# Install Drivers and Allocate Devices with DRA

Install Drivers and Allocate Devices with DRAFEATURE STATE: Kubernetes v1.34 [stable] (enabled by default: true)This tutorial shows you how to install Dynamic Resource Allocation (DRA) drivers in your cluster and how to use them in conjunction with the DRA APIs to allocate devices to Pods. This page is intended for cluster administrators.Dynamic Resource Allocation (DRA) lets a cluster manage availability and allocation of hardware resources to satisfy Pod-based claims for hardware requirements and preferences. To support this, a mixture of Kubernetes built-in components (like the Kubernetes scheduler, kubelet, and kube-controller-manager) and third-party drivers from device owners (called DRA drivers) share the responsibility to advertise, allocate, prepare, mount, healthcheck, unprepare, and cleanup resources throughout the Pod lifecycle. These components share information via a series of DRA specific APIs in the resource.k8s.io API group including DeviceClasses, ResourceSlices, ResourceClaims, as well as new fields in the Pod spec itself.ObjectivesDeploy an example DRA driverDeploy a Pod requesting a hardware claim using DRA APIsDelete a Pod that has a claimBefore you beginYour cluster should support RBAC. You can try this tutorial with a cluster using a different authorization mechanism, but in that case you will have to adapt the steps around defining roles and permissions.You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:iximiuz LabsKillercodaKodeKloudPlay with KubernetesThis tutorial has been tested with Linux nodes, though it may also work with other types of nodes.Your Kubernetes server must be version v1.34.To check the version, enter kubectl version.If your cluster is not currently running Kubernetes 1.34 then please check the documentation for the version of Kubernetes that you plan to use.Explore the initial cluster stateYou can spend some time to observe the initial state of a cluster with DRA enabled, especially if you have not used these APIs extensively before. If you set up a new cluster for this tutorial, with no driver installed and no Pod claims yet to satisfy, the output of these commands won't show any resources.Get a list of DeviceClasses:kubectl get deviceclasses The output is similar to this:No resources found Get a list of ResourceSlices:kubectl get resourceslices The output is similar to this:No resources found Get a list of ResourceClaims and ResourceClaimTemplateskubectl get resourceclaims -A kubectl get resourceclaimtemplates -A The output is similar to this:No resources found No resources found At this point, you have confirmed that DRA is enabled and configured properly in the cluster, and that no DRA drivers have advertised any resources to the DRA APIs yet.Install an example DRA driverDRA drivers are third-party applications that run on each node of your cluster to interface with the hardware of that node and Kubernetes' built-in DRA components. The installation procedure depends on the driver you choose, but is likely deployed as a DaemonSet to all or a selection of the nodes (using selectors or similar mechanisms) in your cluster.Check your driver's documentation for specific installation instructions, which might include a Helm chart, a set of manifests, or other deployment tooling.This tutorial uses an example driver which can be found in the kubernetes-sigs/dra-example-driver repository to demonstrate driver installation. This example driver advertises simulated GPUs to Kubernetes for your Pods to interact with.Prepare your cluster for driver installationTo simplify cleanup, create a namespace named dra-tutorial:Create the namespace:kubectl create namespace dra-tutorial In a production environment, you would likely be using a previously released or qualified image from the driver vendor or your own organization, and your nodes would need to have access to the image registry where the driver image is hosted. In this tutorial, you will use a publicly released image of the dra-example-driver to simulate access to a DRA driver image.Confirm your nodes have access to the image by running the following from within one of your cluster's nodes:docker pull registry.k8s.io/dra-example-driver/dra-example-driver:v0.2.0 Deploy the DRA driver componentsFor this tutorial, you will install the critical example resource driver components individually with kubectl.Create the DeviceClass representing the device types this DRA driver supports:dra/driver-install/deviceclass.yaml apiVersion: resource.k8s.io/v1 kind: DeviceClass metadata: name: gpu.example.com spec: selectors: - cel: expression: "device.driver == 'gpu.example.com'"kubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/deviceclass.yaml Create the ServiceAccount, ClusterRole and ClusterRoleBinding that will be used by the driver to gain permissions to interact with the Kubernetes API on this cluster:Create the Service Account:dra/driver-install/serviceaccount.yaml apiVersion: v1 kind: ServiceAccount metadata: name: dra-example-driver-service-account namespace: dra-tutorial labels: app.kubernetes.io/name: dra-example-driver app.kubernetes.io/instance: dra-example-driverkubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/serviceaccount.yaml Create the ClusterRole:dra/driver-install/clusterrole.yaml apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: dra-example-driver-role rules: - apiGroups: ["resource.k8s.io"] resources: ["resourceclaims"] verbs: ["get"] - apiGroups: [""] resources: ["nodes"] verbs: ["get"] - apiGroups: ["resource.k8s.io"] resources: ["resourceslices"] verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]kubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/clusterrole.yaml Create the ClusterRoleBinding:dra/driver-install/clusterrolebinding.yaml apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: dra-example-driver-role-binding subjects: - kind: ServiceAccount name: dra-example-driver-service-account namespace: dra-tutorial roleRef: kind: ClusterRole name: dra-example-driver-role apiGroup: rbac.authorization.k8s.iokubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/clusterrolebinding.yaml Create a PriorityClass for the DRA driver. The PriorityClass prevents preemption of th DRA driver component, which is responsible for important lifecycle operations for Pods with claims. Learn more about pod priority and preemption here.dra/driver-install/priorityclass.yaml apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: dra-driver-high-priority value: 1000000 globalDefault: false description: "This priority class should be used for DRA driver pods only."kubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/priorityclass.yaml Deploy the actual DRA driver as a DaemonSet configured to run the example driver binary with the permissions provisioned above. The DaemonSet has the permissions that you granted to the ServiceAccount in the previous steps.dra/driver-install/daemonset.yaml apiVersion: apps/v1 kind: DaemonSet metadata: name: dra-example-driver-kubeletplugin namespace: dra-tutorial labels: app.kubernetes.io/name: dra-example-driver spec: selector: matchLabels: app.kubernetes.io/name: dra-example-driver updateStrategy: type: RollingUpdate template: metadata: labels: app.kubernetes.io/name: dra-example-driver spec: priorityClassName: dra-driver-high-priority serviceAccountName: dra-example-driver-service-account securityContext: {} containers: - name: plugin securityContext: privileged: true image: registry.k8s.io/dra-example-driver/dra-example-driver:v0.2.0 imagePullPolicy: IfNotPresent command: ["dra-example-kubeletplugin"] resources: {} # Production drivers should always implement a liveness probe # For the tutorial we simply omit it # livenessProbe: # grpc: # port: 51515 # service: liveness # failureThreshold: 3 # periodSeconds: 10 env: - name: CDI_ROOT value: /var/run/cdi - name: KUBELET_REGISTRAR_DIRECTORY_PATH value: "/var/lib/kubelet/plugins_registry" - name: KUBELET_PLUGINS_DIRECTORY_PATH value: "/var/lib/kubelet/plugins" - name: NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName - name: NAMESPACE valueFrom: fieldRef: fieldPath: metadata.namespace # Simulated number of devices the example driver will pretend to have. - name: NUM_DEVICES value: "9" - name: HEALTHCHECK_PORT value: "51515" volumeMounts: - name: plugins-registry mountPath: "/var/lib/kubelet/plugins_registry" - name: plugins mountPath: "/var/lib/kubelet/plugins" - name: cdi mountPath: /var/run/cdi volumes: - name: plugins-registry hostPath: path: "/var/lib/kubelet/plugins_registry" - name: plugins hostPath: path: "/var/lib/kubelet/plugins" - name: cdi hostPath: path: /var/run/cdikubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/daemonset.yaml The DaemonSet is configured with the volume mounts necessary to interact with the underlying Container Device Interface (CDI) directory, and to expose its socket to kubelet via the kubelet/plugins directory.Verify the DRA driver installationGet a list of the Pods of the DRA driver DaemonSet across all worker nodes:kubectl get pod -l app.kubernetes.io/name=dra-example-driver -n dra-tutorial The output is similar to this:NAME READY STATUS RESTARTS AGE dra-example-driver-kubeletplugin-4sk2x 1/1 Running 0 13s dra-example-driver-kubeletplugin-cttr2 1/1 Running 0 13s The initial responsibility of each node's local DRA driver is to update the cluster with what devices are available to Pods on that node, by publishing its metadata to the ResourceSlices API. You can check that API to see that each node with a driver is advertising the device class it represents.Check for available ResourceSlices:kubectl get resourceslices The output is similar to this:NAME NODE DRIVER POOL AGE kind-worker-gpu.example.com-k69gd kind-worker gpu.example.com kind-worker 19s kind-worker2-gpu.example.com-qdgpn kind-worker2 gpu.example.com kind-worker2 19s At this point, you have successfully installed the example DRA driver, and confirmed its initial configuration. You're now ready to use DRA to schedule Pods.Claim resources and deploy a PodTo request resources using DRA, you create ResourceClaims or ResourceClaimTemplates that define the resources that your Pods need. In the example driver, a memory capacity attribute is exposed for mock GPU devices. This section shows you how to use Common Expression Language to express your requirements in a ResourceClaim, select that ResourceClaim in a Pod specification, and observe the resource allocation.This tutorial showcases only one basic example of a DRA ResourceClaim. Read Dynamic Resource Allocation to learn more about ResourceClaims.Create the ResourceClaimIn this section, you create a ResourceClaim and reference it in a Pod. Whatever the claim, the deviceClassName is a required field, narrowing down the scope of the request to a specific device class. The request itself can include a Common Expression Language expression that references attributes that may be advertised by the driver managing that device class.In this example, you will create a request for any GPU advertising over 10Gi memory capacity. The attribute exposing capacity from the example driver takes the form device.capacity['gpu.example.com'].memory. Note also that the name of the claim is set to some-gpu.dra/driver-install/example/resourceclaim.yaml apiVersion: resource.k8s.io/v1 kind: ResourceClaim metadata: name: some-gpu namespace: dra-tutorial spec: devices: requests: - name: some-gpu exactly: deviceClassName: gpu.example.com selectors: - cel: expression: "device.capacity['gpu.example.com'].memory.compareTo(quantity('10Gi')) >= 0"kubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/example/resourceclaim.yaml Create the Pod that references that ResourceClaimBelow is the Pod manifest referencing the ResourceClaim you just made, some-gpu, in the spec.resourceClaims.resourceClaimName field. The local name for that claim, gpu, is then used in the spec.containers.resources.claims.name field to allocate the claim to the Pod's underlying container.dra/driver-install/example/pod.yaml apiVersion: v1 kind: Pod metadata: name: pod0 namespace: dra-tutorial labels: app: pod spec: containers: - name: ctr0 image: ubuntu:24.04 command: ["bash", "-c"] args: ["export; trap 'exit 0' TERM; sleep 9999 & wait"] resources: claims: - name: gpu resourceClaims: - name: gpu resourceClaimName: some-gpukubectl apply --server-side -f http://k8s.io/examples/dra/driver-install/example/pod.yaml Confirm the pod has deployed:kubectl get pod pod0 -n dra-tutorial The output is similar to this:NAME READY STATUS RESTARTS AGE pod0 1/1 Running 0 9s Explore the DRA stateAfter you create the Pod, the cluster tries to schedule that Pod to a node where Kubernetes can satisfy the ResourceClaim. In this tutorial, the DRA driver is deployed on all nodes, and is advertising mock GPUs on all nodes, all of which have enough capacity advertised to satisfy the Pod's claim, so Kubernetes can schedule this Pod on any node and can allocate any of the mock GPUs on that node.When Kubernetes allocates a mock GPU to a Pod, the example driver adds environment variables in each container it is allocated to in order to indicate which GPUs would have been injected into them by a real resource driver and how they would have been configured, so you can check those environment variables to see how the Pods have been handled by the system.Check the Pod logs, which report the name of the mock GPU that was allocated:kubectl logs pod0 -c ctr0 -n dra-tutorial | grep -E "GPU_DEVICE_[0-9]+=" | grep -v "RESOURCE_CLAIM" The output is similar to this:declare -x GPU_DEVICE_0="gpu-0" Check the state of the ResourceClaim object:kubectl get resourceclaims -n dra-tutorial The output is similar to this:NAME STATE AGE some-gpu allocated,reserved 34s In this output, the STATE column shows that the ResourceClaim is allocated and reserved.Check the details of the some-gpu ResourceClaim. The status stanza of the ResourceClaim has information about the allocated device and the Pod it has been reserved for:kubectl get resourceclaim some-gpu -n dra-tutorial -o yaml The output is similar to this: 1apiVersion: resource.k8s.io/v1 2kind: ResourceClaim 3metadata: 4 creationTimestamp: "2025-08-20T18:17:31Z" 5 finalizers: 6 - resource.kubernetes.io/delete-protection 7 name: some-gpu 8 namespace: dra-tutorial 9 resourceVersion: "2326" 10 uid: d3e48dbf-40da-47c3-a7b9-f7d54d1051c3 11spec: 12 devices: 13 requests: 14 - exactly: 15 allocationMode: ExactCount 16 count: 1 17 deviceClassName: gpu.example.com 18 selectors: 19 - cel: 20 expression: device.capacity['gpu.example.com'].memory.compareTo(quantity('10Gi')) 21 >= 0 22 name: some-gpu 23status: 24 allocation: 25 devices: 26 results: 27 - device: gpu-0 28 driver: gpu.example.com 29 pool: kind-worker 30 request: some-gpu 31 nodeSelector: 32 nodeSelectorTerms: 33 - matchFields: 34 - key: metadata.name 35 operator: In 36 values: 37 - kind-worker 38 reservedFor: 39 - name: pod0 40 resource: pods 41 uid: c4dadf20-392a-474d-a47b-ab82080c8bd7To check how the driver handled device allocation, get the logs for the driver DaemonSet Pods:kubectl logs -l app.kubernetes.io/name=dra-example-driver -n dra-tutorial The output is similar to this:I0820 18:17:44.131324 1 driver.go:106] PrepareResourceClaims is called: number of claims: 1 I0820 18:17:44.135056 1 driver.go:133] Returning newly prepared devices for claim 'd3e48dbf-40da-47c3-a7b9-f7d54d1051c3': [{[some-gpu] kind-worker gpu-0 [k8s.gpu.example.com/gpu=common k8s.gpu.example.com/gpu=d3e48dbf-40da-47c3-a7b9-f7d54d1051c3-gpu-0]}] You have now successfully deployed a Pod that claims devices using DRA, verified that the Pod was scheduled to an appropriate node, and saw that the associated DRA APIs kinds were updated with the allocation status.Delete a Pod that has a claimWhen a Pod with a claim is deleted, the DRA driver deallocates the resource so it can be available for future scheduling. To validate this behavior, delete the Pod that you created in the previous steps and watch the corresponding changes to the ResourceClaim and driver.Delete the pod0 Pod:kubectl delete pod pod0 -n dra-tutorial The output is similar to this:pod "pod0" deleted Observe the DRA stateWhen the Pod is deleted, the driver deallocates the device from the ResourceClaim and updates the ResourceClaim resource in the Kubernetes API. The ResourceClaim has a pending state until it's referenced in a new Pod.Check the state of the some-gpu ResourceClaim:kubectl get resourceclaims -n dra-tutorial The output is similar to this:NAME STATE AGE some-gpu pending 76s Verify that the driver has processed unpreparing the device for this claim by checking the driver logs:kubectl logs -l app.kubernetes.io/name=dra-example-driver -n dra-tutorial The output is similar to this:I0820 18:22:15.629376 1 driver.go:138] UnprepareResourceClaims is called: number of claims: 1 You have now deleted a Pod that had a claim, and observed that the driver took action to unprepare the underlying hardware resource and update the DRA APIs to reflect that the resource is available again for future scheduling.Cleaning upTo clean up the resources that you created in this tutorial, follow these steps:kubectl delete namespace dra-tutorial kubectl delete deviceclass gpu.example.com kubectl delete clusterrole dra-example-driver-role kubectl delete clusterrolebinding dra-example-driver-role-binding kubectl delete priorityclass dra-driver-high-priority What's nextLearn more about DRAAllocate Devices to Workloads with DRA