使用 TensorFlow Serving 和 Docker 快速部署机器学习服务

摘要：从实验到生产，简单快速部署机器学习模型一直是一个挑战。总结上面我们快速实践了使用和部署机器学习服务的过程，可以看到，提供了非常方便和高效的模型管理，配合，可以快速搭建起机器学习服务。

从实验到生产，简单快速部署机器学习模型一直是一个挑战。这个过程要做的就是将训练好的模型对外提供预测服务。在生产中，这个过程需要可重现，隔离和安全。这里，我们使用基于Docker的TensorFlow Serving来简单地完成这个过程。TensorFlow 从1.8版本开始支持Docker部署，包括CPU和GPU，非常方便。

获取模型的第一步当然是训练一个模型，但是这不是本篇的重点，所以我们使用一个已经训练好的模型，比如ResNet。TensorFlow Serving 使用SavedModel这种格式来保存其模型，SavedModel是一种独立于语言的，可恢复，密集的序列化格式，支持使用更高级别的系统和工具来生成，使用和转换TensorFlow模型。这里我们直接下载一个预训练好的模型：

$ mkdir /tmp/resnet
$ curl -s https://storage.googleapis.com/download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC_jpg.tar.gz | tar --strip-components=2 -C /tmp/resnet -xvz

如果是使用其他框架比如Keras生成的模型，则需要将模型转换为SavedModel格式，比如：

from keras.models import Sequential
from keras import backend as K
import tensorflow as tf

model = Sequential()
# 中间省略模型构建

# 模型转换为SavedModel
signature = tf.saved_model.signature_def_utils.predict_signature_def(
    inputs={"input_param": model.input}, outputs={"type": model.output})
builder = tf.saved_model.builder.SavedModelBuilder("/tmp/output_model_path/1/")
builder.add_meta_graph_and_variables(
    sess=K.get_session(),
    tags=[tf.saved_model.tag_constants.SERVING],
    signature_def_map={
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            signature
    })
builder.save()

下载完成后，文件目录树为：

$ tree /tmp/resnet
/tmp/resnet
└── 1538687457
    ├── saved_model.pb
    └── variables
        ├── variables.data-00000-of-00001
        └── variables.index

使用Docker部署模型服务：

$ docker pull tensorflow/serving
$ docker run -p 8500:8500 -p 8501:8501 --name tfserving_resnet 
--mount type=bind,source=/tmp/resnet,target=/models/resnet 
-e MODEL_NAME=resnet -t tensorflow/serving

其中，8500端口对于TensorFlow Serving提供的gRPC端口，8501为REST API服务端口。-e MODEL_NAME=resnet指出TensorFlow Serving需要加载的模型名称，这里为resnet。上述命令输出为

2019-03-04 02:52:26.610387: I tensorflow_serving/model_servers/server.cc:82] Building single TensorFlow model file config:  model_name: resnet model_base_path: /models/resnet
2019-03-04 02:52:26.618200: I tensorflow_serving/model_servers/server_core.cc:461] Adding/updating models.
2019-03-04 02:52:26.618628: I tensorflow_serving/model_servers/server_core.cc:558]  (Re-)adding model: resnet
2019-03-04 02:52:26.745813: I tensorflow_serving/core/basic_manager.cc:739] Successfully reserved resources to load servable {name: resnet version: 1538687457}
2019-03-04 02:52:26.745901: I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: resnet version: 1538687457}
2019-03-04 02:52:26.745935: I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: resnet version: 1538687457}
2019-03-04 02:52:26.747590: I external/org_tensorflow/tensorflow/contrib/session_bundle/bundle_shim.cc:363] Attempting to load native SavedModelBundle in bundle-shim from: /models/resnet/1538687457
2019-03-04 02:52:26.747705: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /models/resnet/1538687457
2019-03-04 02:52:26.795363: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2019-03-04 02:52:26.828614: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-03-04 02:52:26.923902: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:162] Restoring SavedModel bundle.
2019-03-04 02:52:28.098479: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:138] Running MainOp with key saved_model_main_op on SavedModel bundle.
2019-03-04 02:52:28.144510: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:259] SavedModel load for tags { serve }; Status: success. Took 1396689 microseconds.
2019-03-04 02:52:28.146646: I tensorflow_serving/servables/tensorflow/saved_model_warmup.cc:83] No warmup data file found at /models/resnet/1538687457/assets.extra/tf_serving_warmup_requests
2019-03-04 02:52:28.168063: I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: resnet version: 1538687457}
2019-03-04 02:52:28.174902: I tensorflow_serving/model_servers/server.cc:286] Running gRPC ModelServer at 0.0.0.0:8500 ...
[warn] getaddrinfo: address family for nodename not supported
2019-03-04 02:52:28.186724: I tensorflow_serving/model_servers/server.cc:302] Exporting HTTP/REST API at:localhost:8501 ...
[evhttp_server.cc : 237] RAW: Entering the event loop ...

我们可以看到，TensorFlow Serving使用1538687457作为模型的版本号。我们使用curl命令来查看一下启动的服务状态，也可以看到提供服务的模型版本以及模型状态。

$ curl http://localhost:8501/v1/models/resnet
{
 "model_version_status": [
  {
   "version": "1538687457",
   "state": "AVAILABLE",
   "status": {
    "error_code": "OK",
    "error_message": ""
   }
  }
 ]
}

很多时候我们需要查看模型的输出和输出参数的具体形式，TensorFlow提供了一个saved_model_cli命令来查看模型的输入和输出参数：

$ saved_model_cli show --dir /tmp/resnet/1538687457/ --all

MetaGraphDef with tag-set: "serve" contains the following SignatureDefs:

signature_def["predict"]:
  The given SavedModel SignatureDef contains the following input(s):
    inputs["image_bytes"] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs["classes"] tensor_info:
        dtype: DT_INT64
        shape: (-1)
        name: ArgMax:0
    outputs["probabilities"] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1001)
        name: softmax_tensor:0
  Method name is: tensorflow/serving/predict

signature_def["serving_default"]:
  The given SavedModel SignatureDef contains the following input(s):
    inputs["image_bytes"] tensor_info:
        dtype: DT_STRING
        shape: (-1)
        name: input_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs["classes"] tensor_info:
        dtype: DT_INT64
        shape: (-1)
        name: ArgMax:0
    outputs["probabilities"] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 1001)
        name: softmax_tensor:0
  Method name is: tensorflow/serving/predict

注意到signature_def，inputs的名称，类型和输出，这些参数在接下来的模型预测请求中需要。

TensorFlow Serving提供REST API和gRPC两种请求方式，接下来将具体这两种方式。

我们下载一个客户端脚本，这个脚本会下载一张猫的图片，同时使用这张图片来计算服务请求时间。

$ curl -o /tmp/resnet/resnet_client.py https://raw.githubusercontent.com/tensorflow/serving/master/tensorflow_serving/example/resnet_client.py

以下脚本使用requests库来请求接口，使用图片的base64编码字符串作为请求内容，返回图片分类，并计算了平均处理时间。

from __future__ import print_function

import base64
import requests

# The server URL specifies the endpoint of your server running the ResNet
# model with the name "resnet" and using the predict interface.
SERVER_URL = "http://localhost:8501/v1/models/resnet:predict"

# The image URL is the location of the image we should send to the server
IMAGE_URL = "https://tensorflow.org/images/blogs/serving/cat.jpg"


def main():
  # Download the image
  dl_request = requests.get(IMAGE_URL, stream=True)
  dl_request.raise_for_status()

  # Compose a JSON Predict request (send JPEG image in base64).
  jpeg_bytes = base64.b64encode(dl_request.content).decode("utf-8")
  predict_request = "{"instances" : [{"b64": "%s"}]}" % jpeg_bytes

  # Send few requests to warm-up the model.
  for _ in range(3):
    response = requests.post(SERVER_URL, data=predict_request)
    response.raise_for_status()

  # Send few actual requests and report average latency.
  total_time = 0
  num_requests = 10
  for _ in range(num_requests):
    response = requests.post(SERVER_URL, data=predict_request)
    response.raise_for_status()
    total_time += response.elapsed.total_seconds()
    prediction = response.json()["predictions"][0]

  print("Prediction class: {}, avg latency: {} ms".format(
      prediction["classes"], (total_time*1000)/num_requests))


if __name__ == "__main__":
  main()

输出结果为

$ python resnet_client.py
Prediction class: 286, avg latency: 210.12310000000002 ms

让我们下载另一个客户端脚本，这个脚本使用gRPC作为服务，传入图片并获取输出结果。这个脚本需要安装tensorflow-serving-api这个库。

$ curl -o /tmp/resnet/resnet_client_grpc.py https://raw.githubusercontent.com/tensorflow/serving/master/tensorflow_serving/example/resnet_client_grpc.py
$ pip install tensorflow-serving-api

脚本内容：

from __future__ import print_function

# This is a placeholder for a Google-internal import.

import grpc
import requests
import tensorflow as tf

from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc

# The image URL is the location of the image we should send to the server
IMAGE_URL = "https://tensorflow.org/images/blogs/serving/cat.jpg"

tf.app.flags.DEFINE_string("server", "localhost:8500",
                           "PredictionService host:port")
tf.app.flags.DEFINE_string("image", "", "path to image in JPEG format")
FLAGS = tf.app.flags.FLAGS


def main(_):
  if FLAGS.image:
    with open(FLAGS.image, "rb") as f:
      data = f.read()
  else:
    # Download the image since we weren"t given one
    dl_request = requests.get(IMAGE_URL, stream=True)
    dl_request.raise_for_status()
    data = dl_request.content

  channel = grpc.insecure_channel(FLAGS.server)
  stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
  # Send request
  # See prediction_service.proto for gRPC request/response details.
  request = predict_pb2.PredictRequest()
  request.model_spec.name = "resnet"
  request.model_spec.signature_name = "serving_default"
  request.inputs["image_bytes"].CopyFrom(
      tf.contrib.util.make_tensor_proto(data, shape=[1]))
  result = stub.Predict(request, 10.0)  # 10 secs timeout
  print(result)


if __name__ == "__main__":
  tf.app.run()

输出的结果可以看到图片的分类，概率和使用的模型信息：

$ python resnet_client_grpc.py
outputs {
  key: "classes"
  value {
    dtype: DT_INT64
    tensor_shape {
      dim {
        size: 1
      }
    }
    int64_val: 286
  }
}
outputs {
  key: "probabilities"
  value {
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 1001
      }
    }
    float_val: 2.4162832232832443e-06
    float_val: 1.9012182974620373e-06
    float_val: 2.7247710022493266e-05
    float_val: 4.426385658007348e-07
    ...(中间省略)
    float_val: 1.4636580090154894e-05
    float_val: 5.812107133351674e-07
    float_val: 6.599806511076167e-05
    float_val: 0.0012952701654285192
  }
}
model_spec {
  name: "resnet"
  version {
    value: 1538687457
  }
  signature_name: "serving_default"
}

TensorFlows serving有时会有输出如下的日志：

Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

TensorFlow Serving已发布Docker镜像旨在尽可能多地使用CPU架构，因此省略了一些优化以最大限度地提高兼容性。如果你没有看到此消息，则你的二进制文件可能已针对你的CPU进行了优化。根据你的模型执行的操作，这些优化可能会对你的服务性能产生重大影响。幸运的是，编译优化的TensorFlow Serving二进制非常简单。官方已经提供了自动化脚本，分以下两部进行：

# 1. 编译开发版本
$ docker build -t $USER/tensorflow-serving-devel -f Dockerfile.devel https://github.com/tensorflow/serving.git#:tensorflow_serving/tools/docker

# 2. 生产新的镜像
$ docker build -t $USER/tensorflow-serving --build-arg TF_SERVING_BUILD_IMAGE=$USER/tensorflow-serving-devel https://github.com/tensorflow/serving.git#:tensorflow_serving/tools/docker

之后，使用新编译的$USER/tensorflow-serving重新启动服务即可。

上面我们快速实践了使用TensorFlow Serving和Docker部署机器学习服务的过程，可以看到，TensorFlow Serving提供了非常方便和高效的模型管理，配合Docker，可以快速搭建起机器学习服务。

Serving ML Quickly with TensorFlow Serving and Docker

Train and serve a TensorFlow model with TensorFlow Serving

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