# Device Plugin > **Note:** The AMD GPU Operator also supports [DRA (Dynamic Resource Allocation)](../dra/dra-driver.md) as an alternative to the traditional Device Plugin. DRA provides scheduler-driven GPU allocation, fine-grained device selection, and GPU sharing capabilities. The Device Plugin and DRA driver **cannot be enabled at the same time**. ## Configure device plugin To start the Device Plugin along with the GPU Operator configure fields under the ``` spec/devicePlugin ``` field in deviceconfig Custom Resource(CR) ```yaml devicePlugin: # Enable the device plugin (default: true) # Cannot be enabled at the same time as the DRA driver enableDevicePlugin: true # Specify the device plugin image # default value is rocm/k8s-device-plugin:latest devicePluginImage: rocm/k8s-device-plugin:latest # The device plugin arguments is used to pass supported flags and their values while starting device plugin daemonset devicePluginArguments: resource_naming_strategy: single # Specify the node labeller image # default value is rocm/k8s-device-plugin:labeller-latest nodeLabellerImage: rocm/k8s-device-plugin:labeller-latest # The node labeller arguments is used to pass supported flags while starting node labeller daemonset nodeLabellerArguments: - compute-partitioning-supported - memory-partitioning-supported - compute-memory-partition # Specify whether to bring up node labeller component # default value is true enableNodeLabeller: True ``` The **device-plugin** pods start after updating the **DeviceConfig** CR ```bash #kubectl get pods -n kube-amd-gpu NAME READY STATUS RESTARTS AGE amd-gpu-operator-gpu-operator-charts-controller-manager-77tpmgn 1/1 Running 0 4h9m amd-gpu-operator-kmm-controller-6d459dffcf-lbgtt 1/1 Running 0 4h9m amd-gpu-operator-kmm-webhook-server-5fdc8b995-qgj49 1/1 Running 0 4h9m amd-gpu-operator-node-feature-discovery-gc-78989c896-7lh8t 1/1 Running 0 3h48m amd-gpu-operator-node-feature-discovery-master-b8bffc48b-6rnz6 1/1 Running 0 4h9m amd-gpu-operator-node-feature-discovery-worker-m9lwn 1/1 Running 0 4h9m test-deviceconfig-device-plugin-rk5f4 1/1 Running 0 134m test-deviceconfig-node-labeller-bxk7x 1/1 Running 0 134m ```
Note: The Device Plugin name will be prefixed with the name of your DeviceConfig custom resource

## Device Plugin DeviceConfig | Field Name | Details | | --- | --- | | **EnableDevicePlugin** | Enable/Disable device plugin with True/False. Cannot be enabled simultaneously with DRA driver. | | **DevicePluginImage** | Device plugin image | | **DevicePluginImagePullPolicy** | One of Always, Never, IfNotPresent. | | **NodeLabellerImage** | Node labeller image | | **NodeLabellerImagePullPolicy** | One of Always, Never, IfNotPresent. | | **EnableNodeLabeller** | Enable/Disable node labeller with True/False | | **DevicePluginArguments** | The flag/values to pass on to Device Plugin | | **NodeLabellerArguments** | The flags to pass on to Node Labeller |
1. Both the `ImagePullPolicy` fields default to `Always` if `:latest` tag is specified on the respective Image, or defaults to `IfNotPresent` otherwise. This is default k8s behaviour for `ImagePullPolicy` 2. `DevicePluginArguments` is of type `map[string]string`. Currently supported key value pairs to set under `DevicePluginArguments` are: -> "resource_naming_strategy": {"single", "mixed"} 3. `NodeLabellerArguments` is of type `[]string`. Currently supported flags to set under `NodeLabellerArguments` are: - {"compute-memory-partition", "compute-partitioning-supported", "memory-partitioning-supported"} - For the above new partition labels, the labels being set under this field will be applied by nodelabeller on the node The below labels are enabled by nodelabeller by default internally: - {"vram", "cu-count", "simd-count", "device-id", "family", "product-name", "driver-version"} ## How to choose Resource Naming Strategy To customize the way device plugin reports gpu resources to kubernetes as allocatable k8s resources, use the `single` or `mixed` resource naming strategy in **DeviceConfig** CR Before understanding each strategy, please note the definition of homogeneous and heterogeneous nodes Homogeneous node: A node whose gpu's follow the same compute-memory partition style -> Example: A node of 8 GPU's where all 8 GPU's are following CPX-NPS4 partition style Heterogeneous node: A node whose gpu's follow different compute-memory partition styles -> Example: A node of 8 GPU's where 5 GPU's are following SPX-NPS1 and 3 GPU's are following CPX-NPS1 ### Single In `single` mode, the device plugin reports all gpu's (regardless of whether they are whole gpu's or partitions of a gpu) under the resource name `amd.com/gpu` This mode is supported for homogeneous nodes but not supported for heterogeneous nodes A node which has 8 GPUs where all GPUs are not partitioned will report its resources as: ```bash amd.com/gpu: 8 ``` A node which has 8 GPUs where all GPUs are partitioned using CPX-NPS4 style will report its resources as: ```bash amd.com/gpu: 64 ``` ### Mixed In `mixed` mode, the device plugin reports all gpu's under a name which matches its partition style. This mode is supported for both homogeneous nodes and heterogeneous nodes A node which has 8 GPUs which are all partitioned using CPX-NPS4 style will report its resources as: ```bash amd.com/cpx_nps4: 64 ``` A node which has 8 GPUs where 5 GPU's are following SPX-NPS1 and 3 GPU's are following CPX-NPS1 will report its resources as: ```bash amd.com/spx_nps1: 5 amd.com/cpx_nps1: 24 ``` #### **Notes** - If `resource_naming_strategy` is not passed using `DevicePluginArguments` field in CR, then device plugin will internally default to `single` resource naming strategy. This maintains backwards compatibility with earlier release of device plugin with reported resource name of `amd.com/gpu` - If a node has GPUs which do not support partitioning, such as MI210, then the GPUs are reported under resource name `amd.com/gpu` regardless of the resource naming strategy - These different naming styles of resources, for example, `amd.com/cpx_nps1` should be followed when requesting for resources in a pod spec