Dynamic Resource Allocation

• 1: DRA API Objects
• 2: How DRA Works
• 3: Observability of Dynamic Resources
• 4: DRA Features
• 5: Device Taints and Tolerations

1 - DRA API Objects

This page describes the Kubernetes API kinds that dynamic resource allocation (DRA) uses to categorize, request, and allocate devices.

DRA terminology

DRA uses the following Kubernetes API kinds to provide the core allocation functionality. All of these API kinds are included in the resource.k8s.io/v1 API group.

DeviceClass

Defines a category of devices that can be claimed and how to select specific device attributes in claims. The DeviceClass parameters can match zero or more devices in ResourceSlices. To claim devices from a DeviceClass, ResourceClaims select specific device attributes.

ResourceClaim

Describes a request for access to attached resources, such as devices, in the cluster. ResourceClaims provide Pods with access to a specific resource. ResourceClaims can be created by workload operators or generated by Kubernetes based on a ResourceClaimTemplate.

ResourceClaimTemplate

Defines a template that Kubernetes uses to create per-Pod ResourceClaims for a workload. ResourceClaimTemplates provide Pods with access to separate, similar resources. Each ResourceClaim that Kubernetes generates from the template is bound to a specific Pod. When the Pod terminates, Kubernetes deletes the corresponding ResourceClaim.

ResourceSlice

Represents one or more resources that are attached to nodes, such as devices. Drivers create and manage ResourceSlices in the cluster. When a ResourceClaim is created and used in a Pod, Kubernetes uses ResourceSlices to find nodes that have access to the claimed resources. Kubernetes allocates resources to the ResourceClaim and schedules the Pod onto a node that can access the resources.

DeviceClass

A DeviceClass lets cluster admins or device drivers define categories of devices in the cluster. DeviceClasses tell operators what devices they can request and how they can request those devices. You can use common expression language (CEL) to select devices based on specific attributes. A ResourceClaim that references the DeviceClass can then request specific configurations within the DeviceClass.

To create a DeviceClass, see Set Up DRA in a Cluster.

ResourceClaims and ResourceClaimTemplates

A ResourceClaim defines the resources that a workload needs. Every ResourceClaim has requests that reference a DeviceClass and select devices from that DeviceClass. ResourceClaims can also use selectors to filter for devices that meet specific requirements, and can use constraints to limit the devices that can satisfy a request. ResourceClaims can be created by workload operators or can be generated by Kubernetes based on a ResourceClaimTemplate. A ResourceClaimTemplate defines a template that Kubernetes can use to auto-generate ResourceClaims for Pods.

Use cases for ResourceClaims and ResourceClaimTemplates

The method that you use depends on your requirements, as follows:

• ResourceClaim: you want multiple Pods to share access to specific devices. You manually manage the lifecycle of ResourceClaims that you create.
• ResourceClaimTemplate: you want Pods to have independent access to separate, similarly-configured devices. Kubernetes generates ResourceClaims from the specification in the ResourceClaimTemplate. The lifetime of each generated ResourceClaim is bound to the lifetime of the corresponding Pod.
• PodGroup ResourceClaimTemplate: you want PodGroups to have independent access to separate, similarly-configured devices that can be shared by their Pods. Kubernetes generates one ResourceClaim for the PodGroup from the specification in the ResourceClaimTemplate. The lifetime of each generated ResourceClaim is bound to the lifetime of the corresponding PodGroup. This requires the DRAWorkloadResourceClaims feature to be enabled.

When you define a workload, you can use Common Expression Language (CEL) to filter for specific device attributes or capacity. The available parameters for filtering depend on the device and the drivers.

If you directly reference a specific ResourceClaim in a Pod, that ResourceClaim must already exist in the same namespace as the Pod. If the ResourceClaim doesn't exist in the namespace, the Pod won't schedule. This behavior is similar to how a PersistentVolumeClaim must exist in the same namespace as a Pod that references it.

You can reference an auto-generated ResourceClaim in a Pod, but this isn't recommended because auto-generated ResourceClaims are bound to the lifetime of the Pod or PodGroup that triggered the generation.

To learn how to claim resources using one of these methods, see Allocate Devices to Workloads with DRA.

Prioritized list

You can provide a prioritized list of subrequests for requests in a ResourceClaim or ResourceClaimTemplate. The scheduler will then select the first subrequest that can be allocated. This allows users to specify alternative devices that can be used by the workload if the primary choice is not available.

In the example below, the ResourceClaimTemplate requested a device with the color black and the size large. If a device with those attributes is not available, the pod cannot be scheduled. With the prioritized list feature, a second alternative can be specified, which requests two devices with the color white and size small. The large black device will be allocated if it is available. If it is not, but two small white devices are available, the pod will still be able to run.

apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: prioritized-list-claim-template
spec:
  spec:
    devices:
      requests:
      - name: req-0
        firstAvailable:
        - name: large-black
          deviceClassName: resource.example.com
          selectors:
          - cel:
              expression: |-
                device.attributes["resource-driver.example.com"].color == "black" &&
                device.attributes["resource-driver.example.com"].size == "large"
        - name: small-white
          deviceClassName: resource.example.com
          selectors:
          - cel:
              expression: |-
                device.attributes["resource-driver.example.com"].color == "white" &&
                device.attributes["resource-driver.example.com"].size == "small"
          count: 2

If the pod is eligible for multiple nodes in the cluster, the scheduler will use the index of chosen subrequests from any prioritized lists as one of the inputs when it scores each node. So nodes that can allocate devices requested in a higher ranked subrequest are more likely to be chosen than nodes that can only allocate devices for lower ranked subrequests.

The decision is made on a per-Pod basis, so if the Pod is a member of a ReplicaSet or similar grouping, you cannot rely on all the members of the group having the same subrequest chosen. Your workload must be able to accommodate this.

Workload ResourceClaims

When you organize Pods with the Workload API, you can reserve ResourceClaims for entire PodGroups instead of individual Pods and generate ResourceClaimTemplates for a PodGroup instead of a single Pod, allowing the Pods within a PodGroup to share access to devices allocated to the generated ResourceClaim.

This feature targets two problems:

• The ResourceClaim API's status.reservedFor list can only contain 256 items. Since kube-scheduler only records individual Pods in that list, only 256 Pods can share a ResourceClaim. By allowing PodGroups to be recorded in status.reservedFor, many more than 256 Pods can share a ResourceClaim.
• Pods can only share a ResourceClaim when its exact name is known. For complex workloads that replicate groups of Pods, ResourceClaims shared by the Pods in each group need to be created and deleted explicitly when the set of groups scales up and down. By generating ResourceClaims for each PodGroup, a single ResourceClaimTemplate can form the basis for ResourceClaims that are both replicated automatically and shareable among the Pods in a PodGroup.

The PodGroup API defines a spec.resourceClaims field with the same structure and similar meaning as the spec.resourceClaims field in the Pod API:

apiVersion: scheduling.k8s.io/v1alpha2
kind: PodGroup
metadata:
  name: training-group
  namespace: some-ns
spec:
  ...
  resourceClaims:
  - name: pg-claim
    resourceClaimName: my-pg-claim
  - name: pg-claim-template
    resourceClaimTemplateName: my-pg-template

Like claims made by Pods, claims for PodGroups defining a resourceClaimName refer to a ResourceClaim by name. Claims defining a resourceClaimTemplateName refer to a ResourceClaimTemplate which replicates into one ResourceClaim for the entire PodGroup that can be shared amongst its Pods.

When a Pod defines a claim with a name, resourceClaimName, and resourceClaimTemplateName that all match one of its PodGroup's spec.resourceClaims, then kube-scheduler reserves the ResourceClaim for the PodGroup instead of the Pod. If the Pod's claim does not match one made by its PodGroup, then kube-scheduler reserves the ResourceClaim for the Pod. In either case, reservation is recorded in the ResourceClaim's status.reservedFor. PodGroup reservations and the corresponding resource allocation persist in the ResourceClaim until the PodGroup is deleted, even if the group no longer has any Pods.

When a Pod claim matching a PodGroup claim defines a resourceClaimTemplateName, then one ResourceClaim is generated for the PodGroup. Other Pods in the group defining the same claim will share that generated ResourceClaim instead of prompting a new ResourceClaim to be generated for each Pod. Whether or not a resourceClaimTemplateName claim matches a PodGroup claim, the name of the generated ResourceClaim is recorded in the Pod's status.resourceClaimStatuses.

ResourceClaims generated from a ResourceClaimTemplate for a PodGroup follow the lifecycle of the PodGroup. The ResourceClaim is first created when both the PodGroup and its ResourceClaimTemplate exist. The ResourceClaim is deleted after the PodGroup has been deleted and the ResourceClaim is no longer reserved.

Consider the following example:

apiVersion: scheduling.k8s.io/v1alpha2
kind: PodGroup
metadata:
  name: training-group
  namespace: some-ns
spec:
  ...
  resourceClaims:
  - name: pg-claim
    resourceClaimName: my-pg-claim
  - name: pg-claim-template
    resourceClaimTemplateName: my-pg-template
---
apiVersion: v1
kind: Pod
metadata:
  name: training-group-pod-1
  namespace: some-ns
spec:
  ...
  schedulingGroup:
    podGroupName: training-group
  resourceClaims:
  - name: pod-claim
    resourceClaimName: my-pod-claim
  - name: pod-claim-template
    resourceClaimTemplateName: my-pod-template
  - name: pg-claim
    resourceClaimName: my-pg-claim
  - name: pg-claim-template
    resourceClaimTemplateName: my-pg-template

In this example, the training-group PodGroup has one Pod named training-group-pod-1. The Pod's pod-claim and pod-claim-template claims do not match any claim made by the PodGroup, so those claims are not affected by the PodGroup: ResourceClaim my-pod-claim becomes reserved for the Pod and a ResourceClaim is generated from ResourceClaimTemplate my-pod-template and also becomes reserved for the Pod. The pg-claim and pg-claim-template do match claims made by the PodGroup. ResourceClaim my-pg-claim becomes reserved for the PodGroup and a ResourceClaim is generated from ResourceClaimTemplate my-pg-template and also becomes reserved for the PodGroup.

Associating ResourceClaims with Workload API resources is controlled by the DRAWorkloadResourceClaims feature gate in the kube-apiserver, kube-controller-manager, kube-scheduler, and kubelet.

ResourceSlice

Each ResourceSlice represents one or more devices in a pool. The pool is managed by a device driver, which creates and manages ResourceSlices. The resources in a pool might be represented by a single ResourceSlice or span multiple ResourceSlices.

ResourceSlices provide useful information to device users and to the scheduler, and are crucial for dynamic resource allocation. Every ResourceSlice must include the following information:

• Resource pool: a group of one or more resources that the driver manages. The pool can span more than one ResourceSlice. Changes to the resources in a pool must be propagated across all of the ResourceSlices in that pool. The device driver that manages the pool is responsible for ensuring that this propagation happens.
• Devices: devices in the managed pool. A ResourceSlice can list every device in a pool or a subset of the devices in a pool. The ResourceSlice defines device information like attributes, versions, and capacity. Device users can select devices for allocation by filtering for device information in ResourceClaims or in DeviceClasses.
• Nodes: the nodes that can access the resources. Drivers can choose which nodes can access the resources, whether that's all of the nodes in the cluster, a single named node, or nodes that have specific node labels.

Drivers use a controller to reconcile ResourceSlices in the cluster with the information that the driver has to publish. This controller overwrites any manual changes, such as cluster users creating or modifying ResourceSlices.

Consider the following example ResourceSlice:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: cat-slice
spec:
  driver: "resource-driver.example.com"
  pool:
    generation: 1
    name: "black-cat-pool"
    resourceSliceCount: 1
  # The allNodes field defines whether any node in the cluster can access the device.
  allNodes: true
  devices:
  - name: "large-black-cat"
    attributes:
      color:
        string: "black"
      size:
        string: "large"
      cat:
        bool: true

This ResourceSlice is managed by the resource-driver.example.com driver in the black-cat-pool pool. The allNodes: true field indicates that any node in the cluster can access the devices. There's one device in the ResourceSlice, named large-black-cat, with the following attributes:

• color: black
• size: large
• cat: true

A DeviceClass could select this ResourceSlice by using these attributes, and a ResourceClaim could filter for specific devices in that DeviceClass.

Naming and prioritization

The order in which the Kubernetes scheduler evaluates devices for allocation is determined by the lexicographical sorting of ResourceSlice and resource pool names. The scheduler uses a first-fit strategy, meaning it selects the first available device that satisfies the claim's requirements.

This allows the priority of resource allocation to be influenced by the names assigned to pools and ResourceSlices. Note that pools without binding conditions are always evaluated before those with binding conditions, regardless of their names.

For drivers built using the k8s.io/dynamic-resources/kubeletplugin Go package or the ResourceSlice controller from that module, these components automatically handle ResourceSlice naming to ensure they are evaluated in the order specified by the driver.

Admin access

You can mark a request in a ResourceClaim or ResourceClaimTemplate as having privileged features for maintenance and troubleshooting tasks. A request with admin access grants access to in-use devices and may enable additional permissions when making the device available in a container:

apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: large-black-cat-claim-template
spec:
  spec:
    devices:
      requests:
      - name: req-0
        exactly:
          deviceClassName: resource.example.com
          allocationMode: All
          adminAccess: true

Admin access is a privileged mode and should not be granted to regular users in multi-tenant clusters. Only users authorized to create ResourceClaim or ResourceClaimTemplate objects in namespaces labeled with resource.kubernetes.io/admin-access: "true" (case-sensitive) can use the adminAccess field. This ensures that non-admin users cannot misuse the feature.

Admin access is controlled by the DRAAdminAccess feature gate in the kube-apiserver, kube-scheduler, and kubelet.

List type attributes

This feature improves the ResourceSlice API, allowing DRA drivers to specify list values for device attributes instead of only scalars. This is useful for modeling more complex internal node topologies, for example when a CPU has adjacency to multiple PCIe roots.

For ResourceClaim authors (end users), this means that the matchAttribute and distinctAttribute work better for these cases.

• matchAttribute — the two attributes must have a non-empty list intersection, rather than be identical (scalar values are treated as single-item lists). This just means that if one driver publishes a single value for, say, the PCIe root, and another driver publishes a list, the constraint is met as long as the single value appears somewhere in the list.
• distinctAttribute — the attribute values must be pairwise-disjoint (no value shared between any two devices)

To help ResourceClaim authors use attributes that may be lists inside CEL expressions, this feature also introduces an includes() CEL function.

# Scalar attribute (backward compatible)
# assume: device.attributes["dra.example.com"].model = "model-a"
device.attributes["dra.example.com"].model.includes("model-a")  # true
device.attributes["dra.example.com"].model.includes("model-b")  # false

# List-type attribute (requires DRAListTypeAttributes)
# assume: device.attributes["dra.example.com"].supported-models= ["model-a", "model-b"]
device.attributes["dra.example.com"].supported-models.includes("model-a")  # true
device.attributes["dra.example.com"].supported-models.includes("model-c")  # false

Details for DRA Driver Authors

By default, each DeviceAttribute holds exactly one scalar value: a boolean, an integer, a string, or a semantic version string. The DRAListTypeAttributes feature gate extends DeviceAttribute with four list-type fields, allowing a device to advertise multiple values for a single attribute:

• bools — a list of boolean values
• ints — a list of 64-bit integer values
• strings — a list of strings (each at most 64 characters)
• versions — a list of semantic version strings per semver.org spec 2.0.0 (each at most 64 characters)

The total number of individual attribute values per device (scalar fields plus all list elements combined) is limited to 48. When any device in a ResourceSlice uses this feature or other advanced features such as taints, the ResourceSlice will be limited to at most 64 devices. use list-type attributes or other advanced features such as taints.

Here is an example of a device advertising multiple supported models using a list-type string attribute:

kind: ResourceSlice
apiVersion: resource.k8s.io/v1
metadata:
  name: example-resourceslice
spec:
  nodeName: worker-1
  pool:
    name: pool
    generation: 1
    resourceSliceCount: 1
  driver: dra.example.com
  devices:
  - name: gpu-0
    attributes:
      dra.example.com/supported-models:
        strings:
        - model-a
        - model-b

List type attributes is controlled by the DRAListTypeAttributes feature gate in the kube-apiserver and kube-scheduler.

2 - How DRA Works

This page describes how Kubernetes allocates devices to workloads with dynamic resource allocation (DRA), and how pre-scheduled Pods interact with the process.

How resource allocation with DRA works

The following sections describe the workflow for the various types of DRA users and for the Kubernetes system during dynamic resource allocation.

Workflow for users

1. Driver creation: device owners or third-party entities create drivers that can create and manage ResourceSlices in the cluster. These drivers optionally also create DeviceClasses that define a category of devices and how to request them.
2. Cluster configuration: cluster admins create clusters, attach devices to nodes, and install the DRA device drivers. Cluster admins optionally create DeviceClasses that define categories of devices and how to request them.
3. Resource claims: workload operators create ResourceClaimTemplates or ResourceClaims that request specific device configurations within a DeviceClass. In the same step, workload operators modify their Kubernetes manifests to request those ResourceClaimTemplates or ResourceClaims.

Workflow for Kubernetes

1. ResourceSlice creation: drivers in the cluster create ResourceSlices that represent one or more devices in a managed pool of similar devices.

2. Workload creation: the cluster control plane checks new workloads for references to ResourceClaimTemplates or to specific ResourceClaims.

   • If the workload uses a ResourceClaimTemplate, a controller named the resourceclaim-controller generates ResourceClaims for the workload.
   • If the workload uses a specific ResourceClaim, Kubernetes checks whether that ResourceClaim exists in the cluster. If the ResourceClaim doesn't exist, the Pods won't deploy.

3. ResourceSlice filtering: for every Pod, Kubernetes checks the ResourceSlices in the cluster to find a device that satisfies all of the following criteria:

   • The nodes that can access the resources are eligible to run the Pod.
   • The ResourceSlice has unallocated resources that match the requirements of the Pod's ResourceClaim.

4. Resource allocation: after finding an eligible ResourceSlice for a Pod's ResourceClaim, the Kubernetes scheduler updates the ResourceClaim with the allocation details. The scheduler uses a first-fit strategy and evaluates pools and ResourceSlices in lexicographical order by their names. Drivers can prioritize specific slices or pools by naming them appropriately. For details, see Naming and prioritization.

5. Pod scheduling: when resource allocation is complete, the scheduler places the Pod on a node that can access the allocated resource. The device driver and the kubelet on that node configure the device and the Pod's access to the device.

Pre-scheduled Pods

When you - or another API client - create a Pod with spec.nodeName already set, the scheduler gets bypassed. If some ResourceClaim needed by that Pod does not exist yet, is not allocated or not reserved for the Pod, then the kubelet will fail to run the Pod and re-check periodically because those requirements might still get fulfilled later.

Such a situation can also arise when support for dynamic resource allocation was not enabled in the scheduler at the time when the Pod got scheduled (version skew, configuration, feature gate, etc.). kube-controller-manager detects this and tries to make the Pod runnable by reserving the required ResourceClaims. However, this only works if those were allocated by the scheduler for some other pod.

It is better to avoid bypassing the scheduler because a Pod that is assigned to a node blocks normal resources (RAM, CPU) that then cannot be used for other Pods while the Pod is stuck. To make a Pod run on a specific node while still going through the normal scheduling flow, create the Pod with a node selector that exactly matches the desired node:

apiVersion: v1
kind: Pod
metadata:
  name: pod-with-cats
spec:
  nodeSelector:
    kubernetes.io/hostname: name-of-the-intended-node
  ...

You may also be able to mutate the incoming Pod, at admission time, to unset the .spec.nodeName field and to use a node selector instead.

Device binding conditions

Device Binding Conditions allow the Kubernetes scheduler to delay Pod binding until external resources, such as fabric-attached GPUs or reprogrammable FPGAs, are confirmed to be ready.

This waiting behavior is implemented in the PreBind phase of the scheduling framework. During this phase, the scheduler checks whether all required device conditions are satisfied before proceeding with binding.

This improves scheduling reliability by avoiding premature binding and enables coordination with external device controllers.

To use this feature, device drivers (typically managed by driver owners) must publish the following fields in the Device section of a ResourceSlice. Cluster administrators must enable the DRADeviceBindingConditions and DRAResourceClaimDeviceStatus feature gates for the scheduler to honor these fields.

bindingConditions

A list of condition types that must be set to True (in the .status.conditions field of the associated ResourceClaim) before the Pod can be bound. These conditions typically represent readiness signals, such as DeviceAttached or DeviceInitialized.

bindingFailureConditions

A list of condition types that, if set to True in status.conditions field of the associated ResourceClaim, indicate a failure state. If any of these conditions are True, the scheduler will abort binding and reschedule the Pod.

bindsToNode

if set to true, the scheduler records the selected node name in the status.allocation.nodeSelector field of the ResourceClaim. This does not affect the Pod's spec.nodeSelector. Instead, it sets a node selector inside the ResourceClaim, which external controllers can use to perform node-specific operations such as device attachment or preparation.

All condition types listed in bindingConditions and bindingFailureConditions are evaluated from the status.conditions field of the ResourceClaim. External controllers are responsible for updating these conditions using standard Kubernetes condition semantics (type, status, reason, message, lastTransitionTime).

The scheduler waits up to 600 seconds (default) for all bindingConditions to become True. If the timeout is reached or any bindingFailureConditions are True, the scheduler clears the allocation and reschedules the Pod. A cluster administration can configure this timeout duration by editing the kube-scheduler configuration file.

An example of configuring this timeout in KubeSchedulerConfiguration is given below:

apiVersion: kubescheduler.config.k8s.io/v1
kind: KubeSchedulerConfiguration
profiles:
- schedulerName: default-scheduler
  pluginConfig:
  - name: DynamicResources
    args:
      apiVersion: kubescheduler.config.k8s.io/v1
      kind: DynamicResourcesArgs
      bindingTimeout: 60s

Example

Here is an example of a ResourceSlice that you might see in a cluster where there's a DRA driver in use, and that driver supports binding conditions:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: gpu-slice-1
spec:
  driver: dra.example.com
  nodeSelector:
    nodeSelectorTerms:
    - matchExpressions:
      - key: accelerator-type
        operator: In
        values:
        - "high-performance"
  pool:
    name: gpu-pool
    generation: 1
    resourceSliceCount: 1
  devices:
    - name: gpu-1
      attributes:
        vendor:
          string: "example"
        model:
          string: "example-gpu"
      bindsToNode: true
      bindingConditions:
        - dra.example.com/is-prepared
      bindingFailureConditions:
        - dra.example.com/preparing-failed

This example ResourceSlice has the following properties:

• The ResourceSlice targets nodes labeled with accelerator-type=high-performance, so that the scheduler uses only a specific set of eligible nodes.
• The scheduler selects one node from the selected group (for example, node-3) and sets the status.allocation.nodeSelector field in the ResourceClaim to that node name.
• The dra.example.com/is-prepared binding condition indicates that the device gpu-1 must be prepared (the is-prepared condition has a status of True) before binding.
• If the gpu-1 device preparation fails (the preparing-failed condition has a status of True), the scheduler aborts binding.
• The scheduler waits up to 600 seconds (default) for the device to become ready.
• External controllers can use the node selector in the ResourceClaim to perform node-specific setup on the selected node.

Device binding conditions is controlled by the DRADeviceBindingConditions feature gate in the kube-apiserver and kube-scheduler.

Node allocatable resources

Devices managed by DRA can have an underlying footprint composed of node-allocatable resources, such as cpu, memory, hugepages, or ephemeral-storage. This feature integrates these DRA-based requests into the scheduler's standard accounting alongside regular Pod spec requests for these resources.

Users (PodSpec authors) can use a mixture of Pod-level resources, container-level resources, and resource claims with associated node-allocatable resources. These devices represent resources like CPUs or memory directly, or they could be accelerators, network interface cards, or other devices that require some host resources when allocated. The DRA driver will populate information in the ResourceSlice that tells the scheduler how to calculate the node allocatable resources when the device is allocated to a ResourceClaim. PodSpec authors do not need to make that calculation themselves.

When authoring a PodSpec using claims for these types of devices, there are a few things to be aware of:

• When Pod-level resources are used, the sum of all container and claim resources must not exceed the Pod-level resources; otherwise, the Pod will fail to schedule.
• A container's total resource requirement is the sum of its container-level resources and any node-allocatable resources from its associated resource claims.
• Claims that consume node allocatable resources cannot be shared between Pods.

Details for DRA Driver Authors

DRA drivers declare this node allocatable resource footprint using the nodeAllocatableResourceMappings field on devices within a ResourceSlice. This mapping translates the requested DRA device or capacity into standard resources that are tracked in the node's status.allocatable (note that extended resources are not supported for this mapping). This is useful both for drivers that directly expose native resources (like a CPU or Memory DRA driver) and for devices that require auxiliary node dependencies (like an accelerator that needs host memory).

This mapping defines the translation of the requested DRA device or capacity units to the corresponding quantity of the node-allocatable resource. The scheduler calculates the exact quantity using:

• Device-based scaling: If capacityKey is not set, the allocationMultiplier multiplies the device count allocated to the claim. The allocationMultiplier defaults to 1 if not specified.
• Capacity-based scaling: If capacityKey is set, it references a capacity name defined in the device's capacity map. The scheduler looks up the amount of that capacity consumed by the claim and multiplies it by the allocationMultiplier.

Example: CPU DRA Driver (Capacity-based scaling)

Here is an example where a CPU DRA driver exposes a CPU socket as a pool of 128 CPUs using DRA consumable capacity. The capacityKey links the consumed cpu.example.com/cpu capacity directly to the node's standard cpu allocatable resource:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: my-node-cpus
spec:
  driver: cpu.example.com
  nodeName: my-node
  pool:
    name: socket-cpus
    generation: 1
    resourceSliceCount: 1
  devices:
  - name: socket0cpus
    allowMultipleAllocations: true
    capacity:
      "cpu.example.com/cpu": "128"
    nodeAllocatableResourceMappings:
      cpu:
        capacityKey: "cpu.example.com/cpu"
        # allocationMultiplier defaults to 1 if omitted
  - name: socket1cpus
    allowMultipleAllocations: true
    capacity:
      "cpu.example.com/cpu": "128"
    nodeAllocatableResourceMappings:
      cpu:
        capacityKey: "cpu.example.com/cpu"
        # allocationMultiplier defaults to 1 if omitted

Example: Accelerator with Auxiliary Resources (Device-based scaling)

Here is an example of a resource slice where an accelerator requires an additional 8Gi of memory per device instance to function:

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: my-node-xpus
spec:
  driver: xpu.example.com
  nodeName: my-node
  pool:
    name: xpu-pool
    generation: 1
    resourceSliceCount: 1
  devices:
  - name: xpu-model-x-001
    attributes:
      example.com/model:
        string: "model-x"
    nodeAllocatableResourceMappings:
      memory:
        allocationMultiplier: "8Gi"

After a Pod is successfully bound to the node, the exact quantities of node-allocatable resources allocated via DRA are included in the Pod's status.nodeAllocatableResourceClaimStatuses field.

Node-allocatable resources is controlled by the DRANodeAllocatableResources feature gate in the kube-apiserver, kube-scheduler, and kubelet. While this feature is alpha, the kubelet does not account for these resources when determining QoS classes, configuring cgroups, or making eviction decisions.

3 - Observability of Dynamic Resources

This page describes how to observe the status and health of resources that are dynamically allocated with DRA.

Observability of dynamic resources

You can check the status of dynamically allocated resources by using any of the following methods:

• kubelet device metrics
• ResourceClaim status
• Device health monitoring

kubelet device metrics

The PodResourcesLister kubelet gRPC service lets you monitor in-use devices. The DynamicResource message provides information that's specific to dynamic resource allocation, such as the device name and the claim name. For details, see Monitoring device plugin resources.

ResourceClaim device status

DRA drivers can report driver-specific device status data for each allocated device in the status.devices field of a ResourceClaim. For example, the driver might list the IP addresses that are assigned to a network interface device. Updating this field requires specific synthetic RBAC permissions, see Hardening Guide - Dynamic Resource Allocation and Harden Dynamic Resource Allocation in Your Cluster.

The accuracy of the information that a driver adds to a ResourceClaim status.devices field depends on the driver. Evaluate drivers to decide whether you can rely on this field as the only source of device information.

If you disable the DRAResourceClaimDeviceStatus feature gate, the status.devices field automatically gets cleared when storing the ResourceClaim. A ResourceClaim device status is supported when it is possible, from a DRA driver, to update an existing ResourceClaim where the status.devices field is set.

For details about the status.devices field, see the ResourceClaim API reference.

Device Health Monitoring

Kubernetes provides a mechanism for monitoring and reporting the health of dynamically allocated infrastructure resources. For stateful applications running on specialized hardware, it is critical to know when a device has failed or become unhealthy. It is also helpful to find out if the device recovers.

To use this functionality, the ResourceHealthStatus feature gate must be enabled (beta and enabled by default since v1.36), and the DRA driver must implement the DRAResourceHealth gRPC service.

When a DRA driver detects that an allocated device has become unhealthy, it reports this status back to the kubelet. This health information is then exposed directly in the Pod's status. The kubelet populates the allocatedResourcesStatus field in the status of each container, detailing the health of each device assigned to that container. Each resource health entry can include an optional message field with additional human-readable context about the health status, such as error details or failure reasons.

If the kubelet does not receive a health update from a DRA driver within a timeout period, the device's health status is marked as "Unknown". DRA drivers can configure this timeout on a per-device basis by setting the health_check_timeout_seconds field in the DeviceHealth gRPC message. If not specified, the kubelet uses a default timeout of 30 seconds. This allows different hardware types (for example, GPUs, FPGAs, or storage devices) to use appropriate timeout values based on their health-reporting characteristics.

This provides crucial visibility for users and controllers to react to hardware failures. For a Pod that is failing, you can inspect this status to determine if the failure was related to an unhealthy device.

Resource pool status

You can query the availability of devices in resource pools using the ResourcePoolStatusRequest API. This provides visibility into how many devices are available, allocated, or unavailable across your cluster's DRA resource pools.

To check resource pool status:

1. Create a ResourcePoolStatusRequest specifying the driver name (required) and optionally a limit on the number of pools returned. You can also limit it to a single pool by specifying a pool name:

   apiVersion: resource.k8s.io/v1beta2
   kind: ResourcePoolStatusRequest
   metadata:
     name: check-gpus
   spec:
     driver: example.com/gpu
     # Optional: filter to a specific pool
     # poolName: my-pool
     # Optional: limit number of pools returned (default: 100, max: 1000)
     # limit: 10
   

2. Wait for the controller to process the request:

   kubectl wait --for=condition=Complete resourcepoolstatusrequest/check-gpus --timeout=30s
   

3. Read the status to see pool availability:

   kubectl get resourcepoolstatusrequest/check-gpus -o yaml
   

   The status includes:

   • poolCount: total number of pools matching the filter (may exceed the number of pools listed if truncated by the limit).
   • pools: a list of pool details, each containing:
     • driver and poolName: identify the pool.
     • generation: the latest pool generation observed across ResourceSlices.
     • resourceSliceCount: the number of ResourceSlices making up the pool.
     • totalDevices: total devices in the pool.
     • allocatedDevices: devices currently allocated to claims.
     • availableDevices: devices available for allocation (totalDevices - allocatedDevices - unavailableDevices).
     • unavailableDevices: devices not available due to taints or other conditions.
     • nodeName: the node associated with the pool, if any.
     • validationError: set when the pool's data could not be fully validated (for example, during a generation rollout). When set, device count fields may be unset.
   • conditions: includes Complete (success) or Failed (error) condition types.

4. Delete the request when done:

   kubectl delete resourcepoolstatusrequest/check-gpus
   

ResourcePoolStatusRequest objects are processed once by a controller in kube-controller-manager. The spec is immutable once created, and the entire object becomes immutable once the status is populated. To get updated availability data, delete and recreate the request. Completed requests are automatically cleaned up after 1 hour.

This feature requires explicit RBAC permissions on the ResourcePoolStatusRequest resource. No default ClusterRoles include this permission.

Resource pool status is controlled by the DRAResourcePoolStatus feature gate in the kube-apiserver and kube-controller-manager.

DRA device metadata in containers

DRA drivers can expose device metadata such as device attributes (PCI bus addresses or mdevUUID for mediated devices) or network configuration directly to containers as JSON files. This lets applications inside the container discover information about allocated devices without querying the Kubernetes API or building custom controllers.

KEP-5304 defines a device metadata protocol that drivers must follow so applications inside the container see a consistent layout across drivers and clusters. The DRA kubelet plugin library implements this protocol for you; the rest of this section describes how to use it.

Device metadata follows the same rules as device access: it is available inside a container only when that container requests the device in its container specification, and not otherwise. For how to request DRA devices in Pods and containers, see Request devices in workloads using DRA.

Device metadata protocol

The protocol consists of four rules:

1. File paths. Metadata files live inside containers under /var/run/kubernetes.io/dra-device-attributes. For a directly referenced ResourceClaim the path is resourceclaims/<claimName>/<requestName>/<driverName>-metadata.json; for a claim created from a ResourceClaimTemplate the path is resourceclaimtemplates/<podClaimName>/<requestName>/<driverName>-metadata.json (where podClaimName is pod.spec.resourceClaims[].name).

   In cases where the ResourceClaim request uses the prioritized list feature, only the top-level request name is used for the <requestName> segment in the file path (that is, the /<subrequest> portion is dropped). Inside the JSON file, the requests[].name field carries the full <request>/<subrequest> reference (for example, gpu/high-memory) so that consumers can identify which alternative was allocated.

   The path constants are defined in k8s.io/dynamic-resource-allocation/api/metadata.

2. JSON API. Each file is a stream of one or more DeviceMetadata objects serialized as versioned JSON with apiVersion and kind, following Kubernetes API conventions. The same metadata is encoded once per supported API version (newest first). All objects in the stream are semantically equivalent; consumers should use the first object they can decode.

3. Generation. When a driver updates a metadata file the embedded metadata.generation field must increase so consumers can detect changes.

4. Container exposure. Files are typically exposed via CDI bind-mounts, but other mechanisms are permitted as long as the file appears at the correct path and is read-only inside the container.

How device metadata works

Device metadata is a driver-side feature that does not require any Kubernetes API changes or feature gates. Using the DRA kubelet plugin library is a common way to implement a driver, but drivers can be built in other ways as well. Drivers that use the kubelet plugin enable this feature by passing the EnableDeviceMetadata and MetadataVersions options when starting the plugin. MetadataVersions specifies which API versions are serialized into the metadata file and must be set explicitly by the driver. Check the documentation of your DRA driver to learn whether device metadata is supported and how to enable it.

When device metadata is enabled, the driver generates metadata files and CDI bind-mount specifications while preparing the allocated devices for the pod, before the consuming containers start. The metadata appears inside containers at the well-known paths as defined above.

When a single request allocates devices from multiple DRA drivers, each driver writes its own metadata file. Containers enumerate *-metadata.json files in the request directory to discover all devices.

The Go package k8s.io/dynamic-resource-allocation/devicemetadata provides utilities for reading and decoding these metadata files by applications inside the container.

Metadata schema

Each metadata file conforms to the DeviceMetadata API (metadata.resource.k8s.io/v1alpha1). The following example shows a metadata file for a GPU device allocated through a ResourceClaimTemplate:

{
  "kind": "DeviceMetadata",
  "apiVersion": "metadata.resource.k8s.io/v1alpha1",
  "metadata": {
    "name": "pod0-gpu-2kqrd",
    "namespace": "gpu-test1",
    "uid": "c7e7b22e-239b-4498-b27c-7f1344481e14",
    "generation": 1
  },
  "podClaimName": "gpu",
  "requests": [
    {
      "name": "gpu",
      "devices": [
        {
          "driver": "gpu.example.com",
          "pool": "worker-0",
          "name": "gpu-0",
          "attributes": {
            "driverVersion": {
              "version": "1.0.0"
            },
            "index": {
              "int": 0
            },
            "model": {
              "string": "LATEST-GPU-MODEL"
            },
            "uuid": {
              "string": "gpu-18db0e85-99e9-c746-8531-ffeb86328b39"
            }
          }
        }
      ]
    }
  ]
}

Immediate and deferred metadata

Drivers provide metadata in one of two ways:

Immediate

The driver populates metadata while preparing the claim on the node and writes the metadata file before the container starts. This is typical for GPU drivers where device information is known at preparation time.

Deferred

In some cases, for example a network driver, the device information is not available during device allocation time but becomes available after the pod sandbox is created. In those cases the driver creates the CDI mount with an empty metadata file and writes the actual metadata later via an NRI hook that runs before the container starts. This ensures applications never see a missing or partially written file. Each update must increment metadata.generation so consumers can detect changes. The MetadataUpdater API in the DRA kubelet plugin library handles generation bookkeeping automatically for driver authors.

In both cases, metadata remains available to each consuming container for the lifetime of that container. Metadata files are cleaned up after all containers in the Pod have terminated.

To learn how to use device metadata in your workloads, see Access DRA device metadata.

Custom drivers

Custom, hand-crafted drivers that do not use the DRA kubelet plugin library must implement the device metadata protocol themselves. That means writing DeviceMetadata JSON at the correct file paths, incrementing metadata.generation on every update, and exposing the files read-only inside the container through CDI or an equivalent mechanism.

4 - DRA Features

This page describes optional DRA features for advanced use cases. They are relevant only with DRA drivers that support them. Each feature notes its maturity and the feature gate that enables it.

Extended resource allocation by DRA

You can provide an extended resource name for a DeviceClass. The scheduler will then select the devices matching the class for the extended resource requests. This allows users to continue using extended resource requests in a pod to request either extended resources provided by device plugin, or DRA devices. The same extended resource can be provided either by device plugin, or DRA on one single cluster node. The same extended resource can be provided by device plugin on some nodes, and DRA on other nodes in the same cluster.

In the example below, the DeviceClass is given an extendedResourceName example.com/gpu. If a pod requested for the extended resource example.com/gpu: 2, it can be scheduled to a node with two or more devices matching the DeviceClass.

apiVersion: resource.k8s.io/v1
kind: DeviceClass
metadata:
  name: gpu.example.com
spec:
  selectors:
  - cel:
      expression: device.driver == 'gpu.example.com' && device.attributes['gpu.example.com'].type
        == 'gpu'
  extendedResourceName: example.com/gpu

In addition, users can use a special extended resource to allocate devices without having to explicitly create a ResourceClaim. Using the extended resource name prefix deviceclass.resource.kubernetes.io/ and the DeviceClass name. This works for any DeviceClass, even if it does not specify an extended resource name. The resulting ResourceClaim will contain a request for an ExactCount of the specified number of devices of that DeviceClass.

Extended resource allocation by DRA is controlled by the DRAExtendedResource feature gate in the kube-apiserver, kube-scheduler, kube-controller-manager, and kubelet.

For a hands-on walkthrough of requesting extended resources, see Assign Extended Resources to a Container.

Partitionable devices

Devices represented in DRA don't necessarily have to be a single unit connected to a single machine, but can also be a logical device comprised of multiple devices connected to multiple machines. These devices might consume overlapping resources of the underlying phyical devices, meaning that when one logical device is allocated other devices will no longer be available.

In the ResourceSlice API, this is represented as a list of named CounterSets, each of which contains a set of named counters. The counters represent the resources available on the physical device that are used by the logical devices advertised through DRA.

Logical devices can specify the ConsumesCounters list. Each entry contains a reference to a CounterSet and a set of named counters with the amounts they will consume. So for a device to be allocatable, the referenced counter sets must have sufficient quantity for the counters referenced by the device.

CounterSets must be specified in separate ResourceSlices from devices. Devices can consume counters from any CounterSet defined in the same resource pool as the device.

Here is an example of two devices, each consuming 6Gi of memory from a shared counter with 8Gi of memory. Thus, only one of the devices can be allocated at any point in time. The scheduler handles this and it is transparent to the consumer as the ResourceClaim API is not affected.

apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: resourceslice-with-countersets
spec:
  nodeName: worker-1
  pool:
    name: pool
    generation: 1
    resourceSliceCount: 2
  driver: dra.example.com
  sharedCounters:
  - name: gpu-1-counters
    counters:
      memory:
        value: 8Gi
---
apiVersion: resource.k8s.io/v1
kind: ResourceSlice
metadata:
  name: resourceslice-with-devices
spec:
  nodeName: worker-1
  pool:
    name: pool
    generation: 1
    resourceSliceCount: 2
  driver: dra.example.com
  devices:
  - name: device-1
    consumesCounters:
    - counterSet: gpu-1-counters
      counters:
        memory:
          value: 6Gi
  - name: device-2
    consumesCounters:
    - counterSet: gpu-1-counters
      counters:
        memory:
          value: 6Gi

Partitionable devices is controlled by the DRAPartitionableDevices feature gate in the kube-apiserver and kube-scheduler.

Consumable capacity

The consumable capacity feature allows the same devices to be consumed by multiple independent ResourceClaims, with the Kubernetes scheduler managing how much of the device's capacity is used up by each claim. This is analogous to how Pods can share the resources on a Node; ResourceClaims can share the resources on a Device.

The device driver can set allowMultipleAllocations field added in .spec.devices of ResourceSlice to allow allocating that device to multiple independent ResourceClaims or to multiple requests within a ResourceClaim.

Users can set capacity field added in spec.devices.requests of ResourceClaim to specify the device resource requirements for each allocation.

For the device that allows multiple allocations, the requested capacity is drawn from — or consumed from — its total capacity, a concept known as consumable capacity. Then, the scheduler ensures that the aggregate consumed capacity across all claims does not exceed the device’s overall capacity. Furthermore, driver authors can use the requestPolicy constraints on individual device capacities to control how those capacities are consumed. For example, the driver author can specify that a given capacity is only consumed in increments of 1Gi.

Here is an example of a network device which allows multiple allocations and contains a consumable bandwidth capacity.

kind: ResourceSlice
apiVersion: resource.k8s.io/v1
metadata:
  name: resourceslice
spec:
  nodeName: worker-1
  pool:
    name: pool
    generation: 1
    resourceSliceCount: 1
  driver: dra.example.com
  devices:
  - name: eth1
    allowMultipleAllocations: true
    attributes:
      name:
        string: "eth1"
    capacity:
      bandwidth:
        requestPolicy:
          default: "1M"
          validRange:
            min: "1M"
            step: "8"
        value: "10G"

The consumable capacity can be requested as shown in the below example.

apiVersion: resource.k8s.io/v1
kind: ResourceClaimTemplate
metadata:
  name: bandwidth-claim-template
spec:
  spec:
    devices:
      requests:
      - name: req-0
        exactly:
          deviceClassName: resource.example.com
          capacity:
            requests:
              bandwidth: 1G

The allocation result will include the consumed capacity and the identifier of the share.

apiVersion: resource.k8s.io/v1
kind: ResourceClaim
...
status:
  allocation:
    devices:
      results:
      - consumedCapacity:
          bandwidth: 1G
        device: eth1
        shareID: "a671734a-e8e5-11e4-8fde-42010af09327"

In this example, a multiply-allocatable device was chosen. However, any resource.example.com device with at least the requested 1G bandwidth could have met the requirement. If a non-multiply-allocatable device were chosen, the allocation would have resulted in the entire device. To force the use of a only multiply-allocatable devices, you can use the CEL criteria device.allowMultipleAllocations == true.

DistinctAttribute constraint

When requesting multiple devices in a ResourceClaim, you can use the DistinctAttribute constraint to ensure that each allocated device has a different value for a specified attribute. This constraint was introduced with the consumable capacity feature.

The DistinctAttribute constraint is particularly useful when working with multiply-allocatable devices. It prevents the scheduler from allocating the same device multiple times within a single ResourceClaim, even when that device allows multiple allocations.

Beyond preventing duplicate allocations, this constraint helps optimize performance by ensuring devices are distributed based on their attributes. For example, you can use it to distribute devices across different NUMA nodes to optimize memory bandwidth and reduce contention.

Granular status authorization

Starting in Kubernetes v1.36, DRA enforces fine-grained authorization checks for updates to ResourceClaim status by using synthetic subresources and node-aware verbs.

For security hardening guidance, including RBAC examples for scheduler and DRA drivers, see Hardening Guide - Dynamic Resource Allocation.

For a step-by-step cluster administrator procedure, see Harden Dynamic Resource Allocation in Your Cluster.

5 - Device Taints and Tolerations

This page describes device taints and tolerations in DRA, which let drivers and admins keep Pods off specific devices, or evict Pods already using them.

Device taints and tolerations

Device taints are similar to node taints: a taint has a string key, a string value, and an effect. The effect is applied to the ResourceClaim which is using a tainted device and to all Pods referencing that ResourceClaim. The "NoSchedule" effect prevents scheduling those Pods. Tainted devices are ignored when trying to allocate a ResourceClaim because using them would prevent scheduling of Pods.

The "NoExecute" effect implies "NoSchedule" and in addition causes eviction of all Pods which have been scheduled already. This eviction is implemented in the device taint eviction controller in kube-controller-manager by deleting affected Pods.

The "None" effect is ignored by the scheduler and eviction controller. DRA drivers can use it to communicate exceptions to admins or other controllers, for example degraded health of a device. Admins can also use it to do dry-runs of pod eviction in DeviceTaintRules (more on that below).

ResourceClaims can tolerate taints. If a taint is tolerated, its effect does not apply. An empty toleration matches all taints. A toleration can be limited to certain effects and/or match certain key/value pairs. A toleration can check that a certain key exists, regardless which value it has, or it can check for specific values of a key. For more information on this matching see the node taint concepts.

Eviction can be delayed by tolerating a taint for a certain duration. That delay starts at the time when a taint gets added to a device, which is recorded in a field of the taint.

Taints apply as described above also to ResourceClaims allocating "all" devices on a node. All devices must be untainted or all of their taints must be tolerated. Allocating a device with admin access (described above) is not exempt either. An admin using that mode must explicitly tolerate all taints to access tainted devices.

Device taints and tolerations is controlled by the DRADeviceTaints feature gate in the kube-apiserver, kube-controller-manager, and kube-scheduler. To use DeviceTaintRules, the resource.k8s.io/v1beta2 API version must be enabled together with the DRADeviceTaintRules feature gate. In contrast to DRADeviceTaints, DRADeviceTaintRules is off by default because of this dependency on the beta API group, which has to be off by default.

You can add taints to devices in the following ways, by using the DeviceTaintRule API kind.

Taints set by the driver

A DRA driver can add taints to the device information that it publishes in ResourceSlices. Consult the documentation of a DRA driver to learn whether the driver uses taints and what their keys and values are.

Taints set by an admin

An admin or a control plane component can taint devices without having to tell the DRA driver to include taints in its device information in ResourceSlices. They do that by creating DeviceTaintRules. Each DeviceTaintRule adds one taint to devices which match the device selector. Without such a selector, no devices are tainted. This makes it harder to accidentally evict all pods using ResourceClaims when leaving out the selector by mistake.

Devices can be selected by giving the name of a DeviceClass, driver, pool, and/or device. The DeviceClass selects all devices that are selected by the selectors in that DeviceClass. With just the driver name, an admin can taint all devices managed by that driver, for example while doing some kind of maintenance of that driver across the entire cluster. Adding a pool name can limit the taint to a single node, if the driver manages node-local devices.

Finally, adding the device name can select one specific device. The device name and pool name can also be used alone, if desired. For example, drivers for node-local devices are encouraged to use the node name as their pool name. Then tainting with that pool name automatically taints all devices on a node.

Drivers might use stable names like "gpu-0" that hide which specific device is currently assigned to that name. To support tainting a specific hardware instance, CEL selectors can be used in a DeviceTaintRule to match a vendor-specific unique ID attribute, if the driver supports one for its hardware.

The taint applies as long as the DeviceTaintRule exists. It can be modified and and removed at any time. Here is one example of a DeviceTaintRule for a fictional DRA driver:

apiVersion: resource.k8s.io/v1
kind: DeviceTaintRule
metadata:
  name: example
spec:
  # The entire hardware installation for this
  # particular driver is broken.
  # Evict all pods and don't schedule new ones.
  deviceSelector:
    driver: dra.example.com
  taint:
    key: dra.example.com/unhealthy
    value: Broken
    effect: NoExecute

The kube-apiserver automatically tracks when this taint was created by setting the timeAdded field in the spec. The toleration period starts at that time stamp. During updates which change the effect (see simulated eviction flow below), the kube-apiserver automatically updates the time stamp. Users can control the time stamp explicitly by setting the field when creating a DeviceTaintRule and by changing it to some different value when updating.

The status contains a condition added by the eviction controller:

kubectl describe devicetaintrules

Name:         example
...
Spec:
  Device Selector:
    Driver:  dra.example.com
  Taint:
    Effect:      NoExecute
    Key:         dra.example.com/unhealthy
    Time Added:  2025-11-05T18:15:37Z
    Value:       Broken
Status:
  Conditions:
    Last Transition Time:  2025-11-05T18:15:37Z
    Message:               1 pod evicted since starting the controller.
    Observed Generation:   1
    Reason:                Completed
    Status:                False
    Type:                  EvictionInProgress
Events:                    <none>

Pods get evicted by deleting them. Usually this happens very quickly, except when a toleration for the taint delays it for a certain period or when there are very many pods which need to be evicted. When it takes longer, the message provides information about the current status:

2 pods need to be evicted in 2 different namespaces. 1 pod evicted since starting the controller.

The condition can be used to check whether an eviction is currently active:

kubectl wait --for=condition=EvictionInProgress=false DeviceTaintRule/example

Beware of the potential race between scheduler and controller observing the new taint at different times, which can lead to pods still being scheduled at a time when the controller thinks that there are none which need to be evicted and thus sets this condition to False. In practice, this race is made very unlikely by updating the status only after an intentional delay of a few seconds.

For effect: None, the message provides information about the number of affected devices, how many of those are allocated, and how many pods would be evicted if the effect was NoExecute. This can be used to do a dry-run before actually triggering eviction:

• Create a DeviceTaintRule with the desired selectors and effect: None.

• Review the message:

  3 published devices selected. 1 allocated device selected.
  1 pod would be evicted in 1 namespace if the effect was NoExecute.
  This information will not be updated again. Recreate the DeviceTaintRule to trigger an update.
  

  Published devices are those listed in ResourceSlices. Tainting them prevents allocation for new pods. Only allocated devices cause eviction of the pods using them.

• Edit the DeviceTaintRule and change the effect into NoExecute.