Dockerfile combining SubT virtual testbed images with CUDA support

subt_cuda.Dockerfile

FROM osrf/subt-virtual-testbed:cloudsim_bridge_latest

RUN curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | sudo apt-key add - &&     echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list &&     echo "deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64 /" | sudo tee /etc/apt/sources.list.d/nvidia-ml.list && sudo apt-get -yy update

ARG cuda_maj=10
ARG cuda_min=0
ARG cuda=$cuda_maj-$cuda_min
ARG cudnn=7

RUN sudo apt-get install -y --no-install-recommends cuda-cudart-$cuda cuda-compat-$cuda cuda-libraries-$cuda cuda-npp-$cuda cuda-nvtx-$cuda cuda-cublas-$cuda libnccl2="*+cuda$cuda_maj.$cuda_min" libcudnn$cudnn="$cudnn.*+cuda$cuda_maj.$cuda_min" && sudo apt-get clean && sudo apt-mark hold libnccl2 && sudo apt-mark hold libcudnn$cudnn && sudo ln -s cuda-$cuda /usr/local/cuda && echo "/usr/local/nvidia/lib" | sudo tee /etc/ld.so.conf.d/nvidia.conf && echo "/usr/local/nvidia/lib64" | sudo tee -a /etc/ld.so.conf.d/nvidia.conf

ENV NVIDIA_VISIBLE_DEVICES=all
# or just compute,utility if you don't need OpenGL, NVENC and other goodies
ENV NVIDIA_DRIVER_CAPABILITIES=all
ENV NVIDIA_REQUIRE_CUDA="cuda>=$cuda_maj.$cuda_min"

# Install Tensorflow 1.15 for Python 2
#RUN sudo apt-get -yy update && sudo apt-get -yy install python-pip python-numpy && sudo -H pip install --no-cache-dir --upgrade pip && sudo -H pip install --no-cache-dir 'tensorflow-gpu<2' && yes y | sudo -H pip uninstall pip && sudo apt-get -yy install --reinstall python-pip && sudo apt-get clean

# Install Pytorch for Python 2
#RUN sudo apt-get -yy update && sudo apt-get -yy install python-pip python-numpy python-future && sudo -H pip install --no-cache-dir torch && sudo apt-get clean

ENTRYPOINT []

CMD ["/bin/bash"]