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Trying to understand how to leverage GPU ready applications on the Nvidia NGC web site (Nvidia GPU Cloud). Download docker containers and buidl your own on premise catalog. Can't wrap myself around the problem of how to integrate containers with the Openlava scheduler.
# 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