Developers Tutorial

1. Project Setup

Initialize Project

Create a new project directory and install the necessary dependencies.


mkdir ezeni-dev && cd $_
npm init -y
npm install @ezeniia/sdk

Create Configuration

Configure the SDK with your API key and network settings.


// config.js
module.exports = {
    apiKey: "YOUR_API_KEY",
    network: "mainnet",
    defaultNodes: ["node-01", "node-02"]
};

2. Core Implementation

Implement the AI Controller

Use the Ezeni Iia SDK to manage decentralized AI computations across the network.


const { AIController } = require('@ezeniai/sdk');

const controller = new AIController({
    configPath: "./config.js",
    autoSync: true,
    modelType: "vision-transformer"
});

await controller.init(); // Load trained model weights


Initialization successful.
[info] Connected to 3 primary nodes.
[ready] Model "vision-transformer" loaded.

Continue to Advanced Features →

3. Performance Optimization

Distribute Computation

Assign tasks across multiple nodes to increase throughput using dynamic load balancing.


controller.distribute({
    tasks: 500,
    nodeBalance: true,
    batch: "medium"
});

Model Training

Optimize for different AI models using custom training profiles.


controller.train({
    profile: "high-performance-gpu",
    maxIterations: 1000
});

Performance Monitoring

Real-time metrics to ensure optimal resource utilization across the network.


const stats = controller.getPerformance();
console.log(JSON.stringify(stats, null, 2));

4. Deployment

Model Deployment

Publish your model to the decentralized AI network for live execution.

Deployment Status:
✓ Node-01: Running 🟢
✓ Node-03: Synced 🟢
⚠️ Node-02: Low capacity 🟡

Deploy Now

Ready to Build?

Start deploying AI applications today with the Ezeni iia decentralized infrastructure platform.

🛠 Developer Resources

Developer TUTORIAL