TOOLS

Build and run nGraph-TF bridge in Docker

To run nGraph in Docker, choose one of two ways to create your container:

Use docker-build.sh to build two docker images with tags: - devel-<version> that serves as a development image which includes the prerequisites and the source - <release> that includes an installation of the bridge and itsruntime dependencies
Use Dockerfile.ubuntu.18.04 by itself to set up a build environment that you can use to then manually build Tensorflow, nGraph, and the bridge in a virtualenv.

Clone the ngraph-bridge repo:

  git clone https://github.com/tensorflow/ngraph-bridge.git

Navigate into the tools directory and run the installation script:
```
  cd ngraph-bridge/tools
  ./docker-build.sh
```
If you want to use build options such as --use_prebuilt_tensorflow or --use_grappler_optimizer, set an input argument when running the installation script.
```
  ./docker-build.sh '--use_prebuilt_tensorflow --disable_cpp_api'
```
For more information about build options, see here. There may be some build options not supported with this method, so if your customized build is failing, Method 2 is recommended.
When the multi-stage docker build is complete, you will be able to run a container with Tensorflow and nGraph using the ngraph-bridge image. For instance:
```
  docker run -it ngraph-bridge:0.23
```
Note: If running behind a proxy, you will need to set -e http_proxy=<http_proxy> and -e https_proxy=<https_proxy> variables in order to run the test script.
After running the container, you can perform an inference test by running:
```
  python examples/infer_image.py
```

Clone the ngraph-bridge repo:

  git clone https://github.com/tensorflow/ngraph-bridge.git

Navigate into the tools directory and build the devel image:

  cd ngraph-bridge/tools
  docker build -t ngraph-bridge:devel -f=Dockerfile.ubuntu18.04.devel .

Build the install image:

  docker build --build-arg base_image=ngraph-bridge:devel -t ngraph-bridge:master -f=Dockerfile.ubuntu18.04.install .

After the build completes, you will be able to run applications using the container:
```
  python examples/infer_image.py
```