Container Service for Kubernetes:Deploy a DeepSeek distilled model inference service on ACK

Download the DeepSeek-R1-Distill-Qwen-7B model from ModelScope.

git lfs install
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B.git
cd DeepSeek-R1-Distill-Qwen-7B/
git lfs pull

Create a directory in OSS and upload the model files.

ossutil mkdir oss://<your-bucket-name>/models/DeepSeek-R1-Distill-Qwen-7B
ossutil cp -r ./DeepSeek-R1-Distill-Qwen-7B oss://<your-bucket-name>/models/DeepSeek-R1-Distill-Qwen-7B

Create a persistent volume (PV) and persistent volume claim (PVC) named llm-model for the target cluster. See Use statically provisioned ossfs 1.0 volumes.

apiVersion: v1
kind: Secret
metadata:
  name: oss-secret
stringData:
  akId: <your-oss-ak> # Your AccessKey ID for accessing OSS.
  akSecret: <your-oss-sk> # Your AccessKey secret for accessing OSS.
---
apiVersion: v1
kind: PersistentVolume
metadata:
  name: llm-model
  labels:
    alicloud-pvname: llm-model
spec:
  capacity:
    storage: 30Gi 
  accessModes:
    - ReadOnlyMany
  persistentVolumeReclaimPolicy: Retain
  csi:
    driver: ossplugin.csi.alibabacloud.com
    volumeHandle: llm-model
    nodePublishSecretRef:
      name: oss-secret
      namespace: default
    volumeAttributes:
      bucket: <your-bucket-name> # The name of your bucket.
      url: <your-bucket-endpoint> # The endpoint, such as oss-cn-hangzhou-internal.aliyuncs.com.
      otherOpts: "-o umask=022 -o max_stat_cache_size=0 -o allow_other"
      path: <your-model-path> # In this example, the path is /models/DeepSeek-R1-Distill-Qwen-7B/.
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: llm-model
spec:
  accessModes:
    - ReadOnlyMany
  resources:
    requests:
      storage: 30Gi
  selector:
    matchLabels:
      alicloud-pvname: llm-model

Start the inference service named deepseek.

arena serve kserve \
    --name=deepseek \
    --image=kube-ai-registry.cn-shanghai.cr.aliyuncs.com/kube-ai/vllm:v0.6.6 \
    --gpus=1 \
    --cpu=4 \
    --memory=12Gi \
    --data=llm-model:/models/DeepSeek-R1-Distill-Qwen-7B \
    "vllm serve /models/DeepSeek-R1-Distill-Qwen-7B --port 8080 --trust-remote-code --served-model-name deepseek-r1 --max-model-len 32768 --gpu-memory-utilization 0.95 --enforce-eager"

Parameters:

Expected output:

inferenceservice.serving.kserve.io/deepseek created
INFO[0003] The Job deepseek has been submitted successfully
INFO[0003] You can run `arena serve get deepseek --type kserve -n default` to check the job status

Check the deployment status of the KServe inference service.

arena serve get deepseek

Expected output:

Name:       deepseek
Namespace:  default
Type:       KServe
Version:    1
Desired:    1
Available:  1
Age:        3m
Address:    http://deepseek-default.example.com
Port:       :80
GPU:        1
Instances:
  NAME                                 STATUS   AGE  READY  RESTARTS  GPU  NODE
  ----                                 ------   ---  -----  --------  ---  ----
  deepseek-predictor-7cd4d568fd-fznfg  Running  3m   1/1    0         1    cn-beijing.172.16.1.77

The output confirms the inference service is deployed.

Send a request to the inference service via the NGINX Ingress gateway IP.

# Get the IP address of the NGINX Ingress.
NGINX_INGRESS_IP=$(kubectl -n kube-system get svc nginx-ingress-lb -ojsonpath='{.status.loadBalancer.ingress[0].ip}')
# Get the hostname of the inference service.
SERVICE_HOSTNAME=$(kubectl get inferenceservice deepseek -o jsonpath='{.status.url}' | cut -d "/" -f 3)
# Send a request to the inference service.
curl -H "Host: $SERVICE_HOSTNAME" -H "Content-Type: application/json" http://$NGINX_INGRESS_IP:80/v1/chat/completions -d '{"model": "deepseek-r1", "messages": [{"role": "user", "content": "Say this is a test!"}], "max_tokens": 512, "temperature": 0.7, "top_p": 0.9, "seed": 10}'

Expected output:

{"id":"chatcmpl-0fe3044126252c994d470e84807d4a0a","object":"chat.completion","created":1738828016,"model":"deepseek-r1","choices":[{"index":0,"message":{"role":"assistant","content":"<think>\n\n</think>\n\nIt seems like you're testing or sharing some information. How can I assist you further? If you have any questions or need help with something, feel free to ask!","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":9,"total_tokens":48,"completion_tokens":39,"prompt_tokens_details":null},"prompt_logprobs":null}