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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。
获得训练好的模型
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
部署模型
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”: “”
}
}
]
}
查看模型输入输出
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。
使用模型接口预测：REST 和 gRPC
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。
REST
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
gRPC
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”
}
性能
通过编译优化的 TensorFlow Serving 二进制来提高性能
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。
总结
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。
参考
Serving ML Quickly with TensorFlow Serving and Docker
Train and serve a TensorFlow model with TensorFlow Serving
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