> For the complete documentation index, see [llms.txt](https://ai-docs.fptcloud.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-docs.fptcloud.com/fpt-gpu-cloud/gpu-cluster/managed-k8s-with-metal-cloud/tutorial/deploy-applications.md).

# Deploy applications

**Step 1:** Check the GPU configuration using the following command:

kubectl get nodes -o json | jq '.items\[].metadata.labels'

Example: The image below shows a worker using Metal Cloud GPU H100, with the strategy configuration: all-disable, status: success.

![](/files/d91fa8b65bb165cd23b7cc1f057eb363efc014de)

**Step 2:** Check the GPU instance configuration on the worker by SSHing into the node and typing the following command:

Nvidia-smi

The example below shows that the GPU driver has been successfully installed and is running with 8 GPUs in None mode.

![](/files/d49f1793600f10c8e936e8e9a33798bce5359981)

**👉 Example of deploying an application using the GPU:**

```
#Syntax:  
nvidia.com/gpu: <number-of-GPUs> 
#Example:  
nvidia.com/gpu: 1 
 
#Example deployment using GPU 
apiVersion: apps/v1 
kind: Deployment 
metadata: 
  name: example-gpu-app 
spec: 
  replicas: 1 
  selector: 
    matchLabels: 
      component: gpu-app 
  template: 
    metadata: 
      labels: 
        component: gpu-app 
    spec: 
      containers: 
        - name: gpu-container 
          securityContext: 
            capabilities: 
              add: 
                - SYS_ADMIN 
          resources: 
            limits: 
              nvidia.com/gpu: 1 
          image: nvidia/samples:dcgmproftester-2.0.10-cuda11.0-ubuntu18.04 
          command: ["/bin/sh", "-c"] 
          args: 
            - while true; do /usr/bin/dcgmproftester11 --no-dcgm-validation -t 1004 -d 300; sleep 30; 
```


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