From e6b7a4f8c4f5583b3692e43a6024db3521fdb02c Mon Sep 17 00:00:00 2001
From: Jiawei Wang <wangjiawei04@baidu.com>
Date: Mon, 23 Mar 2020 11:55:14 +0800
Subject: [PATCH] Update README.md

---
 .../examples/criteo_ctr_with_cube/README.md   | 31 ++++++++++---------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/python/examples/criteo_ctr_with_cube/README.md b/python/examples/criteo_ctr_with_cube/README.md
index 7a1740df..bea10248 100755
--- a/python/examples/criteo_ctr_with_cube/README.md
+++ b/python/examples/criteo_ctr_with_cube/README.md
@@ -1,32 +1,35 @@
-## 带稀疏参数服务器的CTR预测服务
+## Criteo CTR with Sparse Parameter Server
+
+([简体中文](README_CN.md)|English)
+
+### Get Sample Dataset
 
-### 获取样例数据
 ```
 sh get_data.sh
 ```
 
-### 保存模型和配置文件
+### Train and Save Model
 ```
 python local_train.py
 ```
-执行脚本后会在当前目录生成ctr_server_model和ctr_client_config文件夹,以及ctr_server_model_kv, ctr_client_conf_kv。
+the trained model will be in ./ctr_server_model and ./ctr_client_config, and ctr_server_model_kv, ctr_client_conf_kv。
 
-### 启动稀疏参数服务器
+### Start Sparse Parameter Server
 ```
 cp ../../../build_server/core/predictor/seq_generator seq_generator
 cp ../../../build_server/output/bin/cube* ./cube/
 sh cube_prepare.sh &
 ```
 
-此处，模型当中的稀疏参数会被存放在稀疏参数服务器Cube当中，关于稀疏参数服务器Cube的介绍，请阅读[单机版稀疏参数服务器Cube](../../../doc/CUBE_LOCAL_CN.md)
+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)
 
-### 启动RPC预测服务，服务端线程数为4（可在test_server.py配置）
+### Start RPC Predictor, the number of serving thread is 4（configurable in test_server.py）
 
 ```
 python test_server.py ctr_serving_model_kv 
 ```
 
-### 执行预测
+### Run Prediction
 
 ```
 python test_client.py ctr_client_conf/serving_client_conf.prototxt ./raw_data
@@ -34,17 +37,17 @@ python test_client.py ctr_client_conf/serving_client_conf.prototxt ./raw_data
 
 ### Benchmark
 
-设备 ：Intel(R) Xeon(R) CPU 6148 @ 2.40GHz 
+CPU ：Intel(R) Xeon(R) CPU 6148 @ 2.40GHz 
 
-模型 ：[Criteo CTR](https://github.com/PaddlePaddle/Serving/blob/develop/python/examples/ctr_criteo_with_cube/network_conf.py)
+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
 
-执行
+Run
 ```
 bash benchmark.sh
 ```
-客户端每个线程会发送1000个batch
+1000 batches will be sent by every client
 
 | client  thread num | prepro | client infer | op0    | op1   | op2    | postpro | avg_latency | qps   |
 | ------------------ | ------ | ------------ | ------ | ----- | ------ | ------- | ----- | ----- |
@@ -54,10 +57,10 @@ bash benchmark.sh
 | 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 |
 
-平均每个线程耗时图如下
+the average latency of threads
 
 ![avg cost](../../../doc/criteo-cube-benchmark-avgcost.png)
 
-每个线程QPS耗时如下
+The QPS is 
 
 ![qps](../../../doc/criteo-cube-benchmark-qps.png)
-- 
GitLab