利用flask来访问tensorflow-serving服务

参考githup地址:https://github.com/aaxwaz/Serving-TensorFlow-Model 

启动服务：

bazel build //tensorflow_serving/model_servers:tensorflow_model_server
nohup   bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server  --port=9000 --model_name=example_model --model_base_path=/Users/shuubiasahi/Desktop/modelserving > log.log &

Flask服务代码启动:

"""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')
    #return jsonify(di)
@app.route('/test', methods=['GET'])
def test_serv():
    return ("Hello")
if __name__ == "__main__":
    app.run()

flask客户端调用:

import requests, json

data =[1,0.5,2.0]
headers = {'content-type': 'application/json'}
r = requests.post("http://127.0.0.1:5000/inference", data=json.dumps(data),
                  headers=headers)
print(r.json()  )

结果：

{'result': 'outputs {\n  key: "output"\n  value {\n    dtype: DT_FLOAT\n    tensor_shape {\n      dim {\n        size: 1\n      }\n      dim {\n        size: 1\n      }\n    }\n    float_val: 7.69464254379\n  }\n}\n', 'label': 8.0}