# Using Ascend GPU in the Application This section explains how to use Ascend GPU on the DCE 5.0 platform. ## Prerequisites - The Ascend GPU driver has been installed on the current cluster. - The GPU cards in the current cluster have not undergone any virtualization operations or been occupied by other applications. ## Configuration through the User Interface 1. Confirm whether the cluster has detected the GPU cards. Click __Clusters__ -> __Cluster Settings__ -> __Addon Plugins__ and check if the corresponding GPU type has been automatically enabled and detected. Currently, the cluster will automatically enable __GPU__ , and set the GPU type as __Ascend__ . 2. Deploy the workload by clicking on __Clusters__ -> __Workloads__ . Deploy the workload using an image, and after selecting the type (Ascend), configure the number of physical cards that the application will use: **Number of Physical Cards (huawei.com/Ascend910)**: Indicates the number of physical cards that the current Pod needs to mount. The input value must be an integer and **less than or equal to** the number of cards on the host machine. > If there are issues with the above configuration, scheduling failures and resource allocation problems may occur. ## YAML Configuration To apply for GPU resources in the workload, add the `huawei.com/Ascend910` parameter in the resource request and limit configuration to specify the physical card resources used by the application. ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: full-Ascend-gpu-demo namespace: default spec: replicas: 1 selector: matchLabels: app: full-Ascend-gpu-demo template: metadata: labels: app: full-Ascend-gpu-demo spec: containers: - image: nginx:perl name: container-0 resources: limits: cpu: 250m huawei.com/Ascend910: '1' memory: 512Mi requests: cpu: 250m memory: 512Mi imagePullSecrets: - name: default-secret ```