criteo_ctr_with_cube

Criteo CTR with Sparse Parameter Indexing Service

(简体中文|English)

Get Sample Dataset

go to directory examples/C++/PaddleRec/criteo_ctr_with_cube

sh get_data.sh

Download Model and Sparse Parameter Sequence Files

wget https://paddle-serving.bj.bcebos.com/unittest/ctr_cube_unittest.tar.gz
tar xf ctr_cube_unittest.tar.gz
mv models/ctr_client_conf ./
mv models/ctr_serving_model_kv ./
mv models/data ./cube/

the model will be in ./ctr_server_model_kv and ./ctr_client_config.

Start Sparse Parameter Indexing Service

wget https://paddle-serving.bj.bcebos.com/others/cube_app.tar.gz
tar xf cube_app.tar.gz
mv cube_app/cube* ./cube/
sh cube_prepare.sh &

Here, the sparse parameter is loaded by cube sparse parameter indexing service Cube.

Start RPC Predictor, the number of serving thread is 4（configurable in test_server.py）

python3 test_server.py ctr_serving_model_kv 

Run Prediction

python3 test_client.py ctr_client_conf/serving_client_conf.prototxt ./raw_data

Benchmark

CPU ：Intel(R) Xeon(R) CPU 6148 @ 2.40GHz

Model ：Criteo CTR

server core/thread num ： 4/8

Run

bash benchmark.sh

1000 batches will be sent by every client

client thread num	prepro	client infer	op0	op1	op2	postpro	avg_latency	qps
1	0.035	1.596	0.021	0.518	0.0024	0.0025	6.774	147.7
2	0.034	1.780	0.027	0.463	0.0020	0.0023	6.931	288.3
4	0.038	2.954	0.025	0.455	0.0019	0.0027	8.378	477.5
8	0.044	8.230	0.028	0.464	0.0023	0.0034	14.191	563.8
16	0.048	21.037	0.028	0.455	0.0025	0.0041	27.236	587.5

the average latency of threads

The QPS is