# Part 4: Workload

## What Will You Do

In this part of the self-paced exercise, you will deploy a "GPU workload" to your Amazon EKS cluster that has a GPU node group.

---

## Step 1: Namespace

In a typical production environment, administrators will have already created a "Kubernetes Namespace" for your workload. In this exercise, let us go ahead and create a namespace.

- Open Terminal (on macOS/Linux) or Command Prompt (Windows) and navigate to the folder where you forked the Git repository
- Navigate to the folder "/getstarted/gpueks/workload"
- Type the command

```
    rctl create ns -f namespace.yaml
    ```

This step creates a namespace in your project. The controller can create a namespace on "multiple clusters" based on its placement policy.

```
    rctl publish ns gputest
    ```

### Verify

To verify that the namespace was successfully created on your EKS cluster, run the following kubectl command

```
    kubectl get ns gputest
    ```

You should see results like the following. Note that the namespace was successfully created on your EKS cluster.

```
    NAME                     STATUS   AGE
    gputest                  Active   4s
    ```

---

## Step 2: Deploy Workload

The "gputest.yaml" file contains the declarative specification for our GPU Workload. Let us review it.

```
    name: gputest
    namespace: gputest
    project: default
    type: NativeYaml
    clusters: demo-gpu-eks
    payload: ./gpu-job.yaml
    ```

Note that the workload's name is "gputest" and it is of type "k8s YAML". The actual k8s YAML file is in the payload "gpu-job.yaml".

```
    rctl create workload gputest.yaml
    ```

If there were no errors, you should see a message like below

```
    Workload created successfully
    ```

Now, let us publish the newly created workload to the downstream clusters. The workload can be deployed to multiple clusters as per the configured "placement policy". In this case, you are deploying to a single EKS cluster with the name "demo-gpueks".

```
    rctl publish workload gputest
    ```

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## Step 4: Verify

In the web console, click on Applications -> Workloads. You should see something like the following.

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## Step 5: GPU Dashboard

The GPU workload you deployed will consume the GPU attached to the EKS cluster. Administrators that wish to view GPU metrics have access to an "integrated GPU dashboard"

- In your project, navigate to **Infrastructure -> Clusters**
- Click on **GPUs** in the cluster card to open the GPU selector

- Click **Go To GPU**

You should see something like the following

---

## Recap

Congratulations! At this point, you have successfully

- Configured and provisioned an Amazon EKS cluster with a GPU node group
- Deployed a "GPU Workload" to the EKS Cluster and reviewed the integrated "GPU Dashboards"