I have created a new Tensorflow op, I have compiled and tested it locally and it works.

I now want to use it with Google Cloud ML engine which requires compiling the op in each of the machines in the cloud.

I have managed to include the C++ files and the compiler script in the distribution using `setup.py`:

```
from setuptools import setup, find_package

setup(
    name='trainer',
    version='0.1',
    packages=find_packages(),
    package_data={'CPP': ['*.cc', '*.h', 'compile.sh']},
    description = 'Package discription'
)

```

Now I have to run `compile.sh` which contains:

```
TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())')

g++ -std=c++11 -D_GLIBCXX_USE_CXX11_ABI=0 -shared target.cc dependency_1.cc -o taget.so -fPIC -I$TF_INC -I$TF_INC/external/nsync/public -O2

```

The script can be run from python code using `subprocess.Popen()` so that is not the issue.

The issue is that I don't know how the directory tree is structured within google cloud and so I don't know where to run this script, or how to access its output later on to make use of the new op.