Python/C++ code interoperability

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Theta GPU Nodes

These are the steps to build code that has Python/C++ code interoperability. 1. Login to a ThetaGPU head node

ssh thetagpusn1

1. Request an interactive session on an A100 GPU

qsub -n 1 -q default -A datascience -I -t 1:00:00

Following this, we need to execute a few commands to get setup with an appropriately optimized tensorflow. These are: 3. Activate the TensorFlow 2.2 singularity container:

singularity exec -B /lus:/lus --nv /lus/theta-fs0/projects/datascience/thetaGPU/containers/tf2_20.08-py3.sif bash

2. Setup access to the internet

export HTTP_PROXY= 

Now that we can access the internet, we need to set up a virtual environment in Python (these commands should only be run the first time)

python -m pip install --user virtualenv 
export VENV_LOCATION=/home/rmaulik/THETAGPU_TF_ENV # Add your path here 
python -m virtualenv --system-site-packages $VENV_LOCATION 
source $VENV_LOCATION/bin/activate 
python -m pip install cmake 
python -m pip install matplotlib 
python -m pip install sklearn

cmake is required to build our C++ app and link to Python, and other packages may be pip installed as needed in your Python code. An example CMakeLists.txt file for building with Python/C interoperability with examples can be found here.