深度学习Tensorflow生产环境部署（下·模型部署篇）

前一篇讲过环境的部署篇，这一次就讲讲从代码角度如何导出pb模型，如何进行服务调用。

1 hello world篇

部署完docker后，如果是cpu环境，可以直接拉取tensorflow/serving，如果是GPU环境则麻烦点，具体参考前一篇，这里就不再赘述了。

cpu版本的可以直接拉取tensorflow/serving，docker会自动拉取latest版本：

docker pull tensorflow/serving

如果想要指定tensorflow的版本，可以去这里查看：https://hub.docker.com/r/tensorflow/serving/tags/

比如我需要的是1.12.0版本的tf，那么也可以拉取指定的版本：

docker pull tensorflow/serving:1.12.0

拉取完镜像，需要下载一个hello world的程序代码。

mkdir -p /tmp/tfserving
cd /tmp/tfserving
git clone https://github.com/tensorflow/serving

tensorflow/serving的github中有对应的测试模型，模型其实就是 y = 0.5 * x + 2。即输入一个数，输出是对应的y。

运行下面的命令，在docker中部署服务：

docker run -p 8501:8501 --mount type=bind,source=/tmp/serving/tensorflow_serving/servables/tensorflow/testdata/saved_model_half_plus_two_cpu,target=/models/half_plus_two -e MODEL_NAME=half_plus_two -t tensorflow/serving &

上面的命令中，把/tmp/serving/tensorflow_serving/servables/tensorflow/testdata/saved_model_half_plus_two_cpu路径挂载到/models/half_plus_two,这样tensorflow_serving就可以加载models下的模型了，然后开放内部8501的http接口。

执行docker ps查看服务列表：

➜  ~ docker ps
CONTAINER ID        IMAGE                COMMAND                  CREATED             STATUS              PORTS                              NAMES
7decb4286057        tensorflow/serving   "/usr/bin/tf_serving…"   7 seconds ago       Up 6 seconds        8500/tcp, 0.0.0.0:8501->8501/tcp   eager_dewdney

发送一个http请求测试一下：

curl -d '{"instances": [1.0, 2.0, 5.0]}' -X POST http://localhost:8501/v1/models/half_plus_two:predict
{
    "predictions": [2.5, 3.0, 4.5
    ]
}%

2 mnist篇

由于前面的例子，serving工程下只有pb模型，没有模型的训练和导出，因此看不出其中的门道。这一部分就直接基于手写体识别的例子，展示一下如何从tensorflow训练代码导出模型，又如何通过grpc服务进行模型的调用。

训练和导出:

#! /usr/bin/env python
"""
训练并导出Softmax回归模型，使用SaveModel导出训练模型并添加签名。
"""
from __future__ import print_function
import os
import sys
# This is a placeholder for a Google-internal import.
import tensorflow as tf
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
import basic.mnist_input_data as mnist_input_data
# 定义模型参数
tf.app.flags.DEFINE_integer('training_iteration', 10, 'number of training iterations.')
tf.app.flags.DEFINE_integer('model_version', 2, 'version number of the model.')
tf.app.flags.DEFINE_string('work_dir', './tmp', 'Working directory.')
FLAGS = tf.app.flags.FLAGS
def main(_):
    # 参数校验
    # if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
    #     print('Usage: mnist_saved_model.py [--training_iteration=x] '
    #           '[--model_version=y] export_dir')
    #     sys.exit(-1)
    # if FLAGS.training_iteration <= 0:
    #     print('Please specify a positive value for training iteration.')
    #     sys.exit(-1)
    # if FLAGS.model_version <= 0:
    #     print('Please specify a positive value for version number.')
    #     sys.exit(-1)
    # Train model
    print('Training model...')
    mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
    sess = tf.InteractiveSession()
    serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
    feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32), }
    tf_example = tf.parse_example(serialized_tf_example, feature_configs)
    x = tf.identity(tf_example['x'], name='x')  # use tf.identity() to assign name
    y_ = tf.placeholder('float', shape=[None, 10])
    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))
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    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))
    for _ in range(FLAGS.training_iteration):
        batch = mnist.train.next_batch(50)
        train_step.run(feed_dict={x: batch[0], y_: batch[1]})
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
    print('training accuracy %g' % sess.run(
        accuracy, feed_dict={
            x: mnist.test.images,
            y_: mnist.test.labels
        }))
    print('Done training!')
    # 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 = "/Users/xingoo/PycharmProjects/ml-in-action/实践-tensorflow/01-官方文档-学习和使用ML/save_model"
    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)
    # 配置导出地址，创建SaveModel
    builder = tf.saved_model.builder.SavedModelBuilder(export_path)
    # Build the signature_def_map.
    # 创建TensorInfo，包含type,shape,name
    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)
    # 分类签名：算法类型+输入+输出（概率和名字）
    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)
    tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
    # 预测签名：输入的x和输出的y
    prediction_signature = (
        tf.saved_model.signature_def_utils.build_signature_def(
            inputs={'images': tensor_info_x},
            outputs={'scores': tensor_info_y},
            method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
    # 构建图和变量的信息：
    """
    sess                会话
    tags                标签，默认提供serving、train、eval、gpu、tpu
    signature_def_map   签名
    main_op             初始化？
    strip_default_attrs strip?
    """
    # predict_images就是服务调用的方法
    # serving_default是没有输入签名时，使用的方法
    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,
        },
        main_op=tf.tables_initializer(),
        strip_default_attrs=True)
    # 保存
    builder.save()
    print('Done exporting!')
if __name__ == '__main__':
    tf.app.run()

执行后，在当前目录中就有一个save_model文件，保存了各个版本的pb模型文件。

然后基于grpc部署服务：

docker run -p 8500:8500 --mount type=bind,source=/Users/xingoo/PycharmProjects/ml-in-action/01-实践-tensorflow/01-官方文档-学习和使用ML/save_model,target=/models/mnist -e MODEL_NAME=mnist -t tensorflow/serving &

服务部署成功，查看一下docker列表：

➜  ~ docker ps
CONTAINER ID        IMAGE                COMMAND                  CREATED             STATUS              PORTS                              NAMES
39a06cc35961        tensorflow/serving   "/usr/bin/tf_serving…"   4 seconds ago       Up 3 seconds        0.0.0.0:8500->8500/tcp, 8501/tcp   hardcore_galileo

然后编写对应的client代码：

import tensorflow as tf
import basic.mnist_input_data as mnist_input_data
import grpc
import numpy as np
import sys
import threading
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
tf.app.flags.DEFINE_integer('concurrency', 1, 'maximum number of concurrent inference requests')
tf.app.flags.DEFINE_integer('num_tests', 100, 'Number of test images')
tf.app.flags.DEFINE_string('server', 'localhost:8500', 'PredictionService host:port')
tf.app.flags.DEFINE_string('work_dir', './tmp', 'Working directory. ')
FLAGS = tf.app.flags.FLAGS
test_data_set = mnist_input_data.read_data_sets(FLAGS.work_dir).test
channel = grpc.insecure_channel(FLAGS.server)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
class _ResultCounter(object):
    """Counter for the prediction results."""
    def __init__(self, num_tests, concurrency):
        self._num_tests = num_tests
        self._concurrency = concurrency
        self._error = 0
        self._done = 0
        self._active = 0
        self._condition = threading.Condition()
    def inc_error(self):
        with self._condition:
            self._error += 1
    def inc_done(self):
        with self._condition:
            self._done += 1
            self._condition.notify()
    def dec_active(self):
        with self._condition:
            self._active -= 1
            self._condition.notify()
    def get_error_rate(self):
        with self._condition:
            while self._done != self._num_tests:
                self._condition.wait()
            return self._error / float(self._num_tests)
    def throttle(self):
        with self._condition:
            while self._active == self._concurrency:
                self._condition.wait()
            self._active += 1
def _create_rpc_callback(label, result_counter):
    def _callback(result_future):
        exception = result_future.exception()
        if exception:
            result_counter.inc_error()
            print(exception)
        else:
            response = np.array(result_future.result().outputs['scores'].float_val)
            prediction = np.argmax(response)
            sys.stdout.write("%s - %s\n" % (label, prediction))
            sys.stdout.flush()
        result_counter.inc_done()
        result_counter.dec_active()
    return _callback
result_counter = _ResultCounter(FLAGS.num_tests, FLAGS.concurrency)
for i in range(FLAGS.num_tests):
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'mnist'
    request.model_spec.signature_name = 'predict_images'
    image, label = test_data_set.next_batch(1)
    request.inputs['images'].CopyFrom(tf.contrib.util.make_tensor_proto(image[0], shape=[1, image[0].size]))
    result_counter.throttle()
    result_future = stub.Predict.future(request, 5.0)  # 5 seconds
    result_future.add_done_callback(_create_rpc_callback(label[0], result_counter))
print(result_counter.get_error_rate())

得到对应的输出：

3 - 3
6 - 6
9 - 9
3 - 3
1 - 1
4 - 9
1 - 5
7 - 9
6 - 6
9 - 9
0.0