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CTR Prediction Service

English | 简体中文

This example is based on the ctr prediction service trained on the criteo dataset. For more detail information please refer to Paddle Serving

Build Image for Service (Optional)

The test service is build on registry.baidubce.com/paddlepaddle/serving:0.6.0-devel, and have published in public image register registry.baidubce.com/paddleflow-public/criteoctr-serving:latest. If you need run the service in GPU or other device, please refer to Docker Images and build the model server image as below.

  1. Download Paddle Serving
$ wget https://github.com/PaddlePaddle/Serving/archive/refs/tags/v0.6.0.tar.gz
$ tar xzvf Serving-0.6.0.tar.gz
$ mv Serving-0.6.0 Serving
$ cd Serving
  1. Write Dockerfile
FROM registry.baidubce.com/paddlepaddle/serving:0.6.0-devel

WORKDIR /home

COPY . /home/Serving

WORKDIR /home/Serving

# install depandences
RUN pip install -r python/requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple && \
    pip install paddle-serving-server==0.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple && \
    pip install paddle-serving-client==0.6.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

WORKDIR /home/Serving/python/examples/criteo_ctr

RUN wget https://paddle-serving.bj.bcebos.com/criteo_ctr_example/criteo_ctr_demo_model.tar.gz && \
    tar xzf criteo_ctr_demo_model.tar.gz && rm -rf criteo_ctr_demo_model.tar.gz && \
    mv models/ctr_client_conf . && mv models/ctr_serving_model .

ENTRYPOINT ["python3", "-m", "paddle_serving_server.serve", "--model", "ctr_serving_model/", "--port", "9292"]
  1. Build Image
docker build . -t registry.baidubce.com/paddleflow-public/criteoctr-serving:latest

Create PaddleService

  1. Prepare YAML File
# criteoctr.yaml
apiVersion: elasticserving.paddlepaddle.org/v1
kind: PaddleService
metadata:
  name: paddleservice-criteoctr
  namespace: paddleservice-system
spec:
  default:
    arg: python3 -m paddle_serving_server.serve --model ctr_serving_model/ --port 9292
    containerImage: registry.baidubce.com/paddleflow-public/criteoctr-serving
    port: 9292
    tag: latest
  runtimeVersion: paddleserving
  service:
    minScale: 1
  1. Create PaddleService
$ kubectl apply -f criteoctr.yaml
paddleservice.elasticserving.paddlepaddle.org/paddleservice-criteoctr created

Check The Status of Service

  1. check the status of criteo ctr model service
# Check service in namespace paddleservice-system
kubectl get svc -n paddleservice-system | grep paddleservice-criteoctr

# Check knative service in namespace paddleservice-system
kubectl get ksvc paddleservice-criteoctr -n paddleservice-system

# Check pods in namespace paddleservice-system
kubectl get pods -n paddleservice-system
  1. Obtain ClusterIP
$ kubectl get svc paddleservice-criteoctr-default-private -n paddleservice-system

Test Model Service

The model service supports HTTP / BRPC / GRPC client, refer to criteo ctr service to obtain the code of client. It should be noted that you need to replace the service IP address and port in the client code with the cluster-ip and port from the paddleservice-criteoctr-default-private service mentioned above.

For example, modify the code of test_client.py as follows

 client.load_client_config(sys.argv[1])
- client.connect(["127.0.0.1:9292"])
+ client.connect(["172.16.183.200:80"])
 reader = CriteoReader(1000001)