Ubuntu 16.04 TensorFlow Servering

Prerequisites

Bazel

Using Bazel custom APT repository (recommended)

• Install JDK 8

$ sudo apt-get install openjdk-8-jdk

• Add Bazel distribution URI as a package source (one time setup)

$ echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
$ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

If you want to install the testing version of Bazel,replace stable with testing.

• Install and update Bazel

$ sudo apt-get update && sudo apt-get install bazel

Once installed,you can upgrade to a newer version of Bazel with:

$ sudo apt-get upgrade bazel

gRPC Python

$ sudo pip install grpcio

Packages dependencies

$ sudo apt-get update && sudo apt-get install -y   build-essential   curl   libcurl3-dev   git   libfreetype6-dev   libpng12-dev   libzmq3-dev   pkg-config   python-dev   python-numpy   python-pip   software-properties-common   swig   zip   zlib1g-dev

TensorFlow Serving Python API PIP package

$ pip install tensorflow-serving-api

Installing from source

Clone the TensorFlow Serving repository

$ git clone --recurse-submodules https://github.com/tensorflow/serving
$ cd serving

–recurse-submodules is required to fetch TensorFlow,gRPC,and other libraries that TensorFlow Serving depends on.
Note that these instructions will install the latest master branch of TensorFlow Serving. If you want to install a specific branch (such as a release branch),pass -b <branchname> to the git clone command.

Install prerequisites

Follow the Prerequisites section above to install all dependencies. To configure TensorFlow,run

$ cd tensorflow
$ ./configure
$ cd ..

Build

$ bazel build -c opt tensorflow_serving/...

Binaries are placed in the bazel-bin directory,and can be run using a command like:

$ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server

To test your installation,execute:

$ sudo pip install autograd # handle ImportError: No module named autograd
$ bazel test -c opt tensorflow_serving/...

Serving a TensorFlow Model

Train And Export TensorFlow Model

• step 0

$ rm -rf /tmp/mnist_model

• step 1

$ bazel build -c opt //tensorflow_serving/example:mnist_saved_model
$ bazel-bin/tensorflow_serving/example/mnist_saved_model /tmp/mnist_model
Training model...

...

Done training!
Exporting trained model to /tmp/mnist_model
Done exporting!

OR

$ python tensorflow_serving/example/mnist_saved_model.py /tmp/mnist_model

list the model files

$ ls /tmp/mnist_model
1
$ ls /tmp/mnist_model/1
saved_model.pb variables

Each version sub-directory contains the following files:

• saved_model.pb is the serialized tensorflow::SavedModel. It includes one or more
• graph definitions of the model,as well as metadata of the model such as signatures.
  variables are files that hold the serialized variables of the graphs.

Load Exported Model With Standard TensorFlow ModelServer

$ bazel build -c opt //tensorflow_serving/model_servers:tensorflow_model_server # do not use
$ bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=mnist --model_base_path=/tmp/mnist_model/

Test The Server

$ bazel build -c opt //tensorflow_serving/example:mnist_client
$ bazel-bin/tensorflow_serving/example/mnist_client --num_tests=1000 --server=localhost:9000
...
Inference error rate: 10.4%

OR

$ python tensorflow_serving/example/mnist_client.py --num_tests=1000 --server=localhost:9000

Reference

• tensorflow servering installation
• Bazel-install-ubuntu
• grpc
• gRPC Python
• serving_basic
• mnist

（编辑：安卓应用网）

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