tf_serving-模型训练、导出、部署（解析）

参考：
1、https://tensorflow.google.cn/
2、https://www.tensorflow.org/
3、https://zhuanlan.zhihu.com/p/23361413

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参考官网Serving a TensorFlow Model以及TensorFlow Serving 尝尝鲜，对Serving a TensorFlow Model进行分层次解析。

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模型训练

以官网mnist_saved_model.py进行解析，简单化

代码：

cd ~/serving/tensorflow_servingmkdir testcd test vim mnist_saved_model.py# 写入以下内容

# -*- coding: UTF-8 -*-from tensorflow.examples.tutorials.mnist import input_dataimport tensorflow as tfimport numpy as npimport argparseimport ostf.app.flags.DEFINE_integer('training_iteration', 100,                            'number of training iterations.')tf.app.flags.DEFINE_integer('model_version', 1, 'version number of the model.')tf.app.flags.DEFINE_string('work_dir', '/tmp/model', 'Working directory.')FLAGS = tf.app.flags.FLAGSmnist = input_data.read_data_sets("/tmp/MNIST_data", one_hot=True)sess = tf.InteractiveSession()x=tf.placeholder(tf.float32,[None,28*28*1],name='x')y_=tf.placeholder(tf.float32,[None,10],name='y_')with tf.variable_scope('test'):    w = tf.Variable(tf.zeros([784, 10]))    b = tf.Variable(tf.zeros([10]))    # sess.run(tf.global_variables_initializer())    y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')    # cross_entropy = -tf.reduce_sum(y_ * tf.log(y))    cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))    # values, indices = tf.nn.top_k(y, 10)    # table = tf.contrib.lookup.index_to_string_table_from_tensor(    #       tf.constant([str(i) for i in range(10)]))    # prediction_classes = table.lookup(tf.to_int64(indices))saver = tf.train.Saver()# 验证之前是否已经保存了检查点文件ckpt = tf.train.get_checkpoint_state(FLAGS.work_dir)if ckpt and ckpt.model_checkpoint_path:    saver.restore(sess, ckpt.model_checkpoint_path)else:    tf.global_variables_initializer().run()for step in range(FLAGS.training_iteration):    batch = mnist.train.next_batch(50)    train_step.run(feed_dict={x: batch[0], y_: batch[1]})    if step%20==0:        saver.save(sess, os.path.join(FLAGS.work_dir,'test.ckpt'), global_step=step)print('training accuracy %g' % sess.run(      accuracy, feed_dict={x: mnist.test.images,                           y_: mnist.test.labels}))print('Done training!')

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模型导出

# 以上完成模型训练，并将变量参数保存# 接下来完成模型导出，导入到pb文件# Export model# WARNING(break-tutorial-inline-code): The following code snippet is# in-lined in tutorials, please update tutorial documents accordingly# whenever code changes.# export_path_base = sys.argv[-1]export_path_base = os.path.join('/tmp','test')export_path = os.path.join(  tf.compat.as_bytes(export_path_base),  tf.compat.as_bytes(str(FLAGS.model_version)))print ('Exporting trained model to', export_path)builder = tf.saved_model.builder.SavedModelBuilder(export_path)"""# -----------------虚线部分可以舍弃------------------------------------------# Build the signature_def_map.classification_inputs = tf.saved_model.utils.build_tensor_info(  serialized_tf_example)classification_outputs_classes = tf.saved_model.utils.build_tensor_info(  prediction_classes)classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)classification_signature = (  tf.saved_model.signature_def_utils.build_signature_def(      inputs={          tf.saved_model.signature_constants.CLASSIFY_INPUTS:              classification_inputs      },      outputs={          tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:              classification_outputs_classes,          tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:              classification_outputs_scores      },      method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))# --------------------------------------------------------------------------"""tensor_info_x = tf.saved_model.utils.build_tensor_info(x) # 输入tensor_info_y = tf.saved_model.utils.build_tensor_info(y) # 输出prediction_signature = (  tf.saved_model.signature_def_utils.build_signature_def(      inputs={'x': tensor_info_x},      outputs={'y': tensor_info_y},      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')builder.add_meta_graph_and_variables(  sess, [tf.saved_model.tag_constants.SERVING],  signature_def_map={      'predict_images':          prediction_signature,      # tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:      #     classification_signature,  },  legacy_init_op=legacy_init_op)builder.save()print('Done exporting!')

# 直接运行 cd ~/servingpython tensorflow_serving/test/mnist_saved_model.py# 或者bazel build -c opt //tensorflow_serving/test:mnist_saved_modelbazel-bin/tensorflow_serving/test/mnist_saved_model /tmp/test

导出结果：

模型部署

# 启动tensorflow servingbazel build -c opt //tensorflow_serving/model_servers:tensorflow_model_server# 启动刚才导出的模型bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=test --model_base_path=/tmp/test# 或者tensorflow_model_server --port=9000 --model_name=test --model_base_path=/tmp/test

或者
如果安装 tensorflow-model-server

客户端

接下来我们写一个简单的 Client 来调用下我们部署好的 Model。
参考mnist_client.py

cd ~/serving/tensorflow_serving/testvim test_client.py# 写入以下内容from grpc.beta import implementationsimport numpy as npimport tensorflow as tffrom tensorflow_serving.apis import predict_pb2from tensorflow_serving.apis import prediction_service_pb2from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/MNIST_data", one_hot=True)tf.app.flags.DEFINE_string('server', 'localhost:9000',                           'PredictionService host:port')FLAGS = tf.app.flags.FLAGSn_samples = 100host, port = FLAGS.server.split(':')channel = implementations.insecure_channel(host, int(port))stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)# Generate test data# x_data = np.arange(n_samples, step=1, dtype=np.float32)# x_data = np.reshape(x_data, (n_samples, 1))x_data=mnist.test.imagesy_data=mnist.test.labels# Send requestrequest = predict_pb2.PredictRequest()request.model_spec.name = 'test'request.inputs['x'].CopyFrom(tf.contrib.util.make_tensor_proto(x_data, shape=[100, 28*28*1]))result = stub.Predict(request, 10.0)  # 10 secs timeoutprint(result)

cd ~/tensorflow_serving/testvim BUILD# 添加以下语句py_binary(    name = "test_client",    srcs = [        "test_client.py",    ],    deps = [        "//tensorflow_serving/apis:predict_proto_py_pb2",        "//tensorflow_serving/apis:prediction_service_proto_py_pb2",        "@org_tensorflow//tensorflow:tensorflow_py",    ],)

最后编译运行，就能看到在线预测结果啦！

cd ~/servingbazel build //tensorflow_serving/test:test_client && ./bazel-bin/tensorflow_serving/test/test_client# 或bazel build -c opt //tensorflow_serving/test:test_clientbazel-bin/tensorflow_serving/test/test_client #--num_tests=1000 --server=localhost:9000# 或python tensorflow_serving/test/test_client.py #--num_tests=1000 --server=localhost:9000