参考：
https://tf.wiki/zh_hans/deployment/serving.html#
https://tensorflow.google.cn/tfx/serving/docker

1. 安装 Docker

以下均为 centos7 环境
参考文章：docker安装等操作

2. 使用 Docker 部署

• 拉镜像 docker pull tensorflow/serving
  
• 模型路径，版本号1,2，默认加载最大数字的
  
  
• bash 输入命令，注意路径，mymodel跟请求的代码保持一致

docker run -p 8501:8501 --mount type=bind,source=/home/dnn/project/tf2,target=/models/mymodel -e MODEL_NAME=mymodel -t tensorflow/serving

• 手写数字识别 模型代码+导出模型

# 手写数字 tf 实现
import numpy as np
import tensorflow as tfclass MNistLoader():def __init__(self):data = tf.keras.datasets.mnist# 加载数据(self.train_data, self.train_label), (self.test_data, self.test_label) = data.load_data()# 扩展维度，灰度图1通道 [batch_size, 28, 28, chanels=1]self.train_data = np.expand_dims(self.train_data.astype(np.float32) / 255.0, axis=-1)self.test_data = np.expand_dims(self.test_data.astype(np.float32) / 255.0, axis=-1)self.train_label = self.train_label.astype(np.int32)self.test_label = self.test_label.astype(np.int32)# 样本个数self.num_train_data, self.num_test_data = self.train_data.shape[0], self.test_data.shape[0]def get_batch(self, batch_size):# 从训练集里随机取出 batch_size 个样本idx = np.random.randint(0, self.num_train_data, batch_size)return self.train_data[idx, :], self.train_label[idx]# 自定义多层感知机模型
class MLPmodel(tf.keras.Model):def __init__(self):super().__init__()# 除第一维以外的维度展平self.flatten = tf.keras.layers.Flatten()self.dense1 = tf.keras.layers.Dense(units=100, activation='relu')self.dense2 = tf.keras.layers.Dense(units=10)@tf.function # 计算图模式，导出需要def call(self, input):x = self.flatten(input)x = self.dense1(x)x = self.dense2(x)output = tf.nn.softmax(x)return outputnum_epochs = 5
batch_size = 50
learning_rate = 1e-4mymodel = MLPmodel()data_loader = MNistLoader()mymodel.compile(optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate),loss=tf.keras.losses.sparse_categorical_crossentropy,metrics=[tf.keras.metrics.sparse_categorical_accuracy]
)
mymodel.fit(data_loader.train_data, data_loader.train_label,batch_size=batch_size,epochs=num_epochs)# 导出模型
tf.saved_model.save(mymodel, "./2")
# 载入模型
mymodel = tf.saved_model.load('./2')res = mymodel.call(data_loader.test_data)
print(res)

3. 请求服务

3.1 手写数字例子

import numpy as np
import tensorflow as tf
import json
import requests
import matplotlib.pyplot as pltclass MNistLoader():def __init__(self):data = tf.keras.datasets.mnist# 加载数据(self.train_data, self.train_label), (self.test_data, self.test_label) = data.load_data()# 扩展维度，灰度图1通道 [batch_size, 28, 28, chanels=1]self.train_data = np.expand_dims(self.train_data.astype(np.float32) / 255.0, axis=-1)self.test_data = np.expand_dims(self.test_data.astype(np.float32) / 255.0, axis=-1)self.train_label = self.train_label.astype(np.int32)self.test_label = self.test_label.astype(np.int32)# 样本个数self.num_train_data, self.num_test_data = self.train_data.shape[0], self.test_data.shape[0]def get_batch(self, batch_size):# 从训练集里随机取出 batch_size 个样本idx = np.random.randint(0, self.num_train_data, batch_size)return self.train_data[idx, :], self.train_label[idx]size = 6
dataloader = MNistLoader()data = json.dumps({'instances': dataloader.test_data[0:size].tolist()
})headers = {'content-type': 'application/json'}json_response = requests.post(url='http://localhost:8501/v1/models/mymodel:predict',data=data, headers=headers
)pred = np.array(json.loads(json_response.text)['predictions'])
print("预测结果：", np.argmax(pred, axis=-1))# 打印图片
plt.subplots(1, size)
idx = 1
for img in dataloader.test_data[0:size]:plt.subplot(1, size, idx)plt.imshow(img)idx += 1
plt.show()

输出：

预测结果： [7 2 1 0 4 1]

跟图片显示一致

3.2 猫狗分类例子

模型代码

训练完后，导出模型

tf.saved_model.save(model, './3')

import numpy as np
import tensorflow as tf
import json
import requests
import matplotlib.pyplot as plttest_pic = ["./dogs-vs-cats/test/1.jpg","./dogs-vs-cats/test/7.jpg"]# 狗1， 猫0
test_data = np.array([_decode_and_resize(f).numpy() for f in test_pic])data = json.dumps({'instances': test_data.tolist()
})headers = {'content-type': 'application/json'}json_response = requests.post(url='http://localhost:8501/v1/models/mymodel:predict',data=data, headers=headers
)
pred = np.array(json.loads(json_response.text)['predictions'])
print("预测结果：", np.argmax(pred, axis=-1))for img in test_data:plt.imshow(img)plt.show()

输出：

预测结果： [1 0] # 狗，猫