139、TensorFlow Serving 实现模型的部署(二) TextCnn文本分类模型

昨晚终于实现了Tensorflow模型的部署 使用TensorFlow Serving

1、使用Docker 获取Tensorflow Serving的镜像，Docker在国内的需要将镜像的Repository地址设置为阿里云的加速地址，这个大家可以自己去CSDN上面找

然后启动docker

2、使用Tensorflow 的 SaveModelBuilder保存Tensorflow的计算图模型，并且设置Signature,

Signature主要用来标识模型的输入值的名称和类型

        builder = saved_model_builder.SavedModelBuilder(export_path)

        classification_inputs = utils.build_tensor_info(cnn.input_x)
        classification_dropout_keep_prob = utils.build_tensor_info(cnn.dropout_keep_prob)
        classification_outputs_classes = utils.build_tensor_info(prediction_classes)
        classification_outputs_scores = utils.build_tensor_info(cnn.scores)

        classification_signature = signature_def_utils.build_signature_def(
        inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs,
                     signature_constants.CLASSIFY_INPUTS:classification_dropout_keep_prob
                     },
        outputs={
              signature_constants.CLASSIFY_OUTPUT_CLASSES:
              classification_outputs_classes,
              signature_constants.CLASSIFY_OUTPUT_SCORES:
              classification_outputs_scores
         },
         method_name=signature_constants.CLASSIFY_METHOD_NAME)

        tensor_info_x = utils.build_tensor_info(cnn.input_x)
        tensor_info_y = utils.build_tensor_info(cnn.predictions)
        tensor_info_dropout_keep_prob = utils.build_tensor_info(cnn.dropout_keep_prob)

        prediction_signature = signature_def_utils.build_signature_def(
               inputs={'inputX': tensor_info_x,
                            'input_dropout_keep_prob':tensor_info_dropout_keep_prob},
               outputs={'predictClass': tensor_info_y},
        method_name=signature_constants.PREDICT_METHOD_NAME)

        legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')

        #add the sigs to the servable
        builder.add_meta_graph_and_variables(
                sess, [tag_constants.SERVING],
                signature_def_map={
                    'textclassified':
                    prediction_signature,
                    signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
                    classification_signature,
         },
         legacy_init_op=legacy_init_op)
         #save it!
        builder.save(True)

保存之后的计算图的结构可以从下面这里看见，下面这里只给出模型的signature部分，因为signature里面定义了你到时候call restful接口的参数名称和类型

  signature_def {
    key: "serving_default"
    value {
      inputs {
        key: "inputs"
        value {
          name: "dropout_keep_prob:0"
          dtype: DT_FLOAT
          tensor_shape {
            unknown_rank: true
          }
        }
      }
      outputs {
        key: "classes"
        value {
          name: "index_to_string_Lookup:0"
          dtype: DT_STRING
          tensor_shape {
            dim {
              size: 1
            }
          }
        }
      }
      outputs {
        key: "scores"
        value {
          name: "output/scores:0"
          dtype: DT_FLOAT
          tensor_shape {
            dim {
              size: -1
            }
            dim {
              size: 2
            }
          }
        }
      }
      method_name: "tensorflow/serving/classify"
    }
  }
  signature_def {
    key: "textclassified"
    value {
      inputs {
        key: "inputX"
        value {
          name: "input_x:0"
          dtype: DT_INT32
          tensor_shape {
            dim {
              size: -1
            }
            dim {
              size: 40
            }
          }
        }
      }
      inputs {
        key: "input_dropout_keep_prob"
        value {
          name: "dropout_keep_prob:0"
          dtype: DT_FLOAT
          tensor_shape {
            unknown_rank: true
          }
        }
      }
      outputs {
        key: "predictClass"
        value {
          name: "output/predictions:0"
          dtype: DT_INT64
          tensor_shape {
            dim {
              size: -1
            }
          }
        }
      }
      method_name: "tensorflow/serving/predict"
    }
  }
}

从上面的Signature定义可以看出 到时候call restfull 接口需要传两个参数，

int32类型的名称为inputX参数

float类型名称为input_drop_out_keep_prob的参数

上面的protocol buffer 中

textclassified表示使用TextCnn卷积神经网络来进行预测，然后预测功能的名称叫做textclassified

3、将模型部署到Tensorflow Serving 上面

首先把模型通过工具传输到docker上面

模型的结构如下

传到docker上面，然后在外边套一个文件夹名字起位模型的名字，叫做

text_classified_model
然后执行下面这条命令运行tensorflow/serving

docker run -p 8500:8500 --mount type=bind,source=/home/docker/model/text_classified_model,target=/mo
dels/text_classified_model -e MODEL_NAME=text_classified_model -t tensorflow/serving

source表示模型在docker上面的路径

target表示模型在docker中TensorFlow/serving container上面的路径

然后输入如下所示

上面显示运行了两个接口一个是REST API 接口，端口是8501

另一个是gRPC接口端口是8500

gRPC是HTTP/2协议，REST API 是HTTP/1协议

区别是gRPC只有POST/GET两种请求方式

REST API还有其余很多种 列如 PUT/DELETE 等

4、客户端调用gPRC接口

需要传两个参数，

一个是

inputX

另一个是

input_dropout_keep_prob

'''
Created on 2018年10月17日

@author: 95890
'''

"""Send text to tensorflow serving and gets result
"""

# This is a placeholder for a Google-internal import.

from grpc.beta import implementations
import tensorflow as tf
import data_helpers
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from tensorflow.contrib import learn
import numpy as np

tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")
tf.flags.DEFINE_string('server', '192.168.99.100:8500',
                           'PredictionService host:port')
FLAGS = tf.flags.FLAGS

x_text=[]
y=[]
max_document_length=40

def main(_):

  testStr =["wisegirls is its low-key quality and genuine"]

  if x_text.__len__()==0:
      x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
      max_document_length = max([len(x.split(" ")) for x in x_text])

  vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
  vocab_processor.fit(x_text)
  x = np.array(list(vocab_processor.fit_transform(testStr)))

  host, port = FLAGS.server.split(':')
  channel = implementations.insecure_channel(host, int(port))
  stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
  request = predict_pb2.PredictRequest()
  request.model_spec.name = "text_classified_model"
  request.model_spec.signature_name = 'textclassified'
  dropout_keep_prob = np.float(1.0)

  request.inputs['inputX'].CopyFrom(
  tf.contrib.util.make_tensor_proto(x, shape=[1,40],dtype=np.int32))

  request.inputs['input_dropout_keep_prob'].CopyFrom(
  tf.contrib.util.make_tensor_proto(dropout_keep_prob, shape=[1],dtype=np.float))

  result = stub.Predict(request, 10.0)  # 10 secs timeout
  print(result)

if __name__ == '__main__':
  tf.app.run()

调用的结果如下所示

outputs {
  key: "predictClass"
  value {
    dtype: DT_INT64
    tensor_shape {
      dim {
        size: 1
      }
    }
    int64_val: 1
  }
}
model_spec {
  name: "text_classified_model"
  version {
    value: 1
  }
  signature_name: "textclassified"
}

从上面的结果可以看出，我们传入了一句话

wisegirls is its low-key quality and genuine

分类的结果

predictClass

int64_val: 1

分成第一类

这个真的是神经网络的部署呀。

啦啦啦 ，  Tensorflow真的很牛，上至浏览器，下到手机，一次训练，一次导出。处处运行。

没有不敢想，只有不敢做

The Full version can be find here

https://github.com/weizhenzhao/TextCNN_Tensorflow_Serving/tree/master

Thanks

WeiZhen