99、Tensorflow Serving 實現模型的部署
阿新 • • 發佈：2019-01-03
昨晚終於實現了Tensorflow模型的部署 使用TensorFlow Serving
1、使用Docker 獲取Tensorflow Serving的映象，Docker在國內的需要將映象的Repository地址設定為阿里雲的加速地址，這個大家可以自己去CSDN上面找
然後啟動docker
2、使用Tensorflow 的 SaveModelBuilder儲存Tensorflow的計算圖模型，並且設定Signature,
Signature主要用來標識模型的輸入值的名稱和型別
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)
儲存之後的計算圖的結構可以從下面這裡看見，下面這裡只給出模型的signature部分，因為signature裡面定義了你到時候call restful介面的引數名稱和型別
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" } } }
從上面的Signature定義可以看出 到時候call restfull 介面需要傳兩個引數，
int32型別的名稱為inputX引數
float型別名稱為input_drop_out_keep_prob的引數
上面的protocol buffer 中
textclassified表示使用TextCnn卷積神經網路來進行預測，然後預測功能的名稱叫做textclassified
3、將模型部署到Tensorflow Serving 上面
首先把模型通過工具傳輸到docker上面
模型的結構如下
傳到docker上面，然後在外邊套一個資料夾名字起位模型的名字，叫做
text_classified_model 然後執行下面這條命令執行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/servingsource表示模型在docker上面的路徑
target表示模型在docker中TensorFlow/serving container上面的路徑
然後輸入如下所示
另一個是gRPC介面埠是8500
gRPC是HTTP/2協議，REST API 是HTTP/1協議
區別是gRPC只有POST/GET兩種請求方式
REST API還有其餘很多種 列如 PUT/DELETE 等
4、客戶端呼叫gPRC介面
需要傳兩個引數，
一個是
inputX
另一個是
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()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"
}從上面的結果可以看出，我們傳入了一句話
wisegirls is its low-key quality and genuine
分類的結果
predictClass
int64_val: 1
分成第一類
這個真的是神經網路的部署呀。
啦啦啦 ， Tensorflow真的很牛，上至瀏覽器，下到手機，一次訓練，一次匯出。處處執行。
沒有不敢想，只有不敢做
The Full version can be find here
https://github.com/weizhenzhao/TextCNN_Tensorflow_Serving/tree/master
Thanks
WeiZhen