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DanteGPU's command line tool or Python SDK for programmatic use.

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# 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**
   - Hierarchical config system
   - Multiple override layers
   - Environment variable support
   - TOML-based configuration
   - Secure secrets handling

2. **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
   ```

3. **API Endpoints**
   - `/api/v1/vms` - VM management
   - `/api/v1/gpus` - GPU operations
   - `/api/v1/metrics` - Performance metrics
   - RESTful design principles
   - JSON payload support

4. **Monitoring System**
   - Resource metrics collection
   - Performance tracking
   - Health monitoring
   - Metrics retention management
   - 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
   ```

2. **Configuration**
   ```bash
   # Generate default config
   ./target/release/gpu-share init
   
   # Edit configuration (optional)
   vim config/default.toml
   ```

3. **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

```bash
# 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](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](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

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

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