Integrations

Integration Support

Support for third-party integrations varies. When noted below, the integration is supported out of the box with NVIDIA Run:ai. For other integrations, our Customer Success team has prior experience assisting customers with setup. In many cases, the NVIDIA Enterprise Support Portal may include additional reference documentation provided on an as-is basis.

Tool	Category	NVIDIA Run:ai support details	Additional Information
Apache Airflow	Orchestration	Community Support	It is possible to schedule Airflow workflows with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Apache Airflow.
Argo workflows	Orchestration	Community Support	It is possible to schedule Argo workflows with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Argo Workflows.
ClearML	Experiment tracking	Community Support	It is possible to schedule ClearML workloads with the NVIDIA Run:ai Scheduler.
Docker Registry	Repositories	Supported	NVIDIA Run:ai allows using a docker registry as a Credential asset
GitHub	Storage	Supported	NVIDIA Run:ai communicates with GitHub by defining it as a data source asset
Hugging Face	Repositories	Supported	NVIDIA Run:ai provides an out of the box integration with Hugging Face
JupyterHub	Development	Community Support	It is possible to submit NVIDIA Run:ai workloads via JupyterHub.
Jupyter Notebook	Development	Supported	NVIDIA Run:ai provides integrated support with Jupyter Notebooks. See Jupyter Notebook quick start example.
Karpenter	Cost Optimization	Supported	NVIDIA Run:ai provides out of the box support for Karpenter to save cloud costs. Integration notes with Karpenter can be found here.
Kubeflow MPI	Training	Supported	NVIDIA Run:ai provides out of the box support for submitting MPI workloads via API, CLI or UI. See Distributed training for more details.
Kubeflow notebooks	Development	Community Support	It is possible to launch a Kubeflow notebook with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Kubeflow.
Kubeflow Pipelines	Orchestration	Community Support	It is possible to schedule kubeflow pipelines with the NVIDIA Run:ai Scheduler. Sample code: How to integrate NVIDIA Run:ai with Kubeflow.
MLFlow	Model Serving	Community Support	It is possible to use ML Flow together with the NVIDIA Run:ai Scheduler.
PyCharm	Development	Supported	Containers created by NVIDIA Run:ai can be accessed via PyCharm.
PyTorch	Training	Supported	NVIDIA Run:ai provides out of the box support for submitting PyTorch workloads via API, CLI or UI. See Distributed training for more details.
Ray	training, inference, data processing.	Community Support	It is possible to schedule Ray jobs with the NVIDIA Run:ai Scheduler. Sample code: How to Integrate NVIDIA Run:ai with Ray.
SeldonX	Orchestration	Community Support	It is possible to schedule Seldon Core workloads with the NVIDIA Run:ai Scheduler.
Spark	Orchestration	Community Support	It is possible to schedule Spark workflows with the NVIDIA Run:ai Scheduler.
S3	Storage	Supported	NVIDIA Run:ai communicates with S3 by defining a data source asset
TensorBoard	Experiment tracking	Supported	NVIDIA Run:ai comes with a preset TensorBoard Environment asset
TensorFlow	Training	Supported	NVIDIA Run:ai provides out of the box support for submitting TensorFlow workloads via API, CLI or UI. See Distributed training for more details.
Triton	Orchestration	Supported	Usage via docker base image
VScode	Development	Supported	Containers created by NVIDIA Run:ai can be accessed via Visual Studio Code. You can automatically launch Visual Studio code web from the NVIDIA Run:ai console.
Weights & Biases	Experiment tracking	Community Support	It is possible to schedule W&B workloads with the NVIDIA Run:ai Scheduler. Sample code: How to integrate with Weights and Biases.
XGBoost	Training	Supported	NVIDIA Run:ai provides out of the box support for submitting XGBoost via API, CLI or UI. See Distributed training for more details.

Kubernetes Workloads Integration

Kubernetes has several built-in resources that encapsulate running Pods. These are called Kubernetes Workloads and should not be confused with NVIDIA Run:ai workloads.

Examples of such resources are a Deployment that manages a stateless application, or a Job that runs tasks to completion.

A NVIDIA Run:ai workload encapsulates all the resources needed to run and creates/deletes them together. Since NVIDIA Run:ai is an open platform, it allows the scheduling of any Kubernetes Workflow.

For more information, see Kubernetes Workloads Integration.