> 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/ai-factory-guideline-jp/fpt-gpu-cloud/gpu-cluster/gpu-cluster-managed-k8s-with-metal-cloud/chtoriaru/5-manjido-gpu-kurasutdeno-gpu-woshitaapurikshonnodepuroi.md).
# 5. マネージド GPU クラスターでの GPU を使用したアプリケーションのデプロイ
{% stepper %}
{% step %}
**ステップ 1:** 次のコマンドで GPU の構成を確認します。
```
kubectl get nodes -o json | jq '.items[].metadata.labels'
```
**例：**下の図は、Metal Cloud GPU H100 を使用するワーカー、構成戦略：all-disable、ステータス：success を示しています。
{% endstep %}
{% step %}
**ステップ 2：**ワーカーの GPU インスタンスの設定を確認するには、ノードに ssh で接続し、次のコマンドを入力します。
```
Nvidia-smi
```
以下の例は、GPU ドライバーが正常にインストールされ、8 つの GPU モード None で動作していることを示しています。
**GPU を使用したアプリケーションの実装例**
```
#Syntax:
nvidia.com/gpu:
#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;
```
{% endstep %}
{% endstepper %}
---
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