Calcul et mise à l'échelle automatique

Créez un fichier de configuration de cluster nommé dra-eks-cluster.yaml :

---
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: dra-eks-cluster
  region: us-west-2
  version: '1.33'

managedNodeGroups:
- name: gpu-dra-nodes
  amiFamily: AmazonLinux2023
  instanceType: g6.12xlarge
  desiredCapacity: 2
  minSize: 1
  maxSize: 3

  labels:
    node-type: "gpu-dra"
    nvidia.com/gpu.present: "true"

  taints:
  - key: nvidia.com/gpu
    value: "true"
    effect: NoSchedule

Créez le cluster :

eksctl create cluster -f dra-eks-cluster.yaml

Ajoutez le référentiel Helm du plugin pour appareils NVIDIA :

helm repo add nvidia https://nvidia.github.io/k8s-device-plugin
helm repo update

Créez des valeurs personnalisées pour le plug-in de l'appareil :

cat <<EOF > nvidia-device-plugin-values.yaml
gfd:
  enabled: true
nfd:
  enabled: true
tolerations:
  - key: [nvidia.com/gpu](http://nvidia.com/gpu)
    operator: Exists
    effect: NoSchedule
EOF

Installez le plug-in pour appareil NVIDIA :

helm install nvidia-device-plugin nvidia/nvidia-device-plugin \
 --namespace nvidia-device-plugin \
 --create-namespace \
 --version v0.17.1 \
 --values nvidia-device-plugin-values.yaml

Créez un fichier de dra-driver-values.yaml valeurs pour le pilote DRA :

---
nvidiaDriverRoot: /

gpuResourcesEnabledOverride: true

resources:
  gpus:
    enabled: true
  computeDomains:
    enabled: true  # Enable for GB200 IMEX support

controller:
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

kubeletPlugin:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: "nvidia.com/gpu.present"
            operator: In
            values: ["true"]
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

Ajoutez le référentiel NVIDIA NGC Helm :

helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

Installez le pilote NVIDIA DRA :

helm install nvidia-dra-driver nvidia/nvidia-dra-driver-gpu \
 --version="25.3.0-rc.2" \
 --namespace nvidia-dra-driver \
 --create-namespace \
 --values dra-driver-values.yaml

Vérifiez que les ressources de l'API DRA sont disponibles :

kubectl api-resources | grep [resource.k8s.io/v1beta1](http://resource.k8s.io/v1beta1)

Le résultat attendu est le suivant :

deviceclasses [resource.k8s.io/v1beta1](http://resource.k8s.io/v1beta1) false DeviceClass
resourceclaims [resource.k8s.io/v1beta1](http://resource.k8s.io/v1beta1) true ResourceClaim
resourceclaimtemplates [resource.k8s.io/v1beta1](http://resource.k8s.io/v1beta1) true ResourceClaimTemplate
resourceslices [resource.k8s.io/v1beta1](http://resource.k8s.io/v1beta1) false ResourceSlice

Vérifiez les classes d'appareils disponibles :

kubectl get deviceclasses

Voici un exemple de résultat attendu :

NAME                                        AGE
compute-domain-daemon.nvidia.com            4h39m
compute-domain-default-channel.nvidia.com   4h39m
gpu.nvidia.com                              4h39m
mig.nvidia.com                              4h39m

Lorsqu'une instance de GPU G6 nouvellement créée rejoint votre cluster Amazon EKS avec le DRA activé, les actions suivantes se produisent :

Créez une base ResourceClaimTemplate pour l'allocation du GPU avec un fichier nommé basic-gpu-claim-template.yaml :

---
apiVersion: v1
kind: Namespace
metadata:
  name: gpu-test1

---
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  namespace: gpu-test1
  name: single-gpu
spec:
  spec:
    devices:
      requests:
      - name: gpu
        deviceClassName: gpu.nvidia.com

Appliquez le modèle :

kubectl apply -f basic-gpu-claim-template.yaml

Vérifiez le statut :

kubectl get resourceclaimtemplates -n gpu-test1

Voici un exemple de sortie :

NAME         AGE
single-gpu   9m16s

Créez un Pod qui utilise le ResourceClaimTemplate avec un fichier nommé basic-gpu-pod.yaml :

---
apiVersion: v1
kind: Pod
metadata:
  namespace: gpu-test1
  name: gpu-pod
  labels:
    app: pod
spec:
  containers:
  - name: ctr0
    image: ubuntu:22.04
    command: ["bash", "-c"]
    args: ["nvidia-smi -L; trap 'exit 0' TERM; sleep 9999 & wait"]
    resources:
      claims:
      - name: gpu0
  resourceClaims:
  - name: gpu0
    resourceClaimTemplateName: single-gpu
  nodeSelector:
    NodeGroupType: gpu-dra
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: "nvidia.com/gpu"
    operator: "Exists"
    effect: "NoSchedule"

Appliquez et surveillez le Pod :

kubectl apply -f basic-gpu-pod.yaml

Vérifiez l'état du Pod :

kubectl get pod -n gpu-test1

Voici un exemple de sortie attendue :

NAME      READY   STATUS    RESTARTS   AGE
gpu-pod   1/1     Running   0          13m

Vérifiez le ResourceClaim statut :

kubectl get resourceclaims -n gpu-test1

Voici un exemple de sortie attendue :

NAME                 STATE                AGE
gpu-pod-gpu0-l76cg   allocated,reserved   9m6s

Consultez les journaux du pod pour consulter les informations du processeur graphique :

kubectl logs gpu-pod -n gpu-test1

Voici un exemple de sortie attendue :

GPU 0: NVIDIA L4 (UUID: GPU-da7c24d7-c7e3-ed3b-418c-bcecc32af7c5)

Définissez un ResourceClaimTemplate pour le découpage temporel avec un fichier nommé : timeslicing-claim-template.yaml

---
apiVersion: v1
kind: Namespace
metadata:
  name: timeslicing-gpu

---
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: timeslicing-gpu-template
  namespace: timeslicing-gpu
spec:
  spec:
    devices:
      requests:
      - name: shared-gpu
        deviceClassName: gpu.nvidia.com
      config:
      - requests: ["shared-gpu"]
        opaque:
          driver: gpu.nvidia.com
          parameters:
            apiVersion: resource.nvidia.com/v1beta1
            kind: GpuConfig
            sharing:
              strategy: TimeSlicing

Définissez un pod en utilisant le time-slicing avec un fichier nommé : timeslicing-pod.yaml

---
# Pod 1 - Inference workload
apiVersion: v1
kind: Pod
metadata:
  name: inference-pod-1
  namespace: timeslicing-gpu
  labels:
    app: gpu-inference
spec:
  restartPolicy: Never
  containers:
  - name: inference-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "-c"]
    args:
    - |
      import torch
      import time
      import os
      print(f"=== POD 1 STARTING ===")
      print(f"GPU available: {torch.cuda.is_available()}")
      print(f"GPU count: {torch.cuda.device_count()}")
      if torch.cuda.is_available():
          device = torch.cuda.current_device()
          print(f"Current GPU: {torch.cuda.get_device_name(device)}")
          print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1024**3:.1f} GB")
          # Simulate inference workload
          for i in range(20):
              x = torch.randn(1000, 1000).cuda()
              y = torch.mm(x, x.t())
              print(f"Pod 1 - Iteration {i+1} completed at {time.strftime('%H:%M:%S')}")
              time.sleep(60)
      else:
          print("No GPU available!")
          time.sleep(5)
    resources:
      claims:
      - name: shared-gpu-claim
  resourceClaims:
  - name: shared-gpu-claim
    resourceClaimTemplateName: timeslicing-gpu-template
  nodeSelector:
    NodeGroupType: "gpu-dra"
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule


---
# Pod 2 - Training workload
apiVersion: v1
kind: Pod
metadata:
  name: training-pod-2
  namespace: timeslicing-gpu
  labels:
    app: gpu-training
spec:
  restartPolicy: Never
  containers:
  - name: training-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "-c"]
    args:
    - |
      import torch
      import time
      import os
      print(f"=== POD 2 STARTING ===")
      print(f"GPU available: {torch.cuda.is_available()}")
      print(f"GPU count: {torch.cuda.device_count()}")
      if torch.cuda.is_available():
          device = torch.cuda.current_device()
          print(f"Current GPU: {torch.cuda.get_device_name(device)}")
          print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1024**3:.1f} GB")
          # Simulate training workload with heavier compute
          for i in range(15):
              x = torch.randn(2000, 2000).cuda()
              y = torch.mm(x, x.t())
              loss = torch.sum(y)
              print(f"Pod 2 - Training step {i+1}, Loss: {loss.item():.2f} at {time.strftime('%H:%M:%S')}")
              time.sleep(5)
      else:
          print("No GPU available!")
          time.sleep(60)
    resources:
      claims:
      - name: shared-gpu-claim-2
  resourceClaims:
  - name: shared-gpu-claim-2
    resourceClaimTemplateName: timeslicing-gpu-template
  nodeSelector:
    NodeGroupType: "gpu-dra"
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

Appliquez le modèle et le pod :

kubectl apply -f timeslicing-claim-template.yaml
kubectl apply -f timeslicing-pod.yaml

Surveillez les demandes de ressources :

kubectl get resourceclaims -n timeslicing-gpu -w

Voici un exemple de sortie :

NAME                                      STATE                AGE
inference-pod-1-shared-gpu-claim-9p97x    allocated,reserved   21s
training-pod-2-shared-gpu-claim-2-qghnb   pending              21s
inference-pod-1-shared-gpu-claim-9p97x    pending              105s
training-pod-2-shared-gpu-claim-2-qghnb   pending              105s
inference-pod-1-shared-gpu-claim-9p97x    pending              105s
training-pod-2-shared-gpu-claim-2-qghnb   allocated,reserved   105s
inference-pod-1-shared-gpu-claim-9p97x    pending              105s

Définissez un ResourceClaimTemplate pour MPS avec un fichier nommé mps-claim-template.yaml :

---
apiVersion: v1
kind: Namespace
metadata:
  name: mps-gpu

---
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: mps-gpu-template
  namespace: mps-gpu
spec:
  spec:
    devices:
      requests:
      - name: shared-gpu
        deviceClassName: gpu.nvidia.com
      config:
      - requests: ["shared-gpu"]
        opaque:
          driver: gpu.nvidia.com
          parameters:
            apiVersion: resource.nvidia.com/v1beta1
            kind: GpuConfig
            sharing:
              strategy: MPS

Définissez un Pod à l'aide de MPS avec un fichier nommé mps-pod.yaml :

---
# Single Pod with Multiple Containers sharing GPU via MPS
apiVersion: v1
kind: Pod
metadata:
  name: mps-multi-container-pod
  namespace: mps-gpu
  labels:
    app: mps-demo
spec:
  restartPolicy: Never
  containers:
  # Container 1 - Inference workload
  - name: inference-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "-c"]
    args:
    - |
      import torch
      import torch.nn as nn
      import time
      import os

      print(f"=== INFERENCE CONTAINER STARTING ===")
      print(f"Process ID: {os.getpid()}")
      print(f"GPU available: {torch.cuda.is_available()}")
      print(f"GPU count: {torch.cuda.device_count()}")

      if torch.cuda.is_available():
          device = torch.cuda.current_device()
          print(f"Current GPU: {torch.cuda.get_device_name(device)}")
          print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1024**3:.1f} GB")

          # Create inference model
          model = nn.Sequential(
              nn.Linear(1000, 500),
              nn.ReLU(),
              nn.Linear(500, 100)
          ).cuda()

          # Run inference
          for i in range(1, 999999):
              with torch.no_grad():
                  x = torch.randn(128, 1000).cuda()
                  output = model(x)
                  result = torch.sum(output)
                  print(f"Inference Container PID {os.getpid()}: Batch {i}, Result: {result.item():.2f} at {time.strftime('%H:%M:%S')}")
              time.sleep(2)
      else:
          print("No GPU available!")
          time.sleep(60)
    resources:
      claims:
      - name: shared-gpu-claim
        request: shared-gpu

  # Container 2 - Training workload
  - name: training-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "-c"]
    args:
    - |
      import torch
      import torch.nn as nn
      import time
      import os

      print(f"=== TRAINING CONTAINER STARTING ===")
      print(f"Process ID: {os.getpid()}")
      print(f"GPU available: {torch.cuda.is_available()}")
      print(f"GPU count: {torch.cuda.device_count()}")

      if torch.cuda.is_available():
          device = torch.cuda.current_device()
          print(f"Current GPU: {torch.cuda.get_device_name(device)}")
          print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1024**3:.1f} GB")

          # Create training model
          model = nn.Sequential(
              nn.Linear(2000, 1000),
              nn.ReLU(),
              nn.Linear(1000, 500),
              nn.ReLU(),
              nn.Linear(500, 10)
          ).cuda()

          criterion = nn.MSELoss()
          optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

          # Run training
          for epoch in range(1, 999999):
              x = torch.randn(64, 2000).cuda()
              target = torch.randn(64, 10).cuda()

              optimizer.zero_grad()
              output = model(x)
              loss = criterion(output, target)
              loss.backward()
              optimizer.step()

              print(f"Training Container PID {os.getpid()}: Epoch {epoch}, Loss: {loss.item():.4f} at {time.strftime('%H:%M:%S')}")
              time.sleep(3)
      else:
          print("No GPU available!")
          time.sleep(60)
    resources:
      claims:
      - name: shared-gpu-claim
        request: shared-gpu

  resourceClaims:
  - name: shared-gpu-claim
    resourceClaimTemplateName: mps-gpu-template

  nodeSelector:
    NodeGroupType: "gpu-dra"
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

Appliquez le modèle et créez plusieurs pods MPS :

kubectl apply -f mps-claim-template.yaml
kubectl apply -f mps-pod.yaml

Surveillez les demandes de ressources :

kubectl get resourceclaims -n mps-gpu -w

Voici un exemple de sortie :

NAME                                             STATE                AGE
mps-multi-container-pod-shared-gpu-claim-2p9kx   allocated,reserved   86s

Ajoutez le référentiel NVIDIA GPU Operator :

helm repo add nvidia https://nvidia.github.io/gpu-operator
helm repo update

Créez un gpu-operator-values.yaml fichier :

driver:
  enabled: false

mig:
  strategy: mixed

migManager:
  enabled: true
  env:
    - name: WITH_REBOOT
      value: "true"
  config:
    create: true
    name: custom-mig-parted-configs
    default: "all-disabled"
    data:
      config.yaml: |-
        version: v1
        mig-configs:
          all-disabled:
            - devices: all
              mig-enabled: false

          # P4D profiles (A100 40GB)
          p4d-half-balanced:
            - devices: [0, 1, 2, 3]
              mig-enabled: true
              mig-devices:
                "1g.5gb": 2
                "2g.10gb": 1
                "3g.20gb": 1
            - devices: [4, 5, 6, 7]
              mig-enabled: false

          # P4DE profiles (A100 80GB)
          p4de-half-balanced:
            - devices: [0, 1, 2, 3]
              mig-enabled: true
              mig-devices:
                "1g.10gb": 2
                "2g.20gb": 1
                "3g.40gb": 1
            - devices: [4, 5, 6, 7]
              mig-enabled: false

devicePlugin:
  enabled: true
  config:
    name: ""
    create: false
    default: ""

toolkit:
  enabled: true

nfd:
  enabled: true

gfd:
  enabled: true

dcgmExporter:
  enabled: true
  serviceMonitor:
    enabled: true
    interval: 15s
    honorLabels: false
    additionalLabels:
      release: kube-prometheus-stack

nodeStatusExporter:
  enabled: false

operator:
  defaultRuntime: containerd
  runtimeClass: nvidia
  resources:
    limits:
      cpu: 500m
      memory: 350Mi
    requests:
      cpu: 200m
      memory: 100Mi

daemonsets:
  tolerations:
    - key: "nvidia.com/gpu"
      operator: "Exists"
      effect: "NoSchedule"
  nodeSelector:
    accelerator: nvidia
  priorityClassName: system-node-critical

Installez GPU Operator à l'aide du gpu-operator-values.yaml fichier :

helm install gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --create-namespace \
  --version v25.3.1 \
  --values gpu-operator-values.yaml

Ce graphique Helm déploie les composants suivants et plusieurs profils MIG :

Vérifiez les pods de déploiement :

kubectl get pods -n gpu-operator

Voici un exemple de sortie :

NAME                                                              READY   STATUS      RESTARTS        AGE
gpu-feature-discovery-27rdq                                       1/1     Running     0               3h31m
gpu-operator-555774698d-48brn                                     1/1     Running     0               4h8m
nvidia-container-toolkit-daemonset-sxmh9                          1/1     Running     1 (3h32m ago)   4h1m
nvidia-cuda-validator-qb77g                                       0/1     Completed   0               3h31m
nvidia-dcgm-exporter-cvzd7                                        1/1     Running     0               3h31m
nvidia-device-plugin-daemonset-5ljm5                              1/1     Running     0               3h31m
nvidia-gpu-operator-node-feature-discovery-gc-67f66fc557-q5wkt    1/1     Running     0               4h8m
nvidia-gpu-operator-node-feature-discovery-master-5d8ffddcsl6s6   1/1     Running     0               4h8m
nvidia-gpu-operator-node-feature-discovery-worker-6t4w7           1/1     Running     1 (3h32m ago)   4h1m
nvidia-gpu-operator-node-feature-discovery-worker-9w7g8           1/1     Running     0               4h8m
nvidia-gpu-operator-node-feature-discovery-worker-k5fgs           1/1     Running     0               4h8m
nvidia-mig-manager-zvf54                                          1/1     Running     1 (3h32m ago)   3h35m

Créez un cluster Amazon EKS avec un groupe de nœuds gérés par P4de pour tester les exemples MIG :

apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: dra-eks-cluster
  region: us-east-1
  version: '1.33'

managedNodeGroups:
# P4DE MIG Node Group with Capacity Block Reservation
- name: p4de-mig-nodes
  amiFamily: AmazonLinux2023
  instanceType: p4de.24xlarge

  # Capacity settings
  desiredCapacity: 0
  minSize: 0
  maxSize: 1

  # Use specific subnet in us-east-1b for capacity reservation
  subnets:
    - us-east-1b

  # AL2023 NodeConfig for RAID0 local storage only
  nodeadmConfig:
    apiVersion: node.eks.aws/v1alpha1
    kind: NodeConfig
    spec:
      instance:
        localStorage:
          strategy: RAID0

  # Node labels for MIG configuration
  labels:
    nvidia.com/gpu.present: "true"
    nvidia.com/gpu.product: "A100-SXM4-80GB"
    nvidia.com/mig.config: "p4de-half-balanced"
    node-type: "p4de"
    vpc.amazonaws.com/efa.present: "true"
    accelerator: "nvidia"

  # Node taints
  taints:
    - key: nvidia.com/gpu
      value: "true"
      effect: NoSchedule

  # EFA support
  efaEnabled: true

  # Placement group for high-performance networking
  placementGroup:
    groupName: p4de-placement-group
    strategy: cluster

  # Capacity Block Reservation (CBR)
  # Ensure CBR ID matches the subnet AZ with the Nodegroup subnet
  spot: false
  capacityReservation:
    capacityReservationTarget:
      capacityReservationId: "cr-abcdefghij"  # Replace with your capacity reservation ID

NVIDIA GPU Operator utilise l'étiquette ajoutée aux nœuds nvidia.com/mig.config: "p4de-half-balanced" et partitionne le GPU avec le profil donné.

Connectez-vous à l'p4deinstance.

Exécutez la commande suivante :

nvidia-smi -L

Vous devriez voir l'exemple de sortie suivant :

[root@ip-100-64-173-145 bin]# nvidia-smi -L
GPU 0: NVIDIA A100-SXM4-80GB (UUID: GPU-ab52e33c-be48-38f2-119e-b62b9935925a)
  MIG 3g.40gb     Device  0: (UUID: MIG-da972af8-a20a-5f51-849f-bc0439f7970e)
  MIG 2g.20gb     Device  1: (UUID: MIG-7f9768b7-11a6-5de9-a8aa-e9c424400da4)
  MIG 1g.10gb     Device  2: (UUID: MIG-498adad6-6cf7-53af-9d1a-10cfd1fa53b2)
  MIG 1g.10gb     Device  3: (UUID: MIG-3f55ef65-1991-571a-ac50-0dbf50d80c5a)
GPU 1: NVIDIA A100-SXM4-80GB (UUID: GPU-0eabeccc-7498-c282-0ac7-d3c09f6af0c8)
  MIG 3g.40gb     Device  0: (UUID: MIG-80543849-ea3b-595b-b162-847568fe6e0e)
  MIG 2g.20gb     Device  1: (UUID: MIG-3af1958f-fac4-59f1-8477-9f8d08c55029)
  MIG 1g.10gb     Device  2: (UUID: MIG-401088d2-716f-527b-a970-b1fc7a4ac6b2)
  MIG 1g.10gb     Device  3: (UUID: MIG-8c56c75e-5141-501c-8f43-8cf22f422569)
GPU 2: NVIDIA A100-SXM4-80GB (UUID: GPU-1c7a1289-243f-7872-a35c-1d2d8af22dd0)
  MIG 3g.40gb     Device  0: (UUID: MIG-e9b44486-09fc-591a-b904-0d378caf2276)
  MIG 2g.20gb     Device  1: (UUID: MIG-ded93941-9f64-56a3-a9b1-a129c6edf6e4)
  MIG 1g.10gb     Device  2: (UUID: MIG-6c317d83-a078-5c25-9fa3-c8308b379aa1)
  MIG 1g.10gb     Device  3: (UUID: MIG-2b070d39-d4e9-5b11-bda6-e903372e3d08)
GPU 3: NVIDIA A100-SXM4-80GB (UUID: GPU-9a6250e2-5c59-10b7-2da8-b61d8a937233)
  MIG 3g.40gb     Device  0: (UUID: MIG-20e3cd87-7a57-5f1b-82e7-97b14ab1a5aa)
  MIG 2g.20gb     Device  1: (UUID: MIG-04430354-1575-5b42-95f4-bda6901f1ace)
  MIG 1g.10gb     Device  2: (UUID: MIG-d62ec8b6-e097-5e99-a60c-abf8eb906f91)
  MIG 1g.10gb     Device  3: (UUID: MIG-fce20069-2baa-5dd4-988a-cead08348ada)
GPU 4: NVIDIA A100-SXM4-80GB (UUID: GPU-5d09daf0-c2eb-75fd-3919-7ad8fafa5f86)
GPU 5: NVIDIA A100-SXM4-80GB (UUID: GPU-99194e04-ab2a-b519-4793-81cb2e8e9179)
GPU 6: NVIDIA A100-SXM4-80GB (UUID: GPU-c1a1910f-465a-e16f-5af1-c6aafe499cd6)
GPU 7: NVIDIA A100-SXM4-80GB (UUID: GPU-c2cfafbc-fd6e-2679-e955-2a9e09377f78)

Créez mig-claim-template.yaml pour contenir le MIG resourceclaimtemplates :

apiVersion: v1
kind: Namespace
metadata:
  name: mig-gpu

---
# Template for 3g.40gb MIG instance (Large training)
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: mig-large-template
  namespace: mig-gpu
spec:
  spec:
    devices:
      requests:
      - name: mig-large
        deviceClassName: mig.nvidia.com
        selectors:
        - cel:
            expression: |
              device.attributes['gpu.nvidia.com'].profile == '3g.40gb'

---
# Template for 2g.20gb MIG instance (Medium training)
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: mig-medium-template
  namespace: mig-gpu
spec:
  spec:
    devices:
      requests:
      - name: mig-medium
        deviceClassName: mig.nvidia.com
        selectors:
        - cel:
            expression: |
              device.attributes['gpu.nvidia.com'].profile == '2g.20gb'

---
# Template for 1g.10gb MIG instance (Small inference)
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: mig-small-template
  namespace: mig-gpu
spec:
  spec:
    devices:
      requests:
      - name: mig-small
        deviceClassName: mig.nvidia.com
        selectors:
        - cel:
            expression: |
              device.attributes['gpu.nvidia.com'].profile == '1g.10gb'

Appliquez les trois modèles :

kubectl apply -f mig-claim-template.yaml

Exécutez la commande suivante :

kubectl get resourceclaimtemplates -n mig-gpu

Voici un exemple de sortie :

NAME                  AGE
mig-large-template    71m
mig-medium-template   71m
mig-small-template    71m

Créez mig-pod.yaml pour planifier plusieurs tâches afin d'en tirer parti resourceclaimtemplates :

---
# ConfigMap containing Python scripts for MIG pods
apiVersion: v1
kind: ConfigMap
metadata:
  name: mig-scripts-configmap
  namespace: mig-gpu
data:
  large-training-script.py: |
    import torch
    import torch.nn as nn
    import torch.optim as optim
    import time
    import os

    print(f"=== LARGE TRAINING POD (3g.40gb) ===")
    print(f"Process ID: {os.getpid()}")
    print(f"GPU available: {torch.cuda.is_available()}")
    print(f"GPU count: {torch.cuda.device_count()}")

    if torch.cuda.is_available():
        device = torch.cuda.current_device()
        print(f"Using GPU: {torch.cuda.get_device_name(device)}")
        print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1e9:.1f} GB")

        # Large model for 3g.40gb instance
        model = nn.Sequential(
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        ).cuda()

        optimizer = optim.Adam(model.parameters())
        criterion = nn.CrossEntropyLoss()

        print(f"Model parameters: {sum(p.numel() for p in model.parameters())}")

        # Training loop
        for epoch in range(100):
            # Large batch for 3g.40gb
            x = torch.randn(256, 2048).cuda()
            y = torch.randint(0, 10, (256,)).cuda()

            optimizer.zero_grad()
            output = model(x)
            loss = criterion(output, y)
            loss.backward()
            optimizer.step()

            if epoch % 10 == 0:
                print(f"Large Training - Epoch {epoch}, Loss: {loss.item():.4f}, GPU Memory: {torch.cuda.memory_allocated()/1e9:.2f}GB")
            time.sleep(3)

        print("Large training completed on 3g.40gb MIG instance")

  medium-training-script.py: |
    import torch
    import torch.nn as nn
    import torch.optim as optim
    import time
    import os

    print(f"=== MEDIUM TRAINING POD (2g.20gb) ===")
    print(f"Process ID: {os.getpid()}")
    print(f"GPU available: {torch.cuda.is_available()}")
    print(f"GPU count: {torch.cuda.device_count()}")

    if torch.cuda.is_available():
        device = torch.cuda.current_device()
        print(f"Using GPU: {torch.cuda.get_device_name(device)}")
        print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1e9:.1f} GB")

        # Medium model for 2g.20gb instance
        model = nn.Sequential(
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        ).cuda()

        optimizer = optim.Adam(model.parameters())
        criterion = nn.CrossEntropyLoss()

        print(f"Model parameters: {sum(p.numel() for p in model.parameters())}")

        # Training loop
        for epoch in range(100):
            # Medium batch for 2g.20gb
            x = torch.randn(128, 1024).cuda()
            y = torch.randint(0, 10, (128,)).cuda()

            optimizer.zero_grad()
            output = model(x)
            loss = criterion(output, y)
            loss.backward()
            optimizer.step()

            if epoch % 10 == 0:
                print(f"Medium Training - Epoch {epoch}, Loss: {loss.item():.4f}, GPU Memory: {torch.cuda.memory_allocated()/1e9:.2f}GB")
            time.sleep(4)

        print("Medium training completed on 2g.20gb MIG instance")

  small-inference-script.py: |
    import torch
    import torch.nn as nn
    import time
    import os

    print(f"=== SMALL INFERENCE POD (1g.10gb) ===")
    print(f"Process ID: {os.getpid()}")
    print(f"GPU available: {torch.cuda.is_available()}")
    print(f"GPU count: {torch.cuda.device_count()}")

    if torch.cuda.is_available():
        device = torch.cuda.current_device()
        print(f"Using GPU: {torch.cuda.get_device_name(device)}")
        print(f"GPU Memory: {torch.cuda.get_device_properties(device).total_memory / 1e9:.1f} GB")

        # Small model for 1g.10gb instance
        model = nn.Sequential(
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
        ).cuda()

        print(f"Model parameters: {sum(p.numel() for p in model.parameters())}")

        # Inference loop
        for i in range(200):
            with torch.no_grad():
                # Small batch for 1g.10gb
                x = torch.randn(32, 512).cuda()
                output = model(x)
                prediction = torch.argmax(output, dim=1)

                if i % 20 == 0:
                    print(f"Small Inference - Batch {i}, Predictions: {prediction[:5].tolist()}, GPU Memory: {torch.cuda.memory_allocated()/1e9:.2f}GB")
            time.sleep(2)

        print("Small inference completed on 1g.10gb MIG instance")

---
# Pod 1: Large training workload (3g.40gb)
apiVersion: v1
kind: Pod
metadata:
  name: mig-large-training-pod
  namespace: mig-gpu
  labels:
    app: mig-large-training
    workload-type: training
spec:
  restartPolicy: Never
  containers:
  - name: large-training-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "/scripts/large-training-script.py"]
    volumeMounts:
    - name: script-volume
      mountPath: /scripts
      readOnly: true
    resources:
      claims:
      - name: mig-large-claim
  resourceClaims:
  - name: mig-large-claim
    resourceClaimTemplateName: mig-large-template
  nodeSelector:
    node.kubernetes.io/instance-type: p4de.24xlarge
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
  volumes:
  - name: script-volume
    configMap:
      name: mig-scripts-configmap
      defaultMode: 0755

---
# Pod 2: Medium training workload (2g.20gb) - can run on SAME GPU as Pod 1
apiVersion: v1
kind: Pod
metadata:
  name: mig-medium-training-pod
  namespace: mig-gpu
  labels:
    app: mig-medium-training
    workload-type: training
spec:
  restartPolicy: Never
  containers:
  - name: medium-training-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "/scripts/medium-training-script.py"]
    volumeMounts:
    - name: script-volume
      mountPath: /scripts
      readOnly: true
    resources:
      claims:
      - name: mig-medium-claim
  resourceClaims:
  - name: mig-medium-claim
    resourceClaimTemplateName: mig-medium-template
  nodeSelector:
    node.kubernetes.io/instance-type: p4de.24xlarge
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
  volumes:
  - name: script-volume
    configMap:
      name: mig-scripts-configmap
      defaultMode: 0755

---
# Pod 3: Small inference workload (1g.10gb) - can run on SAME GPU as Pod 1 & 2
apiVersion: v1
kind: Pod
metadata:
  name: mig-small-inference-pod
  namespace: mig-gpu
  labels:
    app: mig-small-inference
    workload-type: inference
spec:
  restartPolicy: Never
  containers:
  - name: small-inference-container
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["python", "/scripts/small-inference-script.py"]
    volumeMounts:
    - name: script-volume
      mountPath: /scripts
      readOnly: true
    resources:
      claims:
      - name: mig-small-claim
  resourceClaims:
  - name: mig-small-claim
    resourceClaimTemplateName: mig-small-template
  nodeSelector:
    node.kubernetes.io/instance-type: p4de.24xlarge
    nvidia.com/gpu.present: "true"
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule
  volumes:
  - name: script-volume
    configMap:
      name: mig-scripts-configmap
      defaultMode: 0755

Appliquez cette spécification, qui devrait déployer trois pods :

kubctl apply -f mig-pod.yaml

Ces pods doivent être programmés par le pilote DRA.

Consultez les journaux du pilote DRA Pod et vous verrez un résultat similaire à celui-ci :

I0717 21:50:22.925811 1 driver.go:87] NodePrepareResource is called: number of claims: 1
I0717 21:50:22.932499 1 driver.go:129] Returning newly prepared devices for claim '933e9c72-6fd6-49c5-933c-a896407dc6d1': [&Device{RequestNames:[mig-large],PoolName:ip-100-64-173-145.ec2.internal,DeviceName:gpu-0-mig-9-4-4,CDIDeviceIDs:[k8s.gpu.nvidia.com/device=**gpu-0-mig-9-4-4**],}]
I0717 21:50:23.186472 1 driver.go:87] NodePrepareResource is called: number of claims: 1
I0717 21:50:23.191226 1 driver.go:129] Returning newly prepared devices for claim '61e5ddd2-8c2e-4c19-93ae-d317fecb44a4': [&Device{RequestNames:[mig-medium],PoolName:ip-100-64-173-145.ec2.internal,DeviceName:gpu-2-mig-14-0-2,CDIDeviceIDs:[k8s.gpu.nvidia.com/device=**gpu-2-mig-14-0-2**],}]
I0717 21:50:23.450024 1 driver.go:87] NodePrepareResource is called: number of claims: 1
I0717 21:50:23.455991 1 driver.go:129] Returning newly prepared devices for claim '1eda9b2c-2ea6-401e-96d0-90e9b3c111b5': [&Device{RequestNames:[mig-small],PoolName:ip-100-64-173-145.ec2.internal,DeviceName:gpu-1-mig-19-2-1,CDIDeviceIDs:[k8s.gpu.nvidia.com/device=**gpu-1-mig-19-2-1**],}]

Vérifiez le resourceclaims pour connaître l'état du Pod :

kubectl get resourceclaims -n mig-gpu -w

Voici un exemple de sortie :

NAME                                             STATE                AGE
mig-large-training-pod-mig-large-claim-6dpn8     pending              0s
mig-large-training-pod-mig-large-claim-6dpn8     pending              0s
mig-large-training-pod-mig-large-claim-6dpn8     allocated,reserved   0s
mig-medium-training-pod-mig-medium-claim-bk596   pending              0s
mig-medium-training-pod-mig-medium-claim-bk596   pending              0s
mig-medium-training-pod-mig-medium-claim-bk596   allocated,reserved   0s
mig-small-inference-pod-mig-small-claim-d2t58    pending              0s
mig-small-inference-pod-mig-small-claim-d2t58    pending              0s
mig-small-inference-pod-mig-small-claim-d2t58    allocated,reserved   0s

Comme vous pouvez le constater, tous les pods sont passés de « en attente » à « allocated,reserved par le pilote DRA ».

Exécutez nvidia-smi depuis le nœud. Vous remarquerez que trois processeurs Python sont en cours d'exécution :

root@ip-100-64-173-145 bin]# nvidia-smi
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 570.158.01 Driver Version: 570.158.01 CUDA Version: 12.8 |
|-----------------------------------------+------------------------+----------------------+
| 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 A100-SXM4-80GB On | 00000000:10:1C.0 Off | On |
| N/A 63C P0 127W / 400W | 569MiB / 81920MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA A100-SXM4-80GB On | 00000000:10:1D.0 Off | On |
| N/A 56C P0 121W / 400W | 374MiB / 81920MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 2 NVIDIA A100-SXM4-80GB On | 00000000:20:1C.0 Off | On |
| N/A 63C P0 128W / 400W | 467MiB / 81920MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 3 NVIDIA A100-SXM4-80GB On | 00000000:20:1D.0 Off | On |
| N/A 57C P0 118W / 400W | 249MiB / 81920MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+------------------------+----------------------+
| 4 NVIDIA A100-SXM4-80GB On | 00000000:90:1C.0 Off | 0 |
| N/A 51C P0 77W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 5 NVIDIA A100-SXM4-80GB On | 00000000:90:1D.0 Off | 0 |
| N/A 46C P0 69W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 6 NVIDIA A100-SXM4-80GB On | 00000000:A0:1C.0 Off | 0 |
| N/A 52C P0 74W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+
| 7 NVIDIA A100-SXM4-80GB On | 00000000:A0:1D.0 Off | 0 |
| N/A 47C P0 72W / 400W | 0MiB / 81920MiB | 0% Default |
| | | Disabled |
+-----------------------------------------+------------------------+----------------------+


+-----------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+----------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+==================================+===========+=======================|
| 0 2 0 0 | 428MiB / 40192MiB | 42 0 | 3 0 2 0 0 |
| | 2MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 3 0 1 | 71MiB / 19968MiB | 28 0 | 2 0 1 0 0 |
| | 0MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 9 0 2 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 0 10 0 3 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 1 0 0 | 107MiB / 40192MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 5 0 1 | 71MiB / 19968MiB | 28 0 | 2 0 1 0 0 |
| | 0MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 13 0 2 | 161MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 2MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 1 14 0 3 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 2 1 0 0 | 107MiB / 40192MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 2 5 0 1 | 289MiB / 19968MiB | 28 0 | 2 0 1 0 0 |
| | 2MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 2 13 0 2 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 2 14 0 3 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 3 1 0 0 | 107MiB / 40192MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 3 5 0 1 | 71MiB / 19968MiB | 28 0 | 2 0 1 0 0 |
| | 0MiB / 16383MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 3 13 0 2 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+
| 3 14 0 3 | 36MiB / 9728MiB | 14 0 | 1 0 0 0 0 |
| | 0MiB / 8191MiB | | |
+------------------+----------------------------------+-----------+-----------------------+


+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
**| 0 2 0 64080 C python 312MiB |
| 1 13 0 64085 C python 118MiB |
| 2 5 0 64073 C python 210MiB |**
+-----------------------------------------------------------------------------------------+

Définissez un ComputeDomain pour l'entraînement multi-nœuds avec un fichier nommé imex-compute-domain.yaml :

apiVersion: resource.nvidia.com/v1beta1
kind: ComputeDomain
metadata:
  name: distributed-training-domain
  namespace: default
spec:
  numNodes: 2
  channel:
    resourceClaimTemplate:
      name: imex-channel-template

Définissez un Pod en utilisant les canaux IMEX avec un fichier nommé imex-pod.yaml :

apiVersion: v1
kind: Pod
metadata:
  name: imex-distributed-training
  namespace: default
  labels:
    app: imex-training
spec:
  affinity:
    nodeAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
        nodeSelectorTerms:
        - matchExpressions:
          - key: nvidia.com/gpu.clique
            operator: Exists
  containers:
  - name: distributed-training
    image: nvcr.io/nvidia/pytorch:25.04-py3
    command: ["bash", "-c"]
    args:
    - |
      echo "=== IMEX Channel Verification ==="
      ls -la /dev/nvidia-caps-imex-channels/
      echo ""

      echo "=== GPU Information ==="
      nvidia-smi
      echo ""

      echo "=== NCCL Test (if available) ==="
      python -c "
      import torch
      import torch.distributed as dist
      import os

      print(f'CUDA available: {torch.cuda.is_available()}')
      print(f'CUDA device count: {torch.cuda.device_count()}')

      if torch.cuda.is_available():
          for i in range(torch.cuda.device_count()):
              print(f'GPU {i}: {torch.cuda.get_device_name(i)}')

      # Check for IMEX environment variables
      imex_vars = [k for k in os.environ.keys() if 'IMEX' in k or 'NVLINK' in k]
      if imex_vars:
          print('IMEX Environment Variables:')
          for var in imex_vars:
              print(f'  {var}={os.environ[var]}')

      print('IMEX channel verification completed')
      "

      # Keep container running for inspection
      sleep 3600
    resources:
      claims:
      - name: imex-channel-0
      - name: imex-channel-1
  resourceClaims:
  - name: imex-channel-0
    resourceClaimTemplateName: imex-channel-template
  - name: imex-channel-1
    resourceClaimTemplateName: imex-channel-template
  tolerations:
  - key: nvidia.com/gpu
    operator: Exists
    effect: NoSchedule

Déployez IMEX en appliquant les modèles ComputeDomain et :

kubectl apply -f imex-claim-template.yaml
kubectl apply -f imex-compute-domain.yaml
kubectl apply -f imex-pod.yaml

Vérifiez le ComputeDomain statut.

kubectl get computedomain distributed-training-domain

Surveillez le déploiement du daemon IMEX.

kubectl get pods -n nvidia-dra-driver -l [resource.nvidia.com/computeDomain](http://resource.nvidia.com/computeDomain)

Vérifiez les canaux IMEX dans le Pod :

kubectl exec imex-distributed-training -- ls -la /dev/nvidia-caps-imex-channels/

Consultez les journaux du Pod :

kubectl logs imex-distributed-training

Voici un exemple de résultat attendu :

=== IMEX Channel Verification ===
total 0
drwxr-xr-x. 2 root root 80 Jul 8 10:45 .
drwxr-xr-x. 6 root root 380 Jul 8 10:45 ..
crw-rw-rw-. 1 root root 241, 0 Jul 8 10:45 channel0
crw-rw-rw-. 1 root root 241, 1 Jul 8 10:45 channel1