Deploy GPU workloads in Autopilot

This page shows you how to request hardware accelerators (GPUs) in your Google Kubernetes Engine (GKE) Autopilot workloads.

Autopilot provides the specialized Accelerator compute class to run GPU Pods. With this compute class, GKE places Pods on GPU nodes, providing the Pods with access to advanced capabilities on the virtual machine (VM). You can also optionally run GPU Pods without selecting the Accelerator compute class. To learn more about the benefits of the Accelerator compute class, see When to use specific compute classes.

Pricing

Autopilot bills you differently depending whether you requested the Accelerator compute class to run your GPU workloads.

Use the Accelerator compute class? Pricing Compatibility with GKE capabilities
You're billed for the Compute Engine hardware that runs your GPU workloads, plus an Autopilot premium for automatic node management and scalability. For details, see Autopilot mode pricing.

Compatible with the following:
You're billed based on the GPU Pod resource requests. For details, see the "GPU Pods" section in Kubernetes Engine pricing.

Compatible with the following:

Before you begin

Before you start, make sure you have performed the following tasks:

  • Enable the Google Kubernetes Engine API.
    • Enable Google Kubernetes Engine API
  • If you want to use the Google Cloud CLI for this task, install and then initialize the gcloud CLI. If you previously installed the gcloud CLI, get the latest version by running gcloud components update.

  • Ensure that you have a GKE Autopilot cluster running one of the following versions:

    • Accelerator compute class: Any patch version of 1.28 starting with 1.28.6-gke.1095000
      • NVIDIA H100 (80GB) GPUs: 1.28.6-gke.1369000 or later, and 1.29.1-gke.1575000 or later
      • Multiple GPU Pods per VM: 1.29.2-gke.1355000 or later

    • No compute class selection:

      • NVIDIA L4 GPUs: 1.28.3-gke.1203000 or later
      • NVIDIA A100 (80GB) GPUs: 1.27 or later
      • All other GPUs: 1.24.2-gke.1800 or later

  • Ensure that you have enough GPU quotas available in your project. You must have enough Compute Engine GPU quota for the GPU models that you want to create in each region. If you require additional GPU quota, request GPU quota.

Limitations

  • Time-sharing GPUs and multi-instance GPUs are available with Autopilot on GKE version 1.29.3-gke.1093000 and later.
  • GPU availability depends on the Google Cloud region of your Autopilot cluster, and your GPU quota. To find a GPU model by region or zone, see GPU regions and zones availability.
  • For NVIDIA A100 (80GB) GPUs, you're charged a fixed price for the Local SSDs attached to the nodes, regardless of whether your Pods use that capacity.
  • For GKE versions prior to 1.29.2-gke.1355000, if you explicitly request a specific existing GPU node for your Pod, the Pod must consume all the GPU resources on the node. For example, if the existing node has 8 GPUs and your Pod's containers request a total of 4 GPUs, Autopilot rejects the Pod.
  • For GKE version 1.29.2-gke.1355000 or later, if you want multiple GPU pods to fit into a single node, the sum of GPU requests for those pods must be less than or equal to the number of GPU resources attached to that node. For example, a node with a gke-accelerator-count of 4 could accommodate up to four Pods that request one GPU each.

Placing multiple Pods on a single GPU node is useful in situations like the following:

  • You have capacity reservations for large Accelerator machine types and you run single-GPU workloads, so deploying one Pod per node would waste the other GPUs on that machine
  • You have GPU workloads that must run on the same host

In these situations, we recommend that you use all of the GPUs on the node by ensuring that the sum of Pod GPU resource requests on the node is equal to the number of GPUs attached to the node.

Request GPUs in your containers

To request GPU resources for your containers, add the following fields to your Pod specification. Depending on your workload requirements, you can optionally omit the cloud.google.com/compute-class: "Accelerator" and the cloud.google.com/gke-accelerator-count fields.

apiVersion: v1
kind: Pod
metadata:
  name: my-gpu-pod
spec:
  nodeSelector:
    cloud.google.com/compute-class: "Accelerator"
    cloud.google.com/gke-accelerator: GPU_TYPE
    cloud.google.com/gke-accelerator-count: GPU_COUNT
  containers:
  - name: my-gpu-container
    image: nvidia/cuda:11.0.3-runtime-ubuntu20.04
    command: ["/bin/bash", "-c", "--"]
    args: ["while true; do sleep 600; done;"]
    resources:
      limits:
        nvidia.com/gpu: GPU_QUANTITY

Replace the following:

  • GPU_TYPE: the type of GPU hardware. Allowed values are the following:
    • nvidia-h100-80gb: NVIDIA H100 (80GB) (only available with Accelerator compute class)
    • nvidia-a100-80gb: NVIDIA A100 (80GB)
    • nvidia-tesla-a100: NVIDIA A100 (40GB)
    • nvidia-l4: NVIDIA L4
    • nvidia-tesla-t4: NVIDIA T4
  • GPU_COUNT: the total number of GPUs available to attach to the node. Must be greater than or equal to GPU_QUANTITY and a supported GPU quantity for the GPU type you selected. If you omit this nodeSelector, Autopilot places one Pod on each GPU node.
  • GPU_QUANTITY: the number of GPUs to allocate to the container. Must be less than or equal to GPU_COUNT and a supported GPU quantity for the GPU type you selected.

You must specify both the GPU type and the GPU quantity in your Pod specification. If you omit either of these values, Autopilot rejects your Pod.

When you deploy this manifest, Autopilot automatically installs the default NVIDIA drivers for the node GKE version. In version 1.29.2-gke.1108000 and later, you can optionally choose to install the latest driver version for that GKE version by adding the following node selector to your manifest:

spec:
  nodeSelector:
    cloud.google.com/gke-gpu-driver-version: "DRIVER_VERSION"

Replace DRIVER_VERSION with one of the following values:

  • default - the default, stable driver for your node GKE version. If you omit the nodeSelector in your manifest, this is the default option.
  • latest - the latest available driver version for your node GKE version.

CPU and memory requests for Autopilot GPU Pods

When defining your GPU Pods, you should also request CPU and memory resources so that your containers perform as expected. Autopilot enforces specific CPU and memory minimums, maximums, and defaults based on the GPU type and quantity. If you run multiple GPU Pods on a single node, specify the CPU and memory, otherwise it will default to the node's entire capacity. For details, refer to Resource requests in Autopilot.

Your Pod specification should look similar to the following example, which requests four T4 GPUs:

apiVersion: v1
kind: Pod
metadata:
  name: t4-pod
spec:
  nodeSelector:
    cloud.google.com/compute-class: "Accelerator"
    cloud.google.com/gke-accelerator: "nvidia-tesla-t4"
  containers:
  - name: t4-container-1
    image: nvidia/cuda:11.0.3-runtime-ubuntu20.04
    command: ["/bin/bash", "-c", "--"]
    args: ["while true; do sleep 600; done;"]
    resources:
      limits:
        nvidia.com/gpu: 3
        cpu: "54"
        memory: "54Gi"
      requests:
        cpu: "54"
        memory: "54Gi"
  - name: t4-container-2
    image: nvidia/cuda:11.0.3-runtime-ubuntu20.04
    command: ["/bin/bash", "-c", "--"]
    args: ["while true; do sleep 600; done;"]
    resources:
      limits:
        nvidia.com/gpu: 1
        cpu: "18"
        memory: "18Gi"
      requests:
        cpu: "18"
        memory: "18Gi"

This manifest specifies limits for CPU and memory resources. If you omit the limits for CPU or memory in GKE version 1.29.2-gke.1060000 and later, GKE gives your Pods the Burstable QoS class and lets your Pods burst into unused resources from the sum of resource requests on the node. For more information, see Configure Pod bursting in GKE.

Ephemeral storage requests for Autopilot GPU Pods

You can also request ephemeral storage in Pods that need short-lived storage. The maximum available ephemeral storage and the type of storage hardware used depends on the type and quantity of GPUs the Pod requests. You can use Local SSD for ephemeral storage if using NVIDIA L4 GPUs, the Accelerator compute class, and running GKE patch version 1.28.6-gke.1369000 and later or 1.29.1-gke.1575000 and later.

To use Local SSD for ephemeral storage, add the cloud.google.com/gke-ephemeral-storage-local-ssd: "true" nodeSelector to your workload manifest. See the example manifest in Use Local SSD-backed ephemeral storage with Autopilot clusters. The NVIDIA H100 (80GB) GPUs and NVIDIA A100 (80GB) GPUs always use Local SSDs for ephemeral storage, and you can't specify this node selector for those GPUs.

Verify GPU allocation

To check that a deployed GPU workload has the requested GPUs, run the following command:

kubectl describe node NODE_NAME

Replace NODE_NAME with the name of the node on which the Pod was scheduled.

The output is similar to the following:


apiVersion: v1
kind: Node
metadata:
...
  labels:
    ...
    cloud.google.com/gke-accelerator: nvidia-tesla-t4
    cloud.google.com/gke-accelerator-count: "1"
    cloud.google.com/machine-family: custom-48
    ...
...

Check GPU driver version

In Autopilot clusters, GKE automatically installs NVIDIA device drivers on all GPU nodes. To find the driver version that GKE installed in your cluster, run the following command:

kubectl logs --label=k8s-app=nvidia-gpu-device-plugin \
    --container="nvidia-gpu-device-plugin" \
    --tail=-1 \
    --namespace=kube-system | grep Driver

The output is similar to the following:

I1206 18:37:08.251742    5851 metrics.go:144] nvml initialized successfully. Driver version: 535.104.12

How GPU allocation works in Autopilot

After you request a GPU type and a quantity for the containers in a Pod and deploy the Pod, the following happens:

  1. If no allocatable GPU node exists, Autopilot provisions a new GPU node to schedule the Pod. Autopilot automatically installs NVIDIA's drivers to facilitate the hardware.
  2. Autopilot adds node taints to the GPU node and adds the corresponding tolerations to the Pod. This prevents GKE from scheduling other Pods on the GPU node.

Autopilot places exactly one GPU Pod on each GPU node, as well as any GKE-managed workloads that run on all nodes, and any DaemonSets that you configure to tolerate all node taints.

Run DaemonSets on every node

You might want to run DaemonSets on every node, even nodes with applied taints. For example, some logging and monitoring agents must run on every node in the cluster. You can configure those DaemonSets to ignore node taints so that GKE places those workloads on every node.

To run DaemonSets on every node in your cluster, including your GPU nodes, add the following toleration to your specification:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: logging-agent
spec:
  tolerations:
  - key: ""
    operator: "Exists"
    effect: ""
  containers:
  - name: logging-agent-v1
    image: IMAGE_PATH

To run DaemonSets on specific GPU nodes in your cluster, add the following to your specification:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: logging-agent
spec:
  nodeSelector:
    cloud.google.com/gke-accelerator: "GPU_TYPE"
  tolerations:
  - key: ""
    operator: "Exists"
    effect: ""
  containers:
  - name: logging-agent-v1
    image: IMAGE_PATH

Replace GPU_TYPE with the type of GPU in your target nodes. Can be one of the following:

  • nvidia-h100-80gb: NVIDIA H100 (80GB) (only available with Accelerator compute class)
  • nvidia-a100-80gb: NVIDIA A100 (80GB)
  • nvidia-tesla-a100: NVIDIA A100 (40GB)
  • nvidia-l4: NVIDIA L4
  • nvidia-tesla-t4: NVIDIA T4

GPU use cases in Autopilot

You can allocate GPUs to containers in Autopilot Pods to facilitate workloads such as the following:

  • Machine learning (ML) inference
  • ML training
  • Rendering

Supported GPU quantities

When you request GPUs in your Pod specification, you must use the following quantities based on the GPU type:

GPU quantities
NVIDIA L4
nvidia-l4		 1, 2, 4, 8
NVIDIA T4
nvidia-tesla-t4		 1, 2, 4
NVIDIA A100 (40GB)
nvidia-tesla-a100		 1, 2, 4, 8, 16
NVIDIA A100 (80GB)
nvidia-a100-80gb		 1, 2, 4, 8
NVIDIA H100 (80GB)
nvidia-h100-80gb		 8

If you request a GPU quantity that isn't supported for that type, Autopilot rejects your Pod.

Monitor GPU nodes

If your GKE cluster has system metrics enabled, then the following metrics are available in Cloud Monitoring to monitor your GPU workload performance:

  • Duty Cycle (container/accelerator/duty_cycle): Percentage of time over the past sample period (10 seconds) during which the accelerator was actively processing. Between 1 and 100.
  • Memory Usage (container/accelerator/memory_used): Amount of accelerator memory allocated in bytes.
  • Memory Capacity (container/accelerator/memory_total): Total accelerator memory in bytes.

You can use predefined dashboards to monitor your clusters with GPU nodes. For more information, see View observability metrics. For general information about monitoring your clusters and their resources, refer to Observability for GKE.

View usage metrics for workloads

You view your workload GPU usage metrics from the Workloads dashboard in the Google Cloud console.

To view your workload GPU usage, perform the following steps:

  1. Go to the Workloads page in the Google Cloud console.

    Go to Workloads
  2. Select a workload.

The Workloads dashboard displays charts for GPU memory usage and capacity, and GPU duty cycle.

What's next