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• https://blog.exxactcorp.com/installing-using-docker-nv-docker-centos-7/

# Assumes CentOS 7
# Assumes NVIDIA Driver is installed as per requirements ( < 340.29 )
# Install DOCKER
sudo curl -fsSL https://get.docker.com/ | sh
# Start DOCKER
sudo systemctl start docker
# Add dockeruser, usermod change
sudo adduser dockeruser
usermod -aG docker dockeruser
# Install NV-DOCKER
# GET NVIDIA-DOCKER
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker-1.0.1-1.x86_64.rpm
# INSTALL
sudo rpm -i /tmp/nvidia-docker*.rpm
# Start NV-DOCKER Service
systemctl start nvidia-docker

systemctl status docker
systemctl status nvidia-docker

# fetch image and run command in container 
# then remove container, image remains

nvidia-docker run --rm nvidia/cuda nvidia-smi

# or 
docker pull nvidia/cuda

Pull down other containers, for example from Nvidia Catalog Register (nvcr.io)

NGC Deep Learning Ready Docker Containers:
NVIDIA DIGITS - nvcr.io/nvidia/digits
TensorFlow - nvcr.io/nvidia/tensorflow
Caffe - nvcr.io/nvidia/caffe
NVIDIA CUDA - nvcr.io/nvidia/cuda (9.2, 10.1, 10.0)
PyTorch - nvcr.io/nvidia/pytorch
RapidsAI - nvcr.io/nvidia/rapidsai/rapidsai

Additional Docker Images:
Portainer Docker Management - portrainer/portainer

# in the catalog you can also find 
docker pull nvcr.io/hpc/gromacs:2018.2
docker pull nvcr.io/hpc/lammps:24Oct2018
docker pull nvcr.io/hpc/namd:2.13-multinode
docker pull nvcr.io/partners/matlab:r2019b
# not all at the latest versions
# and amber would have to be custom build on top of nvidia/cuda

Make GPUs available to container and set some settings

# DIGITS example
# if you passed GPU ID 2,3 for example, the container would still see the GPUs as ID 0,1
NV_GPU=0,1 nvidia-docker run --name digits -d -p 5000:5000 nvidia/digits

# list containers running
nvidia-docker ps

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