Practical Examples and Developer Guidelines 🛠️

Advanced

Practical Use of MCP:

  • Serialize your trained model using ONNX:
import onnx
# Export model
onnx_model = onnx.load('model.pth')
# Save with MCP protocol considerations
onnx.save(onnx_model, 'model.onnx')
  • Deploy Model Securely:
scp model.onnx user@edge-device:/models/

Practical Use of API:

  • Create a REST endpoint with Flask:
from flask import Flask, jsonify
app = Flask(__name__)

@app.route('/api/status')
def status():
    return jsonify({'status': 'ok', 'version': '1.0'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)
  • Call API from a client:
import requests
response = requests.get('http://server:8080/api/status')
print(response.json())

Guidelines:

  • Choose MCP for model portability and integrity.
  • Use API for interactive, real-time data exchange.
  • Always secure sensitive communication.