DanteGPU - GPU Share VM Manager

DanteGPU is a sophisticated virtual machine management system designed specifically for AI workload distribution and GPU resource sharing. Built with Rust, it provides a robust, high-performance solution for managing VMs with GPU passthrough capabilities.

https://github.com/user-attachments/assets/3332166a-5dff-4398-b879-5965f29e33e0

Overview

DanteGPU serves as the core component of the GPU Share Platform, offering: - VM lifecycle management with GPU passthrough - Real-time resource monitoring - Automated GPU management - RESTful API interface - CLI tools for system management

Key Features

VM Management

• Full lifecycle control (create, start, stop, delete)
• GPU passthrough support
• Resource allocation optimization
• Template-based VM creation
• Automated recovery mechanisms

GPU Management

• Automated device discovery
• Dynamic GPU allocation
• Multi-vendor support (NVIDIA, AMD)
• Performance metrics tracking
• Resource isolation

Monitoring System

• Real-time resource tracking
• Performance metrics collection
• GPU utilization monitoring
• Memory usage tracking
• Temperature and power monitoring

API & CLI Interface

• RESTful API endpoints
• Git-style CLI commands
• Colored terminal output
• Async command processing
• Comprehensive error handling

🔧 Technical Architecture

Core Components

1. Configuration Management
2. Hierarchical config system
3. Multiple override layers
4. Environment variable support
5. TOML-based configuration

6. Secure secrets handling

7. CLI System bash gpu-share ├── serve [--port] # API server management ├── vm # VM operations │ ├── list # List all VMs │ ├── create # Create new VM │ ├── start # Start VM │ ├── stop # Stop VM │ └── delete # Remove VM ├── gpu # GPU management │ ├── list # List GPUs │ ├── attach # Attach GPU to VM │ └── detach # Detach GPU from VM └── init # Generate config

8. API Endpoints

9. /api/v1/vms - VM management
10. /api/v1/gpus - GPU operations
11. /api/v1/metrics - Performance metrics
12. RESTful design principles

13. JSON payload support

14. Monitoring System

15. Resource metrics collection
16. Performance tracking
17. Health monitoring
18. Metrics retention management
19. Real-time alerts

🛠 Prerequisites

• System Requirements
• Linux kernel with IOMMU support
• QEMU/KVM virtualization
• Libvirt daemon
• Compatible GPU (NVIDIA/AMD)

• Rust toolchain (latest stable)

• Optional Components

• NVIDIA driver (for NVIDIA GPUs)
• AMD driver (for AMD GPUs)
• Docker (for containerized deployment)

📦 Installation

1. System Setup ```bash # Install dependencies sudo apt install qemu-kvm libvirt-daemon-system

# Clone repository git clone https://github.com/yourusername/gpu-share-vm-manager cd gpu-share-vm-manager

# Build project cargo build --release ```

1. Configuration ```bash # Generate default config ./target/release/gpu-share init

# Edit configuration (optional) vim config/default.toml ```

1. Start Service bash # Run API server ./target/release/gpu-share serve --port 3000

Security Considerations

• Input validation on all endpoints
• Resource limits enforcement
• Secure configuration management
• Environment variable protection
• API authentication (coming soon)
• Resource isolation

Usage Examples

# Create new VM with GPU
gpu-share vm create --name ai-worker-01 --memory 8192 --vcpus 4 --gpu

# List available GPUs
gpu-share gpu list

# Attach GPU to VM
gpu-share gpu attach --vm-name ai-worker-01 --gpu-id 0

🔍 Monitoring & Metrics

• CPU usage tracking
• Memory utilization
• GPU metrics
• Utilization percentage
• Memory usage
• Temperature
• Power consumption
• Performance analytics
• Resource optimization

🤝 Contributing

We welcome contributions! Please see our CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

📝 License

MIT License

Project Status

Currently in active development. Features being worked on: - Enhanced GPU scheduling - Multi-node support - Advanced monitoring - Security enhancements - Performance optimizations

📚 Documentation

Full documentation available in /docs: - Installation Guide - Configuration Reference - API Documentation - Development Guide - Security Guidelines

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Remember: With great GPU power comes great electricity bills! 🔋