使用 TensorFlow Serving 和 Docker 快速部署機器學習服務

時間  2019-11-07
標籤 使用 tensorflow serving docker 快速 部署 機器 學習 服務 欄目 Docker 简体版
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從實驗到生產，簡單快速部署機器學習模型一直是一個挑戰。這個過程要作的就是將訓練好的模型對外提供預測服務。在生產中，這個過程須要可重現，隔離和安全。這裏，咱們使用基於Docker的TensorFlow Serving來簡單地完成這個過程。TensorFlow 從1.8版本開始支持Docker部署，包括CPU和GPU，很是方便。node

得到訓練好的模型

獲取模型的第一步固然是訓練一個模型，可是這不是本篇的重點，因此咱們使用一個已經訓練好的模型，好比ResNet。TensorFlow Serving 使用SavedModel這種格式來保存其模型，SavedModel是一種獨立於語言的，可恢復，密集的序列化格式，支持使用更高級別的系統和工具來生成，使用和轉換TensorFlow模型。這裏咱們直接下載一個預訓練好的模型：python

$ 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

若是是使用其餘框架好比Keras生成的模型，則須要將模型轉換爲SavedModel格式，好比：git

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

model = Sequential()
# 中間省略模型構建

# 模型轉換爲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()

下載完成後，文件目錄樹爲：github

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

部署模型

使用Docker部署模型服務：docker

$ 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端口對於TensorFlow Serving提供的gRPC端口，8501爲REST API服務端口。-e MODEL_NAME=resnet指出TensorFlow Serving須要加載的模型名稱，這裏爲resnet。上述命令輸出爲shell

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 ...

咱們能夠看到，TensorFlow Serving使用1538687457做爲模型的版本號。咱們使用curl命令來查看一下啓動的服務狀態，也能夠看到提供服務的模型版本以及模型狀態。json

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

查看模型輸入輸出

不少時候咱們須要查看模型的輸出和輸出參數的具體形式，TensorFlow提供了一個saved_model_cli命令來查看模型的輸入和輸出參數：api

$ 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的名稱，類型和輸出，這些參數在接下來的模型預測請求中須要。安全

使用模型接口預測：REST和gRPC

TensorFlow Serving提供REST API和gRPC兩種請求方式，接下來將具體這兩種方式。session

REST

咱們下載一個客戶端腳本，這個腳本會下載一張貓的圖片，同時使用這張圖片來計算服務請求時間。

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

如下腳本使用requests庫來請求接口，使用圖片的base64編碼字符串做爲請求內容，返回圖片分類，並計算了平均處理時間。

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()

輸出結果爲

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

gRPC

讓咱們下載另外一個客戶端腳本，這個腳本使用gRPC做爲服務，傳入圖片並獲取輸出結果。這個腳本須要安裝tensorflow-serving-api這個庫。

$ 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

腳本內容：

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()

輸出的結果能夠看到圖片的分類，機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
    ...(中間省略)
    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"
}

性能

經過編譯優化的TensorFlow Serving二進制來提升性能

TensorFlows serving有時會有輸出以下的日誌：

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

TensorFlow Serving已發佈Docker鏡像旨在儘量多地使用CPU架構，所以省略了一些優化以最大限度地提升兼容性。若是你沒有看到此消息，則你的二進制文件可能已針對你的CPU進行了優化。根據你的模型執行的操做，這些優化可能會對你的服務性能產生重大影響。幸運的是，編譯優化的TensorFlow Serving二進制很是簡單。官方已經提供了自動化腳本，分如下兩部進行：

# 1. 編譯開發版本
$ docker build -t $USER/tensorflow-serving-devel -f Dockerfile.devel https://github.com/tensorflow/serving.git#:tensorflow_serving/tools/docker

# 2. 生產新的鏡像
$ 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

以後，使用新編譯的$USER/tensorflow-serving從新啓動服務便可。

總結

上面咱們快速實踐了使用TensorFlow Serving和Docker部署機器學習服務的過程，能夠看到，TensorFlow Serving提供了很是方便和高效的模型管理，配合Docker，能夠快速搭建起機器學習服務。

參考

GitHub repo: qiwihui/blog

Follow me: @qiwihui

Site: QIWIHUI

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