Quota for CML workloads

A Machine Learning (ML) Workspace is allocated a set amount of resources, based on the configured parameters at provisioning time. Within a workspace, resources available for workloads can be further subdivided into quotas at user level.

Quota Management is implemented at user level. By default 8 GB memory and 2 vCPU cores are configured for each user. The configured resources are sufficient for running sessions but neither the spark workloads nor the executors find additional resources. CML administrators can configure custom quota for the user under the Site Administration page.

If the quota for a user is exhausted, the workload remains in the pending state until the required resources are available.

If the quota for users is modified, it will be reflected when the next workload is submitted.

GPU resources can be edited if the workspace is provisioned with GPU resources.