Cloud Parallel File Storage:Mount a CPFS for Lingjun file system in ACS

kubectl

1. Save this YAML content as cpfs-pv-pvc.yaml.

   Choose the YAML that corresponds to the compute type of the pod you are mounting to.

   Mount to a GPU pod

   Important

   This method is only supported for mounting to pods with specific GPU models. For details, see Supported GPU models in ACS.

   apiVersion: v1
   kind: PersistentVolume
   metadata:
     name: cpfs-test
     labels:
       alicloud-pvname: cpfs-test
   spec:
     accessModes:
     - ReadWriteMany
     capacity:
       storage: 10Ti
     csi:
       driver: povplugin.csi.alibabacloud.com
       volumeAttributes:
         filesystemId: bmcpfs-*****
         path: /
       volumeHandle: bmcpfs-*****
   ---
   apiVersion: v1
   kind: PersistentVolumeClaim
   metadata:
     name: cpfs-test
   spec:
     accessModes:
     - ReadWriteMany
     selector:
       matchLabels:
         alicloud-pvname: cpfs-test
     resources:
       requests:
         storage: 10Ti

   • PV parameters

     Parameter	Description
     labels	A label for the PV. The PVC uses this label in its selector to bind to the PV.
     accessModes	The access mode of the PV.
     capacity.storage	The capacity of the volume.
     csi.driver	The driver type. Set it to povplugin.csi.alibabacloud.com.
     csi.volumeAttributes	The attributes of the CPFS volume. filesystemId: The ID of the CPFS for Lingjun file system. path: The subdirectory to mount. The default is /, which mounts the root directory of the CPFS file system. You can specify a subdirectory, such as /dir. If the specified subdirectory does not exist, it is created automatically during mounting.
     csi.volumeHandle	The ID of the CPFS for Lingjun file system.

   • PVC parameters

     Parameter	Description
     accessModes	The access mode requested by the PVC.
     selector	Binds the PVC to a PV with a matching label.
     resources.requests.storage	The storage capacity allocated to the pod. This value must not exceed the PV capacity.

   Mount to a CPU pod

   apiVersion: v1
   kind: PersistentVolume
   metadata:
     name: cpfs-test
     labels:
       alicloud-pvname: cpfs-test
   spec:
     accessModes:
     - ReadWriteMany
     capacity:
       storage: 10Ti
     csi:
       driver: nasplugin.csi.alibabacloud.com
       volumeAttributes:
         mountProtocol: efc
         server: cpfs-***-vpc-***.cn-wulanchabu.cpfs.aliyuncs.com
         path: /
       volumeHandle: bmcpfs-*****
   ---
   apiVersion: v1
   kind: PersistentVolumeClaim
   metadata:
     name: cpfs-test
   spec:
     accessModes:
     - ReadWriteMany
     selector:
       matchLabels:
         alicloud-pvname: cpfs-test
     resources:
       requests:
         storage: 10Ti

   • PV parameters

     Parameter	Description
     labels	A label for the PV. The PVC uses this label in its selector to bind to the PV.
     accessModes	The access mode of the PV.
     capacity.storage	The capacity of the volume.
     csi.driver	The driver type. Set it to nasplugin.csi.alibabacloud.com.
     csi.volumeAttributes	The attributes of the CPFS volume. mountProtocol: The mount protocol. Set it to efc. server: The domain name of the VPC mount target for the CPFS file system. path: The subdirectory to mount. The default is /, which mounts the root directory of the CPFS file system. You can specify a subdirectory, such as /dir.
     csi.volumeHandle	The ID of the CPFS for Lingjun file system.

   • PVC parameters

     Parameter	Description
     accessModes	The access mode requested by the PVC.
     selector	Binds the PVC to a PV with a matching label.
     resources.requests.storage	The storage capacity allocated to the pod. This value must not exceed the PV capacity.

2. Create the PV and PVC.

   kubectl create -f cpfs-pv-pvc.yaml

3. Verify that the PVC is bound to the PV.

   kubectl get pvc cpfs-test

   Example output:

   NAME        STATUS   VOLUME           CAPACITY   ACCESS MODES   STORAGECLASS    VOLUMEATTRIBUTESCLASS   AGE
   cpfs-test   Bound    cpfs-test        10Ti       RWX            <unset>         <unset>                 10s

Console

1. Log on to the ACS console.

2. On the Clusters, click the name of the cluster to go to the cluster management page.

3. In the left-side navigation pane of the cluster management page, choose Volumes > Persistent Volume Claims.

4. On the Persistent Volume Claims page, click Create.

5. In the dialog box that appears, configure the parameters and then click Create.

   This table describes the parameters for creating a PV and PVC simultaneously. You can also create them separately.

   Note

   The ACS console wizard currently does not support creating a CPFS for Lingjun PVC for use with CPU pods. Use the kubectl method for this scenario.

   Parameter	Description	Example
   PVC Type	Select CPFS.	CPFS
   Name	Enter a name for the PVC. Follow the format requirements displayed on the screen.	cpfs-test
   Allocation Mode	Select Existing Volume or Create Volume.	Create Volume
   CPFS Type	Select CPFS for Lingjun.	CPFS for Lingjun
   Access Mode	Supported modes are ReadWriteMany and ReadWriteOnce.	ReadWriteMany
   File System ID	Enter the ID of the CPFS for Lingjun file system that you want to mount.	bmcpfs-0115******13q5

6. Check the created PVC and PV.

   On the Persistent Volume Claims and Persistent Volumes pages, find the newly created PVC and PV and verify that they are bound.

kubectl

1. Create a file named cpfs-test.yaml using the following YAML content.

   GPU-accelerated application

   The following YAML example creates a Deployment that consists of two pods. Both pods declare that they want to use GPU computing power through the alibabacloud.com/compute-class: gpu label, and request storage resources through a PVC named cpfs-test. The mount path for both is /data.

   Note

   For information about specific GPU models, see Specify GPU models and driver versions for ACS GPU-accelerated pods.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: cpfs-test
     labels:
       app: cpfs-test
   spec:
     replicas: 2
     selector:
       matchLabels:
         app: cpfs-test
     template:
       metadata:
         labels:
           app: cpfs-test
           # Set the compute class to GPU
           alibabacloud.com/compute-class: gpu
           # Specify the GPU model, fill in as needed, such as T4
           alibabacloud.com/gpu-model-series: T4
           alibabacloud.com/compute-qos: default
       spec:
         containers:
         - name: nginx
           image: registry.cn-hangzhou.aliyuncs.com/acs-sample/nginx:latest
           ports:
           - containerPort: 80
           volumeMounts:
             - name: pvc-cpfs
               mountPath: /data
         volumes:
           - name: pvc-cpfs
             persistentVolumeClaim:
               claimName: cpfs-test

   CPU-accelerated application

   The following YAML example creates a Deployment that consists of two pods. It requests storage resources through a PVC named cpfs-test. The mount path for both is /data.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: cpfs-test
     labels:
       app: cpfs-test
   spec:
     replicas: 2
     selector:
       matchLabels:
         app: cpfs-test
     template:
       metadata:
         labels:
           app: cpfs-test
       spec:
         containers:
         - name: nginx
           image: registry.cn-hangzhou.aliyuncs.com/acs-sample/nginx:latest
           ports:
           - containerPort: 80
           volumeMounts:
             - name: pvc-cpfs
               mountPath: /data
         volumes:
           - name: pvc-cpfs
             persistentVolumeClaim:
               claimName: cpfs-test

2. Create the Deployment and mount the CPFS volume.

   kubectl create -f cpfs-test.yaml

3. View the deployment status of the pods in the Deployment.

   kubectl get pod | grep cpfs-test

   Expected output shows that two pods have been created:

   cpfs-test-****-***a   1/1     Running   0          45s
   cpfs-test-****-***b   1/1     Running   0          45s

4. View the mount path.

   The example command below is expected to return the data in the mounted directory of the CPFS for LINGJUN file system. By default, it is empty.

   kubectl exec cpfs-test-****-***a -- ls /data

Console

1. In the left-side navigation pane of the cluster management page, choose Workloads > Deployments.

2. On the Deployments page, click Create from Image.

3. Configure the Deployment parameters and click Create.

   Take note of the following parameters. Keep the default settings for other parameters. For more information, see Create a stateless application using a Deployment.

   GPU-accelerated application

   Configuration page	Parameter	Description	Example
   Basic Information	Name:	The name of the Deployment. Enter a custom name. The name must follow the format requirements displayed on the interface.	cpfs-test
   	Replicas:	Configure the number of replicas for the Deployment.	2
   	Type	Select the compute type for the pod. Note For information about specific GPU models, see Specify GPU models and driver versions for ACS GPU-accelerated pods.	GPU, T4
   Container	Image Name:	Enter the image address used to deploy the application.	registry.cn-hangzhou.aliyuncs.com/acs-sample/nginx:latest
   	Required Resources	Set the required GPU, vCPU, and memory resources.	GPU: 1 CPU: 2 vCPUs Memory: 2 GiB
   	Volume:	Click Add PVC, and then configure the parameters. Mount Source: Select the PVC you created earlier. Container Path: Enter the container path to which the CPFS file system will be mounted.	Mount source: pvc-cpfs Container path: /data

   CPU-accelerated application

   Configuration page	Parameter	Description	Example
   Basic Information	Application Name	The name of the Deployment. Enter a custom name. The name must follow the format requirements displayed on the interface.	cpfs-test
   	Replicas	Configure the number of replicas for the Deployment.	2
   	Type	Select the compute type for the pod.	CPU, general-purpose
   Container	Image Name	Enter the image address used to deploy the application.	registry.cn-hangzhou.aliyuncs.com/acs-sample/nginx:latest
   	Required Resources	Set the required vCPU and memory resources.	CPU: 0.25 vCPUs Memory: 0.5 GiB
   	Volume	Click Add PVC, and then configure the parameters. Mount Source: Select the PVC you created earlier. Container Path: Enter the container path to which the CPFS file system will be mounted.	Mount source: pvc-cpfs Container path: /data

4. View the application deployment status.

   1. On the Deployments page, click the application name.

   2. On the Pods tab, confirm that the pods are running normally (status is Running).