Managing deployment jobs

A job is a way of running a batch deployment, Data Refinery flow, Streams flow, or a script in Watson Machine Learning. You can choose to run a job manually or on a schedule you specify. After you create one or more jobs, you can view and manage them from the Jobs tab of your deployment space.

From the Jobs tab of your space, you can:

  • See the list of the jobs in your space
  • View the details of each job. You can change the schedule settings of a job and pick a different environment definition.
  • Monitor job runs
  • Create jobs
  • Delete jobs

You can also create jobs when you create a batch deployment.

You create a Data Refinery job from the Assets page:

  1. Click Create job from the Actions menu next to the name of the Data Refinery flow.
  2. Complete the job details and run the job. Output is saved to the specified output file.

Creating jobs from the Jobs tab

To create a job:

  1. From your deployment space, click the Jobs tab and then click New job.
  2. Enter a job name and description.
  3. Select the deployment you want to run.
  4. Select the environment runtime for your job.
  5. Optional: Select to schedule the job. Select the start date for this schedule. Click the calendar to select a date. Specify the start time, the time zone, and the repeat settings which depend on the frequency you selected.
  6. Create the job. You can create and run the job immediately, for example if you didn’t specify a schedule, or you can create the job and run it later, either manually or as specified in the schedule.

Scheduling a batch deployment

When you create a batch deployment, you can optionally schedule a job for the deployment. Scheduling a deployment creates a job that will display on the Jobs page of the deployment space.

Note that if you exclude certain week days, the job might not run as you would expect. The reason is due to a discrepancy between the timezone of the user who creates the schedule, and the timezone of the master node where the job runs.

Viewing jobs in a space

You can view all of the jobs that exist for your deployment space from the Jobs page. You can delete a job from this page.

To view the details of a specific job, click the job. From the job’s details page, you can:

  • View the runs for that job and the status of each run. If a run failed, you can select the run and view the log tail or download the entire log file to help you troubleshoot the run. A failed run might be related to a temporary connection or environment problem. Try running the job again. If the job still fails, you can send the log to Customer Support.
  • Edit schedule settings or pick another environment definition.
  • Run the job manually by clicking the run icon from the job action bar. You must deselect the schedule to run the job manually.

Deleting jobs

When a job completes, either by executing successfully or failing, the meta data is stored by default for 30 days. You can retrieve meta data from the deployment endpoint using the GET method during the 30 days.

We recommend that when you delete a job programmatically, specify the query parameter hard_delete=true for one of the following Watson Machine Learning API methods to completely remove the job meta data.

V4 Watson Machine Learning API method:

  • DELETE /ml/v4/deployment_jobs/{JobsID}

v4 beta API methods:

  • DELETE /v4/jobs/{JobID}
  • DELETE /v4/deployment_jobs/{JobID}’

To keep the meta data for longer than 30 days, change the query parameter from the default of auto_delete=true to auto_delete=false for the POST method to override the default and preserve the meta data.

V4 Watson Machine Learning API method:

  • POST /ml/v4/deployment_jobs/{JobsID}

v4 beta API methods:

  • POST /v4/jobs/{JobID}
  • POST /v4/deployment_jobs/{JobID}’