Manage cross-cluster embodied intelligence workloads through a unified cloud-native platform spanning cloud GPU training, cross-cluster collaboration, and edge device deployment across heterogeneous resources such as GPU clusters, robot arms, sensors, and cameras.
Explore the complete documentation on Read the Docs — start with the Quick Start, then continue with the platform user guide or administrator guide.
- [2026/08] RLark is now open-source.
- Embodied AI Workload Orchestration: From cloud GPU training (RL/LLM) to edge deployment (robot arm, sensor, camera), unified declarative Job/Workflow/Task abstraction across the full pipeline
- Multi-Runtime Data Plane: Kubernetes provides unified management for cloud GPU clusters and edge devices across the complete training-to-deployment lifecycle; Docker and Raw runtime support will extend coverage to lightweight edge scenarios where Kubernetes is not suitable
- Cross-Cluster Resource Abstraction: Unify multi-site GPU clusters and edge devices via Domain (virtual network domain) and Node (compute node) CRDs, with the control plane running on kcp
- Declarative Training Jobs: Multi-layer abstraction (Job/Workflow/Task) with DAG-based training pipelines and declarative Ray cluster definition
- Cross-Cluster Pod Networking: Virtual network based on TUN devices + gVisor netstack + SSH tunnels, enabling Pod-to-Pod communication without NAT traversal — cloud GPUs and edge robots communicate directly
- Certificate System: Dual-layer X.509 + SSH certificates for Agent access, Domain-scoped cross-cluster forwarding authentication, and user SSH authentication
- Observability: Prometheus metrics, real-time Pod log streaming, and web management UI
Follow the Quick Start Guide on Read the Docs to choose one of the verified flows:
- One-click CLI: deploy the control plane and two kind data-plane clusters, then verify cross-cluster Pod networking.
- UI-based flow: create clusters and a Domain in the web console, deploy two kind data planes, schedule a Job across them, and verify connectivity.
The complete, searchable, and versioned documentation is published on Read the Docs. Use these rendered guides as the primary entry points:
| Guide | Description |
|---|---|
| Quick Start | Verified one-click and UI-based local deployment flows |
| Core Concepts | Domain, Job, Task, Workflow, and other concepts |
| Platform User Guide | Web console, clusters, jobs, workflows, storage, and SSH keys |
| Administrator Guide | Control plane, data plane, networking, security, and operations |
| Developer Guide | Local development, project layout, debugging, and extensions |
| API Reference | Gateway REST API routes and behavior |
| Architecture | Components, interactions, and data flows |
Repository-specific references remain available alongside the code:
| Reference | Description |
|---|---|
| Embodied Runtime | Robot (ROS) and camera hardware management on edge nodes |
| Web UI | Frontend management console |
| Python SDK | Python client for robot/camera gRPC services |
| Go SDK | Go client for embodied-runtime gRPC stubs |
| Proto Definitions | gRPC service definitions for embodied-runtime |
Prefer Chinese? Visit the 中文 Read the Docs 站点.
- Language: Go (control plane/agent) + TypeScript (frontend)
- Orchestration: Kubernetes (kcp + kind)
- Networking: TUN device + gVisor netstack + SSH tunnel
- Certificates: X.509 mTLS + SSH certificates
- Database: PostgreSQL (Bun ORM)
- Monitoring: Prometheus
- Frontend: React + Vite + TypeScript
We welcome contributions! Please see CONTRIBUTING.md for guidelines, and CODE_OF_CONDUCT.md for our community standards.
RLark is licensed under the Apache License 2.0.