> This page is for Self-Hosted, version v3.0 (default).
> For other versions, use one of these documentation indexes:
> - v3.0 (default): https://docs.nvidia.com/run-ai/self-hosted/v3.0/llms.txt
> - v2.26: https://docs.nvidia.com/run-ai/self-hosted/v2.26/llms.txt
> - v2.25: https://docs.nvidia.com/run-ai/self-hosted/v2.25/llms.txt
> - v2.24: https://docs.nvidia.com/run-ai/self-hosted/v2.24/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/run-ai/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/run-ai/_mcp/server.

# Community Support Integrations

The following integrations are not supported out of the box by NVIDIA Run:ai but are commonly used alongside the platform. Reference guides are available for each.

| Tool | Category | Additional Information |
| --- | --- | --- |
| Apache Airflow | Orchestration | It is possible to schedule Airflow workflows with the NVIDIA Run:ai Scheduler. Sample code: [How to integrate NVIDIA Run:ai with Apache Airflow](https://enterprise-support.nvidia.com/s/article/How-to-integrate-Run-ai-with-Apache-Airflow). |
| Argo workflows | Orchestration | It is possible to schedule Argo workflows with the NVIDIA Run:ai Scheduler. Sample code: [How to integrate NVIDIA Run:ai with Argo Workflows](https://enterprise-support.nvidia.com/s/article/How-to-integrate-Run-ai-with-Argo-Workflows). |
| Dataiku | AI platform | It is possible to run and govern GPU workloads using Dataiku with the NVIDIA Run:ai Scheduler. See [From Stranded GPUs to Governed Inference](https://community.dataiku.com/discussion/45842/from-stranded-gpus-to-governed-inference-dataiku-and-nvidia-run-ai-for-nvidia-nim-at-scale) for more details. |
| ClearML | Experiment tracking | It is possible to schedule ClearML workloads with the NVIDIA Run:ai Scheduler. |
| JupyterHub | Development | It is possible to submit NVIDIA Run:ai workloads via JupyterHub. |
| Kubeflow notebooks | Development | It is possible to launch a Kubeflow notebook with the NVIDIA Run:ai Scheduler. Sample code: [How to integrate NVIDIA Run:ai with Kubeflow](https://enterprise-support.nvidia.com/s/article/How-to-integrate-Run-ai-with-Kubeflow). |
| Kubeflow Pipelines | Orchestration | It is possible to schedule kubeflow pipelines with the NVIDIA Run:ai Scheduler. Sample code: [How to integrate NVIDIA Run:ai with Kubeflow](https://enterprise-support.nvidia.com/s/article/How-to-integrate-Run-ai-with-Kubeflow). |
| MLFlow | Model Serving | It is possible to use ML Flow together with the NVIDIA Run:ai Scheduler. |
| Ray | Training, inference, data processing | It is possible to schedule Ray jobs with the NVIDIA Run:ai Scheduler. Sample code: [How to Integrate NVIDIA Run:ai with Ray](https://enterprise-support.nvidia.com/s/article/How-to-Integrate-Run-ai-with-Ray). |
| SeldonX | Orchestration | It is possible to schedule Seldon Core workloads with the NVIDIA Run:ai Scheduler. |
| Spark | Orchestration | It is possible to schedule Spark workflows with the NVIDIA Run:ai Scheduler. |
| Weights & Biases | Experiment tracking | It is possible to schedule W&B workloads with the NVIDIA Run:ai Scheduler. Sample code: [How to integrate with Weights and Biases](https://enterprise-support.nvidia.com/s/article/How-to-integrate-with-Weights-and-Biases). |