标签：api   The   localhost   sequence   metadata   tree   you   tran   环境   

官网：https://tensorflow.google.cn/tfx/guide/serving

步骤1：保存pb模型

# 为模型每一个参数添加name
# ner demo: https://github.com/buppt/ChineseNER
self.input_x = tf.placeholder(tf.int32, shape=[None, None], name=‘input_x‘)
self.input_y = tf.placeholder(tf.int32, shape=[None, None], name=‘input_y‘)
self.seq_length = tf.placeholder(tf.int32, shape=[None], name=‘sequence_length‘)
self.keep_pro = tf.placeholder(tf.float32, name=‘drop_out‘)
self.global_step = tf.Variable(0, trainable=False, name=‘global_step‘)

# 保存模型时添加签名
def save_model(self, sess, input, seq_length, keep_pro, logit, transition_params):
    model_output_path = ‘output/model/1/‘
    if os.path.exists(model_output_path):
        shutil.rmtree(model_output_path)
    # tf.saved_model.simple_save(sess,
    #                            model_output_path,
    #                            inputs={"input": input},
    #                            outputs={"logit": logit,
    #                                     "transition_params": transition_params})
    builder = tf.saved_model.builder.SavedModelBuilder(model_output_path)

    signature = tf.saved_model.predict_signature_def(inputs={"input": input,
                                                             "sequence_length": seq_length,
                                                             "drop_out": keep_pro},
                                                     outputs={"logit": logit,
                                                              "transition_params": transition_params})
    builder.add_meta_graph_and_variables(sess=sess,
                                         tags=[‘serve‘],
                                         signature_def_map={‘predict‘: signature})
    builder.save()

步骤2：运行模型：

1. 下载docker tensorflow/serving

# Download the TensorFlow Serving Docker image and repo
docker pull tensorflow/serving

# Start TensorFlow Serving container and open the REST API port
# -p 端口映射
# -v 卷映射，本地地址:docker目标地址
# -e 环境变量MODEL_NAME和卷映射目标地址保持一致
docker run -p 8501:8501 -p 8500:8500 -v /D/04_project/tf_tools/tf_serving/ner:/models/ner -e MODEL_NAME=ner -t tensorflow/serving

成功提示如下：

查看docker tfserving状态

http://localhost:8501/v1/models/ner
http://localhost:8501/v1/models/ner/metadata

结果如下：

使用grpc或者rest api调用

注意事项：

1. 入参每个字段都要添加签名，否则会提示缺少tensor，
   例如缺少sequence_length参数会提示：
   "error": "You must feed a value for placeholder tensor ‘sequence_length‘ with dtype int32 and shape [?]\n\t [[{{node sequence_length}}]]"

tfserving部署模型

标签：api   The   localhost   sequence   metadata   tree   you   tran   环境   

原文地址：https://www.cnblogs.com/bincoding/p/13266685.html