A Job can be launched in multiple ways to accommodate a variety of workflows.
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From the navigation pane, click Jobs. The Jobs dashboard shows all Jobs for your project, sorted into tabs by Job status.
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Click Run to start a new Job.
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In the Start Run window, configure the Job.
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Enter the File Name of the script you want Domino to execute, followed by any arguments for the script.
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Select the Hardware Tier to use for this job.
In a Domino Nexus deployment, hardware tiers in the dropdown are grouped by data plane, such as
GCP europe-central2below. TheLocaldata plane corresponds to running the execution in the Domino control plane cluster.
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Select the Environment for the Job. By default, the latest version of your environment is used.
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To select another version, click Change.
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Select a Revision.
Note
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If your administrator has enabled volume provisioning recommendations, you can select a volume size automatically recommended by Domino, instead of the default volume size.
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Click Next.
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If necessary, attach a compute cluster to your execution. To learn more about clusters, see the following:
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Click Next.
Domino shows you what data will be mounted in your execution, based on the selected data plane.
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In the example above, this execution is running in the
AWS us-west-2data plane. -
We can see that the project’s dataset will not be available because it is only available in the
Localdata plane. -
We can also see that the
fsx-for-lustreEDV will be mounted because it has been added to this project and is available for the selected remote data plane.
Tip -
You can also configure your job to create new snapshots of datasets it mounts upon execution completion. Created snapshots are shown under the details tab for a job.
You can start a Job directly from the project Code page.
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Go to the file you want to run and click the gear icon at the end of the row in the files list, then click Run.
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Use the window to configure the Run. This window has additional options to set up recurring scheduled Jobs and select different hardware tiers.
- Title
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In this field, enter a name for the Job. This name will be the label for the Job in the Jobs dashboard.
- Parameters
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If there are any arguments or parameters you’d like to pass to your script, enter them here.
- Hardware tier
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This dropdown list lets you set the hardware tier used by the Job.
- Schedule to repeat
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In this section, specify if you want the run to execute only once, or to repeat on a schedule.
- Publish after complete
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Check this if you want Domino to republish a Model API once the Job completes successfully.
From your workstation, you can start Jobs through the Domino CLI. Follow the instructions to install the Domino CLI on your system.
After you have logged in and opened a project, you can start Jobs with:
domino run <filename>
Examples
domino run calculateStats.r
domino run runModel.m model1 .05
domino run --wait runModel.m model1 .05
domino run --direct "pip freeze | grep pandas"
You can use the Domino API to start a Job with a POST request to:
For more information about how to send valid configuration data, see the API docs.
A Launcher is a web form that rests on top of a script that Domino can execute in a Job. You can use a Launcher to pass arguments to your script from UI fields in a customized web form.
Use Domino to schedule Jobs in advance, and set them to execute on a regular schedule. These can be useful when you have a data source that is updated regularly.
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From the navigation pane, click Jobs.
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Go to the Active tab.
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Select the checkboxes for the Jobs to stop and click the Stop icon.
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From the Jobs page, select the run that you want.
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Go to Run > Re-Run with Original Version.
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From the Jobs page, select the run that you want.
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Go to Run > Re-Run with Latest Version. For batch runs, you can run the Job with the same input arguments used previously, while using the latest versions of your files.
For small duration jobs (less than 10 minutes), customers can save cost by using Spot instances. Select hardware tier that uses node pool using Spot instances.
If AWS interrupts a spot instance, the job may fail to start or complete. If this happens, we suggest retrying the job after some duration (more than 30 minutes). If the issue persists the remediation is to change the hardware tier of the job to use a non-spot node pool.
Learn how to automate Jobs and view results in the Jobs dashboard.