Cloudera Docs

Quota for Cloudera AI workloads

Quota management is implemented for both user and team level. A Cloudera AI Workbench is allocated a set amount of resources based on configured parameters at provisioning time. Within a workbench, resources available for workloads can be further subdivided into quotas at user and/or team level.

User and team naming limitations

Follow the kubernetes label naming conventions when naming users and teams:
  • The name must be 63 characters or less.
  • The name must begin and end with an alphanumeric character ([a-z0-9A-Z]).
  • The name can contain dashes (-), underscores (_), dots (.), and alphanumerics, except for the beginning and ending characters.

User quota

By default, 8 GiB memory and 2 vCPU cores are configured for each user. Such resources are sufficient for running simple sessions but might not be sufficient for the spark workloads if the executors cannot find enough resources. The Cloudera AI administrators can configure a custom quota for the user on the Site Administration Page.

In Cloudera AI on premises 1.5.5 SP1 and higher releases custom GPU quota can be defined for users.

Figure 1. User quota settings


Figure 2. User quota settings for Cloudera AI on premises 1.5.5 SP1 and lower releases


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

Any modifications to the user quota take effect immediately.

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

Team quota

By default, 8 GiB memory and 2 vCPU cores are configured for each team in Cloudera AI on premises 1.5.5 SP2 and higher releases. The Cloudera AI Administrator can configure a custom quota for the user on the Site Administration page.

Figure 3. Team quota settings


  1. As a Cloudera AI Administrator, configure a custom quota for a team under Site Administration > Quotas tab.
  2. Configure a quota for a team, at the Quotas Teams tab.
  3. Click the Add Team Quota and a popup enables you to add a custom team quota.
Figure 4. Custom quota

In Cloudera AI on premises 1.5.5 SP2 and higher releases custom GPU quotas can also be configured for teams.

If the quota for the team is used up, the workload remains in PENDING state until the required resources are available.

Any modifications to the team quota take effect immediately.

Quota enforcement

The resources are allocated based on the project context from which the workloads are created.

  • If a workload is created in a project, which is developed in users' context, it will always take the user quota.

  • If a workload is created in a project which is developed in a team's context, it will take resources from the team's quota. This can either be a custom quota allocated to the team or the team's default quota.