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
Get Started with MATLAB
Step 1: Orient yourself to DominoStep 2: Create a Domino ProjectStep 3: Configure Your Domino ProjectStep 4: Start a MATLAB WorkspaceStep 5: Fetch and Save Your DataStep 6: Develop Your ModelStep 7: Clean Up Your Workspace
Step 8: Deploy Your Model
Scheduled JobsLaunchers
Step 9: Working with Domino Datasets
Domino Reference
Projects
Projects Overview
Revert Projects and Files
Revert a ProjectRevert a File
Projects PortfolioReference ProjectsProject Goals in Domino 4+
Git Integration
Git Repositories in DominoGit-based Projects with CodeSyncWorking from a Commit ID in Git
Jira Integration in DominoUpload 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 Git in Your WorkspaceRecreate A Workspace From A Previous CommitUse Visual Studio Code in Domino WorkspacesPersist RStudio PreferencesAccess Multiple Hosted Applications in one Workspace Session
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
On-Demand Ray
On-Demand Ray OverviewValidated Ray VersionConfigure PrerequisitesWork with your ClusterManage DependenciesWork with Data
On-Demand Dask
On-Demand Dask OverviewValidated Dask VersionConfigure PrerequisitesWork with Your ClusterManage DependenciesWork with Data
Customize the Domino Software Environment
Environment ManagementDomino Standard EnvironmentsInstall Packages and DependenciesAdd Workspace IDEsAdding Jupyter Kernels
Partner Environments for Domino
Use MATLAB as a WorkspaceUse Stata as a WorkspaceUse SAS as a WorkspaceNVIDIA NGC Containers
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 MirrorsUse TensorBoard in Jupyter Workspaces
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
App Publishing OverviewGet Started with DashGet Started with ShinyGet Started with FlaskContent Security Policies for Web Apps
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
Model Monitoring and Remediation
Monitor WorkflowsData Drift and Quality Monitoring
Set up Monitoring for Model APIs
Set up Prediction CaptureSet up Drift DetectionSet up Model Quality MonitoringSet up NotificationsSet Scheduled ChecksSet up Cohort Analysis
Set up Model Monitor
Connect a Data SourceRegister a ModelSet up Drift DetectionSet up Model Quality MonitoringSet up Cohort AnalysisSet up NotificationsSet Scheduled ChecksUnregister a Model
Use Monitoring
Access the Monitor DashboardAnalyze Data DriftAnalyze Model QualityExclude Features from Scheduled Checks
Remediation
Cohort Analysis
Review the Cohort Analysis
Remediate a Model API
Monitor Settings
API TokenHealth DashboardNotification ChannelsTest Defaults
Monitoring Config JSON
Supported Binning Methods
Model Monitoring APIsTroubleshoot the Model Monitor
Connect to your Data
Data in Domino
Datasets OverviewProject FilesDatasets Best Practices
Connect to Data Sources
External Data VolumesDomino Data Sources
Connect to External Data
Connect Domino to DataRobotConnect to Amazon S3 from DominoConnect to BigQuery from DominoConnect to Generic S3 from DominoConnect to IBM DB2 from DominoConnect to IBM Netezza from DominoConnect to Impala from DominoConnect to MSSQL from DominoConnect to MySQL from DominoConnect to Okera from DominoConnect to Oracle Database from DominoConnect to PostgreSQL from DominoConnect to Redshift from DominoConnect to Snowflake from DominoConnect to Teradata from Domino
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 KeysDomino TokenOrganizations 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
>
Connect to your Data
>
Connect to Data Sources
>
External Data Volumes

External Data Volumes

Important

Domino projects can access data stored in external data volumes. Domino supports the following data volumes:

  • Network File Systems (generic NFS and AWS EFS)

  • Windows Share (SMB)

When configured for use with your Domino deployment, external data volumes are automatically mounted to supported Domino executions. External volumes are supported in the following Domino executions:

  • Jobs (including Scheduled Jobs)

  • Workspaces

  • Apps

  • Launchers

  • On-demand Spark clusters

Mount an external volume

  1. Click Data in the navigation menu.

    external-data-volumes-1

  2. Scroll to the "External Data Volumes" section.

    external-data-volumes-2

  3. Click Add External Volume.

    external-data-volumes-3

  4. Select an available volume. Click into the text area to expand a dropdown menu of available volumes, or type into the text area to quickly search for a volume.

    external-data-volumes-3-1

    Important
  5. Click Add. If your volume is successfully mounted, it’ll be listed in a table in the "External Data Volumes" section of Data.

    external-data-volumes-4

View mounted volumes

  1. Click Data in the navigation pane.

    external-data-volumes-1-2

  2. Scroll to the "External Data Volumes" section. If no volumes have been mounted, you’ll be prompted to mount a volume. If the project already has mounted volumes, they’ll appear here listed in a table along with the volume’s properties.

    external-data-volumes-5

Important

Volume Censorship Levels

Partial volume censorship

You might encounter mounted volumes that are "greyed out" in your volume table. This means that the volume(s) have been mounted to your project, but that you do not have access to them. To gain access to the volumes, contact your Domino administrator.

external-data-volumes-9

Full volume censorship

Your project may contain volumes that have been mounted to the project but are not listed (that is, fully censored from view) in the volume table. A notification banner will appear above the table informing you about this. To gain access to the volumes, contact your Domino administrator.

external-data-volumes-10

Properties of mounted volumes

Mounted volumes are listed in a table in the Data section of your project. The table will also display the following properties of the volume:

  • Name – An alias for the volume. To change this setting, contact your Domino administrator.

  • Type – The type of volume. Domino supports NFS, AWS EFS, and Windows Share (SMB).

  • Description – A description of the volume, set by your Domino administrator. To change the description, contact your Domino administrator.

  • Mount Path – The mount path of the volume: /domino/edv/name-of-volume. Use this mount path when using the volume in a Job, Workspace, or other supported Domino execution.

    external-data-volumes-8

Use a mounted volume

By default, external volumes that are mounted to your project (and that you have access to) are also automatically mounted in supported executions. At the moment, supported Domino executions include Jobs (including Scheduled Jobs), Workspaces, Apps, Launchers, and on-demand Spark clusters. You can access a volume within an execution by referencing the mount path of the volume(s).

Mounted volumes in a Job

external-data-volumes-11

Mounted volumes in a Workspace

external-data-volumes-12

Mounted volumes in an App

external-data-volumes-13

Mounted volumes with a Launcher

Important

Mounted volumes with an on-demand Spark cluster

External data volumes are mounted at the full mount path on the driver and all Spark executors.

external-data-volumes-14

Unmount a volume

  1. Click Data in the Domino sidebar menu.

    external-data-volumes-6-1

  2. Click on the three vertical dots to the right of the corresponding entry in the table and then click on Remove in the menu that appears.

    external-data-volumes-6

  3. Confirm (or cancel) removal in the subsequent modal that appears.

    external-data-volumes-7

Current Limitations

  • Model APIs do not support external volumes.

  • External data volume actions are not exposed by the Domino REST API or Domino CLI.

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