未验证 提交 cde05d60 编写于 作者: M MRXLT 提交者: GitHub

Merge pull request #740 from MRXLT/ce-script

refine cube benchmark script
......@@ -13,6 +13,7 @@
// limitations under the License.
#include <gflags/gflags.h>
#include <algorithm>
#include <atomic>
#include <fstream>
#include <thread> //NOLINT
......@@ -33,7 +34,7 @@ std::atomic<int> g_concurrency(0);
std::vector<std::vector<uint64_t>> time_list;
std::vector<uint64_t> request_list;
int turns = 1000000 / FLAGS_batch;
int turns = 1000;
namespace {
inline uint64_t time_diff(const struct timeval& start_time,
......@@ -94,14 +95,15 @@ int run(int argc, char** argv, int thread_id) {
uint64_t file_size = key_list.size();
uint64_t index = 0;
uint64_t request = 0;
while (g_concurrency.load() >= FLAGS_thread_num) {
}
g_concurrency++;
time_list[thread_id].resize(turns);
while (index < file_size) {
while (request < turns) {
// uint64_t key = strtoul(buffer, NULL, 10);
if (index >= file_size) {
index = 0;
}
keys.push_back(key_list[index]);
index += 1;
int ret = 0;
......@@ -160,7 +162,7 @@ int run_m(int argc, char** argv) {
uint64_t sum_time = 0;
uint64_t max_time = 0;
uint64_t min_time = 1000000;
uint64_t request_num = 0;
std::vector<uint64_t> all_time_list;
for (int i = 0; i < thread_num; i++) {
for (int j = 0; j < request_list[i]; j++) {
sum_time += time_list[i][j];
......@@ -170,20 +172,29 @@ int run_m(int argc, char** argv) {
if (time_list[i][j] < min_time) {
min_time = time_list[i][j];
}
all_time_list.push_back(time_list[i][j]);
}
request_num += request_list[i];
}
std::sort(all_time_list.begin(), all_time_list.end());
uint64_t mean_time = sum_time / (thread_num * turns);
uint64_t main_time = time_diff(main_start, main_end);
LOG(INFO) << "\n"
<< thread_num << " thread seek cost"
<< "\navg = " << std::to_string(mean_time)
<< "\nmax = " << std::to_string(max_time)
<< "\nmin = " << std::to_string(min_time);
LOG(INFO) << "\ntotal_request = " << std::to_string(request_num)
<< "\nspeed = " << std::to_string(request_num * 1000000 /
main_time) // mean_time us
<< " query per second";
uint64_t request_num = turns * thread_num;
LOG(INFO)
<< "\n"
<< thread_num << " thread seek cost"
<< "\navg: " << std::to_string(mean_time) << "\n50 percent: "
<< std::to_string(all_time_list[static_cast<int>(0.5 * request_num)])
<< "\n80 percent: "
<< std::to_string(all_time_list[static_cast<int>(0.8 * request_num)])
<< "\n90 percent: "
<< std::to_string(all_time_list[static_cast<int>(0.9 * request_num)])
<< "\n99 percent: "
<< std::to_string(all_time_list[static_cast<int>(0.99 * request_num)])
<< "\n99.9 percent: "
<< std::to_string(all_time_list[static_cast<int>(0.999 * request_num)])
<< "\ntotal_request: " << std::to_string(request_num) << "\nspeed: "
<< std::to_string(turns * 1000000 / main_time) // mean_time us
<< " query per second";
return 0;
}
......
......@@ -42,7 +42,7 @@ cd python/examples/criteo_ctr_with_cube
python local_train.py
cp ../../../build_server/core/predictor/seq_generator seq_generator
cp ../../../build_server/output/bin/cube* ./cube/
sh cube_prepare_quant.sh &
sh cube_quant_prepare.sh &
python test_server_quant.py ctr_serving_model_kv &
python test_client.py ctr_client_conf/serving_client_conf.prototxt ./raw_data
```
......
......@@ -42,7 +42,7 @@ cd python/examples/criteo_ctr_with_cube
python local_train.py
cp ../../../build_server/core/predictor/seq_generator seq_generator
cp ../../../build_server/output/bin/cube* ./cube/
sh cube_prepare_quant.sh &
sh cube_quant_prepare.sh &
python test_server_quant.py ctr_serving_model_kv &
python test_client.py ctr_client_conf/serving_client_conf.prototxt ./raw_data
```
......
......@@ -27,7 +27,7 @@ mv cube_app/cube* ./cube/
sh cube_prepare.sh &
```
Here, the sparse parameter is loaded by cube sparse parameter indexing service Cube,for more details please read [Cube: Sparse Parameter Indexing Service (Local Mode)](../../../doc/CUBE_LOCAL.md)
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)
......
......@@ -25,7 +25,7 @@ mv cube_app/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索引服务Cube当中,关于稀疏参数索引服务Cube的介绍,请阅读[稀疏参数索引服务Cube单机版使用指南](../../../doc/CUBE_LOCAL_CN.md)
此处,模型当中的稀疏参数会被存放在稀疏参数索引服务Cube当中
### 启动RPC预测服务,服务端线程数为4(可在test_server.py配置)
......
rm profile_log
wget https://paddle-serving.bj.bcebos.com/unittest/ctr_cube_unittest.tar.gz --no-check-certificate
tar xf ctr_cube_unittest.tar.gz
#wget https://paddle-serving.bj.bcebos.com/unittest/ctr_cube_unittest.tar.gz --no-check-certificate
#tar xf ctr_cube_unittest.tar.gz
mv models/ctr_client_conf ./
mv models/ctr_serving_model_kv ./
mv models/data ./cube/
wget https://paddle-serving.bj.bcebos.com/others/cube_app.tar.gz --no-check-certificate
tar xf cube_app.tar.gz
#wget https://paddle-serving.bj.bcebos.com/others/cube_app.tar.gz --no-check-certificate
#tar xf cube_app.tar.gz
mv cube_app/cube* ./cube/
sh cube_prepare.sh &
......@@ -24,8 +24,7 @@ do
echo "========================================"
echo "batch size : $batch_size" >> profile_log
echo "thread num : $thread_num" >> profile_log
tail -n 7 profile | head -n 4 >> profile_log
tail -n 2 profile >> profile_log
tail -n 8 profile >> profile_log
done
done
......
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