Managing deployment jobs
A job is a way of running a batch deployment, script, or notebook in Watson Machine Learning. You can choose to run a job manually or on a schedule that 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 template.
- Monitor job runs
- Delete jobs
See the following sections for various aspects of job management:
Before you begin
You must set up your task credentials by generating an API key. For more information, see Managing task credentials.
Creating a job for a batch deployment
To learn how to create a job for a batch deployment, see Creating jobs in a batch deployment.
Viewing jobs in a space
You can view all of the jobs that exist for your deployment space from the Jobs page. You can also delete a job.
To view the details of a specific job, click the job. From the job's details page, you can do the following:
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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.
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When a job is running, a progress indicator on the information page displays information about relative progress of the run. You can use the progress indicator to monitor a long run.
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Edit schedule settings or pick another environment template.
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Run the job manually by clicking the run icon from the job action bar. You must deselect the schedule to run the job manually.
Managing job metadata retention
The Watson Machine Learning plan that is associated with your IBM Cloud account sets limits on the number of running and stored deployments that you can create. If you exceed your limit, you cannot create new deployments until you delete existing deployments or upgrade your plan. For more information, see Watson Machine Learning plans.
Managing metadata retention and deletion programmatically
If you are managing a job programmatically by using the Python client or REST API, you can retrieve metadata from the deployment endpoint by using the GET method during the 30 days.
To keep the metadata for more or less than 30 days, change the query parameter from the default of retention=30 for the POST method to override the default and preserve the metadata.
Changing the value to retention=-1 cancels the auto-delete and preserves the metadata.
To delete a job programmatically, specify the query parameter hard_delete=true for the Watson Machine Learning DELETE method to completely remove the job metadata.
The following example shows how to use DELETE method:
DELETE /ml/v4/deployment_jobs/{JobsID}
Learn from samples
Refer to Machine learning samples and examples for links to sample notebooks that demonstrate creating batch deployments and jobs by using the Watson Machine Learning REST API and watsonx.ai Python client library.
Parent topic: Managing predictive deployments