domino logo
Tech Ecosystem
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 Overview
Revert Projects and Files
Revert a ProjectRevert a File
Projects PortfolioProject Goals in Domino 4+Upload Files to Domino using your BrowserFork and Merge ProjectsSearchSharing and CollaborationCommentsDomino 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 StorageUse Apache Airflow with Domino
Scheduled Jobs
Domino Workspaces
WorkspacesUse Visual Studio Code in Domino WorkspacesPersist RStudio PreferencesAccess Multiple Hosted Applications in one Workspace SessionUse Domino Workspaces in Safari
Spark on Domino
On-Demand Spark
On-Demand Spark OverviewValidated Spark VersionConfigure PrerequisitesWork with your ClusterManage DependenciesWork with Data
External Hadoop and Spark
Hadoop and Spark OverviewConnect 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 DominoRun Local Spark on a Domino ExecutorUse PySpark in Jupyter WorkspacesKerberos Authentication
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 ProductionExport Model Image
Publish a Web Application
Cross-Origin Security in Domino web appsApp 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
Assets Portfolio Overview
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
Advanced User Configuration Settings
User 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 SupportSupport Bundles
domino logo
About Domino
Domino Data LabKnowledge BaseData Science BlogTraining
User Guide
>
Domino Reference
>
Projects
>
Comments

Comments

Domino makes it easy to collaborate and coordinate with others through commenting.

Comments can be posted on:

  • Jobs

  • Workspaces

  • Results

  • Files

  • Reviews

  • Run comparisons

Domino supports Mathjax and Markdown for richly formatted comments. You can also mention other users to tag and notify your collaborators.

Example comment with MathJax

MathJax formatting

Example comment with MathJax only^

To produce clear and professional-looking symbols and equations, Domino uses MathJax. MathJax automatically formats the mathematical symbols and equations that you enter in comments using LaTeX notation.

Many resources for learning and using MathJax are available online, including the official MathJax Documentation.

For quick reference and examples of commonly used MathJax notation, Domino recommends this tutorial available on the Mathematics meta stack exchange.

Markdown formatting

Example comment with Markdown only

Domino also supports Markdown in comments. Markdown is a lightweight markup language used to add formatting elements to plaintext text documents.

They are especially useful in Domino for formatting longer comments or creating README files.

Read about the basics of Markdown syntax here.

Mention collaborators

Example comment with @ mentioning only

You can mention your collaborators in a comment with @username. Users mentioned in a comment will receive an email notification.

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