Can you run this:

  
      github.com
  
  
    QuantScientist/Deep-Learning-Boot-Camp/blob/master/docker/deps_nvidia_docker.sh 9
#!/usr/bin/env bash

apt-get install nvidia-modprobe

# curl -O -s https://raw.githubusercontent.com/minimaxir/keras-cntk-docker/master/deps_nvidia_docker.sh
if lspci | grep -i 'nvidia'
then
  echo "\nNVIDIA GPU is likely present."
else
  echo "\nNo NVIDIA GPU was detected. Exiting ...\n"
  exit 1
fi

echo "\nChecking for NVIDIA drivers ..."      
# Check for CUDA and try to install.
if ! dpkg-query -W cuda; then
  # The 16.04 installer works with 16.10.
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
  apt-get update


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An then this:

  
      github.com
  
  
    QuantScientist/Deep-Learning-Boot-Camp/blob/master/day02-PyTORCH-and-PyCUDA/PyTorch/build_torch.sh 15
# PyTorch GPU and CPU
# If you dont have CUDA installed, run this first:
# https://github.com/QuantScientist/Deep-Learning-Boot-Camp/blob/master/docker/deps_nvidia_docker.sh

#GPU version
export PATH=/usr/local/cuda-8.0/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
export PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
export CUDA_BIN_PATH=/usr/local/cuda
export CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-8.0


# Build PyTorch from source
git clone https://github.com/pytorch/pytorch.git
cd pytorch
git submodule update --init
#git checkout 4eb448a051a1421de1dda9bd2ddfb34396eb7287 

export TORCH_CUDA_ARCH_LIST="3.5 5.2 6.0 6.1+PTX" 


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?