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Tensorflow Serving Via Docker

namizzz edited this page Jul 13, 2018 · 2 revisions

Tensorflow Serving Via Docker

This directory contains Dockerfiles to make it easy to get up and running with TensorFlow Serving via Docker.


Installing Docker

General installation instructions are on the Docker site, but we give some quick links here:

Which containers exist?

TensorFlow currently maintain the following Dockerfiles:

  • Dockerfile.devel, which is a minimal VM with all of the dependencies needed to build TensorFlow Serving.

Building a container

run;

username@machine_name:~$docker build --pull -t $USER/tensorflow-serving-devel -f tensorflow_serving/tools/docker/Dockerfile.devel .
username@machine_name:~$docker run --name=test_container -it $USER/tensorflow-serving-devel
root@476b31eb9213:
Name, shorthand Description
--tag , -t Name and optionally a tag in the ‘name:tag’ format
--file , -f Name of the Dockerfile (Default is ‘PATH/Dockerfile’)
--pull Always attempt to pull a newer version of the image

Clone, configure, and build TensorFlow Serving in a container

Note: All bazel build commands below use the standard -c opt flag. To further optimize the build, refer to the instructions here.

In the running container, we clone, configure and build TensorFlow Serving code.

git clone -b r1.6 --recurse-submodules https://github.com/tensorflow/serving
cd serving/
git clone --recursive https://github.com/tensorflow/tensorflow.git
cd tensorflow
./configure
cd ..

Next we can either install a TensorFlow ModelServer with apt-get using the instructions here, or build a ModelServer binary using:

bazel build -c opt tensorflow_serving/model_servers:tensorflow_model_server

The rest of this tutorial assumes you compiled the ModelServer locally, in which case the command to run it is bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server. If however you installed the ModelServer using apt-get, simply replace that command with tensorflow_model_server.

Export your model in container

root@476b31eb9213: **Ctrl+P Ctrl+Q**
username@machine_name: sudo docker cp ./your_model/ 476b31eb9213:/serving
username@machine_name: sudo docker attach 476b31eb9213
root@476b31eb9213:

/serving: folder you want to put your model and also rename it in the docker

File Structure of your_model:

├── your_model
  ├── 1                   => It's version of your model, it's also a **must**.
    ├── saved_model.pb
    └── variables
      ├── variables.data-00000-of-00001
      └── variables.index

Run your model using serving format in container

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_name=tensorflow-serving --model_base_path=/tensorflow_serving/ &>test_log &
[1] 26798

**model_base_path **is the absolute path of your exported model in the docker.

Then, check the running log file and you will see

cat test_log 

Creat the Client

  1. pip install grpcio grpcio-tools
  2. go out of the docker and use the serving TF (client code)

Reference

  1. https://www.tensorflow.org/serving/serving_inception

  2. https://www.youtube.com/watch?v=SsaJJsRvwIs&lc=z23evh2p1xiacv4fvacdp434cpgd0yx25vdlosz0g4xw03c010c

    code file: https://drive.google.com/drive/folders/16rP7q4yzoemAJx3aA82icjqiZTGsZtar

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