# Install the Cluster

## System and Network Requirements <a href="#system-and-network-requirements" id="system-and-network-requirements"></a>

Before installing the NVIDIA Run:ai cluster, validate that the [system requirements](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/install-using-helm/system-requirements) and [network requirements ](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/install-using-helm/network-requirements)are met. For air-gapped environments, make sure you have the [software artifacts ](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts)prepared.

Once all the requirements are met, it is highly recommend to use the NVIDIA Run:ai cluster preinstall diagnostics tool to:

* Test the below requirements in addition to failure points related to Kubernetes, NVIDIA, storage, and networking
* Look at additional components installed and analyze their relevance to a successful installation

For more information, see [preinstall diagnostics](https://github.com/run-ai/preinstall-diagnostics). To run the preinstall diagnostics tool, [download](https://runai.jfrog.io/ui/native/pd-cli-prod/preinstall-diagnostics-cli/) the latest version, and run:

{% tabs %}
{% tab title="Connected" %}

```bash
chmod +x ./preinstall-diagnostics-<platform> && \ 
./preinstall-diagnostics-<platform> \
  --domain ${CONTROL_PLANE_FQDN} \
  --cluster-domain ${CLUSTER_FQDN} \
#if the diagnostics image is hosted in a private registry
  --image-pull-secret ${IMAGE_PULL_SECRET_NAME} \
  --image ${PRIVATE_REGISTRY_IMAGE_URL}    
```

{% endtab %}

{% tab title="Air-gapped" %}
In an air-gapped deployment, the diagnostics image is saved, pushed, and pulled manually from the organization's registry.

```bash
#Save the image locally
docker save --output preinstall-diagnostics.tar gcr.io/run-ai-lab/preinstall-diagnostics:${VERSION}
#Load the image to the organization's registry
docker load --input preinstall-diagnostics.tar
docker tag gcr.io/run-ai-lab/preinstall-diagnostics:${VERSION} ${CLIENT_IMAGE_AND_TAG} 
docker push ${CLIENT_IMAGE_AND_TAG}
```

Run the binary with the `--image` parameter to modify the diagnostics image to be used:

```bash
chmod +x ./preinstall-diagnostics-darwin-arm64 && \
./preinstall-diagnostics-darwin-arm64 \
  --domain ${CONTROL_PLANE_FQDN} \
  --cluster-domain ${CLUSTER_FQDN} \
  --image-pull-secret ${IMAGE_PULL_SECRET_NAME} \
  --image ${PRIVATE_REGISTRY_IMAGE_URL}    
```

{% endtab %}
{% endtabs %}

## Helm

NVIDIA Run:ai requires [Helm](https://helm.sh/) 3.14 or later. To install Helm, see [Installing Helm](https://helm.sh/docs/intro/install/). If you are installing an air-gapped version of NVIDIA Run:ai, the NVIDIA Run:ai tar file contains the [helm binary](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts).

## Permissions <a href="#permissions" id="permissions"></a>

Using a Kubernetes user with the `cluster-admin` role to ensure a successful installation is recommended. For more information, see [Using RBAC authorization](https://kubernetes.io/docs/reference/access-authn-authz/rbac/).

## Installation

{% hint style="info" %}
**Note**

* To customize the installation based on your environment, see [Customized installation](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/install-using-helm/custom-install).
* You can store the `clientSecret` as a Kubernetes secret within the cluster instead of using plain text. You can then configure the installation to use it by setting the `controlPlane.existingSecret` and `controlPlane.secretKeys.clientSecret` parameters as described in [Customized installation](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/install-using-helm/custom-install).
  {% endhint %}

### Kubernetes

<details>

<summary>Connected</summary>

Follow the steps below to add a new cluster.

**Note:** When adding a cluster for the first time, the New Cluster form automatically opens when you log in to the NVIDIA Run:ai platform. Other actions are prevented, until the cluster is created.

If this is your first cluster and you have completed the New Cluster form, start at step 3. Otherwise, start at step 1.

1. In the NVIDIA Run:ai platform, go to **Resources**
2. Click **+NEW CLUSTER**
3. Enter a unique name for your cluster
4. Optional: Choose the NVIDIA Run:ai cluster version (latest, by default)
5. Enter the Cluster URL. For more information, see [Fully Qualified Domain Name](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/system-requirements#fully-qualified-domain-name-fqdn) requirement.
6. Click **Continue**

**Installing NVIDIA Run:ai cluster**

In the next Section, the NVIDIA Run:ai cluster installation steps will be presented.

1. Follow the installation instructions and run the commands provided on your Kubernetes cluster.
2. Click **DONE**

The cluster is displayed in the table with the status **Waiting to connect**. Once installation is complete, the cluster status changes to **Connected**.

**Tip:** Use the `--dry-run` flag to gain an understanding of what is being installed before the actual installation. For more details, see see [Understanding cluster access roles.](https://docs.run.ai/latest/admin/config/access-roles/)

</details>

<details>

<summary>Air-gapped</summary>

Follow the steps below to add a new cluster.

**Note:** When adding a cluster for the first time, the New Cluster form automatically opens when you log-in to the NVIDIA Run:ai platform. Other actions are prevented, until the cluster is created.

If this is your first cluster and you have completed the New Cluster form, start at step 3. Otherwise, start at step 1.

1. In the NVIDIA Run:ai platform, go to **Resources**
2. Click **+NEW CLUSTER**
3. Enter a unique name for your cluster
4. Optional: Choose the NVIDIA Run:ai cluster version (latest, by default)
5. Enter the Cluster URL . For more information, see [Fully Qualified Domain Name](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/system-requirements#fully-qualified-domain-name-fqdn) requirement.
6. Click **Continue**

**Installing NVIDIA Run:ai cluster**

In the next Section, the NVIDIA Run:ai cluster installation steps will be presented.

1. Follow the installation instructions and run the commands provided on your Kubernetes cluster.
2. On the second tab of the cluster wizard, when copying the helm command for installation, you will need to use the [pre-provided installation file](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts) instead of using helm repositories. As such:

   * Do not add the helm repository and do not run `helm repo update`.
   * Instead, edit the `helm upgrade` command.
     * Replace `runai/runai-cluster` with `runai-cluster-<VERSION>.tgz`.
     * Add `--set global.image.registry=<DOCKER REGISTRY ADDRESS>` where the registry address is as entered in the [preparation](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts) section
     * Add `--set global.customCA.enabled=true` as described [here](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/system-requirements#local-certificate-authority)

   The command should look like the following:

   ```bash
   helm upgrade -i runai-cluster runai-cluster-<VERSION>.tgz \
       --set controlPlane.url=... \
       --set controlPlane.clientSecret=... \
       --set cluster.uid=... \
       --set cluster.url=... --create-namespace \
       --set global.image.registry=registry.mycompany.local \
       --set global.customCA.enabled=true
   ```
3. Click **DONE**

The cluster is displayed in the table with the status **Waiting to connect**. Once installation is complete, the cluster status changes to **Connected**.

**Tip:** Use the `--dry-run` flag to gain an understanding of what is being installed before the actual installation. For more details, see [Understanding cluster access roles.](https://docs.run.ai/latest/admin/config/access-roles/)

</details>

### OpenShift

<details>

<summary>Connected</summary>

Follow the steps below to add a new cluster.

**Note:** When adding a cluster for the first time, the New Cluster form automatically opens when you log in to the NVIDIA Run:ai platform. Other actions are prevented, until the cluster is created.

If this is your first cluster and you have completed the New Cluster form, start at step 3. Otherwise, start at step 1.

1. In the NVIDIA Run:ai platform, go to **Resources**
2. Click **+NEW CLUSTER**
3. Enter a unique name for your cluster
4. Optional: Choose the NVIDIA Run:ai cluster version (latest, by default)
5. Enter the Cluster URL
6. Click **Continue**

**Installing NVIDIA Run:ai cluster**

In the next Section, the NVIDIA Run:ai cluster installation steps will be presented.

1. Follow the installation instructions and run the commands provided on your Kubernetes cluster.
2. Click **DONE**

The cluster is displayed in the table with the status **Waiting to connect**. Once installation is complete, the cluster status changes to **Connected**.

</details>

<details>

<summary>Air-gapped</summary>

When creating a new cluster, select the **OpenShift** target platform.

Follow the steps below to add a new cluster.

**Note:** When adding a cluster for the first time, the New Cluster form automatically opens when you log in to the NVIDIA Run:ai platform. Other actions are prevented, until the cluster is created.

If this is your first cluster and you have completed the New Cluster form, start at step 3. Otherwise, start at step 1.

1. In the NVIDIA Run:ai platform, go to **Resources**
2. Click **+NEW CLUSTER**
3. Enter a unique name for your cluster
4. Optional: Choose the NVIDIA Run:ai cluster version (latest, by default)
5. Enter the Cluster URL
6. Click **Continue**

**Installing NVIDIA Run:ai cluster**

In the next Section, the NVIDIA Run:ai cluster installation steps will be presented.

1. Follow the installation instructions and run the commands provided on your Kubernetes cluster.
2. On the second tab of the cluster wizard, when copying the helm command for installation, you will need to use the [pre-provided installation file](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts) instead of using helm repositories. As such:

   * Do not add the helm repository and do not run `helm repo update`.
   * Instead, edit the `helm upgrade` command.
     * Replace `runai/runai-cluster` with `runai-cluster-<VERSION>.tgz`.
     * Add `--set global.image.registry=<DOCKER REGISTRY ADDRESS>` where the registry address is as entered in the [preparations](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/preparations#software-artifacts) section
     * Add `--set global.customCA.enabled=true` as described [here](https://run-ai-docs.nvidia.com/self-hosted/2.22/getting-started/installation/system-requirements#local-certificate-authority)

   The command should look like the following:

   ```bash
   helm upgrade -i runai-cluster runai-cluster-<VERSION>.tgz \
       --set controlPlane.url=... \
       --set controlPlane.clientSecret=... \
       --set cluster.uid=... \
       --set cluster.url=... --create-namespace \
       --set global.image.registry=registry.mycompany.local \
       --set global.customCA.enabled=true
   ```
3. Click **DONE**

The cluster is displayed in the table with the status **Waiting to connect**. Once installation is complete, the cluster status changes to **Connected**.

</details>

## Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

If you encounter an issue with the installation, try the troubleshooting scenario below.

### Installation <a href="#installation_1" id="installation_1"></a>

If the NVIDIA Run:ai cluster installation failed, check the installation logs to identify the issue. Run the following script to print the installation logs:

```bash
curl -fsSL https://raw.githubusercontent.com/run-ai/public/main/installation/get-installation-logs.sh
```

### Cluster Status <a href="#cluster-status" id="cluster-status"></a>

If the NVIDIA Run:ai cluster installation completed, but the cluster status did not change its status to Connected, check the cluster [troubleshooting scenarios](https://run-ai-docs.nvidia.com/self-hosted/2.22/infrastructure-setup/procedures/clusters#troubleshooting-scenarios).