Stopping active runtimes

You should stop all active runtimes when you no longer need them.

Jupyter notebook runtimes are started per user and not per notebook. Stopping a notebook kernel doesn't stop the environment runtime in which the kernel is started because you could have started other notebooks in the same environment. You should only stop a notebook runtime if you are sure that no other notebook kernels are active.

Only runtimes that are started for jobs are automatically shut down after the scheduled job has completed. For example, if you schedule to run a notebook once a day for 2 months, the runtime instance will be activated every day for the duration of the scheduled job and deactivated again after the job has finished.

Project users with Admin role can stop all runtimes in the project. Users added to the project with Editor role can stop the runtimes they started, but can't stop other project users’ runtimes. Users added to the project with the Viewer role can't see the runtimes in the project.

You can stop runtimes under Tool runtimes on the Environments page on the Manage tab of your project.

Default idle timeouts

If you don't stop an active runtime when you no longer need it, it is stopped for you after a defined idle time.

The following table shows the default idle timeout values for runtimes by tool and type of runtime.

Default idle timeout values for environment runtimes shown by tool
Tool Runtime idle timeout Notes
Jupyter notebook editor CPU and GPU runtimes: 18 hours
Spark runtimes: 30 minutes
A cluster administrator can disable the default idle timeout for CPU and GPU runtimes if this is desired. See Disabling the default CPU and GPU idle timeout.
Important: Only stop a runtime if you are sure that no notebook kernels are active in the same environment runtime.
JupyterLab CPU and GPU runtimes: 18 hours
Spark runtimes: 30 minutes
RStudio 2 hours
SPSS Modeler 15 minutes

Parent topic: Environments