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RHEL 8 Helm Chart

In this guide we will go from a basic RHEL 8 operating system to a fully functioning Layar installation. This guide is intended as an example installation only and makes configuration decisions that may not be best suited for your environment.



  • 256 GB RAM
  • 16+ CPUs
  • 1TB SSD
  • 4x NVIDIA A100 generation GPUs (or later)


  • The system must have an external DNS entry (not relying on /etc/hosts) with the corresponding IP assigned to the host.
  • Internet access from the installation system is a requirement.
  • Swap must be disabled.
  • SELinux must be disabled or set to Permissive.


Commands should be run as root or prefixed with sudo.

Install docker

yum install -y yum-utils
yum-config-manager \
    --add-repo \
yum -y install docker-ce
systemctl --now enable docker

Increase operating system vm.max_map_count

echo "vm.max_map_count=262144" > /etc/sysctl.d/99-vyasa.conf 
sysctl -p /etc/sysctl.d/99-vyasa.conf

Configure NetworkManager to ignore Calico interfaces

If using NetworkManager, edit the file /etc/NetworkManager/conf.d/calico.conf and set the following:


Restart NetworkManager

systemctl restart NetworkManager

Install kernel development packages and compiler

dnf -y install kernel-devel-$(uname -r) kernel-headers-$(uname -r) gcc

Install and enable the EPEL and CUDA repository

dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
dnf install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
dnf-config-manager --enable epel

Install CUDA 12

dnf module install nvidia-driver:latest-dkms
dnf install -y cuda-12

Install NVIDIA Docker toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
   && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | tee /etc/yum.repos.d/nvidia-docker.repo
yum install -y nvidia-docker2 nvidia-container-runtime

Set docker default runtime

Edit the file /etc/docker/daemon.json and replace its contents with:

"default-runtime": "nvidia",
"runtimes": {
   "nvidia": {
      "path": "/usr/bin/nvidia-container-runtime",    
      "runtimeArgs": []

Restart docker

systemctl restart docker

Add kubernetes repo

cat <<EOF | sudo tee /etc/yum.repos.d/kubernetes.repo
gpgkey=https://packages.cloud.google.com/yum/doc/yum-key.gpg https://packages.cloud.google.com/yum/doc/rpm-package-key.gpg

Install Kubernetes and initialize

dnf install -y kubeadm-1.23.15-00 kubelet-1.23.15-00 kubectl-1.23.15-00
/usr/bin/kubeadm init --kubernetes-version=1.23.15 --token-ttl 0 --pod-network-cidr= -v 5
mkdir -p $HOME/.kube
cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
chown $(id -u):$(id -g) $HOME/.kube/config

Install NGINX Ingress controller

kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/nginx-ingress.yaml

Install Calico CNI

kubectl create -f https://vyasa-static-assets.s3.amazonaws.com/layar/tigera-operator.yaml
kubectl create -f https://vyasa-static-assets.s3.amazonaws.com/layar/custom-resources.yaml

Let head node run pods

/usr/bin/kubectl taint nodes --all node-role.kubernetes.io/master:NoSchedule-

Install local volume provisioner

/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/local-storage-provisioner.yml

Set default storage class

kubectl patch storageclass 'local-path' -p '{"metadata":{"annotations":{"storageclass.kubernetes.io/is-default-class":"true"}}}'

Install Helm

wget -P /usr/local/bin/ https://vyasa-static-assets.s3.amazonaws.com/helm/helm
chmod +x /usr/local/bin/helm

Add the Layar helm repository

helm repo add vyasa https://helm.vyasa.com/charts/ --username vyasahelm --password "sail#away()"
helm repo update

Install Layar

Replace MY_URL with the IP address or DNS name of your system and setting TRITON_GPU_COUNT to n-1 of available GPUs.

helm install layar vyasa/layar --set APPURL=MY_URL --set TRITON_GPU_COUNT=MY_GPU_COUNT