47、模型部署与导出：从PyTorch到生产环境

模型部署与导出：从PyTorch到生产环境

1. 模型部署到生产环境

1.1 基础服务器代码

首先，我们有一段基础的服务器代码，用于加载模型并进行推理：

import sys
import torch
import numpy as np
from flask import Flask, request, jsonify

app = Flask(__name__)

model = ...  # 这里需要定义模型
model.load_state_dict(torch.load(sys.argv[1], map_location='cpu')['model_state'])
model.eval()

def run_inference(in_tensor):
    with torch.no_grad():
        # LunaModel takes a batch and outputs a tuple (scores, probs)
        out_tensor = model(in_tensor.unsqueeze(0))[1].squeeze(0)
        probs = out_tensor.tolist()
        out = {'prob_malignant': probs[1]}
    return out

@app.route("/predict", methods=["POST"])
def predict():
    meta = json.load(request.files['meta'])
    blob = request.files['b