NVIDIA Run:ai
NVIDIA Run:ai (formerly run.ai) is an AI operations and GPU orchestration platform for Kubernetes that pools, schedules, and governs GPU compute across clusters for training, fine-tuning, and inference workloads. It provides fractional GPU sharing, dynamic scheduling, quota and policy management, multi-tenant projects and departments, and workload lifecycle control across SaaS, self-hosted, and multi-tenant deployments. run.ai was founded in Israel, backed by Insight Partners and other investors, and acquired by NVIDIA in 2024; the product is now delivered as NVIDIA Run:ai. Its control-plane REST API exposes programmatic management of clusters, node pools, projects, departments, workloads (workspaces, trainings, inferences, distributed), assets, policies, permissions, service accounts, and audit logs, authenticated with bearer JWT access tokens obtained from client-credentials access keys.
NVIDIA Run:ai publishes 62 APIs on the APIs.io network, including Access Keys API, Access rules API, Administrator Command Line Interface API, and 59 more. Tagged areas include Company, Artificial Intelligence, GPU, Machine Learning, and Kubernetes.
NVIDIA Run:ai’s developer surface includes documentation, API reference, getting-started guide, authentication, CLI, changelog, and 20 more developer resources.
62 APIs
1 MCP Servers
CompanyArtificial IntelligenceGPUMachine LearningKubernetesOrchestrationMLOpsComputeSchedulingInfrastructure
Individual APIs this provider publishes, each with its own machine-readable definition.
Access Keys allow users to authenticate and interact programmatically with the NVIDIA Run:ai API. Each access key consists of a client ID and secret that can be used to obtain a...
Access rules provide user authorization to system resources and entities. It is managed using Role-based access control (RBAC) which is a policy-neutral access control mechanism...
Information specific to the Administrator Command Line Interface.
**DEPRECATED:** Applications have been renamed to Service Accounts. Please use the [Service Accounts](/latest/#tag/Service-Accounts) endpoints instead. Create and manage applica...
The audit log provides audit trail information for user activity, changes to business objects and other important information. For more information, see [Audit log](https://run-...
Use these endpoints to create, manage and delete NVIDIA Run:ai Kubernetes clusters.
A compute resource is a building block that represents compute resources such as GPUs, CPU cores, and CPU memory. The compute resources may consist of multiple physical resource...
Use a ConfigMap as a data source location for data sets that are relevant to the workload being submitted.
Credentials are used to unlock protected resources such as applications, containers, and other assets. For more information, see [Credentials](https://run-ai-docs.nvidia.com/saa...
The Datavolumes API from NVIDIA Run:ai — 5 operation(s) for datavolumes.
Departments, in the hierarchy of resource allocation, are above Projects. A Department can contain multiple Projects, and has its own quotas. A Department's quota supersedes the...
Distributed Training, is the ability to split the training of a model among multiple processors. It is often a necessity when multi-GPU training no longer applies; typically whe...
Distributed inference enables running inference workloads across multiple pods, typically to scale model serving beyond a single container or node. This approach is useful when ...
An environment resource designates the container image, the image pull policy, working directory, security parameters, and others. It exposes all the necessary tools (open sourc...
Workload events that occurred while the workload was running. Use to diagnose issue around workload scheduling.
Use Git as a data source location for data sets that are relevant to the workload being submitted.
Use a HostPath as a data source location for data sets that are relevant to the workload being submitted.
The Idps API from NVIDIA Run:ai — 3 operation(s) for idps.
Inference workloads deploy trained models into a production environment to generate predictions from live data. These workloads are prioritized over Trainings and Workspaces dur...
Use to manage tenant logo files.
"Me" returns the authenticated user's permissions within the system. It provides a comprehensive view of access rules (roles, subjects and scope) assigned to the current user. F...
The Network Topologies API enables administrators to reflect the hierarchical network topology connectivity of nodes in a data center, such as racks, blocks, and other organizat...
Use NFS as a data source location for data sets that are relevant to the workload being submitted.
Node pools assist in managing heterogeneous resources effectively. A node pool is a set of nodes grouped into a bucket of resources using a predefined (for example, GPU-Type) or...
Nodes are worker machines in Kubernetes and may be either a virtual or a physical machine, depending on the cluster. Each Node is managed by the NVIDIA Run:ai control plane. For...
Use to manage notification state.
Use to get notification types.
Notification Channels are the medium through which notifications are sent.
The NVIDIA NIM API provides endpoints to create and manage workloads that deploy NVIDIA Inference Microservices (NIM) through the NIM Operator. These workloads package optimized...
The Permissions API from NVIDIA Run:ai — 2 operation(s) for permissions.
Retrieve data about workload pods from your NVIDIA Run:ai platform.
Policies allow administrators to impose restrictions and set default values for researcher workloads. Restrictions and default values can be placed on CPUs, GPUs, and other reso...
Projects implement resource allocation policies and create segregation between different initiatives. It can represent a team, an individual, or an initiative that shares resour...
Use a PVC as a data source location for data sets that are relevant to the workload being submitted.
Use an images registry to enable the listting of repositories and tags that can be used as a data source location for data sets that are relevant to the workload being submitted.
The Reports API from NVIDIA Run:ai — 5 operation(s) for reports.
The Researcher Command Line Interface API from NVIDIA Run:ai — 9 operation(s) for researcher command line interface.
The Researcher Command Line Interface Deprecated API from NVIDIA Run:ai — 9 operation(s) for researcher command line interface deprecated.
Revisions are associated with an inference workload and represent a snapshot of its configuration. A revision is created on each change to the inference workload.
A role is a group of permissions that can be granted. Permissions are a set of actions that can be applied to entities. For more information, see [Roles](https://run-ai-docs.nvi...
Use an S3 simple storage service as a data source location for data sets that are relevant to the workload being submitted.
Use a credentials as a data source location for data sets that are relevant to the workload being submitted.
Service accounts enable programmatic access to the NVIDIA Run:ai API, allowing applications or automated systems to authenticate and interact securely. Each service account is a...
View and manage configuration settings for your organization.
The storage class configuration API enables administrators to define, manage, and customize how storage classes are used across the NVIDIA Run:ai platform. Through this API, you...
The Storage Classes API retrieves a list of available, pre-defined storage classes in the system.
Use to manage notifications subscriptions.
Templates are a pre-set configuration used to quickly configure and submit workloads using existing assets.
Use tokens to facilitate authentication to the NVIDIA Run:ai API. The API server must be configured to use the NVIDIA Run:ai identity service to validate authentication tokens.
Trainings are dedicated workloads that are specifically used for training models. They are by design preemptible workloads because they are used in unattended sessions where the...
**DEPRECATED:** User Applications have been renamed to Access Keys. Please use the [Access Keys](/latest/#tag/Access-Keys) endpoints instead. User Applications allow users to au...
The Users API from NVIDIA Run:ai — 6 operation(s) for users.
Workload properties define the behavioral and scheduling characteristics of a workload submitted to the NVIDIA Run:ai platform. These properties such as type, category, and prio...
This set of endpoints manages workload templates used to define reusable workload configurations across various workload types in the NVIDIA Run:ai platform. Templates help stan...
Workloads are both native platform workloads, Workspaces, Training and Inference, as well as workloads that originate from third-party ML frameworks, tools, or the broader Kuber...
The Workloads batch API from NVIDIA Run:ai — 1 operation(s) for workloads batch.
The Workloads V2 API allows you to create, retrieve, and delete workloads that originate from third-party ML frameworks, tools, or the broader Kubernetes ecosystem. These worklo...
A Workspace is a simplified tool for researchers to conduct experiments, build AI models, access standard MLOps tools, and collaborate with their peers. Workspaces abstract comp...
Model Context Protocol servers that expose these APIs to AI agents.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
aid: runai
name: NVIDIA Run:ai
description: NVIDIA Run:ai (formerly run.ai) is an AI operations and GPU orchestration platform for Kubernetes that pools,
schedules, and governs GPU compute across clusters for training, fine-tuning, and inference workloads. It provides fractional
GPU sharing, dynamic scheduling, quota and policy management, multi-tenant projects and departments, and workload lifecycle
control across SaaS, self-hosted, and multi-tenant deployments. run.ai was founded in Israel, backed by Insight Partners
and other investors, and acquired by NVIDIA in 2024; the product is now delivered as NVIDIA Run:ai. Its control-plane REST
API exposes programmatic management of clusters, node pools, projects, departments, workloads (workspaces, trainings, inferences,
distributed), assets, policies, permissions, service accounts, and audit logs, authenticated with bearer JWT access tokens
obtained from client-credentials access keys.
url: https://raw.githubusercontent.com/api-evangelist/runai/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- insight-partners
x-acquired-by: nvidia
accessModel:
pricing: unknown
onboarding: self-serve
trial: false
try_now: false
public: false
label: Self-serve signup
confidence: medium
source:
- authentication
generated: '2026-07-22'
method: derived
image: https://avatars.githubusercontent.com/u/37841801?v=4
tags:
- Company
- Artificial Intelligence
- GPU
- Machine Learning
- Kubernetes
- Orchestration
- MLOps
- Compute
- Scheduling
- Infrastructure
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-21'
apis:
- aid: runai:runai-access-keys-api
name: NVIDIA Run:ai Access Keys API
description: 'Access Keys allow users to authenticate and interact programmatically with the NVIDIA Run:ai API.
Each access key consists of a client ID and secret that can be used to obtain authentication tokens.
Access keys can be managed by individual users for their own use, or by administrators for organization-wide access.
For more information, see [Access control](https://run-ai-docs.nvidia.com/saas/infrastructure-setup/authentication/overview/#role-based-access-control).'
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Access Keys
properties:
- type: OpenAPI
url: openapi/runai-access-keys-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-access-rules-api
name: NVIDIA Run:ai Access rules API
description: "Access rules provide user authorization to system resources and entities.\nIt is managed using Role-based\
\ access control (RBAC) which is a policy-neutral \naccess control mechanism defined around roles and privileges. \nThe\
\ components of RBAC make it simple to manage access to system resources and entities.\nFor more information, see [Access\
\ control](https://run-ai-docs.nvidia.com/saas/infrastructure-setup/authentication/overview/#role-based-access-control)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Access rules
properties:
- type: OpenAPI
url: openapi/runai-access-rules-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-administrator-command-line-interface-api
name: NVIDIA Run:ai Administrator Command Line Interface API
description: Information specific to the Administrator Command Line Interface.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Administrator Command Line Interface
properties:
- type: OpenAPI
url: openapi/runai-administrator-command-line-interface-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-ai-applications-api
name: NVIDIA Run:ai AI Applications API
description: AI Applications.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- AI Applications
properties:
- type: OpenAPI
url: openapi/runai-ai-applications-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-applications-api
name: NVIDIA Run:ai Applications API
description: '**DEPRECATED:** Applications have been renamed to Service Accounts. Please use the [Service Accounts](/latest/#tag/Service-Accounts)
endpoints instead.
Create and manage applications in the tenant.'
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Applications
properties:
- type: OpenAPI
url: openapi/runai-applications-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-auditlogs-api
name: NVIDIA Run:ai AuditLogs API
description: The audit log provides audit trail information for user activity, changes to business objects and other important
information. For more information, see [Audit log](https://run-ai-docs.nvidia.com/saas/infrastructure-setup/procedures/event-history).
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- AuditLogs
properties:
- type: OpenAPI
url: openapi/runai-auditlogs-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-clusters-api
name: NVIDIA Run:ai Clusters API
description: Use these endpoints to create, manage and delete NVIDIA Run:ai Kubernetes clusters.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Clusters
properties:
- type: OpenAPI
url: openapi/runai-clusters-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-compute-api
name: NVIDIA Run:ai Compute API
description: "A compute resource is a building block that represents compute resources such as GPUs, CPU cores, and CPU\
\ memory.\nThe compute resources may consist of multiple physical resources, for example, 0.5 GPU, 8 cores and 200 Megabytes\
\ of CPU memory. \nA compute resource is available to a scope and and all of the organizational units within that scope.\
\ \n \nFor more information, see [Compute resource](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/assets/compute-resources)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Compute
properties:
- type: OpenAPI
url: openapi/runai-compute-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-configmap-api
name: NVIDIA Run:ai ConfigMap API
description: Use a ConfigMap as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- ConfigMap
properties:
- type: OpenAPI
url: openapi/runai-configmap-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-credentials-api
name: NVIDIA Run:ai Credentials API
description: Credentials are used to unlock protected resources such as applications, containers, and other assets. For
more information, see [Credentials](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/assets/credentials).
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Credentials
properties:
- type: OpenAPI
url: openapi/runai-credentials-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-datasources-api
name: NVIDIA Run:ai Datasources API
description: Data source assets.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Datasources
properties:
- type: OpenAPI
url: openapi/runai-datasources-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-datavolumes-api
name: NVIDIA Run:ai Datavolumes API
description: The Datavolumes API from NVIDIA Run:ai — 5 operation(s) for datavolumes.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Datavolumes
properties:
- type: OpenAPI
url: openapi/runai-datavolumes-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-departments-api
name: NVIDIA Run:ai Departments API
description: "Departments, in the hierarchy of resource allocation, are above Projects. A Department can contain multiple\
\ Projects, and has its own quotas. \nA Department's quota supersedes the total of the Project quotas in the Department,\
\ so tt is recommended \nthat a Department's quota be the total, or more than of all the Project quotas in the Department.\
\ \nFor further information see, [Working with Departments](https://run-ai-docs.nvidia.com/saas/platform-management/aiinitiatives/organization/departments)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Departments
properties:
- type: OpenAPI
url: openapi/runai-departments-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-distributed-api
name: NVIDIA Run:ai Distributed API
description: "Distributed Training, is the ability to split the training of a model among multiple processors. \nIt is often\
\ a necessity when multi-GPU training no longer applies; \ntypically when you require more GPUs than exist on a single\
\ node. \nEach such split is a pod (see definition above). NVIDIA Run:ai spawns an additional launcher process that manages\
\ and \ncoordinates the other worker pods. For more information, see [Distributed training](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/using-training/distributed-training/distributed-training-models)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Distributed
properties:
- type: OpenAPI
url: openapi/runai-distributed-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-distributed-inferences-api
name: NVIDIA Run:ai Distributed Inferences API
description: "Distributed inference enables running inference workloads across multiple pods, typically to scale model serving\
\ beyond a single container or node. This approach is useful when a single instance cannot meet resource requirements.NVIDIA\
\ Run:ai supports this model using Leader Worker Set (LWS). \nEach pod plays a specific role, either as a leader or worker,\
\ and together they form a coordinated service. NVIDIA Run:ai manages the orchestration and configuration of these pods\
\ to ensure efficient and scalable inference execution"
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Distributed Inferences
properties:
- type: OpenAPI
url: openapi/runai-distributed-inferences-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-environment-api
name: NVIDIA Run:ai Environment API
description: "An environment resource designates the container image, the image pull policy, working directory, security\
\ parameters, and others. \nIt exposes all the necessary tools (open source, 3rd party, or custom tools) along \nwith\
\ their connection interfaces including external node port and the container ports.\nYou can also specify a standard,\
\ distributed, or inference workload architecture for the environment.\nAn environment is a mandatory building block for\
\ the creation of a workload.\nFor more information, see [Environments](https://run-ai-docs.nvidia.com/saas/workloads-in-nvidia-run-ai/assets/environments)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Environment
properties:
- type: OpenAPI
url: openapi/runai-environment-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-events-api
name: NVIDIA Run:ai Events API
description: Workload events that occurred while the workload was running. Use to diagnose issue around workload scheduling.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Events
properties:
- type: OpenAPI
url: openapi/runai-events-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-git-api
name: NVIDIA Run:ai Git API
description: Use Git as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Git
properties:
- type: OpenAPI
url: openapi/runai-git-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-hostpath-api
name: NVIDIA Run:ai HostPath API
description: Use a HostPath as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- HostPath
properties:
- type: OpenAPI
url: openapi/runai-hostpath-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-idps-api
name: NVIDIA Run:ai Idps API
description: The Idps API from NVIDIA Run:ai — 3 operation(s) for idps.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Idps
properties:
- type: OpenAPI
url: openapi/runai-idps-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-inferences-api
name: NVIDIA Run:ai Inferences API
description: Inference workloads deploy trained models into a production environment to generate predictions from live data.
These workloads are prioritized over Trainings and Workspaces during scheduling. NVIDIA Run:ai Inference workloads support
auto-scaling to maintain service-level agreements (SLAs) by dynamically adjusting resources as demand changes.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Inferences
properties:
- type: OpenAPI
url: openapi/runai-inferences-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-logo-api
name: NVIDIA Run:ai Logo API
description: Use to manage tenant logo files.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Logo
properties:
- type: OpenAPI
url: openapi/runai-logo-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-me-api
name: NVIDIA Run:ai Me API
description: "\"Me\" returns the authenticated user's permissions within the system. \nIt provides a comprehensive view\
\ of access rules (roles, subjects and scope) assigned to the current user.\nFor more information see [Access control](https://run-ai-docs.nvidia.com/saas/infrastructure-setup/authentication/overview/#role-based-access-control)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Me
properties:
- type: OpenAPI
url: openapi/runai-me-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-network-topologies-api
name: NVIDIA Run:ai Network Topologies API
description: The Network Topologies API enables administrators to reflect the hierarchical network topology connectivity
of nodes in a data center, such as racks, blocks, and other organizational units, to improve pod scheduling decisions
for communication-intensive AI/ML workloads. To support topology-aware scheduling, this API allows administrators to define
a multi-level network topology element using ordered Kubernetes node labels that represent the network connectivity of
nodes in the data center. Nodes are labeled with the same key-value pairs labels that represent their location in the
data center network. These labels are used by the NVIDIA Run:ai Scheduler to prefer scheduling pods on nodes that are
"closer" to each other, minimizing communication overhead and optimizing workload performance.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Network Topologies
properties:
- type: OpenAPI
url: openapi/runai-network-topologies-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-nfs-api
name: NVIDIA Run:ai NFS API
description: Use NFS as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- NFS
properties:
- type: OpenAPI
url: openapi/runai-nfs-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-nodepools-api
name: NVIDIA Run:ai NodePools API
description: "Node pools assist in managing heterogeneous resources effectively. \nA node pool is a set of nodes grouped\
\ into a bucket of resources using a predefined (for example, GPU-Type) or \nadministrator-defined label (for example,\
\ key & value). \nFor more information, see [Node Pools](https://run-ai-docs.nvidia.com/saas/platform-management/aiinitiatives/resources/node-pools/#introduction)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- NodePools
properties:
- type: OpenAPI
url: openapi/runai-nodepools-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-nodes-api
name: NVIDIA Run:ai Nodes API
description: "Nodes are worker machines in Kubernetes and may be either a virtual or a physical machine, depending on the\
\ cluster. \nEach Node is managed by the NVIDIA Run:ai control plane. For more information, see [Nodes](https://run-ai-docs.nvidia.com/saas/platform-management/aiinitiatives/resources/nodes)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Nodes
properties:
- type: OpenAPI
url: openapi/runai-nodes-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-notification-state-api
name: NVIDIA Run:ai Notification State API
description: Use to manage notification state.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Notification State
properties:
- type: OpenAPI
url: openapi/runai-notification-state-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-notification-types-api
name: NVIDIA Run:ai Notification Types API
description: Use to get notification types.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Notification Types
properties:
- type: OpenAPI
url: openapi/runai-notification-types-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-notificationchannels-api
name: NVIDIA Run:ai NotificationChannels API
description: Notification Channels are the medium through which notifications are sent.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- NotificationChannels
properties:
- type: OpenAPI
url: openapi/runai-notificationchannels-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-nvidia-nim-api
name: NVIDIA Run:ai NVIDIA NIM API
description: 'The NVIDIA NIM API provides endpoints to create and manage workloads that deploy NVIDIA Inference Microservices
(NIM) through the NIM Operator. These workloads package optimized NVIDIA model servers and run as managed services on
the NVIDIA Run:ai platform.
Each request includes NVIDIA Run:ai scheduling metadata (for example, project, priority, and category) and a NIM service
specification that defines the container image, compute resources, environment variables, storage, and networking configuration.
Once submitted, NVIDIA Run:ai handles scheduling, orchestration, and lifecycle management of the NIM service to ensure
reliable and efficient model serving.'
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- NVIDIA NIM
properties:
- type: OpenAPI
url: openapi/runai-nvidia-nim-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-org-unit-api
name: NVIDIA Run:ai Org unit API
description: Org unit.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Org unit
properties:
- type: OpenAPI
url: openapi/runai-org-unit-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-permissions-api
name: NVIDIA Run:ai Permissions API
description: The Permissions API from NVIDIA Run:ai — 2 operation(s) for permissions.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Permissions
properties:
- type: OpenAPI
url: openapi/runai-permissions-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-pods-api
name: NVIDIA Run:ai Pods API
description: Retrieve data about workload pods from your NVIDIA Run:ai platform.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Pods
properties:
- type: OpenAPI
url: openapi/runai-pods-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-policy-api
name: NVIDIA Run:ai Policy API
description: "Policies allow administrators to impose restrictions and set default values for researcher workloads. \nRestrictions\
\ and default values can be placed on CPUs, GPUs, and other resources or entities. \nFor more information, see [Policies](https://run-ai-docs.nvidia.com/saas/platform-management/policies/#introduction)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Policy
properties:
- type: OpenAPI
url: openapi/runai-policy-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-projects-api
name: NVIDIA Run:ai Projects API
description: "Projects implement resource allocation policies and create segregation between \ndifferent initiatives. It\
\ can represent a team, an individual, or an initiative that \nshares resources or has a specific resources budget (quota).\
\ \nSee [Projects](https://run-ai-docs.nvidia.com/saas/platform-management/aiinitiatives/organization/projects) \nfor\
\ more information."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Projects
properties:
- type: OpenAPI
url: openapi/runai-projects-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-pvc-api
name: NVIDIA Run:ai PVC API
description: Use a PVC as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- PVC
properties:
- type: OpenAPI
url: openapi/runai-pvc-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-registry-api
name: NVIDIA Run:ai Registry API
description: Use an images registry to enable the listting of repositories and tags that can be used as a data source location
for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Registry
properties:
- type: OpenAPI
url: openapi/runai-registry-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-reports-api
name: NVIDIA Run:ai Reports API
description: The Reports API from NVIDIA Run:ai — 5 operation(s) for reports.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Reports
properties:
- type: OpenAPI
url: openapi/runai-reports-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-researcher-command-line-interface-api
name: NVIDIA Run:ai Researcher Command Line Interface API
description: The Researcher Command Line Interface API from NVIDIA Run:ai — 9 operation(s) for researcher command line interface.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Researcher Command Line Interface
properties:
- type: OpenAPI
url: openapi/runai-researcher-command-line-interface-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-researcher-command-line-interface-deprecated-api
name: NVIDIA Run:ai Researcher Command Line Interface Deprecated API
description: The Researcher Command Line Interface Deprecated API from NVIDIA Run:ai — 9 operation(s) for researcher command
line interface deprecated.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Researcher Command Line Interface Deprecated
properties:
- type: OpenAPI
url: openapi/runai-researcher-command-line-interface-deprecated-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-revisions-api
name: NVIDIA Run:ai Revisions API
description: Revisions are associated with an inference workload and represent a snapshot of its configuration. A revision
is created on each change to the inference workload.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Revisions
properties:
- type: OpenAPI
url: openapi/runai-revisions-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-roles-api
name: NVIDIA Run:ai Roles API
description: "A role is a group of permissions that can be granted. \nPermissions are a set of actions that can be applied\
\ to entities.\nFor more information, see [Roles](https://run-ai-docs.nvidia.com/saas/infrastructure-setup/authentication/roles)."
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Roles
properties:
- type: OpenAPI
url: openapi/runai-roles-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-s3-api
name: NVIDIA Run:ai S3 API
description: Use an S3 simple storage service as a data source location for data sets that are relevant to the workload
being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- S3
properties:
- type: OpenAPI
url: openapi/runai-s3-api-openapi.yml
- type: Documentation
url: https://run-ai-docs.nvidia.com/api/getting-started/about-the-rest-api.md
- type: Authentication
url: authentication/runai-authentication.yml
- aid: runai:runai-secret-api
name: NVIDIA Run:ai Secret API
description: Use a credentials as a data source location for data sets that are relevant to the workload being submitted.
humanURL: https://run-ai-docs.nvidia.com/api/readme.md
baseURL: https://app.run.ai
tags:
- Secre
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# Full source: https://raw.githubusercontent.com/api-evangelist/runai/refs/heads/main/apis.yml
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