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.
- [2026/09] 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/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: Job/Task abstraction with declarative distributed training and 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
See the RLark Roadmap for planned runtime, account management, and custom workload resource improvements.
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, and other concepts |
| Platform User Guide | Web console, clusters, jobs, 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.