Starting a Job Run

Use Data Science job runs to apply various use cases to created jobs.

Job creation sets the infrastructure and the actual use case artifact, but the job run actually runs the job with the specified parameters. Job runs provision the specified infrastructure, run the job artifact, and then deprovision and destroy the used resources when the job run ends.

    1. Use the Console to sign in to a tenancy with the necessary policies.
    2. Open the navigation menu and click Analytics & AI. Under Machine Learning, click Data Science.
    3. Select the compartment that contains the project with the jobs.

      All projects in the compartment are listed.

    4. Click the name of the project.

      The project details page opens and lists the notebook sessions.

    5. Under Resources, click Jobs.

      A tabular list of jobs in the project is displayed.

    6. Click the name of the job to work with.
    7. Click Start a job run.
    8. (Optional) Select a different compartment for the job.
    9. (Optional) Enter a unique name for the job run (limit of 255 characters). If you don't provide a name, a name is automatically generated.

      For example, jobrun20210808222435.

    10. (Optional) To use logging, click Select, and then ensure that Enable logging is selected.
      1. Select a log group from the list. You can change to a different compartment to specify a log group in a different compartment from the job.
      2. Select one of the following to store all stdout and stderr messages:
        Enable automatic log creation

        Data Science automatically creates a log when the job starts.

        Select a log

        Select a log to use.

      3. Click Select to return to the job run creation page.
    11. (Optional) Override the Bring Your Own Container configuration, that was defined when the job was created, by using these options:
      1. In Environment configuration. click Select. The Set your BYOC environment panel is displayed.
      2. In Repository select a repository from the list. If the repository is in a different compartment, click Change compartment.
      3. In Image select an image from the list.
      4. (Optional) In Entrypoint enter an entry point. To add another, click +Add parameter.
      5. (Optional) In CMD enter a CMD. To add another, click +Add parameter.

        Use CMD as arguments to the ENTRYPOINT or the only command to run in the absence of an ENTRYPOINT.
      6. (Optional) In Image digest enter an image digest.
      7. (Optional) In Signature ID, if using signature verification, enter the OCID of the image signature. For example, ocid1.containerimagesignature.oc1.iad.aaaaaaaaab....
      8. Click Select.
    12. (Optional) Override the default job configuration that was defined when the job was created by using these options:
      Enter or select any of the following values:
      Custom environment variable key

      The environment variables that control the job.


      The value for the custom environment variable key.

      You can click Additional custom environment key to specify more variables.

      Command line arguments

      The command line arguments that you want to use for running the job.

      Maximum runtime (in minutes)

      The maximum number of minutes that the job can run. The service cancels the job run if its runtime exceeds the specified value. The maximum runtime is 30 days (43,200 minutes). We recommend that you configure a maximum runtime on all job runs to prevent runaway job runs.

    13. (Optional) Click Show advanced options to add tags to the job run.
    14. (Optional) Enter the tag namespace (for a defined tag), key, and value to assign tags to the resource.

      To add more than one tag, click Add tag.

      Tagging describes the various tags that you can use organize and find resources including cost-tracking tags.

    15. Click Start.
  • These environment variables control the job.

    Use the Data Science CLI to start job runs as in this example:

    1. Start a job run with:
      oci data-science job-run create \
      --display-name <job_run_name> \
      --compartment-id <compartment_ocid> \
      --project-id <project_ocid> \
      --job-id <job_ocid> \
      --configuration-override-details file://<optional_job_run_configuration_override_json_file> \
      --log-configuration-override-details file://<optional_job_run_logging_configuration_override_json_file>
    2. (Optional) Use this job run configuration override JSON file to override the configurations defined on the parent job:
      jobEnvironmentConfigurationDetails: {
        jobEnvironmentType: "OCIR_CONTAINER",
          image: "",
          imageDigest: "sha256",
        cmd: ["ls", "-h"],
        entrypoint: ["-l"],
          imageSignatureId: "ocid1.containerimagesignature.oc1.iad.0.ociodscdev.aaaaaaaaccutw5qdz6twjzkpgmbojdck3qotqqsbn7ph6xcumu4s32o6v5gq",
          jobConfigurationDetails: {
              jobType: "DEFAULT",
              environmentVariables: <envar-list-object>},
    3. (Optional) Use this job run logging configuration override JSON file to override the logging configuration defined on the parent job:
        "enableLogging": true,
        "enableAutoLogCreation": true,
        "logGroupId": "<log_group_ocid>"
  • The ADS SDK is also a publicly available Python library that you can install with this command:

    pip install oracle-ads

    It provides the wrapper that makes starting job runs from notebooks or on your client machine easy.

    Use the ADS SDK to start job runs.