# Supported Workload Types

NVIDIA Run:ai supports 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. These workloads are managed by the [Resource Interface](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/extending-workload-support#resource-interface-overview) (RI), which ensures they receive the same advanced scheduling, orchestration, and monitoring capabilities as NVIDIA Run:ai native workloads. For more details on feature support, see [Supported features](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/workload-types/supported-features).

All supported workload types are pre-registered using the [Workload Types](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/workloads/workload-properties#post-api-v1-workload-types) API. You can retrieve the full list of these workloads and their current configuration defaults.

{% hint style="info" %}
**Note**

To define and register workload types beyond those provided by NVIDIA Run:ai, see [Extending workload support with Resource Interface](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/extending-workload-support#registering-new-workload-types).
{% endhint %}

## List of Supported Workload Types

The following workload types are already registered in the platform and ready to use:

* **NVIDIA** - [NIM Services](https://docs.nvidia.com/nim-operator/latest/service.html), [DynamoGraphDeployment](https://docs.nvidia.com/dynamo/latest/index.html)
* **Kubernetes** - LeaderWorkerSet (LWS), Deployment, StatefulSet, ReplicaSet, Pod, CronJob, Job ([kubernetes.io](https://kubernetes.io/))
* **Kubeflow** - TFJob, PyTorchJob, MPIJob, XGBoostJob, JAXJob, Notebook, ScheduledWorkflow ([kubeflow.org](http://kubeflow.org))
* **Ray** - RayService, RayCluster, RayJob ([ray.io](http://ray.io))
* **Tekton** - PipelineRun, TaskRun ([tekton.dev](http://tekton.dev))
* **Additional frameworks** - [SeldonDeployment](http://machinelearning.seldon.io), [AMLJob](https://learn.microsoft.com/en-us/cli/azure/ml/job?view=azure-cli-latest\&utm_source=chatgpt.com), [Workflow](http://argoproj.io), [DevWorkspace](http://workspace.devfile.io), [Service](http://serving.knative.dev), [VirtualMachineInstance](http://kubevirt.io/), InferenceService ([KServe](https://kserve.github.io/website/))

## Submitting Supported Workload Types

Supported workload types are submitted as standard Kubernetes YAML manifests**.** Once submitted, the workload is created and appears in the [Workloads](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/workloads) table for monitoring and management. See [Submit supported workload types via YAML](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/submit-via-yaml).

## Workload Types and Defaults

Each workload type is assigned a default category, which determines its default priority and preemptibility. These defaults influence how workloads are scheduled and prioritized within a project, as well as how they are grouped for monitoring and reporting.

{% hint style="info" %}
**Note**

To retrieve the default category, priority and preemptibility per workload type, refer to the [List workload types](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/workloads/workload-properties#get-api-v1-workload-types) API.
{% endhint %}

| Category | Default Priority | Default Preemptibility |
| -------- | ---------------- | ---------------------- |
| Build    | High             | Non-preemptible        |
| Train    | Low              | Preemptible            |
| Deploy   | Very high        | Non-preemptible        |

### Workload Types by Default Category

<table><thead><tr><th>Workload Type</th><th data-type="checkbox">Build</th><th data-type="checkbox">Train</th><th data-type="checkbox">Deploy</th></tr></thead><tbody><tr><td>AMLJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>CronJob</td><td>false</td><td>false</td><td>true</td></tr><tr><td>Deployment</td><td>false</td><td>false</td><td>true</td></tr><tr><td>DevWorkspace</td><td>true</td><td>false</td><td>false</td></tr><tr><td>DynamoGraphDeployment</td><td>false</td><td>false</td><td>true</td></tr><tr><td>InferenceService (KServe)</td><td>false</td><td>false</td><td>true</td></tr><tr><td>JAXJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>Job</td><td>true</td><td>false</td><td>false</td></tr><tr><td>LeaderWorkerSet (LWS)</td><td>false</td><td>false</td><td>true</td></tr><tr><td>MPIJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>NIMCache</td><td>false</td><td>false</td><td>true</td></tr><tr><td>NIMServices</td><td>false</td><td>false</td><td>true</td></tr><tr><td>Notebook</td><td>true</td><td>false</td><td>false</td></tr><tr><td>PipelineRun</td><td>false</td><td>false</td><td>true</td></tr><tr><td>Pod</td><td>false</td><td>false</td><td>true</td></tr><tr><td>PyTorchJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>RayCluster</td><td>false</td><td>true</td><td>false</td></tr><tr><td>RayJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>RayService</td><td>false</td><td>false</td><td>true</td></tr><tr><td>ReplicaSet</td><td>false</td><td>false</td><td>true</td></tr><tr><td>ScheduledWorkflow</td><td>false</td><td>false</td><td>true</td></tr><tr><td>SeldonDeployment</td><td>false</td><td>false</td><td>true</td></tr><tr><td>Service</td><td>false</td><td>false</td><td>true</td></tr><tr><td>SPOTRequest</td><td>false</td><td>false</td><td>false</td></tr><tr><td>StatefulSet</td><td>false</td><td>false</td><td>true</td></tr><tr><td>TaskRun</td><td>true</td><td>false</td><td>false</td></tr><tr><td>TFJob</td><td>false</td><td>true</td><td>false</td></tr><tr><td>VirtualMachineInstance</td><td>false</td><td>true</td><td>false</td></tr><tr><td>Workflow</td><td>false</td><td>false</td><td>true</td></tr><tr><td>XGBoostJob</td><td>false</td><td>true</td><td>false</td></tr></tbody></table>

## Updating Default Category and Priority Mapping

Administrators can change the default priority and category assigned to a workload type by updating the mapping using the [NVIDIA Run:ai API](https://app.gitbook.com/s/b5QLzc5pV7wpXz3CDYyp/):

* To update the priority mapping, see [Workload priority and preemption](https://run-ai-docs.nvidia.com/saas/platform-management/runai-scheduler/scheduling/workload-priority-control#updating-the-default-mapping)
* To update the category mapping, see [Monitor workloads by category](https://run-ai-docs.nvidia.com/saas/platform-management/monitor-performance/workload-categories#update-the-default-category-mapping)