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
Notifications
On-Demand Open MPI
Configure MPI PrerequisitesFile Sync MPI ClustersValidate MPI VersionWork with your ClusterManage Dependencies
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
Use Custom Images as a Compute Environment
Pre-requisites for Automatic Custom Image CompatibilityModify the Default Workspace ToolsCreate a Domino Image with an NGC ContainerCreate a Domino Environment with a Pre-Built ImageManually Modify Images for Domino Compatibility
Partner Environments for Domino
Use MATLAB as a WorkspaceUse Stata as a WorkspaceUse SAS as a Workspace
Advanced Options for Domino Software Environment
Publish in Domino with Custom ImagesInstall 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 ImageExport to NVIDIA Fleet Command
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 Azure Data Lake StorageConnect to BigQuery from DominoConnect to Generic S3 from DominoConnect to Google Cloud StorageConnect 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
>
Scheduled Jobs

Scheduled Jobs

You can schedule Jobs in advance and set them to execute on a regular cadence. These can be useful when you have a data source that is updated regularly.

To schedule a Job:
  1. Go to a project.

  2. From the navigation pane, click Scheduled Jobs.

  3. Click New Scheduled Job.

  4. In the Create a Scheduled Job page, define the job:

    scheduled_job_1.png

    Scheduled Job Name

    Enter the name of the job. The Jobs Dashboard lists each job by this name.

    File Name

    Enter the name of the file to execute. Include any optional arguments to pass to the file.

    Hardware tier

    Select the hardware tier used by the Job.

    Environment

    Select the compute environment used by the Job.

    Data

    Click to expand the section to see the Datasets configuration used by the Job.

  5. Click Next. Then, define the Compute Cluster:

    scheduled_job_2.png

    Attach Compute Cluster

    Use this option to provision and attach a compute cluster to the Job. The remainder of the configurations are explained in:

    • Spark Cluster Settings.

    • Ray Cluster Settings.

    • Dask Cluster Settings.

  6. Click Next. Set up the Schedule:

    scheduled_job_3.png

    Use custom expression

    Enter a custom Quartz CronTrigger expression. For example, if you wanted to run the job on the 5th minute of every day, enter the following: 0 5 * ? * *

    Repeat every

    Set the frequency at which you want the Job to repeat.

    Run sequentially

    Setting a Job to Run sequentially will cause the scheduler to always wait for the last Job it started to complete before starting the next one. For example, if you set up a scheduled Job to run once per hour, and one of the Jobs launched by the scheduler takes 90 minutes to complete, the next hourly Job will not start until the previous one has finished. Otherwise, multiple Jobs from this scheduler will be allowed to run simultaneously. The scheduler will not wait for the previous Job to finish if it’s still running. This mode should be used when your Job doesn’t depend on output from the previous Job.

  7. Click Next. Set up Actions:

    scheduled_job_4.png

    Notify Emails

    Enter a list of email addresses to notify when the Job completes.

    Update Model API

    If a Model API has been publishing from the Project, the selected Model API will be republished after the Job has completed. Use this for retraining and updating a Model API regularly.

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