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View Execution Performance

View Execution Performance

To understand the performance of your experiments, you must analyze many outputs and results. It’s often useful to see an overview of key metrics across all executions so you can quickly identify which experiments are worth investigating further. To do this, use Domino’s diagnostic statistics functionality.

View execution performance
  1. Write a file named dominostats.json to the root of your project directory.

  2. Use keys in this .json file to identify the outputs you’re interested in and add the corresponding values.

    The following is sample R code that writes three key/value pairs to dominostats.json:

    diagnostics = list("R^2" = 0.99, "p-value" = 0.05, "sse" = 10.49)
    library(jsonlite)
    fileConn<-file("dominostats.json")
    writeLines(toJSON(diagnostics), fileConn)
    close(fileConn)

    The following is the same data written to dominostats.json in Python:

    import json
    with open('dominostats.json', 'w') as f:
        f.write(json.dumps({"R^2": 0.99, "p-value": 0.05, "sse": 10.49}))

    The resulting dominostats.json from these code examples looks like this:

    {
      "sse": 10.49,
      "R^2": 0.99,
      "p-value": 0.05
    }

    Domino automatically deletes dominostats.json before each execution. If Domino detects that this file has been written to the project root by a job, it parses the values and shows them as columns on the Jobs dashboard. You can see the keys represented as available columns in the dashboard, and each row contains the corresponding value from a Job.

    jobs timeline

  3. Click Jobs Timeline to expand a line chart of dominostats.json values over time. This chart shows all Jobs listed in the current dashboard. To filter to a specific set of related Jobs, tag the jobs to create a separate dashboard view.

    jobs stats

    The x-axis ticks on the timeline represent individual jobs, and the y-axis represents the values for the statistics in those jobs.

    1. Hold your cursor over the chart to see individual data points as tooltips, and click at a point to open the details for the Job that produced that value.

    2. Click and drag on the chart to zoom in.

    3. Click each stat in the legend to toggle its line on and off.

    jobs timeline

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