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')
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)
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.