This page shows you how to export metrics from a Kubeflow Pipelines component. For details about how to build a component, see the guide to building your own component.

Overview of metrics

Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI.

Export the metrics file

To enable metrics, your program must write out a file named /mlpipeline-metrics.json. For example:

  accuracy = accuracy_score(df['target'], df['predicted'])
  metrics = {
    'metrics': [{
      'name': 'accuracy-score', # The name of the metric. Visualized as the column name in the runs table.
      'numberValue':  accuracy, # The value of the metric. Must be a numeric value.
      'format': "PERCENTAGE",   # The optional format of the metric. Supported values are "RAW" (displayed in raw format) and "PERCENTAGE" (displayed in percentage format).
  with file_io.FileIO('/mlpipeline-metrics.json', 'w') as f:
    json.dump(metrics, f)

See the full example.

The metrics file has the following requirements:

  • The file path must be /mlpipeline-metrics.json.
  • name must follow the pattern ^[a-z]([-a-z0-9]{0,62}[a-z0-9])?$.
  • numberValue must be a numeric value.
  • format can only be PERCENTAGE, RAW, or not set.

View the metrics

To see a visualization of the metrics:

  1. Open the Experiments page in the Kubeflow Pipelines UI.
  2. Click one of your experiments. The Runs page opens showing the top two metrics, where top is determined by prevalence (that is, the metrics with the highest count) and then by metric name. The metrics appear as columns for each run.

The following example shows the accuracy-score and roc-auc-score metrics for two runs within an experiment:

Metrics from a pipeline run

The above example comes from the taxi 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 components, including:

Next step

Visualize the output of your component by writing out metadata for an output viewer.