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dsx-ai-factory/run-ai: Run:ai is the AI workload orchestration platform for Kubernetes. This monorepo contains th – Repository dsx-ai-factory/run-ai. Mô tả: Run:ai is the AI workload orchestration platform for Kubernetes. This monorepo contains the control plane services, in-cluster Kubernetes operator/controllers, shared libraries and clients, the installer, and the local dev environment.. 680 stars, 102 forks, ngôn ngữ chính Go, cập nhật 2026-10-29.
1. Repository làm gì?
Docs | Roadmap | Installation | API Reference | Contributing | License
NVIDIA Run:ai is an open-source AI workload and GPU orchestration platform for managing shared AI infrastructure on Kubernetes.
NVIDIA Run:ai provides a consistent way to run development, training, and inference workloads across Kubernetes environments, whether deployed on-premises, in the cloud, or across hybrid infrastructure. It combines GPU scheduling, resource management, workload orchestration, governance, and observability to help organizations share GPU resources efficiently across teams and clusters.
This monorepo contains the NVIDIA Run:ai control plane and cluster services, installer, CLI, MCP server, documentation, shared client packages, and the local development environment.
NVIDIA Run:ai is made up of two components, both installed on a Kubernetes cluster:
How NVIDIA Run:ai Relates to Other Tools
2. Dữ liệu và cấu trúc đáng chú ý
NVIDIA Run:ai is a scheduling and orchestration layer on top of Kubernetes: it does not replace Kubernetes, a container runtime, or a CI/CD system.
If you need GPU sharing, quota enforcement, and multi-tenant governance across teams sharing one Kubernetes GPU estate, that is the gap NVIDIA Run:ai fills. If you only need to run a single team's jobs on dedicated GPUs, native Kubernetes scheduling may be sufficient.
For a detailed breakdown of individual services and components, see the Service Reference.
The local-dev.sh script builds the NVIDIA Run:ai control plane and cluster services from source and deploys them to a self-contained Kind cluster with a local image registry.
See Local Development Guide for prerequisites (Docker/Podman, make, git, curl, python3, openssl, and minimum CPU/RAM/disk; kind, kubectl, and helm are installed automatically) and all available flags. Go, Node, and Java are not needed on the host: all builds run inside containers.
Each service directory has a Makefile with standard targets (make build, make test, make lint, make validate) and a README describing how to build, test, and work with that service. See the Service Reference for a breakdown of individual services and components.
NVIDIA Run:ai can be deployed on Kubernetes or OpenShift in connected or air-gapped environments. To understand the full installation lifecycle, see the Installation and Configuration Overview.
3. Khả năng ứng dụng và giới hạn
Choose an installation method based on how you want to manage the deployment:
Guided installation (recommended) – Uses the runai-installer CLI with a configuration YAML file. The installer performs preflight checks, installs required dependencies, and deploys the NVIDIA Run:ai control plane and cluster.
Manual installation – Deploys the NVIDIA Run:ai Helm charts directly. Use this path if you manage deployments through your own tooling, such as Argo CD, or require granular control over Helm values.
To build and run NVIDIA Run:ai locally for development, see Usage.
Released NVIDIA Run:ai container images are published to the NVIDIA NGC container registry under nvcr.io/nvidia/runai-oss, and the Helm charts to https://helm.ngc.nvidia.com/nvidia/runai-oss: these are the locations the installer and Helm charts pull from by default. Source releases and their notes are on the GitHub Releases page; see Release Process for how releases are cut and versioned.
Contributions are welcome. Please read Contributing for the development workflow, branch and PR policy, and CI expectations, and Code of Conduct for community guidelines.
NVIDIA Run:ai does not publish a long-range feature roadmap in this repository. What is planned and in progress is tracked in the open:
Release cadence, branch naming, and who cuts a release are documented in Release Process.
Known limitations: the root Makefile targets are placeholders; real build, test, and lint targets live in each service directory (see Building Individual Services). The full CI gate runs remotely and is reported back on pull requests as the Validate Successful Run check.
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