1.安装tensorflow serving
1.1确保当前环境已经安装并可运行tensorflow
从github上下载源码
git clone --recurse-submodules https: //github.com/tensorflow/serving
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进入到serving目录下的tensorflow运行./configure,并安装步骤完成(需将 2问题解决的的步骤全操作完后执行安装步骤)
1.2.编译example代码
bazel build tensorflow_serving/example/... |
1.3.运行mnist例子导出model到/tmp/mnist_export目录下,目录下会根据export_version创建一个目录名为 /tmp/mnist_export/00000001
rm -rf /tmp/mnist_export/(第一次执行不存在,不必操作) bazel-bin/tensorflow_serving/example/mnist_export --training_iteration= 10000 --export_version= 1 /tmp/mnist_export
Training model... ( 'Extracting' , '/tmp/train-images-idx3-ubyte.gz' )
( 'Extracting' , '/tmp/train-labels-idx1-ubyte.gz' )
( 'Extracting' , '/tmp/t10k-images-idx3-ubyte.gz' )
( 'Extracting' , '/tmp/t10k-labels-idx1-ubyte.gz' )
training accuracy 0.9219
Done training! Exporting trained model to /tmp/mnist_export Done exporting! |
1.4 执行inference开启服务,端口9000,目录指向之前导出的目录
bazel-bin/tensorflow_serving/example/mnist_inference --port= 9000 /tmp/mnist_export/ 00000001
I tensorflow_serving/session_bundle/session_bundle.cc: 130 ] Attempting to load a SessionBundle from: /tmp/mnist_export/ 00000001
I tensorflow_serving/session_bundle/session_bundle.cc: 107 ] Running restore op for SessionBundle
I tensorflow_serving/session_bundle/session_bundle.cc: 178 ] Done loading SessionBundle
I tensorflow_serving/example/mnist_inference.cc: 163 ] Running...
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1.6 执行测试client
bazel-bin/tensorflow_serving/example/mnist_client --num_tests= 1000 --server=localhost: 9000
( 'Extracting' , '/tmp/train-images-idx3-ubyte.gz' )
( 'Extracting' , '/tmp/train-labels-idx1-ubyte.gz' )
( 'Extracting' , '/tmp/t10k-images-idx3-ubyte.gz' )
( 'Extracting' , '/tmp/t10k-labels-idx1-ubyte.gz' )
........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ Inference error rate: 9.2 %
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2.问题解决
no such package '@boringssl_git//' : Error cloning repository:https://boringssl.googlesource.com/boringssl: cannot open git-upload-pack and referenced by '//external:libssl' .
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由于GFW把google的很多地址给墙了,所以无法下载相关的内容,修改seving目录下的tensorflow/tensorflow/workspace.bzl 文件相关的repository
git_repository( name = "boringssl_git" ,
#commit = "436432d849b83ab90f18773e4ae1c7a8f148f48d" ,
commit = "db0729054d5964feab9e60089ba2d06a181e78b1" ,
init_submodules = True, ) |
https://github.com/tensorflow/serving/issues/6
运行mnist_client时报错
Traceback (most recent call last): File "/root/tensorflow-serving/bazel-bin/tensorflow_serving/example/mnist_client.runfiles/__main__/tensorflow_serving/example/mnist_client.py" , line 34 , in <module>
from grpc.beta import implementations
ImportError: No module named grpc.beta |
使用pip安装grpcio模块
pip install grpcio |
https://github.com/grpc/grpc/tree/master/src/python/grpcio
export过程报错缺少 manifest_pb2.py 的解决方法:
首先编译serving下的example目录得到
bazel-bin/tensorflow_serving/example/mnist_export.runfiles/org_tensorflow/tensorflow/contrib/session_bundle/manifest_pb2.py
随后copy到python主目录下的lib目录 例如:/usr/lib/python2.7/site-packages/
如果还报相同错误
修改 /usr/lib/python2.7/site-packages/tensorflow/contrib/session_bundle/目录下的exporter.py文件
删除 from tensorflow.contrib.session_bundle import manifest_pb2
增加 import manifest_pb2
解决思路:主要就是PYTHONPATH中缺少manifest_pb2.py,所以需要设置,设置路径可以找到manifest_pb2.py即可
manifest_pb2.py为tensorflow/contrib/session_bundle/manifest.proto 的protobuf生成文件,若有条件可手动生成
3.参考地址
tensorflow serving github : https://github.com/tensorflow/serving
https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/setup.md
bazel http://www.bazel.io/ (需FQ)
4.Serving Framework
4.1.Train
训练模型的过程
4.2.exporter
负责将训练好的模型导出
4.3.Sever
负责存储操作,例如将对象存储到磁盘
4.4.Server
提供grpc server,组织request调用Module,将结果response client
4.5.ModuleManager
负责加载训练好的模型
4.6.Scheduler
负责请求的调度,例如BatchScheduler(buffer 某一批数据才发给Service)
4.7.client
负责发送Request请求接收Response
5.如何编写Serving
5.1 export model
模型训练完成后,需要export model
1) 需要确定 signature : (classification_signature,regression_signature,generic_signature)
classification_signature: input , classes , scores
regression_signature: input , output
generic_signature: map<string,tensor_name>
signature规定了输入和输出的tensor_name, 这个tensor_name应该对应到graph里的tensor
例如 mnist 为classification模型 训练输入了 x 训练出 y 则在export的时候使用:
signature = exporter.classification_signature(input_tensor = x, scores_tensor = y)
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5.2 确定输出的路径
导出model需要一个可存储的路径,这个路径会在inference程序读取时使用
5.3 编写inference
inference主要流程:
1)获得 SessionBundle
std::unique_ptr<SessionBundleFactory> bundle_factory; TF_QCHECK_OK( SessionBundleFactory::Create(session_bundle_config, &bundle_factory)); std::unique_ptr<SessionBundle> bundle( new SessionBundle);
TF_QCHECK_OK(bundle_factory->CreateSessionBundle(bundle_path, &bundle)); |
2) 提供输入与输出的tensor
Tensor input(tensorflow::DT_FLOAT, {1, kImageDataSize}); std::copy_n(request->image_data().begin(), kImageDataSize,
input.flat< float >().data());
std::vector<Tensor> outputs; |
3) 通过signatrue传入input,output到session_bundle并执行
const tensorflow::Status status = bundle_->session->Run(
{{signature_.input().tensor_name(), input}},
{signature_.scores().tensor_name()}, {}, &outputs);
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