domino logo
Get started with Python
Step 0: Orient yourself to DominoStep 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get started with R
Step 0: Orient yourself to Domino (R Tutorial)Step 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Domino Reference
Projects
Projects OverviewProjects PortfolioUpload Files to Domino using your BrowserFork and Merge ProjectsSearchSharing and CollaborationDomino Service FilesystemCompare File RevisionsArchive a Project
Advanced Project Settings
Project DependenciesProject TagsRename a ProjectSet up your Project to Ignore FilesUpload files larger than 550MBExporting Files as a Python or R PackageTransfer Project Ownership
Domino Runs
JobsDiagnostic Statistics with dominostats.jsonNotificationsResultsRun Comparison
Advanced Options for Domino Runs
Run StatesDomino Environment VariablesEnvironment Variables for Secure Credential StorageAccessing the shell for a Domino Run with SSHUse Apache Airflow with Domino
Scheduled Jobs
Domino Workspaces
WorkspacesUse Visual Studio Code in Domino WorkspacesPersist RStudio PreferencesAccess Multiple Hosted Applications in one Workspace Session
Customize the Domino Software Environment
Environment ManagementDomino Standard EnvironmentsInstall Packages and DependenciesAdd Workspace IDEs
Advanced Options for Domino Software Environment
Install Custom Packages in Domino with Git IntegrationAdd Custom DNS Servers to Your Domino EnvironmentConfigure a Compute Environment to User Private Cran/Conda/PyPi MirrorsScala notebooksUse TensorBoard in Jupyter WorkspacesUse MATLAB as a WorkspaceCreate a SAS Data Science Workspace Environment
Publish your Work
Publish a Model API
Model Publishing OverviewModel Invocation SettingsModel Access and CollaborationModel Deployment ConfigurationPromote Projects to Production
Publish a Web Application
App Publishing OverviewGet Started with DashGet Started with ShinyGet Started with Flask
Advanced Web Application Settings in Domino
App Scaling and PerformanceHost HTML Pages from DominoHow to Get the Domino Username of an App Viewer
Launchers
Launchers OverviewAdvanced Launcher Editor
Connect to your Data
Domino Datasets
Datasets OverviewDatasets Best PracticesAbout domino.yamlDatasets Advanced Mode TutorialDatasets Scratch SpacesConvert Legacy Data Sets to Domino Datasets
Data Sources OverviewConnect to Data Sources
Git and Domino
Git Repositories in DominoWork From a Commit ID in Git
Work with Data Best Practices
Work with Big Data in DominoWork with Lots of FilesMove Data Over a Network
Hadoop and Spark
Connect to a Cloudera CDH5 cluster from DominoConnect to a Hortonworks cluster from DominoConnect to a MapR cluster from DominoConnect to an Amazon EMR cluster from DominoHadoop and Spark overviewKerberos authenticationRun local Spark on a Domino executorUse PySpark in Jupyter Workspaces
Advanced User Configuration Settings
Two-factor authenticationUser API KeysOrganizations Overview
Use the Domino Command Line Interface (CLI)
Install the Domino Command Line (CLI)Domino CLI ReferenceDownload Files with the CLIForce-Restore a Local ProjectMove a Project Between Domino DeploymentsUse the Domino CLI Behind a Proxy
Browser Support
Get Help with Domino
Additional ResourcesGet Domino VersionContact Domino Technical Support
domino logo
About Domino
Domino Data LabKnowledge BaseData Science BlogTraining
User Guide
>
Domino Reference
>
Domino Runs
>
Run Comparison

Run Comparison

Job comparison makes it really easy to figure out how two experiments differed in their inputs, and how those changes affected the results that were generated.

Comparing two Jobs is easy. From the Jobs dashboard, check the two Jobs you’re interested in then click the Compare button.

Screen Shot 2019 02 14 at 1.24.11 PM 1

This will generate a report, summarizing the differences between those two Jobs.

Screen Shot 2019 02 14 at 1.26.16 PM

If you are tracking Run Diagnostic Statistics, the comparison view will show you the difference between your stats.

Remember that Domino snapshots the state of all the files in the project before the run starts (the inputs) and snapshots the project after the run completes (the outputs). Any files that were added or modified between the input and outputs are considered results.

Domino will do its best to show you differences as sensibly as it can. For text, we will highlight the lines in the file that are different:

28752072 CD89 4F8E 80C6 B289D0A3AB75

For files that we know how to render, we will render those files side-by-side so you can easily visually compare:

5AAA101D B07E 49D5 B5CA 821D6FEB1F7F

For files Domino doesn’t know how to render, we’ll give you some simple metadata and links to download the exact version so you can look at them on your own computer:

2868CD73 C03D 487F B419 62C32BD323F0

Domino Data LabKnowledge BaseData Science BlogTraining
Copyright © 2022 Domino Data Lab. All rights reserved.