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.
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Write a file named
dominostats.json
to the root of your project directory. -
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. -
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.The x-axis ticks on the timeline represent individual jobs, and the y-axis represents the values for the statistics in those jobs.
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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.
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Click and drag on the chart to zoom in.
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Click each stat in the legend to toggle its line on and off.
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