## Criteo CTR with Sparse Parameter Server ([简体中文](./README_CN.md)|English) ### Get Sample Dataset ``` sh get_data.sh ``` ### Train and Save Model ``` python local_train.py ``` the trained model will be in ./ctr_server_model and ./ctr_client_config, and ctr_server_model_kv, ctr_client_conf_kv。 ### Start Sparse Parameter Server ``` cp ../../../build_server/core/predictor/seq_generator seq_generator cp ../../../build_server/output/bin/cube* ./cube/ sh cube_prepare.sh & ``` Here, the sparse parameter is loaded by cube sparse parameter server,for more details please read [Cube: Sparse Parameter Server (Local Mode)](../../../doc/CUBE_LOCAL.md) ### Start RPC Predictor, the number of serving thread is 4(configurable in test_server.py) ``` python test_server.py ctr_serving_model_kv ``` ### Run Prediction ``` python 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](https://github.com/PaddlePaddle/Serving/blob/develop/python/examples/ctr_criteo_with_cube/network_conf.py) 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 ![avg cost](../../../doc/criteo-cube-benchmark-avgcost.png) The QPS is ![qps](../../../doc/criteo-cube-benchmark-qps.png)