To compile and use TensorFlow Serving, you need to set up some prerequisites.
TensorFlow Serving requires Bazel 0.4.2 or higher. You can find the Bazel installation instructions here.
If you have the prerequisites for Bazel, those instructions consist of the following steps:
Download the relevant binary from here. Let’s say you downloaded bazel-0.4.2-installer-linux-x86_64.sh. You would execute:
cd ~/Downloads chmod +x bazel-0.4.2-installer-linux-x86_64.sh ./bazel-0.4.2-installer-linux-x86_64.sh --user
Set up your environment. Put this in your ~/.bashrc.
To install TensorFlow Serving dependencies, execute the following:
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
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
git clone command.
Follow the Prerequisites section above to install all dependencies. To configure TensorFlow, run
cd tensorflow ./configure cd ..
Consult the TensorFlow install instructions if you encounter any issues with setting up TensorFlow or its dependencies.
TensorFlow Serving uses Bazel to build. Use Bazel commands to build individual targets or the entire source tree.
To build the entire tree, execute:
bazel build tensorflow_serving/...
Binaries are placed in the bazel-bin directory, and can be run using a command like:
To test your installation, execute:
bazel test tensorflow_serving/...
Continuous integration build
Our continuous integration build using TensorFlow ci_build infrastructure offers you simplified development using docker. All you need is git and docker. No need to install all other dependencies manually.
git clone --recursive https://github.com/tensorflow/serving cd serving CI_TENSORFLOW_SUBMODULE_PATH=tensorflow tensorflow/tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow_serving/...
serving directory is mapped into the container. You can develop
outside the docker container (in your favourite editor) and when you run this
build it will build with your changes.