Valhalla

A Personal project of mine to get a better understanding of rust and generative AI its a system analysis tool built that combines real-time resource monitoring with AI-powered security insights.

Features

• Real-time system resource monitoring (CPU, memory, network, disk usage)
• AI-powered security analysis of running processes
• Clean, intuitive metrics visualization

Prerequisites

• Rust (1.75.0 or later)
• Docker and Docker Compose

Getting Started

Local Development Setup

1. Clone the repository:

   git clone https://github.com/Greatlakescoder/valhalla
   cd valhalla

2. Install dependencies:

   cargo install --path .

3. Run the tests:

   cargo test

4. Start the application:

   cargo run

Docker Setup

The application runs in a containerized environment with three main services:

1. Odin Service (Backend API)

   • Runs on port 3000
   • Built from custom Rust backend Dockerfile
   • Handles system analysis and AI processing

2. Frontend Service

   • Runs on port 5173
   • Built with Vite
   • Communicates with Odin service
   • Configurable API URL through environment variables

3. Ollama Server

   • Local AI model server
   • Runs on port 11434
   • GPU-accelerated using NVIDIA support
   • Persistent model storage in /mnt/valhalla/ollama-server

Requirements

• Docker and Docker Compose (v3.8 or later)
• NVIDIA GPU with appropriate drivers
• NVIDIA Container Toolkit installed

Starting the Application

1. Build and start all services:

   docker compose up -d

2. View service logs:

   # All services
   docker compose logs -f
   
   # Specific service
   docker compose logs -f odin
   docker compose logs -f frontend
   docker compose logs -f ollama-server

3. Stop and remove containers:

   docker compose down

Network Configuration

• All services communicate over a bridged network named app-network
• Frontend can access the backend via http://localhost:3000
• Ollama server is restricted to localhost access on port 11434

GPU Support

The Ollama server is configured to utilize all available NVIDIA GPUs for model inference. Ensure your system has:

• NVIDIA GPU drivers installed
• NVIDIA Container Toolkit configured
• Appropriate GPU capabilities

Configuration

Environment Variables

Application Configuration

Update the local file in the configuration folder in project root

Example:

monitor:
  ollama_url: "http://localhost:11434"
  model: mistral
  context_size: 5000
  offline: true

Development

Running Tests

# Run all tests
cargo test

# Run with logging
RUST_LOG=debug cargo test

Code Style

This project follows the official Rust style guidelines. To ensure your code is properly formatted:

# Check formatting
cargo fmt -- --check

# Apply formatting
cargo fmt

# Run clippy for linting
cargo clippy

Deployment

Building for Production

cargo build --release

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• sysinfo crate is key component https://crates.io/crates/sysinfo

Support