Ubuntu 20 Helm Chart

Prerequisites

Hardware

256 GB RAM
16 CPUs
1TB SSD disk
2x V100 32GB or later generation GPUs (A100 strongly preferred)

Additional Requirements

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.

Installation

Commands should be run as root or prefixed with sudo.

Ensure you have Docker 20.10 installed

docker version

Both Client and Server output should show version 20.10.17 or later. If not please follow the instructions for installing docker on Ubuntu here before proceeding.

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:

[keyfile]
unmanaged-devices=interface-name:cali*;interface-name:tunl*;interface-name:vxlan.calico;interface-name:wireguard.cali

Restart NetworkManager

systemctl restart NetworkManager

Install CUDA 11

apt-get install linux-headers-$(uname -r)
distribution=$(. /etc/os-release;echo $ID$VERSION_ID | sed -e 's/\.//g')
wget https://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64/cuda-$distribution.pin 
mv cuda-$distribution.pin/etc/apt/preferences.d/cuda-repository-pin-600
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
echo "deb http://developer.download.nvidia.com/compute/cuda/repos/$distribution/x86_64 /" | sudo tee /etc/apt/sources.list.d/cuda.list
apt-get update
apt install -y cuda-runtime-11-6

Install NVIDIA Docker toolkit

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \   
    && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \   
    && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
apt update
apt-get install -y nvidia-docker2

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

Install Kubernetes

curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key 
add -
echo "deb http://apt.kubernetes.io/ kubernetes-xenial main" >> /etc/apt/sources.list  && apt update
apt 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=10.17.0.0/16 -v 5
mkdir -p $HOME/.kube
sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config
sudo 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

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/layar/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 the system. Set MY_GPU_COUNT to the number of GPUs available to Layar.

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

Uninstall Layar

To uninstall the Layar helm chart, execute the following command:

helm uninstall layar