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How to use NVIDIA MIG technology with Kubernetes

Reviewed on 08 July 2024Published on 19 September 2023
Note
  • Scaleway offers MIG-compatible GPU Instances such as H100 PCIe GPU Instances
  • NVIDIA uses the term GPU instance to designate a MIG partition of a GPU (MIG= Multi-Instance GPU).
  • To avoid confusion, we use the term GPU Instance in this document to refer to the Scaleway GPU Instance, and MIG partition in the context of the MIG feature.

NVIDIA Multi-Instance GPU (MIG) is a powerful feature that allows you to divide a single NVIDIA GPU into multiple smaller partitions, each with its dedicated GPU resources, such as memory and compute units. This technology is particularly valuable in Kubernetes (K8s) environments, where efficient resource allocation is crucial for running diverse workloads efficiently.

In this guide, we will explore the capabilities of NVIDIA MIG within a Kubernetes cluster. We cover the steps required to set up and configure MIG-enabled GPUs to use with Kubernetes, to maximize GPU usage and ensure workload isolation and performance predictability.

Before you start

To complete the actions presented below, you must have:

  • A Scaleway account logged into the console
  • A Kubernetes cluster with a GPU Instance as node
Tip

MIG is fully supported on Scaleway managed Kubernetes clusters (Kapsule and Kosmos).

Configure MIG partitions inside a Kubernetes cluster

  1. Find the name of the pods running the Nvidia Driver:

    % kubectl get pods -n kube-system
    NAME READY STATUS RESTARTS AGE
    cilium-operator-fbff794f4-kff42 1/1 Running 0 4h13m
    cilium-sfkgc 1/1 Running 0 4h12m
    cilium-w768l 1/1 Running 0 4h2m
    coredns-7449449ddc-plr8m 1/1 Running 0 4h11m
    csi-node-44xll 2/2 Running 0 4h2m
    csi-node-pg7vd 2/2 Running 0 4h12m
    gpu-feature-discovery-dgjlx 1/1 Running 0 20m
    gpu-operator-6b8db67bfb-2b5f8 1/1 Running 0 4h11m
    konnectivity-agent-mhcqt 1/1 Running 0 4h12m
    konnectivity-agent-vrgqg 1/1 Running 0 4h2m
    kube-proxy-th6g8 1/1 Running 0 4h12m
    kube-proxy-xcdlj 1/1 Running 0 4h2m
    metrics-server-59fb595596-4xlbb 1/1 Running 0 4h11m
    node-problem-detector-cqxnn 1/1 Running 0 4h2m
    node-problem-detector-kr8v5 1/1 Running 0 4h12m
    nvidia-container-toolkit-daemonset-2jtn8 1/1 Running 0 4h1m
    nvidia-cuda-validator-kcgzv 0/1 Completed 0 20m
    nvidia-dcgm-exporter-5bn4w 1/1 Running 0 20m
    nvidia-device-plugin-daemonset-vvm8s 1/1 Running 0 20m
    nvidia-device-plugin-validator-gk6pt 0/1 Completed 0 20m
    nvidia-driver-daemonset-8t89m 1/1 Running 0 4h1m
    nvidia-gpu-operator-node-feature-discovery-master-6fb7d946phbmb 1/1 Running 0 4h11m
    nvidia-gpu-operator-node-feature-discovery-worker-49bwd 1/1 Running 0 4h11m
    nvidia-gpu-operator-node-feature-discovery-worker-xtnnb 1/1 Running 0 4h2m
    nvidia-mig-manager-gf492 1/1 Running 0 3h58m
    nvidia-operator-validator-m4686 1/1 Running 0 20m
  2. Check the status of NVIDIA SMI in the NVIDIA driver container:

    % kubectl exec nvidia-driver-daemonset-8t89m -t -n kube-system -- nvidia-smi -L
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)

    MIG is currently disabled.

  3. Find the name of the H100 GPU node:

    % kubectl get nodes
    NAME STATUS ROLES AGE VERSION
    scw-k8s-jovial-dubinsky-default-8dcea9ad52bc47 Ready <none> 4h12m v1.27.4
    scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 Ready <none> 4h3m v1.27.4
  4. Configure two 3g.40gb MIG partitions by adding a label on the GPU node:

    % kubectl label nodes scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 nvidia.com/mig.config=all-3g.40gb --overwrite
    node/scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 labeled
  5. Check the status of NVIDIA SMI in the NVIDIA driver container:

    % kubectl exec nvidia-driver-daemonset-8t89m -t -n kube-system -- nvidia-smi -L
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 3g.40gb Device 0: (UUID: MIG-3f77eb92-98ee-5f05-8aef-9ec3d3c24b9d)
    MIG 3g.40gb Device 1: (UUID: MIG-13088296-f5a2-5f84-9e37-6105abda4b4f)

    Two MIG 3g.40gb partitions are available now.

Reconfigure MIG partitions inside a Kubernetes cluster

  1. Reconfigure the GPU into seven MIG 1g.10gb partitions by overwriting the existing labels:

    % kubectl label nodes scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 nvidia.com/mig.config=all-1g.10gb --overwrite
    node/scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 labeled
  2. Check the status of NVIDIA SMI in the NVIDIA driver container:

    % kubectl exec nvidia-driver-daemonset-8t89m -t -n kube-system -- nvidia-smi -L
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-222504cc-4a15-589b-8ec8-dbc02e6fb378)
    MIG 1g.10gb Device 1: (UUID: MIG-fdfd2afa-5cbd-5d1d-b1ae-6f0e13cc0ff8)
    MIG 1g.10gb Device 2: (UUID: MIG-b2925bc6-41ca-5ccd-bf5e-24259386f88e)
    MIG 1g.10gb Device 3: (UUID: MIG-083c76fc-5d21-5322-9d50-c8e21a01852f)
    MIG 1g.10gb Device 4: (UUID: MIG-13d8a181-5bc1-5527-9a0f-9c3f9cc1d89e)
    MIG 1g.10gb Device 5: (UUID: MIG-db99bb81-dde3-5c95-9778-daa291fce210)
    MIG 1g.10gb Device 6: (UUID: MIG-2d636152-57c2-5e73-9654-b1d21d6d21fb)

    Seven MIG 1g.10gb partitions are available now.

  3. Look at the NVIDIA labels on the node (note the label nvidia.com/mig.config=all-1g.10gb and nvidia.com/gpu.product=NVIDIA-H100-PCIe-MIG-1g.10gb):

    % kubectl describe nodes scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 | grep "nvidia.com/"
    nvidia.com/cuda.driver.major=525
    nvidia.com/cuda.driver.minor=105
    nvidia.com/cuda.driver.rev=17
    nvidia.com/cuda.runtime.major=12
    nvidia.com/cuda.runtime.minor=0
    nvidia.com/gfd.timestamp=1692810266
    nvidia.com/gpu-driver-upgrade-state=upgrade-done
    nvidia.com/gpu.compute.major=9
    nvidia.com/gpu.compute.minor=0
    nvidia.com/gpu.count=7
    nvidia.com/gpu.deploy.container-toolkit=true
    nvidia.com/gpu.deploy.dcgm=true
    nvidia.com/gpu.deploy.dcgm-exporter=true
    nvidia.com/gpu.deploy.device-plugin=true
    nvidia.com/gpu.deploy.driver=true
    nvidia.com/gpu.deploy.gpu-feature-discovery=true
    nvidia.com/gpu.deploy.mig-manager=true
    nvidia.com/gpu.deploy.node-status-exporter=true
    nvidia.com/gpu.deploy.nvsm=true
    nvidia.com/gpu.deploy.operator-validator=true
    nvidia.com/gpu.engines.copy=1
    nvidia.com/gpu.engines.decoder=1
    nvidia.com/gpu.engines.encoder=0
    nvidia.com/gpu.engines.jpeg=1
    nvidia.com/gpu.engines.ofa=0
    nvidia.com/gpu.family=hopper
    nvidia.com/gpu.machine=SCW-H100-1-80G
    nvidia.com/gpu.memory=9856
    nvidia.com/gpu.multiprocessors=14
    nvidia.com/gpu.present=true
    nvidia.com/gpu.product=NVIDIA-H100-PCIe-MIG-1g.10gb
    nvidia.com/gpu.replicas=1
    nvidia.com/gpu.slices.ci=1
    nvidia.com/gpu.slices.gi=1
    nvidia.com/mig.capable=true
    nvidia.com/mig.config=all-1g.10gb
    nvidia.com/mig.config.state=success
    nvidia.com/mig.strategy=single
    nvidia.com/gpu-driver-upgrade-enabled: true
    nvidia.com/gpu: 7
    nvidia.com/gpu: 7
    nvidia.com/gpu 0 0

Deploy containers that use NVIDIA MIG technology partitions

  1. Write a deployment file to deploy 8 pods executing NVIDIA SMI. Open a text editor of your choice and create a deployment file deploy-mig.yaml, then paste the following content into the file, save it, and exit the editor:

    apiVersion: v1
    kind: Pod
    metadata:
    name: test-1
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-2
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-3
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-4
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-5
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-6
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-7
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
    ---
    apiVersion: v1
    kind: Pod
    metadata:
    name: test-8
    spec:
    restartPolicy: OnFailure
    containers:
    - name: gpu-test
    image: nvcr.io/nvidia/pytorch:23.07-py3
    command: [ "nvidia-smi" ]
    args: ["-L" ]
    resources:
    limits:
    nvidia.com/gpu: 1
    nodeSelector:
    nvidia.com/gpu.product : NVIDIA-H100-PCIe-MIG-1g.10gb
  2. Deploy the pods:

    % kubectl create -f deploy-mig.yaml
    pod/test-1 created
    pod/test-2 created
    pod/test-3 created
    pod/test-4 created
    pod/test-5 created
    pod/test-6 created
    pod/test-7 created
    pod/test-8 created
  3. Display the logs of the pods. The pods print their UUID with the nvidia-smi command:

    % kubectl get -f deploy-mig.yaml -o name | xargs -I{} kubectl logs {}
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-2d636152-57c2-5e73-9654-b1d21d6d21fb)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-db99bb81-dde3-5c95-9778-daa291fce210)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-13d8a181-5bc1-5527-9a0f-9c3f9cc1d89e)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-b2925bc6-41ca-5ccd-bf5e-24259386f88e)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-fdfd2afa-5cbd-5d1d-b1ae-6f0e13cc0ff8)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-222504cc-4a15-589b-8ec8-dbc02e6fb378)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-083c76fc-5d21-5322-9d50-c8e21a01852f)
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)
    MIG 1g.10gb Device 0: (UUID: MIG-fdfd2afa-5cbd-5d1d-b1ae-6f0e13cc0ff8)

    As you can see, seven pods have been executed on different MIG partitions, while the eighth pod had to wait for one of the seven MIG partitions to become available to be executed.

  4. Clean the deployment:

    % kubectl delete -f deploy-mig.yaml
    pod "test-1" deleted
    pod "test-2" deleted
    pod "test-3" deleted
    pod "test-4" deleted
    pod "test-5" deleted
    pod "test-6" deleted
    pod "test-7" deleted
    pod "test-8" deleted

Disable MIG inside a Kubernetes cluster

  1. Disable MIG by overwriting the Kubernetes labels:

    % kubectl label nodes scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 nvidia.com/mig.config=all-disabled --overwrite
    node/scw-k8s-jovial-dubinsky-pool-h100-93a072191d38 labeled
  2. Check the status of NVIDIA SMI in the driver pod:

    % kubectl exec nvidia-driver-daemonset-8t89m -t -n kube-system -- nvidia-smi -L
    GPU 0: NVIDIA H100 PCIe (UUID: GPU-717ef73c-2d43-4fdc-76d2-1cddef4863bb)

    MIG is disabled and the whole GPU is available.

Tip

To enable autoscaling with Kubernetes Kapsule, it is necessary to designate a tag for the node pool. This tag will be automatically inherited as a label by all nodes within that pool. Therefore, to create a node pool offering H100 nodes configured with the label nvidia.com/mig.config=all-3g.40g, simply assign the tag noprefix=nvidia.com/mig.config=all-3g.40gb to the corresponding Kapsule node pool. All nodes added by the autoscaler will automatically receive the label MIG. Note, that updates to a tag may take up to five minutes to fully propagate.

For more information about NVIDIA MIG, refer to the official NVIDIA MIG user guide and the Kubernetes GPU operator documentation.

See also
How to use NVIDIA MIG technology on GPU InstancesHow to use the scratch storage on H100 GPU Instances
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