There are several ways to distribute machine learning models. One common approach is to use a client-server architecture, where the model is hosted on a server and clients send requests for inference. Another approach is to use frameworks like TensorFlow's distributed training, which allow you to train models across multiple machines.
Example code (client-server architecture):
# Server-side code
from flask import Flask, request, jsonify
app = Flask(__name__)
model = torch.load('trained_model.pt')
@app.route('/predict', methods=['POST'])
def predict():
input_data = request.json['data']
output = model(input_data)
return jsonify({'output': output.tolist()})
if __name__ == '__main__':
app.run()
# Client-side code
import requests
data = ...
response = requests.post('http://localhost:5000/predict', json={'data': data})
output = response.json()['output']
