> For the complete documentation index, see [llms.txt](https://run-ai-docs.nvidia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://run-ai-docs.nvidia.com/self-hosted/workloads-in-nvidia-run-ai/workload-types/supported-workload-types.md). # 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 [Karta](/self-hosted/workloads-in-nvidia-run-ai/workload-types/extending-workload-support.md#karta-overview), 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](/self-hosted/workloads-in-nvidia-run-ai/workload-types/supported-features.md). All supported workload types are pre-registered using the [Workload Types](https://run-ai-docs.nvidia.com/api/2.26/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 Karta](/self-hosted/workloads-in-nvidia-run-ai/workload-types/extending-workload-support.md). {% 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)) * **Slinky** - [NodeSet](https://github.com/SlinkyProject/slurm-operator#nodesets) * **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/)) {% hint style="info" %} **Scope of Slinky NodeSet Support** NVIDIA Run:ai recognizes a [Slinky](https://github.com/SlinkyProject/slurm-operator) `NodeSet` - the group of Slurm compute node (`slurmd`) pods that the Slinky operator runs on Kubernetes - and manages it as a standard workload. The NodeSet receives the same scheduling, prioritization, preemption, and monitoring as any other supported workload type, and appears in the [Workloads](/self-hosted/workloads-in-nvidia-run-ai/workloads.md) table for management. This support is scoped to the `NodeSet` workload as it runs on Kubernetes. It is not an integration with the Slurm control plane: NVIDIA Run:ai does not submit, schedule, or manage Slurm jobs, partitions, accounts, or the Slurm controller (`slurmctld`). {% endhint %} ## 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](/self-hosted/workloads-in-nvidia-run-ai/workloads.md) table for monitoring and management. See [Submit supported workload types via YAML](/self-hosted/workloads-in-nvidia-run-ai/submit-via-yaml.md). ## 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://run-ai-docs.nvidia.com/api/2.26/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
Workload TypeBuildTrainDeploy
AMLJobfalsetruefalse
CronJobfalsefalsetrue
Deploymentfalsefalsetrue
DevWorkspacetruefalsefalse
DynamoGraphDeploymentfalsefalsetrue
InferenceService (KServe)falsefalsetrue
JAXJobfalsetruefalse
Jobtruefalsefalse
JobSetfalsetruefalse
LeaderWorkerSet (LWS)falsefalsetrue
MPIJobfalsetruefalse
NIMCachefalsefalsetrue
NIMServicesfalsefalsetrue
NodeSet (Slinky)falsefalsetrue
Notebooktruefalsefalse
PipelineRunfalsefalsetrue
Podfalsefalsetrue
PyTorchJobfalsetruefalse
RayClusterfalsetruefalse
RayJobfalsetruefalse
RayServicefalsefalsetrue
ReplicaSetfalsefalsetrue
ScheduledWorkflowfalsefalsetrue
SeldonDeploymentfalsefalsetrue
Servicefalsefalsetrue
SPOTRequestfalsefalsefalse
StatefulSetfalsefalsetrue
TaskRuntruefalsefalse
TFJobfalsetruefalse
VirtualMachineInstancefalsetruefalse
Workflowfalsefalsetrue
XGBoostJobfalsetruefalse
## 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://run-ai-docs.nvidia.com/api/2.26/): * To update the priority mapping, see [Workload priority and preemption](/self-hosted/platform-management/runai-scheduler/scheduling/workload-priority-control.md#updating-the-default-mapping) * To update the category mapping, see [Monitor workloads by category](/self-hosted/platform-management/monitor-performance/workload-categories.md#update-the-default-category-mapping) --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://run-ai-docs.nvidia.com/self-hosted/workloads-in-nvidia-run-ai/workload-types/supported-workload-types.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.