AWS

Amazon Web Services (AWS)

Setup Instance

From console:

• CentOS 7 Official AMI with HVM
• Use public-ssh security group
• Use existing key
• Encrypt all disks
• 10g root partion

Once booted, get public and private IPs:

• Add IPs to named
• ssh centos@<public-ip>
• sudo su -
• cat > /root/.ssh/authorized_keys -- add key
• Do not logout yet, verify first
• ssh root@<public-ip>
• userdel -r centos
• Logout of original session
• yum install -y lvm2
• fdisk -l -- determine local volume
• pvcreate <disk>
• vgcreate centos <disk>

Proceed with post installation instructions.

GPU Driver install

# need kernel source which is always the latest so do update first
yum update -y
yum install -y kernel-devel
# if new kernel, then
reboot
yum remove $(rpm -qa | grep ^kernel-3 | grep -v $(uname -r))
curl -s -L https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo \
   | install -m 444 /dev/stdin /etc/yum.repos.d/cuda-rhel7.repo
yum install cuda-toolkit-10-1 nvidia-driver-cuda nvidia-kmod
curl -s -L https://nvidia.github.io/nvidia-container-runtime/centos7/nvidia-container-runtime.repo \
    | install -m 444 /dev/stdin /etc/yum.repos.d/nvidia-container-runtime.repo
yum install -y nvidia-container-runtime
systemctl restart docker

Docker Container Build

See https://github.com/NVIDIA/nvidia-docker/wiki

docker run -it --name=gpu -u root radiasoft/beamsim-jupyter:prod bash <<'EOF'
rpm -i https://developer.download.nvidia.com/compute/cuda/repos/fedora29/x86_64/cuda-repo-fedora29-10.1.243-1.x86_64.rpm
dnf install -y kmodtool kernel-devel
dnf install -y cuda-drivers
EOF
docker commit --change 'USER vagrant' --change 'CMD ["/home/vagrant/.radia-run/tini", "--", "/home/vagrant/.radia-run/start"]' gpu gpu
docker rm gpu

Verify ssh key fingerprint

You have to convert the ssh private key to PEM format then DER format, and finally compute md5:

ssh-keygen -e -m PEM -f ~/.ssh/id_rsa | openssl rsa -RSAPublicKey_in -outform DER | openssl md5 -c