polish prepare.sh and docs
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5609211f17
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8641aed670
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@ -52,6 +52,19 @@ Linux GPU/CPU C++ 服务化部署测试的主程序为`test_serving_infer_cpp.sh
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```
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- 安装 PaddleServing 相关组件,包括serving_client、serving-app,自动编译并安装带自定义OP的 serving_server 包,以及自动下载并解压推理模型
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```bash
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# 安装必要依赖包
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python3.7 -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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python3.7 -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 安装编译自定义OP的serving-server包
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pushd ./deploy/paddleserving
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source build_server.sh python3.7
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popd
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# 测试PP-ShiTu识别模型时需安装faiss包
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python3.7-m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 下载模型与数据
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bash test_tipc/prepare.sh test_tipc/configs/PPLCNet/PPLCNet_x1_0_linux_gpu_normal_normal_serving_cpp_linux_gpu_cpu.txt serving_infer
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```
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@ -52,6 +52,15 @@ Linux GPU/CPU PYTHON 服务化部署测试的主程序为`test_serving_infer_pyt
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```
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- 安装 PaddleServing 相关组件,包括serving-server、serving_client、serving-app,自动下载并解压推理模型
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```bash
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# 安装必要依赖包
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python3.7 -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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python3.7 -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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python3.7 -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 测试PP-ShiTu识别模型时需安装faiss包
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python3.7-m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
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# 下载模型与数据
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bash test_tipc/prepare.sh test_tipc/configs/ResNet50/ResNet50_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt serving_infer
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```
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@ -200,18 +200,7 @@ fi
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if [[ ${MODE} = "serving_infer" ]]; then
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# prepare serving env
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python_name=$(func_parser_value "${lines[2]}")
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${python_name} -m pip install paddle_serving_client==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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${python_name} -m pip install paddle-serving-app==0.9.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
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python_name=$(func_parser_value "${lines[2]}")
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if [[ ${FILENAME} =~ "cpp" ]]; then
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pushd ./deploy/paddleserving
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source build_server.sh ${python_name}
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popd
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else
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${python_name} -m pip install install paddle-serving-server-gpu==0.9.0.post101 -i https://pypi.tuna.tsinghua.edu.cn/simple
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fi
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if [[ ${model_name} =~ "ShiTu" ]]; then
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${python_name} -m pip install faiss-cpu==1.7.1post2 -i https://pypi.tuna.tsinghua.edu.cn/simple
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cls_inference_model_url=$(func_parser_value "${lines[3]}")
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cls_tar_name=$(func_get_url_file_name "${cls_inference_model_url}")
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det_inference_model_url=$(func_parser_value "${lines[4]}")
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@ -104,6 +104,8 @@ function func_serving_cls(){
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if [[ ${use_gpu} = "null" ]]; then
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web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 &"
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eval ${web_service_cpp_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_batchsize_1.log"
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pipeline_cmd="${python_} test_cpp_serving_client.py > ${_save_log_path} 2>&1 "
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@ -116,6 +118,8 @@ function func_serving_cls(){
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else
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web_service_cpp_cmd="${python_} -m paddle_serving_server.serve --model ${serving_server_dir_name} --op GeneralClasOp --port 9292 --gpu_id=${use_gpu} &"
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eval ${web_service_cpp_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}"
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sleep 8s
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_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_pipeline_batchsize_1.log"
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@ -213,6 +217,8 @@ function func_serving_rec(){
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det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
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web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 &"
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eval ${web_service_cpp_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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_save_log_path="${LOG_PATH}/server_infer_cpp_cpu_batchsize_1.log"
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pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
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@ -226,6 +232,8 @@ function func_serving_rec(){
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det_serving_server_dir_name=$(func_get_url_file_name "$det_serving_server_value")
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web_service_cpp_cmd="${python_interp} -m paddle_serving_server.serve --model ../../${det_serving_server_value} ../../${cls_serving_server_value} --op GeneralPicodetOp GeneralFeatureExtractOp --port 9400 --gpu_id=${use_gpu} &"
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eval ${web_service_cpp_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cpp_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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_save_log_path="${LOG_PATH}/server_infer_cpp_gpu_batchsize_1.log"
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pipeline_cmd="${python_interp} ${pipeline_py} > ${_save_log_path} 2>&1 "
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@ -243,7 +251,7 @@ function func_serving_rec(){
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# set cuda device
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GPUID=$2
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if [ ${#GPUID} -le 0 ];then
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env=" "
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env="export CUDA_VISIBLE_DEVICES=0"
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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@ -98,6 +98,8 @@ function func_serving_cls(){
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web_service_cmd="${python_} ${web_service_py} &"
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eval ${web_service_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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for pipeline in ${pipeline_py[*]}; do
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_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
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@ -130,6 +132,8 @@ function func_serving_cls(){
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web_service_cmd="${python_} ${web_service_py} & "
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eval ${web_service_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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for pipeline in ${pipeline_py[*]}; do
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_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
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@ -237,6 +241,8 @@ function func_serving_rec(){
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web_service_cmd="${python} ${web_service_py} &"
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eval ${web_service_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
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sleep 5s
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for pipeline in ${pipeline_py[*]}; do
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_save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_batchsize_1.log"
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web_service_cmd="${python} ${web_service_py} & "
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eval ${web_service_cmd}
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last_status=${PIPESTATUS[0]}
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status_check $last_status "${web_service_cmd}" "${status_log}" "${model_name}"
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sleep 10s
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for pipeline in ${pipeline_py[*]}; do
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_save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_batchsize_1.log"
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# set cuda device
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GPUID=$2
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if [ ${#GPUID} -le 0 ];then
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env=" "
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env="export CUDA_VISIBLE_DEVICES=0"
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else
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env="export CUDA_VISIBLE_DEVICES=${GPUID}"
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fi
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