如何在本地环境中部署亚马逊网络服务SageMaker线性学习者模型

tar -xvzf model.tar.gz -C /path/to/local/directory

/path/to/local/directory/
├── model_algo_1-2021-01-01-00-00-00
│   ├── __init__.py
│   ├── linearlearner_model.py
│   └── model.pth
└── serving
    ├── __init__.py
    └── predict.py

pip install -r /path/to/local/directory/serving/requirements.txt

from flask import Flask, request, jsonify
import predict

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict_endpoint():
    data = request.json
    predictions = predict.predict(data)
    return jsonify(predictions)

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

import torch
from model_algo_1_2021_01_01_00_00_00 import LinearLearnerModel

model = LinearLearnerModel.load_state_dict(torch.load('model.pth'))
model.eval()

def predict(data):
    inputs = preprocess(data)  # 实现数据预处理逻辑
    with torch.no_grad():
        outputs = model(inputs)
    return postprocess(outputs)  # 实现结果后处理逻辑

python app.py

curl -X POST http://localhost:8080/predict -H "Content-Type: application/json" -d '{"input": [1.0, 2.0, 3.0]}'