我创建了一个 tensorflow 模型,保存并测试了它。我真的不知道如何使用 tensorflow 服务,并且我不确定模型的输入和输出节点将其转换为 protobuf,然后使用 tensorflow 服务。所以,我想知道是否可以直接使用flask上的预测功能并加载模型来进行预测?我真的很困惑为什么我们必须仅使用 tensorflow 服务来部署 tensorflow 模型?有没有更简单直接的方法?
最佳答案
可以,但您需要设置TensorFlow Serving 服务器。然后您向服务器发送一个 POST 请求。
此处引用链接:Deploying-keras-models-using-tensorflow-serving-and-flask
此处引用链接:Serving-TensorFlow flask client
"""This script wraps the client into a Flask server. It receives POST request with
prediction data, and forward the data to tensorflow server for inference.
"""
from flask import Flask, render_template, request, url_for, jsonify,Response
import json
import tensorflow as tf
import numpy as np
import os
import argparse
import sys
from datetime import datetime
from grpc.beta import implementations
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
tf.app.flags.DEFINE_string('server', 'localhost:9000', 'PredictionService host:port')
FLAGS = tf.app.flags.FLAGS
app = Flask(__name__)
class mainSessRunning():
def __init__(self):
host, port = FLAGS.server.split(':')
channel = implementations.insecure_channel(host, int(port))
self.stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
self.request = predict_pb2.PredictRequest()
self.request.model_spec.name = 'example_model'
self.request.model_spec.signature_name = 'prediction'
def inference(self, val_x):
# temp_data = numpy.random.randn(100, 3).astype(numpy.float32)
temp_data = val_x.astype(np.float32).reshape(-1, 3)
print("temp_data is:", temp_data)
data, label = temp_data, np.sum(temp_data * np.array([1, 2, 3]).astype(np.float32), 1)
self.request.inputs['input'].CopyFrom(
tf.contrib.util.make_tensor_proto(data, shape=data.shape))
result = self.stub.Predict(self.request, 5.0)
return result, label
run = mainSessRunning()
print("Initialization done. ")
# Define a route for the default URL, which loads the form
@app.route('/inference', methods=['POST'])
def inference():
request_data = request.json
input_data = np.expand_dims(np.array(request_data), 0)
result, label = run.inference(input_data)
di={"result":str(result),'label': label[0].tolist()}
return Response(json.dumps(di), mimetype='application/json')
关于python - Tensorflow:我可以直接在 Flask 中运行我的 tensorflow 模型吗?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/61928192/