Run a large-scale workload with flex-start with queued provisioning

This page shows you how to optimize GPU obtainability for large-scale batch and AI workloads with GPUs using flex-start with queued provisioning powered by Dynamic Workload Scheduler.

Before reading this page, ensure that you're familiar with the following:

This guide is intended for Machine learning (ML) engineers, Platform admins and operators, and for Data and AI specialists who are interested in using Kubernetes container orchestration capabilities for running batch workloads. For more information about common roles and example tasks that we reference in Google Cloud content, see Common GKE Enterprise user roles and tasks.

How flex-start with queued provisioning works

With flex-start with queued provisioning, GKE allocates all requested resources at the same time. Flex-start with queued provisioning uses the following tools:

  • Flex-start with queued provisioning is based on Dynamic Workload Scheduler combined with the Provisioning Request custom resource definition (CRD). These tools manage the capacity allocated based on the available resources and your workload requirements.
  • (Optional) Kueue automates the lifecycle of flex-start with queued provisioning requests. Kueue implements Job queueing and automatically handles the Provisioning Request lifecycle.

To use flex-start with queued provisioning, you have to add the --flex-start and --enable-queued-provisioning flags when you create the node pool.

Best practice:

Use flex-start with queued provisioning for large-scale batch and AI workloads when your workloads meet the following criteria:

  • Your workloads have flexible start times.
  • Your workloads are required to run across multiple nodes simultaneously.

For smaller workloads that can run on a single node, use flex-start provisioning mode. For more information about GPU provisioning in GKE, see Obtain accelerators for AI workloads.

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.

Use node pools with flex-start with queued provisioning

This section applies to Standard clusters only.

You can use any of the following methods to designate that flex-start with queued provisioning can work with specific node pools in your cluster:

Create a node pool

Create a node pool that has flex-start with queued provisioning enabled by using the gcloud CLI:

gcloud container node-pools create NODEPOOL_NAME \
    --cluster=CLUSTER_NAME \
    --location=LOCATION \
    --enable-queued-provisioning \
    --accelerator type=GPU_TYPE,count=AMOUNT,gpu-driver-version=DRIVER_VERSION \
    --machine-type=MACHINE_TYPE \
    --flex-start \
    --enable-autoscaling  \
    --num-nodes=0   \
    --total-max-nodes TOTAL_MAX_NODES  \
    --location-policy=ANY  \
    --reservation-affinity=none  \
    --no-enable-autorepair

Replace the following:

  • NODEPOOL_NAME: The name you choose for the node pool.
  • CLUSTER_NAME: The name of the cluster.
  • LOCATION: The cluster's Compute Engine region, such as us-central1.
  • GPU_TYPE: The GPU type.
  • AMOUNT: The number of GPUs to attach to nodes in the node pool.
  • DRIVER_VERSION: the NVIDIA driver version to install. Can be one of the following:
    • default: Install the default driver version for your GKE version.
    • latest: Install the latest available driver version for your GKE version. Available only for nodes that use Container-Optimized OS.
  • TOTAL_MAX_NODES: the maximum number of nodes to automatically scale for the entire node pool.

  • MACHINE_TYPE: The Compute Engine machine type for your nodes.

    Best practice:

    Use an accelerator-optimized machine type to improve performance and efficiency for AI/ML workloads.

Optionally, you can use the following flags:

  • --node-locations=COMPUTE_ZONES: The comma-separated list of one or more zones where GKE creates the GPU nodes. The zones must be in the same region as the cluster. Choose zones that have available GPUs.
  • --enable-gvnic: This flag enables gVNIC on the GPU node pools to increase network traffic speed.

This command creates a node pool with the following configuration:

  • The --flex-start flag combined with the --enable-queued-provisioning flag instructs GKE to create a node pool with flex-start with queued provisioning enabled and to add the cloud.google.com/gke-queued taint to the node pool.
  • GKE enables queued provisioning and cluster autoscaling.
  • The node pool initially has zero nodes.
  • The --no-enable-autorepair flag disables automatic repairs, which could disrupt workloads that run on repaired nodes.

Enable node auto-provisioning to create node pools for flex-start with queued provisioning

You can use node auto-provisioning to manage node pools for flex-start with queued provisioning for clusters running version 1.29.2-gke.1553000 or later. When you enable node auto-provisioning, GKE creates node pools with the required resources for the associated workload.

To enable node auto-provisioning, consider the following settings and complete the steps in Configure GPU limits:

  • Specify the required resources for flex-start with queued provisioning when you enable the feature. To list the available resourceTypes, run the gcloud compute accelerator-types list command.
  • Use the --no-enable-autoprovisioning-autorepair flag to disable node node auto-repair.
  • Let GKE automatically install GPU drivers in auto-provisioned GPU nodes. For more information, see Installing drivers using node auto-provisioning with GPUs.

Run your batch and AI workloads with flex-start with queued provisioning

To run batch workloads with flex-start with queued provisioning use any of the following configurations:

Best practice:

Use Kueue to run your batch and AI workloads with flex-start with queued provisioning.

Flex-start with queued provisioning for Jobs with Kueue

The following sections show you how to configure the flex-start with queued provisioning for Jobs with Kueue:

  • Flex-start with queued provisioning node pool setup.
  • Reservation and flex-start with queued provisioning node pool setup.

This section uses the samples in the dws-examples directory from the ai-on-gke repository. We have published the samples in the dws-examples directory under the Apache2 license.

You need to have administrator permissions to install Kueue. To gain them, make sure you are granted the IAM role roles/container.admin. To find out more about GKE IAM roles, see Create IAM allow policies guide.

Prepare your environment

  1. In Cloud Shell, run the following command:

    git clone https://github.com/GoogleCloudPlatform/ai-on-gke
cd ai-on-gke/tutorials-and-examples/workflow-orchestration/dws-examples

  2. Install the latest Kueue version in your cluster:

    VERSION=KUEUE_VERSION
kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/download/$VERSION/manifests.yaml

    Replace KUEUE_VERSION with the latest Kueue version.

If you use Kueue in version earlier than 0.7.0, change the Kueue feature gate configuration by setting the ProvisioningACC feature gate to true. See Kueue's feature gates for more detailed explanation and default gate values. For more information about Kueue installation, see Installation.

Create the Kueue resources for the Dynamic Workload Scheduler node pool only setup

With the following manifest, you create a cluster-level queue named dws-cluster-queue and the LocalQueue namespace named dws-local-queue. Jobs that refer to dws-cluster-queue queue in this namespace use flex-start with queued provisioning to get the GPU resources.

apiVersion: kueue.x-k8s.io/v1beta1
kind: ResourceFlavor
metadata:
  name: "default-flavor"
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: AdmissionCheck
metadata:
  name: dws-prov
spec:
  controllerName: kueue.x-k8s.io/provisioning-request
  parameters:
    apiGroup: kueue.x-k8s.io
    kind: ProvisioningRequestConfig
    name: dws-config
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ProvisioningRequestConfig
metadata:
  name: dws-config
spec:
  provisioningClassName: queued-provisioning.gke.io
  managedResources:
  - nvidia.com/gpu
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "dws-cluster-queue"
spec:
  namespaceSelector: {}
  resourceGroups:
  - coveredResources: ["cpu", "memory", "nvidia.com/gpu", "ephemeral-storage"]
    flavors:
    - name: "default-flavor"
      resources:
      - name: "cpu"
        nominalQuota: 1000000000    # "Infinite" quota
      - name: "memory"
        nominalQuota: 1000000000Gi  # "Infinite" quota
      - name: "nvidia.com/gpu"
        nominalQuota: 1000000000    # "Infinite" quota
      - name: "ephemeral-storage"
        nominalQuota: 1000000000Ti  # "Infinite" quota
  admissionChecks:
  - dws-prov
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  namespace: "default"
  name: "dws-local-queue"
spec:
  clusterQueue: "dws-cluster-queue"
---

This cluster's queue has high quota limits and only the flex-start with queued provisioning integration is enabled. For more information about Kueue APIs and how to set up limits, see Kueue concepts.

Deploy the LocalQueue:

kubectl create -f ./dws-queues.yaml

The output is similar to the following:

resourceflavor.kueue.x-k8s.io/default-flavor created
admissioncheck.kueue.x-k8s.io/dws-prov created
provisioningrequestconfig.kueue.x-k8s.io/dws-config created
clusterqueue.kueue.x-k8s.io/dws-cluster-queue created
localqueue.kueue.x-k8s.io/dws-local-queue created

If you want to run Jobs that use flex-start with queued provisioning in other namespaces, you can create additional LocalQueues using the preceding template.

Run your Job

In the following manifest, the sample Job uses flex-start with queued provisioning:

apiVersion: batch/v1
kind: Job
metadata:
  name: sample-job
  namespace: default
  labels:
    kueue.x-k8s.io/queue-name: dws-local-queue
  annotations:
    provreq.kueue.x-k8s.io/maxRunDurationSeconds: "600"
spec:
  parallelism: 1
  completions: 1
  suspend: true
  template:
    spec:
      nodeSelector:
        cloud.google.com/gke-nodepool: NODEPOOL_NAME
      tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
      containers:
      - name: dummy-job
        image: gcr.io/k8s-staging-perf-tests/sleep:v0.0.3
        args: ["120s"]
        resources:
          requests:
            cpu: "100m"
            memory: "100Mi"
            nvidia.com/gpu: 1
          limits:
            cpu: "100m"
            memory: "100Mi"
            nvidia.com/gpu: 1
      restartPolicy: Never

This manifest includes the following fields that are relevant for the flex-start with queued provisioning configuration:

  • The kueue.x-k8s.io/queue-name: dws-local-queue label tells GKE that Kueue is responsible for orchestrating that Job. This label also defines the queue where the Job is queued.
  • The flag suspend: true tells GKE to create the Job resource but to not schedule the Pods yet. Kueue changes that flag to false when the nodes are ready for the Job execution.
  • nodeSelector tells GKE to schedule the Job only on the specified node pool. The value should match NODEPOOL_NAME, the name of the node pool with queued provisioning enabled.

  1. Run your Job:

    kubectl create -f ./job.yaml

    The output is similar to the following:

    job.batch/sample-job created

  2. Check the status of your Job:

    kubectl describe job sample-job

    The output is similar to the following:

    Events:
  Type    Reason            Age    From                        Message
  ----    ------            ----   ----                        -------
  Normal  Suspended         5m17s  job-controller              Job suspended
  Normal  CreatedWorkload   5m17s  batch/job-kueue-controller  Created Workload: default/job-sample-job-7f173
  Normal  Started           3m27s  batch/job-kueue-controller  Admitted by clusterQueue dws-cluster-queue
  Normal  SuccessfulCreate  3m27s  job-controller              Created pod: sample-job-9qsfd
  Normal  Resumed           3m27s  job-controller              Job resumed
  Normal  Completed         12s    job-controller              Job completed

The flex-start with queued provisioning with Kueue integration also supports other workload types available in the open source ecosystem, like the following:

  • RayJob
  • JobSet v0.5.2 or later
  • Kubeflow MPIJob, TFJob, PyTorchJob.
  • Kubernetes Pods that are frequently used by workflow orchestrators
  • Flux mini cluster

For more information about this support, see Kueue's batch user.

Create the Kueue resources for Reservation and Dynamic Workload Scheduler node pool setup

With the following manifest, you create two ResourceFlavors tied to two different node pools: reservation-nodepool and dws-nodepool. The name of these node pools are only exemplary names. Modify these names according to your node pool configuration. Additionally, with the ClusterQueue configuration, incoming Jobs try to use reservation-nodepool, and if there is no capacity then these Jobs use Dynamic Workload Scheduler to get the GPU resources.

apiVersion: kueue.x-k8s.io/v1beta1
kind: ResourceFlavor
metadata:
  name: "reservation"
spec:
  nodeLabels:
    cloud.google.com/gke-nodepool: "reservation-nodepool" # placeholder value
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ResourceFlavor
metadata:
  name: "dws"
spec:
  nodeLabels:
    cloud.google.com/gke-nodepool: "dws-nodepool" # placeholder value
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: "cluster-queue"
spec:
  namespaceSelector: {} # match all.
  resourceGroups:
  - coveredResources: ["cpu", "memory", "nvidia.com/gpu"]
    flavors:
    - name: "reservation" # first we try reservation
      resources:
      - name: "cpu"
        nominalQuota: 9
      - name: "memory"
        nominalQuota: 36Gi
      - name: "nvidia.com/gpu"
        nominalQuota: 9
    - name: "dws"         # if reservation is saturated we try dws
      resources:
      - name: "cpu"
        nominalQuota: 1000000000    # "Infinite" quota
      - name: "memory"
        nominalQuota: 1000000000Gi  # "Infinite" quota
      - name: "nvidia.com/gpu"
        nominalQuota: 1000000000    # "Infinite" quota
  admissionChecksStrategy:
    admissionChecks:
      - name: "dws-prov"
        onFlavors: [dws]
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  namespace: "default"
  name: "user-queue"
spec:
  clusterQueue: "cluster-queue"
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: AdmissionCheck
metadata:
  name: dws-prov
spec:
  controllerName: kueue.x-k8s.io/provisioning-request
  parameters:
    apiGroup: kueue.x-k8s.io
    kind: ProvisioningRequestConfig
    name: dws-config
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: ProvisioningRequestConfig
metadata:
  name: dws-config
spec:
  provisioningClassName: queued-provisioning.gke.io
  managedResources:
  - nvidia.com/gpu

This cluster's queue has high quota limits and only the flex-start with queued provisioning integration is enabled. For more information about Kueue APIs and how to set up limits, see Kueue concepts.

Deploy the manifest using the following command:

kubectl create -f ./dws_and_reservation.yaml

The output is similar to the following:

resourceflavor.kueue.x-k8s.io/reservation created
resourceflavor.kueue.x-k8s.io/dws created
clusterqueue.kueue.x-k8s.io/cluster-queue created
localqueue.kueue.x-k8s.io/user-queue created
admissioncheck.kueue.x-k8s.io/dws-prov created
provisioningrequestconfig.kueue.x-k8s.io/dws-config created

Run your Job

Contrary to the preceding setup, this manifest does not include the nodeSelector field because it's filled by Kueue, depending on the free capacity in the ClusterQueue.

apiVersion: batch/v1
kind: Job
metadata:
  generateName: sample-job-
  namespace: default
  labels:
    kueue.x-k8s.io/queue-name: user-queue
  annotations:
    provreq.kueue.x-k8s.io/maxRunDurationSeconds: "600"
spec:
  parallelism: 1
  completions: 1
  suspend: true
  template:
    spec:
      tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
      containers:
      - name: dummy-job
        image: gcr.io/k8s-staging-perf-tests/sleep:v0.0.3
        args: ["120s"]
        resources:
          requests:
            cpu: "100m"
            memory: "100Mi"
            nvidia.com/gpu: 1
          limits:
            cpu: "100m"
            memory: "100Mi"
            nvidia.com/gpu: 1
      restartPolicy: Never

  1. Run your Job:

    kubectl create -f ./job-without-node-selector.yaml

    The output is similar to the following:

    job.batch/sample-job-v8xwm created

To identify which node pool your Job uses, you need to find out what ResourceFlavor your Job uses.

Troubleshooting

For more information about Kueue's troubleshooting, see Troubleshooting Provisioning Request in Kueue.

Flex-start with queued provisioning for Jobs without Kueue

Define a ProvisioningRequest object

Create a request through the Provisioning Request for each Job. Flex-start with queued provisioning doesn't start the Pods, it only provisions the nodes.

  1. Create the following provisioning-request.yaml manifest:

    Standard 

    apiVersion: v1
kind: PodTemplate
metadata:
  name: POD_TEMPLATE_NAME
  namespace: NAMESPACE_NAME
  labels:
    cloud.google.com/apply-warden-policies: "true"
template:
  spec:
    nodeSelector:
      cloud.google.com/gke-nodepool: NODEPOOL_NAME
      cloud.google.com/gke-flex-start: "true"
    tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
    containers:
      - name: pi
        image: perl
        command: ["/bin/sh"]
        resources:
          limits:
            cpu: "700m"
            nvidia.com/gpu: 1
          requests:
            cpu: "700m"
            nvidia.com/gpu: 1
    restartPolicy: Never
---
apiVersion: autoscaling.x-k8s.io/API_VERSION
kind: ProvisioningRequest
metadata:
  name: PROVISIONING_REQUEST_NAME
  namespace: NAMESPACE_NAME
spec:
  provisioningClassName: queued-provisioning.gke.io
  parameters:
    maxRunDurationSeconds: "MAX_RUN_DURATION_SECONDS"
  podSets:
  - count: COUNT
    podTemplateRef:
      name: POD_TEMPLATE_NAME

    Replace the following:

    • API_VERSION: The version of the API, either v1 or v1beta1. For GKE version 1.31.1-gke.1678000 and later, we recommend using v1 for stability and access to the latest features.
    • NAMESPACE_NAME: The name of your Kubernetes namespace. The namespace must be the same as the namespace of the Pods.
    • PROVISIONING_REQUEST_NAME: The name of the ProvisioningRequest. You'll refer to this name in the Pod annotation.
    • MAX_RUN_DURATION_SECONDS: Optionally, the maximum runtime of a node in seconds, up to the default of seven days. To learn more, see How flex-start with queued provisioning works. You can't change this value after creation of the request. This field is available in GKE version 1.28.5-gke.1355000 or later.
    • COUNT: Number of Pods requested. The nodes are scheduled atomically in one zone.
    • POD_TEMPLATE_NAME: The name of the PodTemplate.
    • NODEPOOL_NAME: The name you choose for the node pool. Remove if you want to use an auto-provisioned node pool.

    GKE might apply validations and mutations to Pods during their creation. The cloud.google.com/apply-warden-policies label allows GKE to apply the same validations and mutations to PodTemplate objects. This label is necessary for GKE to calculate node resource requirements for your Pods. The flex-start with queued provisioning integration supports only one PodSet spec. If you want to mix different Pod templates, use the template that requests the most resources. Mixing different machine types, such as VMs with different GPU types, is not supported.

    Node auto-provisioning 

    apiVersion: v1
kind: PodTemplate
metadata:
  name: POD_TEMPLATE_NAME
  namespace: NAMESPACE_NAME
  labels:
    cloud.google.com/apply-warden-policies: "true"
template:
  spec:
    nodeSelector:
      cloud.google.com/gke-accelerator: GPU_TYPE
      cloud.google.com/gke-flex-start: "true"
    tolerations:
      - key: "nvidia.com/gpu"
        operator: "Exists"
        effect: "NoSchedule"
    containers:
      - name: pi
        image: perl
        command: ["/bin/sh"]
        resources:
          limits:
            cpu: "700m"
            nvidia.com/gpu: 1
          requests:
            cpu: "700m"
            nvidia.com/gpu: 1
    restartPolicy: Never
---
apiVersion: autoscaling.x-k8s.io/API_VERSION
kind: ProvisioningRequest
metadata:
  name: PROVISIONING_REQUEST_NAME
  namespace: NAMESPACE_NAME
spec:
  provisioningClassName: queued-provisioning.gke.io
  parameters:
    maxRunDurationSeconds: "MAX_RUN_DURATION_SECONDS"
  podSets:
  - count: COUNT
    podTemplateRef:
      name: POD_TEMPLATE_NAME

    Replace the following:

    • API_VERSION: The version of the API, either v1 or v1beta1. For GKE version 1.31.1-gke.1678000 and later, we recommend using v1 for stability and access to the latest features.
    • NAMESPACE_NAME: The name of your Kubernetes namespace. The namespace must be the same as the namespace of the Pods.
    • PROVISIONING_REQUEST_NAME: The name of the ProvisioningRequest. You'll refer to this name in the Pod annotation.
    • MAX_RUN_DURATION_SECONDS: Optionally, the maximum runtime of a node in seconds, up to the default of seven days. To learn more, see How flex-start with queued provisioning works. You can't change this value after creation of the request. This field is available in GKE version 1.28.5-gke.1355000 or later.
    • COUNT: Number of Pods requested. The nodes are scheduled atomically in one zone.
    • POD_TEMPLATE_NAME: The name of the PodTemplate.
    • GPU_TYPE: the type of GPU hardware.

    GKE might apply validations and mutations to Pods during their creation. The cloud.google.com/apply-warden-policies label allows GKE to apply the same validations and mutations to PodTemplate objects. This label is necessary for GKE to calculate node resource requirements for your Pods.

  2. Apply the manifest:

    kubectl apply -f provisioning-request.yaml

Configure the Pods

This section uses Kubernetes Jobs to configure the Pods. However, you can also use a Kubernetes JobSet or any other framework like Kubeflow, Ray, or custom controllers. In the Job spec, link the Pods to the ProvisioningRequest using the following annotations:

apiVersion: batch/v1
kind: Job
spec:
  template:
    metadata:
      annotations:
        autoscaling.x-k8s.io/consume-provisioning-request: PROVISIONING_REQUEST_NAME
        autoscaling.x-k8s.io/provisioning-class-name: "queued-provisioning.gke.io"
    spec:
      ...

Prior to GKE version 1.30.3-gke.1854000, you must use the following legacy annotations:

      annotations:
        cluster-autoscaler.kubernetes.io/consume-provisioning-request: PROVISIONING_REQUEST_NAME
        cluster-autoscaler.kubernetes.io/provisioning-class-name: "queued-provisioning.gke.io"

Note that starting with GKE version 1.31.1-gke.1678000 the cluster-autoscaler.kubernetes.io/consume-provisioning-request and cluster-autoscaler.kubernetes.io/provisioning-class-name annotations are deprecated.

The Pod annotation key consume-provisioning-request defines which ProvisioningRequest to consume. GKE uses the consume-provisioning-request and provisioning-class-name annotations to do the following:

  • To schedule the Pods only in the nodes provisioned by flex-start with queued provisioning.
  • To avoid double counting of resource requests between Pods and flex-start with queued provisioning in the cluster autoscaler.
  • To inject safe-to-evict: false annotation, to prevent the cluster autoscaler from moving Pods between nodes and interrupting batch computations. You can change this behavior by specifying safe-to-evict: true in the Pod annotations.

Observe the status of a Provisioning Request

The status of a Provisioning Request defines if a Pod can be scheduled or not. You can use Kubernetes watches to observe changes efficiently or other tooling you already use for tracking statuses of Kubernetes objects. The following table describes the possible status of a Provisioning Request request and each possible outcome:

Provisioning Request status Description Possible outcome
Pending The request was not seen and processed yet. After processing, the request transitions to Accepted or Failed state.
Accepted=true The request is accepted and is waiting for resources to be available. The request should transition to Provisioned state, if resources were found and nodes were provisioned or to Failed state if that was not possible.
Provisioned=true The nodes are ready. You have 10 minutes to start the Pods to consume provisioned resources. After this time, the cluster autoscaler considers the nodes as not needed and removes them.
Failed=true The nodes can't be provisioned due to errors. Failed=true is a terminal state. Troubleshoot the condition based on the information in the Reason and Message fields of the condition. Create and retry a new Provisioning Request request.
Provisioned=false The nodes haven't been provisioned yet.

If Reason=NotProvisioned, this is a temporary state before all resources are available.

If Reason=QuotaExceeded, troubleshoot the condition based on this reason and the information in the Message field of the condition. You might need to request more quota. For more details, see Check if the Provisioning Request is limited by quota section. This Reason is only available with GKE version 1.29.2-gke.1181000 or later.

If Reason=ResourcePoolExhausted, and the Message contains Expected time is indefinite, either select a different zone or region, or adjust the requested resources.

Start the Pods

When the Provisioning Request request reaches the Provisioned=true status, you can run your Job to start the Pods. This avoids proliferation of unschedulable Pods for pending or failed requests, which can impact kube-scheduler and cluster autoscaler performance.

Alternatively, if you don't care about having unschedulable Pods, you can create Pods in parallel with the Provisioning Request request.

Cancel the Provisioning Request request

To cancel the request before it's provisioned, you can delete the ProvisioningRequest:

kubectl delete provreq PROVISIONING_REQUEST_NAME -n NAMESPACE

In most cases, deleting ProvisioningRequest stops nodes from being created. However, depending on timing, for example if nodes were already being provisioned, the nodes might still end up created. In these cases, the cluster autoscaler removes the nodes after 10 minutes if no Pods are created.

Troubleshoot quota issues

All VMs provisioned by Provisioning Request requests use preemptible quotas.

The number of ProvisioningRequests that are in Accepted state is limited by a dedicated quota. You configure the quota for each project, one quota configuration per region.

Check quota in the Google Cloud console

To check the name of the quota limit and current usage in the Google Cloud console, follow these steps:

  1. Go to the Quotas page in the Google Cloud console:

    Go to Quotas

  2. In the Filter box, select the Metric property, enter active_resize_requests, and press Enter.

The default value is 100. To increase the quota, follow the steps listed in Request a higher quota limit guide.

Check if the Provisioning Request request is limited by quota

If your Provisioning Request request is taking longer than expected to be fulfilled, check that the request isn't limited by quota. You might need to request more quota.

For clusters running version 1.29.2-gke.1181000 or later, check whether specific quota limitations are preventing your request from being fulfilled:

kubectl describe provreq PROVISIONING_REQUEST_NAME \
    --namespace NAMESPACE

The output is similar the following:

…
Last Transition Time:  2024-01-03T13:56:08Z
    Message:               Quota 'NVIDIA_P4_GPUS' exceeded. Limit: 1.0 in region europe-west4.
    Observed Generation:   1
    Reason:                QuotaExceeded
    Status:                False
    Type:                  Provisioned
…

In this example, GKE can't deploy nodes because there isn't enough quota in the region of europe-west4.

Migrate node pools from queued provisioning to flex-start provisioning mode

For existing node pools that were created by using the --enable-queued-provisioning flag, run the following command to migrate these node pools to the new flex-start provisioning mode:

  gcloud container node-pools update NODEPOOL_NAME \
    --cluster=CLUSTER_NAME --flex-start

This operation does the following:

  • Update the node pool to a flex-start provisioning mode node pool.
  • Apply the pricing of flex-start provisioning mode nodes.

All nodes on clusters running on 1.32.2-gke.1652000 or later, the minimum version for flex-start provisioning mode nodes, use short-lived upgrades.

What's next