Integrations
The table below summarizes the integration capabilities with various third-party products.
Integration support
Support for integrations varies. Where mentioned below, the integration is supported out of the box with NVIDIA Run:ai. With other integrations, our customer success team has previous experience with integrating with the third party software and many times the community portal will contain additional reference documentation provided on an as-is basis.
The NVIDIA Run:ai community portal is password protected and access is provided to customers and partners.
Triton
Orchestration
Supported
Usage via docker base image
Spark
Orchestration
Community Support
It is possible to schedule Spark workflows with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer support. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to Run Spark job with NVIDIA Run:ai.
Kubeflow Pipelines
Orchestration
Community Support
It is possible to schedule kubeflow pipelines with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer support. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to integrate NVIDIA Run:ai with Kubeflow.
Apache Airflow
Orchestration
Community Support
It is possible to schedule Airflow workflows with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer support. Sample code can be found in the NVIDIA Run:ai customer success community portal: 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. For details, please contact NVIDIA Run:ai customer support. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to integrate NVIDIA Run:ai with Argo Workflows.
SeldonX
Orchestration
Community Support
It is possible to schedule Seldon Core workloads with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer success. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to integrate NVIDIA Run:ai with Seldon Core.
Jupyter Notebook
Development
Supported
NVIDIA Run:ai provides integrated support with Jupyter Notebooks. See Jupyter Notebook quick start example.
JupyterHub
Development
Community Support
It is possible to submit NVIDIA Run:ai workloads via JupyterHub. For more information please, contact NVIDIA Run:ai customer support.
PyCharm
Development
Supported
Containers created by NVIDIA Run:ai can be accessed via PyCharm.
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.
Kubeflow notebooks
Development
Community Support
It is possible to launch a Kubeflow notebook with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer support Sample code can be found in the NVIDIA Run:ai customer success community portal: How to integrate NVIDIA Run:ai with Kubeflow.
Ray
training, inference, data processing.
Community Support
It is possible to schedule Ray jobs with the NVIDIA Run:ai Scheduler. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to Integrate NVIDIA Run:ai with Ray.
TensorBoard
Experiment tracking
Supported
NVIDIA Run:ai comes with a preset TensorBoard Environment asset
Weights & Biases
Experiment tracking
Community Support
It is possible to schedule W&B workloads with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer success.
ClearML
Experiment tracking
Community Support
It is possible to schedule ClearML workloads with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer success.
MLFlow
Model Serving
Community Support
It is possible to use ML Flow together with the NVIDIA Run:ai Scheduler. For details, please contact NVIDIA Run:ai customer support. Sample code can be found in the NVIDIA Run:ai customer success community portal: How to integrate NVIDIA Run:ai with MLFlow.
Hugging Face
Repositories
Supported
NVIDIA Run:ai provides an out of the box integration with Hugging Face
Docker Registry
Repositories
Supported
NVIDIA Run:ai allows using a docker registry as a Credential 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.
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.
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.
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.
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