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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.

  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

    This option causes the scheduler to wait for the last Job to finish before starting the next one. For example, if you set up a scheduled Job to run once an hour, and a Job launched by the scheduler takes 90 minutes to complete, the next hourly Job will not start until the previous one finishes. Use this option if your Job depends on output from the previous Job. If you don’t use this option, Jobs run concurrently.

  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.

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