If you have a project that requires regular updates for tasks (such as data processing or monthly/weekly reports) or a model that needs to be retrained on updated data sources at regular intervals, Data Science & AI Workbench enables you to schedule your deployments to automate project executions. Strategically scheduling deployments minimizes manual intervention and can reduce system load during peak hours, maintaining optimal performance for your cluster.
Scheduled tasks can read data previously committed to a project, but cannot be used to commit any new data to the project. Any data written to a scheduled deployment’s container is deleted immediately after the scheduled task completes. Anaconda recommends you ensure data is read from and written to an external data source.
From a schedule’s details view, you can use the controls at the top of the page to pause or resume, edit, or delete a selected schedule.
If you attempt to delete a schedule that is currently running or is scheduled to run, you will be prompted to confirm that you want to force the deletion.
Each time a scheduled task is executed, its run is automatically logged. You can view all runs associated with a schedule in its details view. Selecting a specific run from the list displays the corresponding log for that execution.
If something went wrong with your deployment, the runs log can be a helpful tool in troubleshooting the issue.