VisioGrid

Table of Contents

• Project Introduction
• Main Features
• Tech Stack
• Installation and Running
• Usage

Project Introduction

VisioGrid is a heterogeneous distributed computing platform developed in Rust, focusing on image recognition. The project aims to establish an efficient distributed system for large-scale image recognition processing. By parallel processing tasks in a multi-agent environment, VisioGrid enhances the efficiency and speed of image recognition.

Main Features

• High Efficiency: Thanks to Tokio's asynchronous runtime and efficient I/O processing, VisioGrid can quickly handle large amounts of image data.
• Stability: Through real-time monitoring of agents and proactive error handling, the system runs stably, avoiding program crashes.
• Image Recognition: Supports multiple image recognition models, flexibly adapting to different application scenarios.

Tech Stack

• Rust: Fully utilizes Rust's high performance and safety features to ensure efficient system operation.
• Actix Web: Provides a user-friendly web management interface for easy system management and monitoring.
• Tokio: Uses Tokio's asynchronous runtime for performance optimization, improving I/O processing efficiency.
• GStreamer: Used for handling media streams and content, supporting efficient processing of multimedia data.
• Ultralytics: Integrates Ultralytics for image recognition, supporting multiple advanced image recognition models.

Installation and Running

Compile from Source Code

1. Clone the Repository

   git clone https://github.com/DaLaw2/VisioGrid
   cd VisioGrid
   

2. Compile the Project

• Compile the Management Node:

  bash Build/ManagementDepend.sh
  cargo build --release --package Management
  

• Compile the Agent Node:

  bash Build/AgentDepend.sh
  cargo build --release --package Agent
  

3. Run the Nodes

• Run the Management Node:

  cargo run --package Management --release
  

• Run the Agent Node:

  cargo run --package Agent --release
  

Using Docker

VisioGrid provides Docker containers that include all necessary dependencies, eliminating the need for manual installation.

1. Build the Management Node Container

   docker build -t management-image Docker/Management
   

2. Run the Management Node Container

   docker run -d --name management management-image
   

3. Build the Agent Container

   docker build -t agent-image Docker/Agent
   

4. Run the Agent Container

   docker run -d --name agent agent-image
   

Usage

Access http://localhost:8080 via a web browser to view the management interface. The management interface provides system monitoring and management functions, allowing users to view system status, add agent nodes, check task progress, etc.

• The Home page provides an overview of the system, including system information, system load, and system logs.

  

• The Agents page displays all connected agent nodes. Clicking on an agent reveals more detailed information.

  

• The Inference page allows you to create a new inference task. After selecting a model and image, you can submit it.

  

• The Tasks page lists all tasks that are in progress, successful, or failed. You can view the execution status of each subtask.

  

• The Configuration page provides system configuration options, allowing users to modify system settings.