Submit Supported Workload Types via YAML

This section describes how to run supported workload types using the NVIDIA Run:ai UI by submitting a YAML manifest directly.

To learn more about workload types in NVIDIA Run:ai and determine what is the most suitable workload type for your goals, see Workload types and features.

Before You Start

Make sure you have created a project or have one created for you.

Note

Via YAML submission is enabled by default. If you do not see it in the menu, contact your administrator to enable it under General settings → Workloads → Via YAML submission.

Supported Workload Types

Supported workload types include a broad range of workloads from the ML and Kubernetes ecosystem that are already registered as workload types in the platform and ready to use. See Supported workload types for more details.

• Your administrator can also register additional workload types for your organization. See Registering new workload types for more details.

• By default, workload types are grouped into Build, Train and Deploy categories. These categories determine how the workload is scheduled and prioritized within a project and how they are grouped for monitoring and reporting.

Note

Some supported workload types require additional installation or cluster preparation before they can be used. Refer to the documentation for each workload type for specific prerequisites.

Workload Priority

By default, supported workload types are assigned a priority based on their workload type. These defaults determine how workloads are scheduled relative to others within the same project, whether they can use over-quota resources, and whether they may be interrupted once running. You can override the defaults by configuring priority and preemptibility. For more details on the default values per workload type, see Workload types and defaults.

Creating a New Workload

1. To create a workload, go to Workload manager → Workloads.

2. Click + NEW WORKLOAD and select Via YAML from the dropdown.

3. In the YAML submission form, select the cluster where the workload will run.

4. Upload or paste your YAML manifest. Hover over Supported workload types to view a full list of available workloads:

   • To upload a file, click UPLOAD YAML FILE and choose your YAML.

   • To paste the YAML, insert it directly into the editor.

5. Select a project:

   • If the namespace is not defined in the YAML, select a project from the submission form. To create a new project, click +NEW PROJECT and refer to Projects for a step-by-step guide.

   • If a project is selected in the form, it overrides the namespace defined in the YAML.

   • Alternatively, set the project directly in the YAML using the metadata.namespace field.

     metadata:
       namespace: my-project-name

6. Set the workload priority. Change this setting only if you want to override the default preemptibility defined for the workload type. Higher priority workloads are scheduled before lower-priority ones:

   • In the UI, select a priority from the dropdown.

   • In the YAML, set priorityClassName under metadata.labels using one of the supported values: very-low, low, medium-low, medium, medium-high, high, very-high:

     metadata:
       labels:
         priorityClassName: high

7. Click CREATE WORKLOAD

Managing and Monitoring

After the workload is created, it is added to the Workloads table, where it can be managed and monitored.

Using CLI

To view the available actions, see the CLI v2 reference.

Using API

To view the available actions, see the Workloads V2 API reference.

Troubleshooting

Generic / unknown errors

Description: Not specific enough to diagnose from the message alone.

Something went wrong

Authentication and permissions

Description: These errors mean you aren’t authorized to submit/manage workloads in the selected scope, or your token issuer is not recognized.

insufficient permissions
Issuer is not familiar

Cluster / API compatibility

Description: This error means the API endpoint you used is not compatible with the target cluster version.

This API is incompatible with the version of cluster '<clusterId>'

Priority / category not available

Description: These errors mean the selected priority or category isn’t supported in the target cluster.

category '<name>' is not available in this cluster
priority '<name>' is not available in this cluster

Workload name validation

Description: These errors mean the workload name is missing, invalid, too long, or already exists.

Workload name must not be empty
The length of a workload name must not exceed 63 characters
The workload name must consist of lower case alphanumeric characters or '-', and start with an alphabetic character.
workload with name '<name>' already exists in this project

Project / cluster selection errors

Description: These errors mean the request context is missing or ambiguous (mainly relevant for API/automation flows).

either projectId or clusterId must be provided
projectId and clusterId are mutually exclusive, provide only one
Failed to read request body

Workload type (GVK) not found or not ready in the cluster

Description: These errors mean NVIDIA Run:ai can’t map your manifest’s GVK (group/version/kind) to a workload type that is registered and ready in the selected cluster. See Supported workload types.

failed to extract resource info: <error>
failed to get workload types: <error>
workload type not found for GVK: <group>/<version>/<kind>
workload type has no cluster status information
workload type has no status information for cluster <clusterId> and version <version>
workload type phase is not available for this cluster
workload type is not ready in cluster <clusterId> (phase: <phase>) - <conditions>

Manifest structure and parsing errors

Description: These errors mean the submitted YAML is not a valid Kubernetes-style manifest, or it can’t be parsed.

these are all the validations missing required field: apiVersion
missing required field: kind
manifest metadata is not a valid map
failed to convert YAML to JSON
failed to unmarshal JSON manifest