Superdesktop

A distributed, AI-driven desktop orchestration platform for high-performance, secure, and scalable remote workspaces.

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Enterprise-grade Security

Zero-trust architecture, mTLS, JWT-based access control, encrypted sync, and detailed audit logs designed for compliance-sensitive environments.

  • Role-based access control
  • Transport & at-rest encryption
  • Audit trails & policy enforcement

AI-driven Orchestration

Predictive scheduling, cost-aware placement, and adaptive resource allocation using light-weight ML models to maximize utilization and reduce latency.

  • Auto-scaling & placement heuristics
  • Workload prioritization and QoS

Modular Deployment

Container-first architecture with optional Kubernetes operator, lightweight agents for compute/storage, and pluggable telemetry backends.

  • Docker Compose for quick demos
  • Kubernetes for production orchestration

Distributed Compute

Aggregate CPU/GPU/NPU resources into single low-latency workspaces.

AI Optimization

Predictive scheduling, automatic placement and cost-aware scaling.

Zero-trust Security

mTLS, JWT, encrypted sync and role-based access controls.

Live Telemetry

Real-time dashboards, logs, and metrics with alerting hooks.

Quick Start

Clone the repo and start the orchestrator and UI using the provided script for a fast local demo.

git clone https://github.com/Chandu00756/Superdesktop.git cd Superdesktop chmod +x start-omega.sh ./start-omega.sh

Default endpoints after startup:

  • http://localhost:8081/omega-new.html — Desktop UI
  • http://127.0.0.1:8443/docs — API docs
  • http://127.0.0.1:7777/health — Health

Policy & Governance

Fine-grained RBAC, org-level quotas, and immutable audit trails to meet enterprise compliance and governance needs.

Snapshots & Persistence

Workspace snapshots, fast restore paths, and versioned disk images for reproducible workspaces.

Edge & Hybrid Deployments

Lightweight agents for edge compute and an optional Kubernetes operator for hybrid cloud deployments.

Example Workloads

Distributed ML Training

Large-scale model training that shards data and aggregates gradients across heterogeneous GPU clusters.

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Common Questions

What makes Superdesktop unique and where is it used?
Superdesktop combines AI-driven resource orchestration with a secure, modular desktop delivery platform. It is designed for high-performance remote workspaces, research clusters, and enterprise environments that require secure, auditable multi-tenant access.
What databases are supported?
Postgres is recommended; the code can fallback to SQLite for dev/test.
How do I contribute?
Open PRs and issues on the GitHub repository; see CONTRIBUTING.md for the process.
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