Title: Prerequisites — UCS Tools Documentation URL Source: https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html Published Time: Thu, 30 Oct 2025 07:23:03 GMT Markdown Content: Deployment System[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#deployment-system "Link to this heading") ----------------------------------------------------------------------------------------------------------------------------- These instructions require Ubuntu Server LTS 22.04 on your system. MicroK8s for Developer System[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#microk8s-for-developer-system "Link to this heading") ----------------------------------------------------------------------------------------------------------------------------------------------------- This document was tested with the following environment: * Ubuntu: 22.04 ### Install MicroK8s[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-microk8s "Link to this heading") Install **MicroK8s** and wait for Kubernetes to start: $ sudo snap install microk8s --classic $ microk8s status --wait-ready $ microk8s start ### Enable Addons[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#enable-addons "Link to this heading") Install the following **MicroK8s** addons: $ microk8s enable dashboard dns gpu helm3 ingress registry storage NVIDIA Cloud Native Stack for Developer System[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#nvidia-cloud-native-stack-for-developer-system "Link to this heading") --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For more information about the cloud native stack, see: [NVIDIA/cloud-native-stack](https://github.com/NVIDIA/cloud-native-stack). ### Install the Ubuntu Operating System[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-the-ubuntu-operating-system "Link to this heading") Download Ubuntu Server from [http://cdimage.ubuntu.com/releases/22.04/release/](http://cdimage.ubuntu.com/releases/22.04/release/). For Ubuntu server installation details, see the Ubuntu Server Installation Guide. Install CUDA Drivers[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cuda-drivers "Link to this heading") ----------------------------------------------------------------------------------------------------------------------------------- Find CUDA installation instructions at: [https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local](https://developer.nvidia.com/cuda-downloads.md?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local). After installing the NVIDIA drivers, reboot your system. Then run this command to verify the drivers loaded correctly: nvidia-smi Expected Output: +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | |-----------------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+======================+======================| | 0 NVIDIA GeForce RTX 4090 On | 00000000:65:00.0 Off | Off | | 0% 30C P8 5W / 450W | 133MiB / 24564MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | 0 N/A N/A 1119 G /usr/lib/xorg/Xorg 107MiB | | 0 N/A N/A 1239 G /usr/bin/gnome-shell 13MiB | +---------------------------------------------------------------------------------------+ ### Install Docker CE[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-docker-ce "Link to this heading") 1. Update the apt package index: $ sudo apt-get update 1. Install packages needed for HTTPS repository access: $ sudo apt-get install -y \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common 1. Add Docker’s official GPG key: $ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - 1. Verify the key fingerprint by searching for the last 8 characters: $ sudo apt-key fingerprint 0EBFCD88 pub rsa4096 2017-02-22 [SCEA] 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88 uid [ unknown] Docker Release (CE deb) sub rsa4096 2017-02-22 [S] 1. Set up the stable repository: $ sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" 1. Update the apt package index: $ sudo apt-get update 1. Install Docker Engine: $ sudo apt-get install -y docker-ce docker-ce-cli containerd.io 1. Verify Docker Engine installation by running the hello-world image: $ sudo docker run hello-world For more information on Docker installation, see [https://docs.docker.com/install/linux/docker-ce/ubuntu/](https://docs.docker.com/install/linux/docker-ce/ubuntu/). ### Install NVIDIA Container Toolkit[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-nvidia-container-toolkit "Link to this heading") 1. Setup the package repository: distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \ && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \ sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list 1. Update the package index: sudo apt update 1. Install NVIDIA Container Toolkit: sudo apt-get install -y nvidia-docker2 1. Update the Docker Default Runtime. 2. Edit the docker daemon configuration to add the following line and save the file: "default-runtime" : "nvidia" Example .. code-block:: text $ sudo nano /etc/docker/daemon.json { "runtimes": { "nvidia": { "path": "nvidia-container-runtime", "runtimeArgs": [] } }, "default-runtime" : "nvidia" } 1. Now execute the below commands to restart the docker daemon: sudo systemctl daemon-reload && sudo systemctl restart docker 1. Validate docker default runtime. 2. Execute the below command to validate docker default runtime as NVIDIA: $ sudo docker info | grep -i runtime Output: .. code-block:: text Runtimes: nvidia runc Default Runtime: nvidia ### Install Containerd[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-containerd "Link to this heading") 1. Set up the repository and update the apt package index: sudo apt-get update 1. Install packages to allow apt to use a repository over HTTPS: sudo apt-get install -y apt-transport-https gnupg-agent libseccomp2 autotools-dev debhelper software-properties-common 1. Configure the prerequisites for Containerd: cat < sudo mkdir -p /etc/containerd > wget https://raw.githubusercontent.com/NVIDIA/cloud-native-stack/master/playbooks/config.toml > sudo mv config.toml /etc/containerd/ && sudo sed -i 's/SystemdCgroup \= false/SystemdCgroup \= true/g' /etc/containerd/config.toml > > > > sudo systemctl enable containerd && sudo systemctl restart containerd For additional information on installing Containerd, please reference Install Containerd with Release Tarball. ### Install Kubernetes[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-kubernetes "Link to this heading") 1. Make sure Containerd has been started and enabled before beginning installation: sudo systemctl start containerd && sudo systemctl enable containerd 1. Execute the following to add apt keys: sudo apt-get update && sudo apt-get install -y apt-transport-https curl curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - sudo mkdir -p /etc/apt/sources.list.d/ 1. Create kubernetes.list: cat <:6443 --token 489oi5.sm34l9uh7dk4z6cm \ --discovery-token-ca-cert-hash sha256:17165b6c4a4b95d73a3a2a83749a957a10161ae34d2dfd02cd730597579b4b34 1. Following the instructions in the output, execute the commands as shown below: > mkdir -p $HOME/.kube > sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config > sudo chown $(id -u):$(id -g) $HOME/.kube/config 1. With the following command, you install a pod-network add-on to the control plane node. We are using calico as the pod-network add-on here: > kubectl apply -f https://docs.projectcalico.org/v3.21/manifests/calico.yaml 1. Update the Calico Daemonset: > kubectl set env daemonset/calico-node -n kube-system IP_AUTODETECTION_METHOD=interface=ens\*,eth\*,enc\*,enp\* 1. Execute the below commands to ensure that all pods are up and running: > kubectl get pods --all-namespaces Output: > NAMESPACE NAME READY STATUS RESTARTS AGE > kube-system calico-kube-controllers-65b8787765-bjc8h 1/1 Running 0 2m8s > kube-system calico-node-c2tmk 1/1 Running 0 2m8s > kube-system coredns-5c98db65d4-d4kgh 1/1 Running 0 9m8s > kube-system coredns-5c98db65d4-h6x8m 1/1 Running 0 9m8s > kube-system etcd-#yourhost 1/1 Running 0 8m25s > kube-system kube-apiserver-#yourhost 1/1 Running 0 8m7s > kube-system kube-controller-manager-#yourhost 1/1 Running 0 8m3s > kube-system kube-proxy-6sh42 1/1 Running 0 9m7s > kube-system kube-scheduler-#yourhost 1/1 Running 0 8m26s 1. The get nodes command shows that the control-plane node is up and ready: > kubectl get nodes > > > Output: > > > > NAME STATUS ROLES AGE VERSION > #yourhost Ready control-plane,master 10m v1.27.0 1. Since we are using a single-node Kubernetes cluster, the cluster will not schedule pods on the control plane node by default. To schedule pods on the control plane node, remove the taint by executing the following command: > kubectl taint nodes --all node-role.kubernetes.io/master- Refer to [kubeadm installation guide](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/) for more information. ### Install the latest version of Helm[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-the-latest-version-of-helm "Link to this heading") Execute the following command to download and [install the latest version of Helm](https://helm.sh/docs/intro/install/#from-script): curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 chmod 700 get_helm.sh ./get_helm.sh ### Add additional node[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#add-additional-node "Link to this heading") Steps below show how to add an additional node to NVIDIA Cloud Native Stack Note If you’re not adding additional nodes, please skip this step and proceed to the next step Installing NVIDIA Network Operator 1. Make sure to install the Containerd and Kubernetes packages on additional nodes. > Prerequisites: 1. Once the prerequisites are completed on the additional nodes, execute the below command on the control-plane node. Then execute the join command output on an additional node to add the additional node to NVIDIA Cloud Native Stack: > sudo kubeadm token create --print-join-command > > > Output: > > > example: > > > > sudo kubeadm join 10.110.0.34:6443 --token kg2h7r.e45g9uyrbm1c0w3k --discovery-token-ca-cert-hash sha256:77fd6571644373ea69074dd4af7b077bbf5bd15a3ed720daee98f4b04a8f524e > > > > Note > > > control-plane node and worker node should not have the same node name. 1. The get nodes command shows that the master and worker nodes are up and ready: > kubectl get nodes > > > Output: > > > > NAME STATUS ROLES AGE VERSION > #yourhost Ready control-plane,master 10m v1.27.0 > #yourhost-worker Ready 10m v1.27.0 ### Install GPU Operator[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-gpu-operator "Link to this heading") 1. Add the NVIDIA repo: > helm repo add nvidia https://helm.ngc.nvidia.com/nvidia 1. Update the Helm repo: > helm repo update 1. Install GPU Operator: > Note > > > As we are preinstalled with NVIDIA Driver and NVIDIA Container Toolkit, we need to set as false when installing the GPU Operator. > > > > helm install --version 23.3.2 --create-namespace --namespace nvidia-gpu-operator --devel nvidia/gpu-operator --set driver.enabled=false,toolkit.enabled=false --wait --generate-name 1. Validate the State of the GPU Operator: > Please note that the installation of the GPU Operator can take a couple of minutes. Installation time depends on your internet speed. > > > > kubectl get pods --all-namespaces | grep -v kube-system > NAMESPACE NAME READY STATUS RESTARTS AGE > default gpu-operator-1622656274-node-feature-discovery-master-5cddq96gq 1/1 Running 0 2m39s > default gpu-operator-1622656274-node-feature-discovery-worker-wr88v 1/1 Running 0 2m39s > default gpu-operator-7db468cfdf-mdrdp 1/1 Running 0 2m39s > gpu-operator-resources gpu-feature-discovery-g425f 1/1 Running 0 2m20s > gpu-operator-resources nvidia-cuda-validator-s6x2p 0/1 Completed 0 48s > gpu-operator-resources nvidia-dcgm-exporter-wtxnx 1/1 Running 0 2m20s > gpu-operator-resources nvidia-dcgm-jbz94 1/1 Running 0 2m20s > gpu-operator-resources nvidia-device-plugin-daemonset-hzzdt 1/1 Running 0 2m20s > gpu-operator-resources nvidia-device-plugin-validator-9nkxq 0/1 Completed 0 17s > gpu-operator-resources nvidia-operator-validator-cw4j5 1/1 Running 0 2m20s > > > Refer to the [GPU Operator page](https://ngc.nvidia.com/catalog/helm-charts/nvidia:gpu-operator.md) on NGC for more information. 1. For multiple worker nodes, execute the below command to fix the CoreDNS and Node Feature Discovery. > kubectl delete pods $(kubectl get pods -n kube-system | grep core | awk '{print $1}') -n kube-system; kubectl delete pod $(kubectl get pods -o wide -n gpu-operator-resources | grep node-feature-discovery | grep -v master | awk '{print $1}') -n gpu-operator-resources > > > _GPU Operator with MIG_ > > > > Note > > > Only A100 and A30 GPUs are supported for GPU Operator with MIG > > > Multi-Instance GPU (MIG) allows GPUs based on the NVIDIA Ampere architecture (such as NVIDIA A100) to be securely partitioned into separate GPU instances for CUDA applications. For more information about enabling the MIG capability, see GPU Operator with MIG. ### Validating the GPU Operator[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#validating-the-gpu-operator "Link to this heading") GPU Operator validates through the nvidia-device-plugin-validation pod and the nvidia-driver-validation pod. Both pods must complete successfully. Check the output from `kubectl get pods --all-namespaces | grep -v kube-system`. If both show completed status, NVIDIA Cloud Native Stack is working correctly. This section provides two examples for manually validating GPU usability from within a pod. 1. Example 1: nvidia-smi > Execute the following: > > > > cat < apiVersion: v1 > kind: Pod > metadata: > name: nvidia-smi > spec: > restartPolicy: OnFailure > containers: > - name: nvidia-smi > image: "nvidia/cuda:12.2.0-base-ubuntu22.04" > args: ["nvidia-smi"] > EOF > > kubectl apply -f nvidia-smi.yaml > kubectl logs nvidia-smi > > > Output: > > > > +---------------------------------------------------------------------------------------+ > | NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | > |-----------------------------------------+----------------------+----------------------+ > | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | > | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | > | | | MIG M. | > |=========================================+======================+======================| > | 0 NVIDIA GeForce RTX 4090 On | 00000000:65:00.0 Off | Off | > | 0% 30C P8 5W / 450W | 133MiB / 24564MiB | 0% Default | > | | | N/A | > +-----------------------------------------+----------------------+----------------------+ > > +---------------------------------------------------------------------------------------+ > | Processes: | > | GPU GI CI PID Type Process name GPU Memory | > | ID ID Usage | > |=======================================================================================| > | 0 N/A N/A 1119 G /usr/lib/xorg/Xorg 107MiB | > | 0 N/A N/A 1239 G /usr/bin/gnome-shell 13MiB | > +---------------------------------------------------------------------------------------+ 1. Example 2: CUDA-Vector-Add > 1. Create a pod YAML file: > > > > > $ cat < > apiVersion: v1 > > kind: Pod > > metadata: > > name: cuda-vector-add > > spec: > > restartPolicy: OnFailure > > containers: > > - name: cuda-vector-add > > image: "k8s.gcr.io/cuda-vector-add:v0.1" > > EOF > > > 1. Execute the below command to create a sample GPU pod: > > > > > $ kubectl apply -f cuda-samples.yaml > > > 1. Confirm the cuda-samples pod was created: > > > > > $ kubectl get pods NVIDIA Cloud Native Stack works as expected if the get pods command shows the pod status as completed. NVIDIA Cloud Native Stack for AWS[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#nvidia-cloud-native-stack-for-aws "Link to this heading") ------------------------------------------------------------------------------------------------------------------------------------------------------------- Instructions in this document were tested with the following environment: ### EC2 Instance Configuration[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#ec2-instance-configuration "Link to this heading") * instance type: g4dn.2xlarge * os: Ubuntu Server 22.04 LTS image with 64-bit (x86) * storage: min 150 GB * network: * keep port 22 open to ssh * additional ports might need opening after deploying the application SSH into the instance using the key you generated at instance creation: ssh -i /path/to/.pem ubuntu@ ### Installing Containerd[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#installing-containerd "Link to this heading") 1. Install packages to allow apt to use a repository over HTTPS: > sudo apt-get update > sudo apt-get install -y apt-transport-https ca-certificates gnupg-agent libseccomp2 autotools-dev debhelper software-properties-common 1. Configure the prerequisites for Containerd: > cat < overlay > br_netfilter > EOF > > sudo modprobe overlay > sudo modprobe br_netfilter 1. Setup required sysctl params; these persist across reboots: > cat < net.bridge.bridge-nf-call-iptables = 1 > net.ipv4.ip_forward = 1 > net.bridge.bridge-nf-call-ip6tables = 1 > EOF 1. Apply sysctl params without reboot: > sudo sysctl --system 1. Download the Containerd tarball: > wget https://github.com/containerd/containerd/releases/download/v1.6.8/cri-containerd-cni-1.6.8-linux-amd64.tar.gz > sudo tar --no-overwrite-dir -C / -xzf cri-containerd-cni-1.6.8-linux-amd64.tar.gz > rm -rf cri-containerd-cni-1.6.8-linux-amd64.tar.gz 1. Install the Containerd: > sudo mkdir -p /etc/containerd > containerd config default | sudo tee /etc/containerd/config.toml > sudo systemctl restart containerd > > > For additional information on installing Containerd, see Install Containerd with Release Tarball. ### Install Kubernetes[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#id5 "Link to this heading") 1. Make sure Containerd has been started and enabled before beginning installation: > sudo systemctl start containerd && sudo systemctl enable containerd 1. Execute the following to install kubelet, kubeadm, and kubectl: > curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add - > > sudo mkdir -p /etc/apt/sources.list.d/ 1. Create Kubernetes.list: > cat < deb https://apt.kubernetes.io/ kubernetes-xenial main > EOF 1. Now execute the commands below: > sudo apt-get update > > sudo apt-get install -y -q kubelet=1.23.12-00 kubectl=1.23.12-00 kubeadm=1.23.12-00 > > sudo apt-mark hold kubelet kubeadm kubectl 1. Initialize the Kubernetes cluster to run as master: > 1. Disable swap: > > > > > sudo swapoff -a > > > > sudo nano /etc/fstab > > > 1. Add a # before all the lines that start with /swap. # is a comment, and the result should look similar to this: > > UUID=e879fda9-4306-4b5b-8512-bba726093f1d / ext4 defaults 0 0 > UUID=DCD4-535C /boot/efi vfat defaults 0 0 > #/swap.img none swap sw 0 0 > 2. Execute the following command: > > > > > sudo kubeadm init --pod-network-cidr=192.168.0.0/16 --cri-socket=/run/containerd/containerd.sock --kubernetes-version="v1.23.12" > > > The output will show you the commands that, when executed, deploy a pod network to the cluster and commands to join the cluster. > > > 1. Following the instructions in the output, execute the commands as shown below: > > > > > mkdir -p $HOME/.kube > sudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/config > sudo chown $(id -u):$(id -g) $HOME/.kube/config > > > 1. With the following command, you install a pod-network add-on to the control plane node. Calico is used as the pod-network add-on here: > > > > > kubectl apply -f https://projectcalico.docs.tigera.io/archive/v3.21/manifests/calico.yaml > > > 1. You can execute the below commands to ensure that all pods are up and running: > > > > > kubectl get pods --all-namespaces > > > Output: > > > > NAMESPACE NAME READY STATUS RESTARTS AGE > kube-system calico-kube-controllers-65b8787765-bjc8h 1/1 Running 0 2m8s > kube-system calico-node-c2tmk 1/1 Running 0 2m8s > kube-system coredns-5c98db65d4-d4kgh 1/1 Running 0 9m8s > kube-system coredns-5c98db65d4-h6x8m 1/1 Running 0 9m8s > kube-system etcd-#hostname 1/1 Running 0 8m25s > kube-system kube-apiserver-#hostname 1/1 Running 0 8m7s > kube-system kube-controller-manager-#hostname 1/1 Running 0 8m3s > kube-system kube-proxy-6sh42 1/1 Running 0 9m7s > kube-system kube-scheduler-#hostname 1/1 Running 0 8m26s 1. The get nodes command shows that the control-plane node is up and ready: > kubectl get nodes > > Output: > > > NAME STATUS ROLES AGE VERSION > #yourhost Ready control-plane 10m v1.23.12 1. Since we are using a single-node Kubernetes cluster, the cluster will not schedule pods on the control plane node by default. To schedule pods on the control plane node, we have to remove the taint by executing the following command: ### Install Helm[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#id7 "Link to this heading") Execute the following command to download Helm 3.10.0: wget https://get.helm.sh/helm-v3.10.0-linux-amd64.tar.gz tar -zxvf helm-v3.10.0-linux-amd64.tar.gz sudo mv linux-amd64/helm /usr/local/bin/helm For additional information about Helm, refer to the Helm 3.10.0 [release notes](https://github.com/helm/helm/releases) and the [Installing Helm guide](https://helm.sh/docs/using_helm/#installing-helm) for more information. ### Install GPU Operator[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#id8 "Link to this heading") 1. Add the NVIDIA helm repo: > helm repo add nvidia https://helm.ngc.nvidia.com/nvidia 1. Update the helm repo: > helm repo update 1. To install GPU Operator for AWS G4 instance with Tesla T4: > helm install --version 23.3.2 --create-namespace --namespace gpu-operator-resources --devel nvidia/gpu-operator --wait --generate-name 1. Validate the state of GPU Operator: > kubectl get pods --all-namespaces | grep -v kube-system > > NAMESPACE NAME READY STATUS RESTARTS AGE > > gpu-operator-resources gpu-operator-1590097431-node-feature-discovery-master-76578jwwt 1/1 Running 0 5m2s > gpu-operator-resources gpu-operator-1590097431-node-feature-discovery-worker-pv5nf 1/1 Running 0 5m2s > gpu-operator-resources gpu-operator-74c97448d9-n75g8 1/1 Running 1 5m2s > gpu-operator-resources gpu-feature-discovery-6986n 1/1 Running 0 5m2s > gpu-operator-resources nvidia-container-toolkit-daemonset-pwhfr 1/1 Running 0 4m58s > gpu-operator-resources nvidia-cuda-validator-8mgr2 0/1 Completed 0 5m3s > gpu-operator-resources nvidia-dcgm-exporter-bdzrz 1/1 Running 0 4m57s > gpu-operator-resources nvidia-device-plugin-daemonset-zmjhn 1/1 Running 0 4m57s > gpu-operator-resources nvidia-device-plugin-validator-spjv7 0/1 Completed 0 4m57s > gpu-operator-resources nvidia-driver-daemonset-7b66v 1/1 Running 0 4m57s > gpu-operator-resources nvidia-operator-validator-phndq 1/1 Running 0 4m57s Note > The installation of GPU Operator can take a couple of minutes. How long it takes depends on your internet speed. Refer to the [GPU Operator page](https://ngc.nvidia.com/catalog/helm-charts/nvidia:gpu-operator.md) on NGC for more information. 1. To verify GPU Operator Installation with nvidia-smi, execute the following: > Execute the following: > > > > cat < apiVersion: v1 > kind: Pod > metadata: > name: nvidia-smi > spec: > restartPolicy: OnFailure > containers: > - name: nvidia-smi > image: "nvidia/cuda:12.2.0-base-ubuntu22.04" > args: ["nvidia-smi"] > EOF > > kubectl apply -f nvidia-smi.yaml > kubectl logs nvidia-smi > > > Output: > > > > +---------------------------------------------------------------------------------------+ > | NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 | > |-----------------------------------------+----------------------+----------------------+ > | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | > | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | > | | | MIG M. | > |=========================================+======================+======================| > | 0 NVIDIA GeForce RTX 4090 On | 00000000:65:00.0 Off | Off | > | 0% 30C P8 5W / 450W | 133MiB / 24564MiB | 0% Default | > | | | N/A | > +-----------------------------------------+----------------------+----------------------+ > > +---------------------------------------------------------------------------------------+ > | Processes: | > | GPU GI CI PID Type Process name GPU Memory | > | ID ID Usage | > |=======================================================================================| > | 0 N/A N/A 1119 G /usr/lib/xorg/Xorg 107MiB | > | 0 N/A N/A 1239 G /usr/bin/gnome-shell 13MiB | > +---------------------------------------------------------------------------------------+ > > > This concludes the AWS cluster setup. AWS Elastic Kubernetes Service (AWS EKS)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#aws-elastic-kubernetes-service-aws-eks "Link to this heading") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### Start Holoscan Ready EKS Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#start-holoscan-ready-eks-cluster "Link to this heading") The Holoscan Ready EKS Cluster connects to your AWS account. It creates all required resources using Terraform with NVIDIA GPU Operator. By default, it creates 1 CPU node (instance-type = `t2.xlarge`, disk-size = 512GB) and 2 GPU nodes (instance-type = `p3.2xlarge`, disk-size = 512GB). Clone the repository: [NVIDIA Terraform Modules / EKS](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/eks). To use NVIDIA Cloud Native Add-On Pack (see section [Install NVIDIA Cloud Native Add-On Pack on AWS EKS (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-eks)), change your working directory to `examples/cnpack`. Otherwise, run commands from the `nvidia-terraform-modules/eks` folder. Follow the repository instructions to create the cluster. After `terraform apply` completes, perform these validation steps. 1. Update your kubeconfig using the AWS CLI: aws eks update-kubeconfig --name tf- --region us-west-2 You should now see the cluster with kubectl and three nodes (1 CPU and 2 GPU nodes by default). kubectl get nodes 1. Validate the GPU operator (this may take ~5 minutes after cluster creation): kubectl get pod -n gpu-operator Pods with `-validator` in their name should show Completed status. All other pods should be running. 1. Verify the driver works by running this command to see NVIDIA-SMI output: kubectl exec -it -n gpu-operator nvidia-device-plugin-daemonset- -- nvidia-smi Your cluster is now ready to use. ### Install NVIDIA Cloud Native Add-On Pack on AWS EKS (Optional)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-nvidia-cloud-native-add-on-pack-on-aws-eks-optional "Link to this heading") The NVIDIA Cloud Native Service Add-on Pack provides tools for creating and operating cloud-native services on Kubernetes clusters. Before proceeding, ensure you have created and can access the CNPack version of Holoscan Ready EKS. See step 1 in [Start Holoscan Ready EKS Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-eks) for details. #### Connect with AWS Managed Services[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#connect-with-aws-managed-services "Link to this heading") 1. In your `nvidia-terraform-modules/eks/examples/cnpack` folder, run `terraform output` to obtain the following information. ~/nvidia-terraform-modules/eks/examples/cnpack $ terraform output amp_ingest_role_arn = "arn:aws:iam::0000000000000:role/amp-ingest-role-f1b" amp_query_endpoint = "https://aps-workspaces.us-west-2.amazonaws.com/workspaces/ws-example/api/v1/query" amp_remotewrite_endpoint = "https://aps-workspaces.us-west-2.amazonaws.com/workspaces/ws-example/api/v1/remote_write" aws_pca_arn = "arn:aws:acm-pca:us-west-2:0000000000000:certificate-authority/example-pca-arn-hash" 1. Download CNPack from NGC. ngc registry resource download-version "nv-holoscan-cloud-native/cnpack/cnpack:0.16.0" cd cnpack_v0.16.0 chmod +x nvidia-cnpack-linux-x86_64 ./nvidia-cnpack-linux-x86_64 --help 1. Create the CNPack configuration using the template below. Name the file `nvidia-platform.yaml`. For EKS integration, we’re enabling fluentbit for log aggregation, prometheus for metrics, and cert-manager. apiVersion: v1alpha3 kind: NvidiaPlatform spec: # The platform block contains general configuration that is important to all components platform: # Required value specifying the Wildcard Domain to configure for ingress. wildcardDomain: "*.my-cluster.my-domain.com" # Required value to specify the port to configure for ingress. externalPort: 443 # Optional infrastructure provider configuration for AWS EKS eks: # The region in-which the cluster is installed. region: us-west-2 # The ingress block configures the ingress controller ingress: # Whether this component should be enabled Default is true. enabled: false # The postgres block configures the postgres operator postgres: # Whether this component should be enabled Default is true. enabled: false # The certManager block configures the certificate management system certManager: # Whether this component should be enabled Default is true. enabled: true # Optional configuration for the AWS Private CA service integration. # # Dependencies: # - EKS Infrastructure provider configuration (spec.platform.eks) awsPCA: # Whether this component should be enabled Default is true. enabled: true # The ARN required to communicate with the AWS Private CA service. arn: # The common name of the configured Private CA. commonName: my-cert.my-domain.com # The domain name of the configured Private CA. domainName: my-domain.com # The trustManager block configures the trust bundle management system # # Dependencies: # - cert-manager trustManager: # Whether this component should be enabled Default is true. enabled: false # The keycloack block configures Keycloak as an OIDC provider # # Dependencies: # - cert-manager # - postgres # - ingress keycloak: # Whether this component should be enabled Default is true. enabled: false # The prometheus block configures the Prometheus metrics service # # Dependencies: # - cert-manager prometheus: # Whether this component should be enabled Default is true. enabled: true # Optional configuration for connecting Prometheus to an AWS Managed Prometheus instance. awsRemoteWrite: # The URL of the AWS managed prometheus service. url: # The ARN required to communicate with the AWS Managed Prometheus Service. arn: # The grafana block configures the Grafana dashboard service # # Dependencies: # - prometheus # - cert-manager # - ingress grafana: # Whether this component should be enabled Default is true. enabled: true # Optional value to override the hostname used to expose grafana. customHostname: my-host.my-cluster.my-domain.com # The elastic block configures the Elastic Cloud on Kubernetes operator elastic: # Whether this component should be enabled Default is true. enabled: true # The fluentbit block configures the fluentbit log aggregation service # # Dependencies: # - Infrastructure provider configuration (spec.platform.eks or spec.platform.aks or empty spec.platform for CNS) fluentbit: # Whether this component should be enabled Default is true. enabled: true 1. Create the CNPack in your cluster with the following command. CNPack will use your KUBECONFIG to connect to the cluster and perform installation of its services. ./nvidia-cnpack-linux-x86_64 create -f nvidia-platform.yaml 1. Validate CNPack installation status by running the following two commands and ensure all the pods are in running state. kubectl get po -n nvidia-platform kubectl get po -n nvidia-monitoring 1. In case you want to delete cnpack from the cluster, run `./nvidia-cnpack-linux-x86_64 delete`: ### Troubleshooting[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#troubleshooting "Link to this heading") If kubectl or terraform commands show errors like “couldn’t get current server API group list: the server has asked for the client to provide credentials”, refresh your AWS credentials in `~/.aws/credentials`. ### Delete Holoscan Ready EKS Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#delete-holoscan-ready-eks-cluster "Link to this heading") Note Delete all applications (Helm charts, Kubernetes resources) before running the destroy command. 1. Refresh your AWS credentials. 2. In the same directory where you ran `terraform apply`, run `terraform destroy` and enter “yes” when prompted. Azure Kubernetes Service (Microsoft AKS)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#azure-kubernetes-service-microsoft-aks "Link to this heading") ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### Start Holoscan Ready AKS Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#start-holoscan-ready-aks-cluster "Link to this heading") The Holoscan Ready AKS Cluster will connect to your Azure account. It creates all the needed resources with terraform along with NVIDIA GPU Operator. By default, it creates 1 CPU node (instance-type = `Standard_D16_v5`, disk-size = 100GB) and 2 GPU nodes (instance-type = `Standard_NC6s_v3`, disk-size = 100GB). Clone the repo [NVIDIA Terraform Modules / AKS](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/aks). If you would like to use NVIDIA Cloud Native Add-On Pack (see section [Install NVIDIA Cloud Native Add-On Pack on Azure AKS (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-aks) for more details) as part of your deployment system, please change your working directory to `examples/cnpack` directory. Otherwise, run the following from the `nvidia-terraform-modules/aks` root. Follow the instructions in the repo to bring up the cluster. Once `terraform apply` completes, you can perform the following validation steps. 1. Update your kube config with az CLI by running the following: az aks get-credentials --name ucf-cnpack-cluster --resource-group ucf-cnpack-cluster-rg You should now see the cluster with your kubectl CLI and you should see three nodes (1 CPU and 2 GPU nodes). (Note: You may need to sign in to Microsoft the first time you run this on the cluster.) kubectl get nodes 1. Validate GPU operator by running the following commands. Note that it may take ~5 minutes after cluster creation for all the GPU Operator resources to be ready: kubectl get pod -n gpu-operator The pods with `-validator` in their name should be in Completed status and all the other pods should be running. We will also validate that the driver is working by running the following command and you should see the NVIDIA-SMI output. kubectl exec -it -n gpu-operator nvidia-device-plugin-daemonset- -- nvidia-smi Now your cluster is ready to be used. ### Install NVIDIA Cloud Native Add-On Pack on Azure AKS (Optional)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-nvidia-cloud-native-add-on-pack-on-azure-aks-optional "Link to this heading") The NVIDIA Cloud Native Service Add-on Pack is a collection of tools designed to support the creation and operation of cloud-native services on a Kubernetes cluster. Before proceeding, please make sure that you have brought up and have access to the CNPack version of Holoscan Ready AKS. See step 1 in [Start Holoscan Ready AKS Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-aks) for more details. #### Connect with Azure Managed Services[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#connect-with-azure-managed-services "Link to this heading") 1. In your nvidia-terraform-modules/aks/examples/cnpack folder, run `terraform output` to obtain the following information. ~/nvidia-terraform-modules/aks/examples/cnpack $ terraform output cluster_managed-client-id = "x123xxxxxxx" fluentbit-secret-name = "fluentbit-secrets" fluentbit-secret-namespace = "nvidia-platform" prometheus-query-url = "https://ucf-cnpack-prom-xxxx.westus2.prometheus.monitor.azure.com" 1. Download CNPack from NGC. ngc registry resource download-version "nv-holoscan-cloud-native/cnpack/cnpack:0.16.0" cd cnpack_v0.16.0 chmod +x nvidia-cnpack-linux-x86_64 ./nvidia-cnpack-linux-x86_64 --help 1. Create the CNPack configuration using the template below. Name the file `nvidia-platform.yaml`. For AKS integration, we’re enabling fluentbit for log aggregation, prometheus for metrics, and cert-manager. apiVersion: v1alpha3 kind: NvidiaPlatform spec: platform: wildcardDomain: "*.holoscandev.nvidia.com" externalPort: 443 aks: {} certManager: enabled: true prometheus: enabled: true aksRemoteWrite: url: "" clientId: " in your terraform output" fluentbit: enabled: true aks: secretName: "" trustManager: enabled: false keycloak: enabled: false grafana: customHostname: grafana.holoscandev.com enabled: false elastic: enabled: false ingress: enabled: false postgres: enabled: false Note #1: To obtain the AKS Remote Write URL, go to the Azure portal. Search for ucf-cnpack-prom (or the name you used in terraform.tfvars file). Select the resource for the Azure Monitor Workspace of the same name. Copy the value of Metrics Ingestion Endpoint into `spec.Prometheus.aksRemoteWrite.url`. 1. Create the CNPack in your cluster with the following command. CNPack will use your KUBECONFIG to connect to the cluster and perform installation of its services. ./nvidia-cnpack_Linux_x86_64 create -f nvidia-platform.yaml 1. Validate CNPack installation status by running the following two commands and ensure all the pods are in running state. kubectl get po -n nvidia-platform kubectl get po -n nvidia-monitoring 1. In case you want to delete cnpack from the cluster, run `./nvidia-cnpack_Linux_x86_64 delete`. ### Troubleshooting[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#id13 "Link to this heading") If at any time, your kubectl or terraform commands shows error like “couldn’t get current server API group list: the server has asked for the client to provide credentials”, please run `az login` again. If you see the following from `terraform apply`: │ Error: chart "gpu-operator" matching vX.Y.Z not found in nvidia index. (try 'helm repo update'): no chart │ version found for gpu-operator-vX.Y.Z Run the helm repo update and then run terraform apply again. ### Delete Holoscan Ready AKS Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#delete-holoscan-ready-aks-cluster "Link to this heading") Note Make sure that all applications (helm charts, other Kubernetes resources) that you installed have been deleted before running the destroy command. 1. Refresh your Azure login by running `az login`. 2. In the same directory where you had ran `terraform apply`, run `terraform destroy` and enter “yes” when prompted. Google Kubernetes Engine (GCP GKE)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#google-kubernetes-engine-gcp-gke "Link to this heading") ------------------------------------------------------------------------------------------------------------------------------------------------------------- ### Start Holoscan Ready GKE Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#start-holoscan-ready-gke-cluster "Link to this heading") The Holoscan Ready GKE Cluster will connect to your GCP account. It creates all the needed resources with terraform along with NVIDIA GPU Operator. By default, it creates 1 CPU node (instance-type = `n1-standard-4`, disk-size = 512GB) and 2 GPU nodes (instance-type = `n1-standard-4` with `nvidia-tesla-v100`, disk-size = 512GB). Clone the repo [NVIDIA Terraform Modules / GKE](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/gke). If you would like to use NVIDIA Cloud Native Add-On Pack (see section [Install NVIDIA Cloud Native Add-On Pack on GKE (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-gke) for more details) as part of your deployment system, change your working directory to `examples/cnpack` directory. Otherwise, run the following from the `nvidia-terraform-modules/gke` root. Follow the instructions in the repo to bring up the cluster. Once `terraform apply` completes, you can perform the following validation steps. 1. Update your kube config with az CLI by running the following: gcloud components install gke-gcloud-auth-plugin gcloud container clusters get-credentials --region= You should now see the cluster with your kubectl CLI and you should see three nodes (1 CPU and 2 GPU nodes). Note It may ask you to sign in to Microsoft the first time you run this on the cluster. kubectl get nodes 1. Validate GPU operator by running the following commands. (Note that it may take ~5 minutes after cluster is created for all the GPU Operator resources to be ready): kubectl get pod -n gpu-operator The pods with `-validator` in their name should be in Completed status and all the other pods should be running. We will also validate that the driver is working by running the following command and you should see the NVIDIA-SMI output. kubectl exec -it -n gpu-operator nvidia-device-plugin-daemonset- -- nvidia-smi Now your cluster is ready to be used. ### Install NVIDIA Cloud Native Add-On Pack on GKE (Optional)[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-nvidia-cloud-native-add-on-pack-on-gke-optional "Link to this heading") The NVIDIA Cloud Native Service Add-on Pack is a collection of tools designed to support the creation and operation of cloud-native services on a Kubernetes cluster. Before proceeding, please make sure that you have brought up and have access to the CNPack version of Holoscan Ready GKE. See step 1 in [Start Holoscan Ready GKE Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-gke) for more details. #### Connect with GCP Managed Services[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#connect-with-gcp-managed-services "Link to this heading") 1. In your nvidia-terraform-modules/gke/examples/cnpack folder, run `terraform output` to obtain the following information. ~/nvidia-terraform-modules/gke/examples/cnpack $ terraform output gcp_service_account_email_for_prometheus = "tf-gke-prom-svc-acct-xxxxxdxxx.com" 1. Download CNPack from NGC. ngc registry resource download-version "nv-holoscan-cloud-native/cnpack/cnpack:0.16.0" cd cnpack_v0.16.0 chmod +x nvidia-cnpack-linux-x86_64 ./nvidia-cnpack-linux-x86_64 --help 1. Create the CNPack configuration using the template below. Name the file `nvidia-platform.yaml`. For GKE integration, we’re enabling fluentbit for log aggregation, prometheus for metrics, and cert-manager. apiVersion: v1alpha3 kind: NvidiaPlatform spec: platform: wildcardDomain: "*.holoscandev.nvidia.com" externalPort: 443 gke: {} certManager: enabled: true prometheus: enabled: true gkeRemoteWrite: gcpServiceAccount: " in your terraform output" fluentbit: enabled: true trustManager: enabled: false keycloak: enabled: false grafana: customHostname: grafana.holoscandev.com enabled: false elastic: enabled: false ingress: enabled: false postgres: enabled: false 1. Create the CNPack in your cluster with the following command. CNPack will use your KUBECONFIG to connect to the cluster and perform installation of its services. ./nvidia-cnpack_Linux_x86_64 create -f nvidia-platform.yaml 1. Validate CNPack installation status by running the following two commands and ensure all the pods are in running state. kubectl get po -n nvidia-platform kubectl get po -n nvidia-monitoring 1. In case you want to delete cnpack from the cluster, run `./nvidia-cnpack_Linux_x86_64 delete`. ### Troubleshooting[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#id17 "Link to this heading") If at any time, your kubectl or terraform commands shows error like “couldn’t get current server API group list: the server has asked for the client to provide credentials”, please run `gcloud auth application-default login` again. ### Delete Holoscan Ready GKE Cluster[#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#delete-holoscan-ready-gke-cluster "Link to this heading") Note Make sure that all applications (helm charts, other Kubernetes resources) that you installed have been deleted before running the destroy command. 1. Refresh your GCP login by running `gcloud auth application-default login`. 2. In the same directory where you ran `terraform apply`, run `terraform state rm kubernetes_namespace_v1.gpu-operator`. Then run `terraform destroy` and enter “yes” when prompted. Links/Buttons: - [#](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#delete-holoscan-ready-gke-cluster) - [NGC Personal Key](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#ngc-personal-key) - [NVIDIA/cloud-native-stack](https://github.com/NVIDIA/cloud-native-stack) - [http://cdimage.ubuntu.com/releases/22.04/release/](http://cdimage.ubuntu.com/releases/22.04/release/) - [https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local](https://developer.nvidia.com/cuda-downloads.md?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=22.04&target_type=deb_local) - [https://docs.docker.com/install/linux/docker-ce/ubuntu/](https://docs.docker.com/install/linux/docker-ce/ubuntu/) - [kubeadm installation guide](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/install-kubeadm/) - [install the latest version of Helm](https://helm.sh/docs/intro/install/#from-script) - [Install Docker CE](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-docker) - [Install NVIDIA Container Toolkit](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-container-toolkit) - [Install Kubernetes](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-k8s) - [GPU Operator page](https://ngc.nvidia.com/catalog/helm-charts/nvidia:gpu-operator.md) - [release notes](https://github.com/helm/helm/releases) - [Installing Helm guide](https://helm.sh/docs/using_helm/#installing-helm) - [NVIDIA Terraform Modules / EKS](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/eks) - [Install NVIDIA Cloud Native Add-On Pack on AWS EKS (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-eks) - [Start Holoscan Ready EKS Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-eks) - [NVIDIA Terraform Modules / AKS](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/aks) - [Install NVIDIA Cloud Native Add-On Pack on Azure AKS (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-aks) - [Start Holoscan Ready AKS Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-aks) - [NVIDIA Terraform Modules / GKE](https://github.com/NVIDIA/nvidia-terraform-modules/tree/main/gke) - [Install NVIDIA Cloud Native Add-On Pack on GKE (Optional)](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-cnpack-gke) - [Start Holoscan Ready GKE Cluster](https://docs.nvidia.com/ucf/2.10.0/text/UCS_Prerequisites.html#install-hcn-gke)