Container Service for Kubernetes:Enable auto scaling based on GPU metrics

Step 1: Deploy Managed Service for Prometheus and ack-alibaba-cloud-metrics-adapter

1. Step 1: Enable Managed Service for Prometheus.

   Note

   You can select Enable Managed Service for Prometheus when you create a cluster. This saves you the need to install Managed Service for Prometheus after the cluster is created.

2. Install and configure ack-alibaba-cloud-metrics-adapter.

   A. Obtain the HTTP API endpoint

   1. Log on to the ARMS console.

   2. In the left-side navigation pane, choose Managed Service for Prometheus > Instances.

   3. In the top navigation bar, select the region where your ACK cluster is deployed. Then, click the name of the Prometheus instance used by your cluster to go to the instance details page.

   4. On the Settings page, click the Settings tab, and copy the internal endpoint in the HTTP API URL section.

   B. Configure the Prometheus URL

   1. Log on to the ACK console. In the left-side navigation pane, choose Marketplace > Marketplace.

   2. On the Marketplace page, click the App Catalog tab. Find and click ack-alibaba-cloud-metrics-adapter.

   3. On the ack-alibaba-cloud-metrics-adapter page, click Deploy.

   4. On the Basic Information wizard page, select a cluster and a namespace, and then click Next.

   5. On the Parameters wizard page, select a chart version from the Chart Version drop-down list, set the Prometheus URL in the Parameters section to the HTTP API endpoint that you obtained, and then click OK.

Step 3: Enable auto scaling based on GPU metrics

The following example shows how to deploy a model inference service on a GPU-accelerated node and perform stress tests on the node to check whether auto scaling can be performed based on GPU metrics.

a. Deploy an inference service

1. Run the following command to deploy the inference service:

   Show sample code

   cat <<EOF | kubectl create -f -
   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: bert-intent-detection
   spec:
     replicas: 1
     selector:
       matchLabels:
         app: bert-intent-detection
     template:
       metadata:
         labels:
           app: bert-intent-detection
       spec:
         containers:
         - name: bert-container
           image: registry.cn-hangzhou.aliyuncs.com/ai-samples/bert-intent-detection:1.0.1
           ports:
           - containerPort: 80
           resources:
             limits:
               nvidia.com/gpu: 1
   ---
   apiVersion: v1
   kind: Service
   metadata:
     name: bert-intent-detection-svc
     labels:
       app: bert-intent-detection
   spec:
     selector:
       app: bert-intent-detection
     ports:
     - protocol: TCP
       name: http
       port: 80
       targetPort: 80
     type: LoadBalancer
   EOF

2. Query the status of the pod and Service.

   • Run the following command to query the status of the pod:

     kubectl get pods -o wide

     Expected output:

     NAME                                    READY   STATUS    RESTARTS   AGE     IP           NODE                        NOMINATED NODE   READINESS GATES
     bert-intent-detection-7b486f6bf-f****   1/1     Running   0          3m24s   10.15.1.17   cn-beijing.192.168.94.107   <none>           <none>

     The output indicates that only one pod is deployed on the GPU-accelerated node 192.168.94.107.

   • Run the following command to query the status of the Service:

     kubectl get svc bert-intent-detection-svc

     Expected output:

     NAME                        TYPE           CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
     bert-intent-detection-svc   LoadBalancer   172.16.186.159   47.95.XX.XX   80:30118/TCP   5m1s

     If the output displays the name of the Service, the Service is deployed.

3. Log on to the node 192.168.94.107 by using SSH and run the following command to query GPU utilization:

   nvidia-smi

   Expected output:

   Wed Feb 16 11:48:07 2022
   +-----------------------------------------------------------------------------+
   | NVIDIA-SMI 450.102.04   Driver Version: 450.102.04   CUDA Version: 11.0     |
   |-------------------------------+----------------------+----------------------+
   | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
   | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
   |                               |                      |               MIG M. |
   |===============================+======================+======================|
   |   0  Tesla V100-SXM2...  On   | 00000000:00:07.0 Off |                    0 |
   | N/A   32C    P0    55W / 300W |  15345MiB / 16160MiB |      0%      Default |
   |                               |                      |                  N/A |
   +-------------------------------+----------------------+----------------------+
   
   +-----------------------------------------------------------------------------+
   | Processes:                                                                  |
   |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
   |        ID   ID                                                   Usage      |
   |=============================================================================|
   |    0   N/A  N/A   2305118      C   python                          15343MiB |
   +-----------------------------------------------------------------------------+

   The output indicates that the inference service is running on the GPU-accelerated node. The GPU utilization is 0 because no request is sent to the service.

4. Run the following command to send requests to the inference service and check whether the service is deployed:

   curl -v  "http://47.95.XX.XX/predict?query=Music"

   Expected output:

   *   Trying 47.95.XX.XX...
   * TCP_NODELAY set
   * Connected to 47.95.XX.XX (47.95.XX.XX) port 80 (#0)
   > GET /predict?query=Music HTTP/1.1
   > Host: 47.95.XX.XX
   > User-Agent: curl/7.64.1
   > Accept: */*
   >
   * HTTP 1.0, assume close after body
   < HTTP/1.0 200 OK
   < Content-Type: text/html; charset=utf-8
   < Content-Length: 9
   < Server: Werkzeug/1.0.1 Python/3.6.9
   < Date: Wed, 16 Feb 2022 03:52:11 GMT
   <
   * Closing connection 0
   PlayMusic # The query result.

   If the HTTP status code 200 and the query result are returned, the inference service is deployed.

b. Configure the HPA

The following example describes how to trigger auto scaling when the GPU utilization of a pod exceeds 20%. The following table describes the metrics that are supported by the Horizontal Pod Autoscaler (HPA).

1. Run the following command to deploy the HPA:

   Clusters that run Kubernetes 1.23 or later

   cat <<EOF | kubectl create -f -
   apiVersion: autoscaling/v2  # Use the HPA configuration for API version autoscaling/v2. 
   kind: HorizontalPodAutoscaler
   metadata:
     name: gpu-hpa
   spec:
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: bert-intent-detection
     minReplicas: 1
     maxReplicas: 10
     metrics:
     - type: Pods
       pods:
         metric:
           name: DCGM_CUSTOM_PROCESS_SM_UTIL
         target:
           type: Utilization
           averageValue: 20 # If the GPU utilization exceeds 20%, pods are scaled out. 
   EOF

   Clusters that run Kubernetes versions earlier than 1.23

   cat <<EOF | kubectl create -f -
   apiVersion: autoscaling/v2beta1  # Use the HPA configuration for API version autoscaling/v2beta1. 
   kind: HorizontalPodAutoscaler
   metadata:
     name: gpu-hpa
   spec:
     scaleTargetRef:
       apiVersion: apps/v1
       kind: Deployment
       name: bert-intent-detection
     minReplicas: 1
     maxReplicas: 10
     metrics:
     - type: Pods
       pods:
         metricName: DCGM_CUSTOM_PROCESS_SM_UTIL # The GPU utilization of pods. 
         targetAverageValue: 20 # If the GPU utilization exceeds 20%, pods are scaled out. 
   EOF

2. Run the following command to query the status of the HPA:

   kubectl get hpa

   Expected output:

   NAME      REFERENCE                          TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
   gpu-hpa   Deployment/bert-intent-detection   0/20      1         10        1          74s

   The expected output indicates that TARGETS displays 0/20. The current GPU utilization is 0. When the GPU utilization exceeds 20%, pods are scaled out.

c. Test auto scaling on the inference service

You can check whether there are changes in the GPU utilization on the GPU Monitoring tab. If the GPU utilization increases, a GPU is used. If no changes are found in the GPU utilization, no GPU is used. To do this, perform the following steps:

1. Log on to the ACK console. In the left-side navigation pane, click Clusters.

2. On the Clusters page, click the name of the cluster that you want to manage. In the left-side navigation pane, choose Operations > Prometheus Monitoring.

3. On the Prometheus Monitoring page, click the GPU Monitoring tab and view changes in the GPU utilization.