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
>
Projects
>
Advanced Project Settings
>
Exporting Files as a Python or R Package

Exporting Files as a Python or R Package

If you organize the files in a project as an installable package, then you can choose to export it as such. When another project import this project, Domino will automatically install the package at runtime, making it available to your code.

To export as a package, configure your project to export files, and select the appropriate language under "code package".

screen shot 2016 02 04 at 4.33.44 PM

The following describes the language-specific pattern required for any package.

R

For an in-depth guide to writing R extensions, see the official manual.

In summary, each R package requires:

  • A directory called R/ containing code files.

  • A directory called man/ containing documentation files

  • A file named DESCRIPTION, with each line following the pattern link:key>: <value[]. Required keys include:

    Package
    Version (for example, 0.1)
    Title
    Description
    Author
    Maintainer (a name followed by an email address in angle brackets, for example, Sample Maintainer <maintainer@example.com>)
    License
  • A file named NAMESPACE that describes the namespace of the package. If you aren’t sure what to put here, exportPattern( "." ) can work in many cases.

Python

For an in-depth guide, see this documentation.

In summary, each Python package requires:

  • A setup.py file. This must contain a setup() function (imported from setuptools), with arguments as described here.

  • A folder containing your Python modules and packages. Usually this is given the same name as the overall package.

  • It’s also a good idea to include some sort of README file.

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