Resource Management for Dedicated Resource Customers
Note
This feature is only available for teams with an upgraded machine learning license with dedicated increased resources (Memory, CPUs, Storage and in some cases GPUs).
Teams with dedicated resources can easily manage and distribute these resources between different Machine Learning workbenches by updating the memory and CPU limits or starting and stopping workbenches to free up resources if needed. Only admins of the team are able to manage these resources and see the overview of all the resources available.
While creating a workbench you will be able to also select the memory, cpu, storage and potentially the gpu (where available) limit for the workbench as required by a specific project you are creating. You can also mark a workbench as productive. Marking a workbench as productive will mean that the resources are blocked for this workbench and will always be available, that is, this workbench will never be shut down automatically in case of longer inactivity. We recommend to mark the workbenches productive if they have scheduled executions planned.
Under the Configuration tab in the side menu you can see the status of all the resources and the current auto shutdown setting. This shows how many total resources are allocated to the team and how much of those resources is available. This screen also shows an overview of how much Memory, CPU and Storage each ML workbench is occupying. You can also see which of the ML workbenches are marked as productive.
Note
This view is only available for admins of the team.
An admin can then start or shutdown any ML workbench from the Configuration page by clicking on the three dots menu next to each row. You can also edit the resources of the workbenches from here using the Edit option on the same menu.
By default the Auto Shutdown time of any non-productive ML workbench is 12 hours of inactivity. This can be updated according to the needs (increase or decrease) by enabling the Auto Shutdown toggle on the Configuration page. You need to enter the auto shutdown time period in hours in the respective field and click the Save button. Auto shutdown helps free up resources that are unnecessarily occupied by idle ML workbenches. An ML workbench that is shut down does not lose any information or data, it only frees up the resources the workbench was using and can be started again at any time.
