Workload Priority Control

The workload priority management feature allows you to change the priority of a workload within a project. The priority determines the workload's position in the project scheduling queue managed by the NVIDIA Run:ai Scheduler. By adjusting the priority, you can increase the likelihood that a workload will be scheduled and preferred over others within the same project, ensuring that critical tasks are given higher priority and resources are allocated efficiently. The workload's priority also affects whether it can consume over-quota resources and whether it is subject to preemption by higher-priority workloads.

You can change the priority of a workload by selecting one of the predefined values from the NVIDIA Run:ai priority dictionary.

Note

This applies only within a single project. It does not impact the scheduling queues or workloads of other projects.

Priority Dictionary

Workload priority is defined by selecting a priority from a predefined list in the NVIDIA Run:ai priority dictionary. Each string corresponds to a specific Kubernetes PriorityClass, which in turn determines scheduling behavior, such as whether the workload is preemptible or allowed to run over quota.

Priority	Kubernetes Value	Preemption	Over Quota
very-low	25	Preemptible	Available
low	40	Preemptible	Available
medium-low	65	Preemptible	Available
medium	80	Preemptible	Available
medium-high	90	Preemptible	Available
high	125	Non-preemptible	Not available
very-high	150	Non-preemptible	Not available

Preemptible vs Non-Preemptible Workloads

• Non-preemptible workloads must run within the project’s deserved quota, cannot use over-quota resources, and will not be interrupted once scheduled.

• Preemptible workloads can use opportunistic compute resources beyond the project’s quota but may be interrupted at any time.

Default Priority per Workload

NVIDIA Run:ai defines the following default mappings of workload types to priorities. To retrieve the default priority per workload type, refer to the List workload types API.

Note

• For more information on workload support, see Introduction to workloads.

• Legacy priority values remain available for backward compatibility

• Changing the priority is not supported for NVCF workloads.

	Workload Types	Default Priority
NVIDIA Run:ai native workloads	Workspaces, Standard training, Distributed training, Custom inference, Hugging Face inference, NVIDIA NIM inference	Workspaces = high Training = low Inference = very-high
NVIDIA	NIM services, NVIDIA Cloud Functions (NVCF)	very-high
Kubernetes	Deployment, StatefulSet, ReplicaSet, Pod, Service, CronJob, Job, JobSet	Most = very-high JobSet = low Job = high
Kubeflow	TFJob, PyTorchJob, MPIJob, XGBoostJob, Notebook, ScheduledWorkflow	Most = low Notebook= = high ScheduledWorkflow = very-high
Ray	RayService, RayCluster, RayJob	RayService = very-high RayCluster, RayJob = low
Tekton	PipelineRun, TaskRun	PipelineRun = very-high TaskRun = high
Additional Frameworks	SeldonDeployment, AMLJob, DevWorkspace, VirtualMachineInstance, KServe, Milvus, Workflow	Most = very-high DevWorkspace = high AMLJob, VirtualMachineInstance = low

Setting Priority During Workload Submission

Note

Changing a workload’s priority may impact its ability to be scheduled. For example, switching a workload from a low priority (which allows over-quota usage) to high priority (which requires in-quota resources) may reduce its chances of being scheduled in cases where the required quota is unavailable.

• Set the priority when submitting NVIDIA Run:ai workloads via the UI, CLI, or API:

  • UI - Set workload priority under General settings (flexible submission only)

  • API - Set using the PriorityClass field

  • CLI - Set using the --priority flag

• Set the workload's priority by adding the following label to your YAML under the metadata.labels section of your workload definition and use the following values, very-low, medium-low, medium, medium-high, high, very-high :

  metadata:
    labels:
      priorityClassName: <priority>

Updating the Default Priority Mapping

Administrators can change the default priority assigned to a workload type by updating the priority mapping using the NVIDIA Run:ai API. To update the priority mapping:

1. Retrieve the list of workload types and their IDs using GET /api/v1/workload-types.

2. Identify the workloadTypeId of the workload type you want to modify.

3. Retrieve the list of available priorities and their IDs using GET /api/v1/workload-priorities.

4. Send a request to update the workload type with the new priority using PUT /api/v1/workload-types/{workloadTypeId} and include the priorityId in the request body.

Using API

Go to the Workload priorities API reference to view the available actions.