Skip to content

S
Serving

PaddlePaddle / Serving
1 年多 前同步成功

通知 186
Star 833
Fork 253
S
Serving
未验证 提交 3cefebf8 编写于 作者: T TeslaZhao 提交者： GitHub
浏览文件
操作

Merge pull request #9 from PaddlePaddle/develop 

Sync from PaddlePaddle/Serving
上级 15b22346 ae55eb3d
显示空白变更内容
内联 并排
Showing with 895 addition and 65 deletion
README.md
浏览文件 @ 3cefebf8
......@@ -55,11 +55,13 @@ You may need to use a domestic mirror source (in China, you can use the Tsinghua
If you need install modules compiled with develop branch, please download packages from [latest packages list](./doc/LATEST_PACKAGES.md) and install with `pip install` command.
Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7 and Ubuntu 16/18.
Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7, Ubuntu 16/18, Windows 10.
Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python2.7/3.6/3.7.
Recommended to install paddle >= 1.8.2.
Recommended to install paddle >= 1.8.4.
For **Windows Users**, please read the document [Paddle Serving for Windows Users](./doc/WINDOWS_TUTORIAL.md)
<h2 align="center"> Pre-built services with Paddle Serving</h2>
......
README_CN.md
浏览文件 @ 3cefebf8
......@@ -57,11 +57,13 @@ pip install paddle-serving-server-gpu==0.3.2.post10 # GPU with CUDA10.0
如果需要使用develop分支编译的安装包，请从[最新安装包列表](./doc/LATEST_PACKAGES.md)中获取下载地址进行下载，使用`pip install`命令进行安装。
paddle-serving-server和paddle-serving-server-gpu安装包支持Centos 6/7和Ubuntu 16/18。
paddle-serving-server和paddle-serving-server-gpu安装包支持Centos 6/7, Ubuntu 16/18和Windows 10。
paddle-serving-client和paddle-serving-app安装包支持Linux和Windows，其中paddle-serving-client仅支持python2.7/3.5/3.6。
推荐安装1.8.2及以上版本的paddle
推荐安装1.8.4及以上版本的paddle
对于**Windows 10 用户**，请参考文档[Windows平台使用Paddle Serving指导](./doc/WINDOWS_TUTORIAL_CN.md)。
<h2 align="center"> Paddle Serving预装的服务 </h2>
......
cmake/paddlepaddle.cmake
浏览文件 @ 3cefebf8
......@@ -114,7 +114,7 @@ ADD_LIBRARY(openblas STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET openblas PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/openblas/lib/libopenblas.a)
ADD_LIBRARY(paddle_fluid SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET paddle_fluid PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_fluid.so)
SET_PROPERTY(TARGET paddle_fluid PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_fluid.a)
if (WITH_TRT)
ADD_LIBRARY(nvinfer SHARED IMPORTED GLOBAL)
......@@ -127,12 +127,17 @@ endif()
ADD_LIBRARY(xxhash STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET xxhash PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/xxhash/lib/libxxhash.a)
ADD_LIBRARY(cryptopp STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET cryptopp PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/cryptopp/lib/libcryptopp.a)
LIST(APPEND external_project_dependencies paddle)
LIST(APPEND paddle_depend_libs
xxhash)
xxhash cryptopp)
if(WITH_TRT)
LIST(APPEND paddle_depend_libs
nvinfer nvinfer_plugin)
endif()
core/predictor/tools/seq_generator.cpp
浏览文件 @ 3cefebf8
......@@ -17,11 +17,11 @@
#include <fstream>
#include <iostream>
#include <memory>
#include <thread>
#include <thread> //NOLINT
#include "core/predictor/framework.pb.h"
#include "quant.h"
#include "seq_file.h"
#include "quant.h" // NOLINT
#include "seq_file.h" // NOLINT
inline uint64_t time_diff(const struct timeval &start_time,
const struct timeval &end_time) {
......@@ -113,13 +113,15 @@ int dump_parameter(const char *input_file, const char *output_file) {
// std::cout << "key_len " << key_len << " value_len " << value_buf_len
// << std::endl;
memcpy(value_buf, tensor_buf + offset, value_buf_len);
seq_file_writer.write((char *)&i, sizeof(i), value_buf, value_buf_len);
seq_file_writer.write(
std::to_string(i).c_str(), sizeof(i), value_buf, value_buf_len);
offset += value_buf_len;
}
return 0;
}
float *read_embedding_table(const char *file1, std::vector<int64_t> &dims) {
float *read_embedding_table(const char *file1,
std::vector<int64_t> &dims) { // NOLINT
std::ifstream is(file1);
// Step 1: is read version, os write version
uint32_t version;
......@@ -242,7 +244,7 @@ int compress_parameter_parallel(const char *file1,
float x = *(emb_table + k * emb_size + e);
int val = round((x - xmin) / scale);
val = std::max(0, val);
val = std::min((int)pow2bits - 1, val);
val = std::min(static_cast<int>(pow2bits) - 1, val);
*(tensor_temp + 2 * sizeof(float) + e) = val;
}
result[k] = tensor_temp;
......@@ -262,7 +264,8 @@ int compress_parameter_parallel(const char *file1,
}
SeqFileWriter seq_file_writer(file2);
for (int64_t i = 0; i < dict_size; i++) {
seq_file_writer.write((char *)&i, sizeof(i), result[i], per_line_size);
seq_file_writer.write(
std::to_string(i).c_str(), sizeof(i), result[i], per_line_size);
}
return 0;
}
......
doc/WINDOWS_TUTORIAL.md 0 → 100644
浏览文件 @ 3cefebf8
## Paddle Serving for Windows Users
(English|[简体中文](./WINDOWS_TUTORIAL_CN.md))
### Summary
This document guides users how to build Paddle Serving service on the Windows platform. Due to the limited support of third-party libraries, the Windows platform currently only supports the use of web services to build local predictor prediction services. If you want to experience all the services, you need to use Docker for Windows to simulate the operating environment of Linux.
### Running Paddle Serving on Native Windows System
**Configure Python environment variables to PATH**: First, you need to add the directory where the Python executable program is located to the PATH. Usually in **System Properties/My Computer Properties**-**Advanced**-**Environment Variables**, click Path and add the path at the beginning. For example, `C:\Users\$USER\AppData\Local\Programs\Python\Python36`, and finally click **OK** continuously. If you enter python on Powershell, you can enter the python interactive interface, indicating that the environment variable configuration is successful.
**Install wget**: Because all the downloads in the tutorial and the built-in model download function in `paddle_serving_app` all use the wget tool, download the binary package at the [link](http://gnuwin32.sourceforge.net/packages/wget.htm), unzip and copy it to `C:\Windows\System32`, if there is a security prompt, you need to pass it.
**Install Git**: For details, see [Git official website](https://git-scm.com/downloads)
**Install the necessary C++ library (optional)**: Some users may encounter the problem that the dll cannot be linked during the `import paddle` stage. It is recommended to [Install Visual Studio Community Edition](https://visualstudio.microsoft.com/), and install the relevant components of C++.
**Install Paddle and Serving**: In Powershell, execute
```
python -m pip install -U paddle_serving_server paddle_serving_client paddle_serving_app paddlepaddle`
```
for GPU users,
```
python -m pip install -U paddle_serving_server_gpu paddle_serving_client paddle_serving_app paddlepaddle-gpu
```
**Git clone Serving Project:**
```
git clone https://github.com/paddlepaddle/Serving
```
**Run OCR example**:
```
cd Serving/python/example/ocr
python -m paddle_serving_app.package --get_model ocr_rec
tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
python ocr_debugger_server.py &
python ocr_web_client.py
```
### Create a new Paddle Serving Web Service on Windows
Currently Windows supports the Local Predictor of the Web Service framework. The server code framework is as follows
```
# filename:your_webservice.py
from paddle_serving_server.web_service import WebService
# If it is the GPU version, please use from paddle_serving_server_gpu.web_service import WebService
class YourWebService(WebService):
def preprocess(self, feed=[], fetch=[]):
#Implement pre-processing here
#feed_dict is key: var names, value: numpy array input
#fetch_names is a list of fetch variable names
The meaning of #is_batch is whether the numpy array in the value of feed_dict contains the batch dimension
return feed_dict, fetch_names, is_batch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
#fetch map is the returned dictionary after prediction, the key is the fetch names given when the process returns, and the value is the var specific value corresponding to the fetch names
#After processing here, the result needs to be converted into a dictionary again, and the type of values should be a list, so that it can be serialized in JSON to facilitate web return
return response
your_service = YourService(name="XXX")
your_service.load_model_config("your_model_path")
your_service.prepare_server(workdir="workdir", port=9292)
# If you are a GPU user, you can refer to the python example under python/examples/ocr
your_service.run_debugger_service()
# Windows platform cannot use run_rpc_service() interface
your_service.run_web_service()
```
Client code example
```
# filename:your_client.py
import requests
import json
import base64
import os, sys
import time
import cv2 # If you need to upload pictures
# Used for image reading, the principle is to use base64 encoding file content
def cv2_to_base64(image):
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/XXX/prediction" # XXX depends on the initial name parameter of the server YourService
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
```
The user only needs to follow the above instructions and implement the relevant content in the corresponding function. For more information, please refer to [How to develop a new Web Service? ](./NEW_WEB_SERVICE.md)
Execute after development
```
python your_webservice.py &
python your_client.py
```
Because the port needs to be occupied, there may be a security prompt during the startup process. Please click through and an IP address will be generated. It should be noted that when the Windows platform starts the service, the local IP address may not be 127.0.0.1. You need to confirm the IP address and then see how the Client should set the access IP.
### Docker for Windows User Guide
The above content is used for native Windows. If users want to experience complete functions, they need to use Docker tools to model Linux systems.
Please refer to [Docker Desktop](https://www.docker.com/products/docker-desktop) to install Docker
After installation, start the docker linux engine and download the relevant image. In the Serving directory
```
docker pull hub.baidubce.com/paddlepaddle/serving:latest-devel
# There is no expose port here, users can set -p to perform port mapping as needed
docker run --rm -dit --name serving_devel -v $PWD:/Serving hub.baidubce.com/paddlepaddle/serving:latest-devel
docker exec -it serving_devel bash
cd /Serving
```
The rest of the operations are exactly the same as the Linux version.
doc/WINDOWS_TUTORIAL_CN.md 0 → 100644
浏览文件 @ 3cefebf8
## Windows平台使用Paddle Serving指导
([English](./WINDOWS_TUTORIAL.md)|简体中文）
### 综述
本文档指导用户如何在Windows平台手把手搭建Paddle Serving服务。由于受限第三方库的支持，Windows平台目前只支持用web service的方式搭建local predictor预测服务。如果想要体验全部的服务，需要使用Docker for Windows，来模拟Linux的运行环境。
### 原生Windows系统运行Paddle Serving
**配置Python环境变量到PATH**：首先需要将Python的可执行程序所在目录加入到PATH当中。通常在**系统属性/我的电脑属性**-**高级**-**环境变量** ，点选Path，并在开头加上路径。例如`C:\Users\$USER\AppData\Local\Programs\Python\Python36`，最后连续点击**确定** 。在Powershell上如果输入python可以进入python交互界面，说明环境变量配置成功。
**安装wget工具**：由于教程当中所有的下载，以及`paddle_serving_app`当中内嵌的模型下载功能，都是用到wget工具，在链接[下载wget](http://gnuwin32.sourceforge.net/packages/wget.htm)，解压后复制到`C:\Windows\System32`下，如有安全提示需要通过。
**安装Git工具**： 详情参见[Git官网](https://git-scm.com/downloads)
**安装必要的C++库（可选）**：部分用户可能会在`import paddle`阶段遇见dll无法链接的问题，建议可以[安装Visual Studio社区版本](`https://visualstudio.microsoft.com/`) ，并且安装C++的相关组件。
**安装Paddle和Serving**：在Powershell，执行
```
python -m pip install -U paddle_serving_server paddle_serving_client paddle_serving_app paddlepaddle`
```
如果是GPU用户
```
python -m pip install -U paddle_serving_server_gpu paddle_serving_client paddle_serving_app paddlepaddle-gpu
```
**下载Serving库**：
```
git clone https://github.com/paddlepaddle/Serving
```
**运行OCR示例**：
```
cd Serving/python/example/ocr
python -m paddle_serving_app.package --get_model ocr_rec
tar -xzvf ocr_rec.tar.gz
python -m paddle_serving_app.package --get_model ocr_det
tar -xzvf ocr_det.tar.gz
python ocr_debugger_server.py &
python ocr_web_client.py
```
### 创建新的Windows支持的Paddle Serving服务
目前Windows支持Web Service框架的Local Predictor。服务端代码框架如下
```
# filename:your_webservice.py
from paddle_serving_server.web_service import WebService
# 如果是GPU版本，请使用 from paddle_serving_server_gpu.web_service import WebService
class YourWebService(WebService):
def preprocess(self, feed=[], fetch=[]):
#在这里实现前处理
#feed_dict是 key: var names, value: numpy array input
#fetch_names 是fetch变量名列表
#is_batch的含义是feed_dict的value里的numpy array是否包含了batch维度
return feed_dict, fetch_names, is_batch
def postprocess(self, feed={}, fetch=[], fetch_map=None):
#fetch map是经过预测之后的返回字典，key是process返回时给定的fetch names，value是对应fetch names的var具体值
#在这里做处理之后，结果需重新转换成字典，并且values的类型应是列表list，这样可以JSON序列化方便web返回
return response
your_service = YourService(name="XXX")
your_service.load_model_config("your_model_path")
your_service.prepare_server(workdir="workdir", port=9292)
# 如果是GPU用户，可以参照python/examples/ocr下的python示例
your_service.run_debugger_service()
# Windows平台不可以使用 run_rpc_service()接口
your_service.run_web_service()
```
客户端代码示例
```
# filename：your_client.py
import requests
import json
import base64
import os, sys
import time
import cv2 # 如果需要上传图片
# 用于图片读取，原理是采用base64编码文件内容
def cv2_to_base64(image):
return base64.b64encode(image).decode(
'utf8') #data.tostring()).decode('utf8')
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:9292/XXX/prediction" # XXX取决于服务端YourService的初始化name参数
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
```
用户只需要按照如上指示，在对应函数中实现相关内容即可。更多信息请参见[如何开发一个新的Web Service？](./NEW_WEB_SERVICE_CN.md)
开发完成后执行
```
python your_webservice.py &
python your_client.py
```
因为需要占用端口，因此启动过程可能会有安全提示，请点选通过，就会有IP地址生成。需要注意的是，Windows平台启动服务时，本地IP地址可能不是127.0.0.1，需要确认好IP地址再看Client应该如何设定访问IP。
### Docker for Windows 使用指南
以上内容用于原生的Windows，如果用户想要体验完整的功能，需要使用Docker工具，来模拟Linux系统。
安装Docker请参考[Docker Desktop](https://www.docker.com/products/docker-desktop)
安装之后启动docker的linux engine，下载相关镜像。在Serving目录下
```
docker pull hub.baidubce.com/paddlepaddle/serving:latest-devel
# 此处没有expose端口，用户可根据需要设置-p来进行端口映射
docker run --rm -dit --name serving_devel -v $PWD:/Serving hub.baidubce.com/paddlepaddle/serving:latest-devel
docker exec -it serving_devel bash
cd /Serving
```
其余操作与Linux版本完全一致。
paddle_inference/inferencer-fluid-cpu/include/fluid_cpu_engine.h
浏览文件 @ 3cefebf8
......@@ -13,7 +13,6 @@
// limitations under the License.
#pragma once
#include <pthread.h>
#include <fstream>
#include <map>
......@@ -29,7 +28,6 @@ namespace paddle_serving {
namespace fluid_cpu {
using configure::SigmoidConf;
class AutoLock {
public:
explicit AutoLock(pthread_mutex_t& mutex) : _mut(mutex) {
......@@ -530,7 +528,60 @@ class FluidCpuAnalysisDirWithSigmoidCore : public FluidCpuWithSigmoidCore {
return 0;
}
};
class FluidCpuAnalysisEncryptCore : public FluidFamilyCore {
public:
void ReadBinaryFile(const std::string& filename, std::string* contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
int create(const predictor::InferEngineCreationParams& params) {
std::string data_path = params.get_path();
if (access(data_path.c_str(), F_OK) == -1) {
LOG(ERROR) << "create paddle predictor failed, path note exits: "
<< data_path;
return -1;
}
std::string model_buffer, params_buffer, key_buffer;
ReadBinaryFile(data_path + "encrypt_model", &model_buffer);
ReadBinaryFile(data_path + "encrypt_params", &params_buffer);
ReadBinaryFile(data_path + "key", &key_buffer);
VLOG(2) << "prepare for encryption model";
auto cipher = paddle::MakeCipher("");
std::string real_model_buffer = cipher->Decrypt(model_buffer, key_buffer);
std::string real_params_buffer = cipher->Decrypt(params_buffer, key_buffer);
paddle::AnalysisConfig analysis_config;
analysis_config.SetModelBuffer(&real_model_buffer[0],
real_model_buffer.size(),
&real_params_buffer[0],
real_params_buffer.size());
analysis_config.DisableGpu();
analysis_config.SetCpuMathLibraryNumThreads(1);
if (params.enable_memory_optimization()) {
analysis_config.EnableMemoryOptim();
}
analysis_config.SwitchSpecifyInputNames(true);
AutoLock lock(GlobalPaddleCreateMutex::instance());
VLOG(2) << "decrypt model file sucess";
_core =
paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
if (NULL == _core.get()) {
LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
return -1;
}
VLOG(2) << "create paddle predictor sucess, path: " << data_path;
return 0;
}
};
} // namespace fluid_cpu
} // namespace paddle_serving
} // namespace baidu
paddle_inference/inferencer-fluid-cpu/src/fluid_cpu_engine.cpp
浏览文件 @ 3cefebf8
......@@ -52,6 +52,13 @@ REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
::baidu::paddle_serving::predictor::InferEngine,
"FLUID_CPU_NATIVE_DIR_SIGMOID");
#if 1
REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
::baidu::paddle_serving::predictor::FluidInferEngine<
FluidCpuAnalysisEncryptCore>,
::baidu::paddle_serving::predictor::InferEngine,
"FLUID_CPU_ANALYSIS_ENCRYPT");
#endif
} // namespace fluid_cpu
} // namespace paddle_serving
} // namespace baidu
paddle_inference/inferencer-fluid-gpu/include/fluid_gpu_engine.h
浏览文件 @ 3cefebf8
......@@ -25,7 +25,6 @@
#include "core/configure/inferencer_configure.pb.h"
#include "core/predictor/framework/infer.h"
#include "paddle_inference_api.h" // NOLINT
DECLARE_int32(gpuid);
namespace baidu {
......@@ -591,6 +590,60 @@ class FluidGpuAnalysisDirWithSigmoidCore : public FluidGpuWithSigmoidCore {
}
};
class FluidGpuAnalysisEncryptCore : public FluidFamilyCore {
public:
void ReadBinaryFile(const std::string& filename, std::string* contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
int create(const predictor::InferEngineCreationParams& params) {
std::string data_path = params.get_path();
if (access(data_path.c_str(), F_OK) == -1) {
LOG(ERROR) << "create paddle predictor failed, path note exits: "
<< data_path;
return -1;
}
std::string model_buffer, params_buffer, key_buffer;
ReadBinaryFile(data_path + "encrypt_model", &model_buffer);
ReadBinaryFile(data_path + "encrypt_params", &params_buffer);
ReadBinaryFile(data_path + "key", &key_buffer);
VLOG(2) << "prepare for encryption model";
auto cipher = paddle::MakeCipher("");
std::string real_model_buffer = cipher->Decrypt(model_buffer, key_buffer);
std::string real_params_buffer = cipher->Decrypt(params_buffer, key_buffer);
paddle::AnalysisConfig analysis_config;
analysis_config.SetModelBuffer(&real_model_buffer[0],
real_model_buffer.size(),
&real_params_buffer[0],
real_params_buffer.size());
analysis_config.EnableUseGpu(100, FLAGS_gpuid);
analysis_config.SetCpuMathLibraryNumThreads(1);
if (params.enable_memory_optimization()) {
analysis_config.EnableMemoryOptim();
}
analysis_config.SwitchSpecifyInputNames(true);
AutoLock lock(GlobalPaddleCreateMutex::instance());
VLOG(2) << "decrypt model file sucess";
_core =
paddle::CreatePaddlePredictor<paddle::AnalysisConfig>(analysis_config);
if (NULL == _core.get()) {
LOG(ERROR) << "create paddle predictor failed, path: " << data_path;
return -1;
}
VLOG(2) << "create paddle predictor sucess, path: " << data_path;
return 0;
}
};
} // namespace fluid_gpu
} // namespace paddle_serving
} // namespace baidu
paddle_inference/inferencer-fluid-gpu/src/fluid_gpu_engine.cpp
浏览文件 @ 3cefebf8
......@@ -54,6 +54,12 @@ REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
::baidu::paddle_serving::predictor::InferEngine,
"FLUID_GPU_NATIVE_DIR_SIGMOID");
REGIST_FACTORY_OBJECT_IMPL_WITH_NAME(
::baidu::paddle_serving::predictor::FluidInferEngine<
FluidGpuAnalysisEncryptCore>,
::baidu::paddle_serving::predictor::InferEngine,
"FLUID_GPU_ANALYSIS_ENCRPT")
} // namespace fluid_gpu
} // namespace paddle_serving
} // namespace baidu
python/examples/encryption/README.md 0 → 100644
浏览文件 @ 3cefebf8
# Encryption Model Prediction
([简体中文](README_CN.md)|English)
## Get Origin Model
The example uses the model file of the fit_a_line example as a origin model
```
sh get_data.sh
```
## Encrypt Model
```
python encrypt.py
```
The key is stored in the `key` file, and the encrypted model file and server-side configuration file are stored in the `encrypt_server` directory.
client-side configuration file are stored in the `encrypt_client` directory.
## Start Encryption Service
CPU Service
```
python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_encryption_model
```
GPU Service
```
python -m paddle_serving_server_gpu.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
```
## Prediction
```
python test_client.py uci_housing_client/serving_client_conf.prototxt
```
python/examples/encryption/README_CN.md 0 → 100644
浏览文件 @ 3cefebf8
# 加密模型预测
(简体中文|[English](README.md))
## 获取明文模型
示例中使用fit_a_line示例的模型文件作为明文模型
```
sh get_data.sh
```
## 模型加密
```
python encrypt.py
```
密钥保存在`key`文件中，加密模型文件以及server端配置文件保存在`encrypt_server`目录下，client端配置文件保存在`encrypt_client`目录下。
## 启动加密预测服务
CPU预测服务
```
python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_encryption_model
```
GPU预测服务
```
python -m paddle_serving_server_gpu.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0
```
## 预测
```
python test_client.py uci_housing_client/serving_client_conf.prototxt
```
python/examples/encryption/encrypt.py 0 → 100644
浏览文件 @ 3cefebf8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle_serving_client.io import inference_model_to_serving
def serving_encryption():
inference_model_to_serving(
dirname="./uci_housing_model",
serving_server="encrypt_server",
serving_client="encrypt_client",
encryption=True)
if __name__ == "__main__":
serving_encryption()
python/examples/encryption/get_data.sh 0 → 100644
浏览文件 @ 3cefebf8
wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing_example/encrypt.tar.gz
tar -xzf encrypt.tar.gz
python/examples/ocr_detection/text_det_client.py → python/examples/encryption/test_client.py
浏览文件 @ 3cefebf8
......@@ -11,37 +11,22 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=doc-string-missing
import os
from paddle_serving_client import Client
from paddle_serving_app.reader import Sequential, File2Image, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes
import sys
client = Client()
client.load_client_config("ocr_det_client/serving_client_conf.prototxt")
client.connect(["127.0.0.1:9494"])
client.load_client_config(sys.argv[1])
client.use_key("./key")
client.connect(["127.0.0.1:9300"], encryption=True)
read_image_file = File2Image()
preprocess = Sequential([
ResizeByFactor(32, 960), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
filter_func = FilterBoxes(10, 10)
import paddle
test_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.test(), buf_size=500),
batch_size=1)
img = read_image_file(name)
ori_h, ori_w, _ = img.shape
img = preprocess(img)
new_h, new_w, _ = img.shape
ratio_list = [float(new_h) / ori_h, float(new_w) / ori_w]
outputs = client.predict(feed={"image": img}, fetch=["concat_1.tmp_0"])
dt_boxes_list = post_func(outputs["concat_1.tmp_0"], [ratio_list])
dt_boxes = filter_func(dt_boxes_list[0], [ori_h, ori_w])
for data in test_reader():
fetch_map = client.predict(feed={"x": data[0][0]}, fetch=["price"])
print("{} {}".format(fetch_map["price"][0], data[0][1][0]))
python/paddle_serving_client/__init__.py
浏览文件 @ 3cefebf8
......@@ -13,16 +13,19 @@
# limitations under the License.
# pylint: disable=doc-string-missing
import paddle_serving_client
import os
from .proto import sdk_configure_pb2 as sdk
from .proto import general_model_config_pb2 as m_config
import google.protobuf.text_format
import numpy as np
import time
import sys
import requests
import json
import base64
import numpy as np
import paddle_serving_client
import google.protobuf.text_format
import grpc
from .proto import sdk_configure_pb2 as sdk
from .proto import general_model_config_pb2 as m_config
from .proto import multi_lang_general_model_service_pb2
sys.path.append(
os.path.join(os.path.abspath(os.path.dirname(__file__)), 'proto'))
......@@ -161,6 +164,7 @@ class Client(object):
self.fetch_names_to_idx_ = {}
self.lod_tensor_set = set()
self.feed_tensor_len = {}
self.key = None
for i, var in enumerate(model_conf.feed_var):
self.feed_names_to_idx_[var.alias_name] = i
......@@ -193,7 +197,28 @@ class Client(object):
else:
self.rpc_timeout_ms = rpc_timeout
def connect(self, endpoints=None):
def use_key(self, key_filename):
with open(key_filename, "r") as f:
self.key = f.read()
def get_serving_port(self, endpoints):
if self.key is not None:
req = json.dumps({"key": base64.b64encode(self.key)})
else:
req = json.dumps({})
r = requests.post("http://" + endpoints[0], req)
result = r.json()
print(result)
if "endpoint_list" not in result:
raise ValueError("server not ready")
else:
endpoints = [
endpoints[0].split(":")[0] + ":" +
str(result["endpoint_list"][0])
]
return endpoints
def connect(self, endpoints=None, encryption=False):
# check whether current endpoint is available
# init from client config
# create predictor here
......@@ -203,6 +228,8 @@ class Client(object):
"You must set the endpoints parameter or use add_variant function to create a variant."
)
else:
if encryption:
endpoints = self.get_serving_port(endpoints)
if self.predictor_sdk_ is None:
self.add_variant('default_tag_{}'.format(id(self)), endpoints,
100)
......
python/paddle_serving_client/io/__init__.py
浏览文件 @ 3cefebf8
......@@ -21,6 +21,9 @@ from paddle.fluid.framework import Program
from paddle.fluid import CPUPlace
from paddle.fluid.io import save_inference_model
import paddle.fluid as fluid
from paddle.fluid.core import CipherUtils
from paddle.fluid.core import CipherFactory
from paddle.fluid.core import Cipher
from ..proto import general_model_config_pb2 as model_conf
import os
......@@ -29,7 +32,10 @@ def save_model(server_model_folder,
client_config_folder,
feed_var_dict,
fetch_var_dict,
main_program=None):
main_program=None,
encryption=False,
key_len=128,
encrypt_conf=None):
executor = Executor(place=CPUPlace())
feed_var_names = [feed_var_dict[x].name for x in feed_var_dict]
......@@ -38,14 +44,29 @@ def save_model(server_model_folder,
for key in sorted(fetch_var_dict.keys()):
target_vars.append(fetch_var_dict[key])
target_var_names.append(key)
if not encryption:
save_inference_model(
server_model_folder,
feed_var_names,
target_vars,
executor,
main_program=main_program)
else:
if encrypt_conf == None:
aes_cipher = CipherFactory.create_cipher()
else:
#todo: more encryption algorithms
pass
key = CipherUtils.gen_key_to_file(128, "key")
params = fluid.io.save_persistables(
executor=executor, dirname=None, main_program=main_program)
model = main_program.desc.serialize_to_string()
if not os.path.exists(server_model_folder):
os.makedirs(server_model_folder)
os.chdir(server_model_folder)
aes_cipher.encrypt_to_file(params, key, "encrypt_params")
aes_cipher.encrypt_to_file(model, key, "encrypt_model")
os.chdir("..")
config = model_conf.GeneralModelConfig()
#int64 = 0; float32 = 1; int32 = 2;
......@@ -116,7 +137,10 @@ def inference_model_to_serving(dirname,
serving_server="serving_server",
serving_client="serving_client",
model_filename=None,
params_filename=None):
params_filename=None,
encryption=False,
key_len=128,
encrypt_conf=None):
place = fluid.CPUPlace()
exe = fluid.Executor(place)
inference_program, feed_target_names, fetch_targets = \
......@@ -127,7 +151,7 @@ def inference_model_to_serving(dirname,
}
fetch_dict = {x.name: x for x in fetch_targets}
save_model(serving_server, serving_client, feed_dict, fetch_dict,
inference_program)
inference_program, encryption, key_len, encrypt_conf)
feed_names = feed_dict.keys()
fetch_names = fetch_dict.keys()
return feed_names, fetch_names
python/paddle_serving_server/__init__.py
浏览文件 @ 3cefebf8
......@@ -157,6 +157,7 @@ class Server(object):
self.cur_path = os.getcwd()
self.use_local_bin = False
self.mkl_flag = False
self.encryption_model = False
self.product_name = None
self.container_id = None
self.model_config_paths = None # for multi-model in a workflow
......@@ -197,6 +198,9 @@ class Server(object):
def set_ir_optimize(self, flag=False):
self.ir_optimization = flag
def use_encryption_model(self, flag=False):
self.encryption_model = flag
def set_product_name(self, product_name=None):
if product_name == None:
raise ValueError("product_name can't be None.")
......@@ -232,8 +236,14 @@ class Server(object):
engine.force_update_static_cache = False
if device == "cpu":
if self.encryption_model:
engine.type = "FLUID_CPU_ANALYSIS_ENCRYPT"
else:
engine.type = "FLUID_CPU_ANALYSIS_DIR"
elif device == "gpu":
if self.encryption_model:
engine.type = "FLUID_GPU_ANALYSIS_ENCRYPT"
else:
engine.type = "FLUID_GPU_ANALYSIS_DIR"
self.model_toolkit_conf.engines.extend([engine])
......
python/paddle_serving_server/serve.py
浏览文件 @ 3cefebf8
......@@ -18,8 +18,14 @@ Usage:
python -m paddle_serving_server.serve --model ./serving_server_model --port 9292
"""
import argparse
from .web_service import WebService
import sys
import json
import base64
import time
from multiprocessing import Process
from web_service import WebService, port_is_available
from flask import Flask, request
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
def parse_args(): # pylint: disable=doc-string-missing
......@@ -53,6 +59,11 @@ def parse_args(): # pylint: disable=doc-string-missing
type=int,
default=512 * 1024 * 1024,
help="Limit sizes of messages")
parser.add_argument(
"--use_encryption_model",
default=False,
action="store_true",
help="Use encryption model")
parser.add_argument(
"--use_multilang",
default=False,
......@@ -71,17 +82,18 @@ def parse_args(): # pylint: disable=doc-string-missing
return parser.parse_args()
def start_standard_model(): # pylint: disable=doc-string-missing
def start_standard_model(serving_port): # pylint: disable=doc-string-missing
args = parse_args()
thread_num = args.thread
model = args.model
port = args.port
port = serving_port
workdir = args.workdir
device = args.device
mem_optim = args.mem_optim_off is False
ir_optim = args.ir_optim
max_body_size = args.max_body_size
use_mkl = args.use_mkl
use_encryption_model = args.use_encryption_model
use_multilang = args.use_multilang
if model == "":
......@@ -111,6 +123,7 @@ def start_standard_model(): # pylint: disable=doc-string-missing
server.use_mkl(use_mkl)
server.set_max_body_size(max_body_size)
server.set_port(port)
server.use_encryption_model(use_encryption_model)
if args.product_name != None:
server.set_product_name(args.product_name)
if args.container_id != None:
......@@ -121,11 +134,88 @@ def start_standard_model(): # pylint: disable=doc-string-missing
server.run_server()
if __name__ == "__main__":
class MainService(BaseHTTPRequestHandler):
def get_available_port(self):
default_port = 12000
for i in range(1000):
if port_is_available(default_port + i):
return default_port + i
def start_serving(self):
start_standard_model(serving_port)
def get_key(self, post_data):
if "key" not in post_data:
return False
else:
key = base64.b64decode(post_data["key"])
with open(args.model + "/key", "w") as f:
f.write(key)
return True
def check_key(self, post_data):
if "key" not in post_data:
return False
else:
key = base64.b64decode(post_data["key"])
with open(args.model + "/key", "r") as f:
cur_key = f.read()
return (key == cur_key)
def start(self, post_data):
post_data = json.loads(post_data)
global p_flag
if not p_flag:
if args.use_encryption_model:
print("waiting key for model")
if not self.get_key(post_data):
print("not found key in request")
return False
global serving_port
global p
serving_port = self.get_available_port()
p = Process(target=self.start_serving)
p.start()
time.sleep(3)
if p.is_alive():
p_flag = True
else:
return False
else:
if p.is_alive():
if not self.check_key(post_data):
return False
else:
return False
return True
def do_POST(self):
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
if self.start(post_data):
response = {"endpoint_list": [serving_port]}
else:
response = {"message": "start serving failed"}
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps(response))
if __name__ == "__main__":
args = parse_args()
if args.name == "None":
start_standard_model()
if args.use_encryption_model:
p_flag = False
p = None
serving_port = 0
server = HTTPServer(('localhost', int(args.port)), MainService)
print(
'Starting encryption server, waiting for key from client, use <Ctrl-C> to stop'
)
server.serve_forever()
else:
start_standard_model(args.port)
else:
service = WebService(name=args.name)
service.load_model_config(args.model)
......
python/paddle_serving_server/web_service.py
浏览文件 @ 3cefebf8
......@@ -25,6 +25,16 @@ from paddle_serving_server import pipeline
from paddle_serving_server.pipeline import Op
def port_is_available(port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port))
if result != 0:
return True
else:
return False
class WebService(object):
def __init__(self, name="default_service"):
self.name = name
......@@ -110,7 +120,7 @@ class WebService(object):
self.mem_optim = mem_optim
self.ir_optim = ir_optim
for i in range(1000):
if self.port_is_available(default_port + i):
if port_is_available(default_port + i):
self.port_list.append(default_port + i)
break
......
python/paddle_serving_server_gpu/__init__.py
浏览文件 @ 3cefebf8
......@@ -68,6 +68,11 @@ def serve_args():
type=int,
default=512 * 1024 * 1024,
help="Limit sizes of messages")
parser.add_argument(
"--use_encryption_model",
default=False,
action="store_true",
help="Use encryption model")
parser.add_argument(
"--use_multilang",
default=False,
......@@ -277,7 +282,8 @@ class Server(object):
def set_trt(self):
self.use_trt = True
def _prepare_engine(self, model_config_paths, device):
def _prepare_engine(self, model_config_paths, device, use_encryption_model):
if self.model_toolkit_conf == None:
self.model_toolkit_conf = server_sdk.ModelToolkitConf()
......@@ -299,8 +305,14 @@ class Server(object):
engine.use_trt = self.use_trt
if device == "cpu":
if use_encryption_model:
engine.type = "FLUID_CPU_ANALYSIS_ENCRPT"
else:
engine.type = "FLUID_CPU_ANALYSIS_DIR"
elif device == "gpu":
if use_encryption_model:
engine.type = "FLUID_GPU_ANALYSIS_ENCRPT"
else:
engine.type = "FLUID_GPU_ANALYSIS_DIR"
self.model_toolkit_conf.engines.extend([engine])
......@@ -458,6 +470,7 @@ class Server(object):
workdir=None,
port=9292,
device="cpu",
use_encryption_model=False,
cube_conf=None):
if workdir == None:
workdir = "./tmp"
......@@ -471,7 +484,8 @@ class Server(object):
self.set_port(port)
self._prepare_resource(workdir, cube_conf)
self._prepare_engine(self.model_config_paths, device)
self._prepare_engine(self.model_config_paths, device,
use_encryption_model)
self._prepare_infer_service(port)
self.workdir = workdir
......
python/paddle_serving_server_gpu/serve.py
浏览文件 @ 3cefebf8
......@@ -19,19 +19,21 @@ Usage:
"""
import argparse
import os
import json
import base64
from multiprocessing import Pool, Process
from paddle_serving_server_gpu import serve_args
from flask import Flask, request
from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
def start_gpu_card_model(index, gpuid, args): # pylint: disable=doc-string-missing
def start_gpu_card_model(index, gpuid, port, args): # pylint: disable=doc-string-missing
gpuid = int(gpuid)
device = "gpu"
port = args.port
if gpuid == -1:
device = "cpu"
elif gpuid >= 0:
port = args.port + index
port = port + index
thread_num = args.thread
model = args.model
mem_optim = args.mem_optim_off is False
......@@ -73,14 +75,20 @@ def start_gpu_card_model(index, gpuid, args): # pylint: disable=doc-string-miss
server.set_container_id(args.container_id)
server.load_model_config(model)
server.prepare_server(workdir=workdir, port=port, device=device)
server.prepare_server(
workdir=workdir,
port=port,
device=device,
use_encryption_model=args.use_encryption_model)
if gpuid >= 0:
server.set_gpuid(gpuid)
server.run_server()
def start_multi_card(args): # pylint: disable=doc-string-missing
def start_multi_card(args, serving_port=None): # pylint: disable=doc-string-missing
gpus = ""
if serving_port == None:
serving_port = args.port
if args.gpu_ids == "":
gpus = []
else:
......@@ -97,14 +105,16 @@ def start_multi_card(args): # pylint: disable=doc-string-missing
env_gpus = []
if len(gpus) <= 0:
print("gpu_ids not set, going to run cpu service.")
start_gpu_card_model(-1, -1, args)
start_gpu_card_model(-1, -1, serving_port, args)
else:
gpu_processes = []
for i, gpu_id in enumerate(gpus):
p = Process(
target=start_gpu_card_model, args=(
target=start_gpu_card_model,
args=(
i,
gpu_id,
serving_port,
args, ))
gpu_processes.append(p)
for p in gpu_processes:
......@@ -113,9 +123,88 @@ def start_multi_card(args): # pylint: disable=doc-string-missing
p.join()
class MainService(BaseHTTPRequestHandler):
def get_available_port(self):
default_port = 12000
for i in range(1000):
if port_is_available(default_port + i):
return default_port + i
def start_serving(self):
start_multi_card(args, serving_port)
def get_key(self, post_data):
if "key" not in post_data:
return False
else:
key = base64.b64decode(post_data["key"])
with open(args.model + "/key", "w") as f:
f.write(key)
return True
def check_key(self, post_data):
if "key" not in post_data:
return False
else:
key = base64.b64decode(post_data["key"])
with open(args.model + "/key", "r") as f:
cur_key = f.read()
return (key == cur_key)
def start(self, post_data):
post_data = json.loads(post_data)
global p_flag
if not p_flag:
if args.use_encryption_model:
print("waiting key for model")
if not self.get_key(post_data):
print("not found key in request")
return False
global serving_port
global p
serving_port = self.get_available_port()
p = Process(target=self.start_serving)
p.start()
time.sleep(3)
if p.is_alive():
p_flag = True
else:
return False
else:
if p.is_alive():
if not self.check_key(post_data):
return False
else:
return False
return True
def do_POST(self):
content_length = int(self.headers['Content-Length'])
post_data = self.rfile.read(content_length)
if self.start(post_data):
response = {"endpoint_list": [serving_port]}
else:
response = {"message": "start serving failed"}
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.end_headers()
self.wfile.write(json.dumps(response))
if __name__ == "__main__":
args = serve_args()
if args.name == "None":
from .web_service import port_is_available
if args.use_encryption_model:
p_flag = False
p = None
serving_port = 0
server = HTTPServer(('localhost', int(args.port)), MainService)
print(
'Starting encryption server, waiting for key from client, use <Ctrl-C> to stop'
)
server.serve_forever()
else:
start_multi_card(args)
else:
from .web_service import WebService
......
python/paddle_serving_server_gpu/web_service.py
浏览文件 @ 3cefebf8
......@@ -28,6 +28,16 @@ from paddle_serving_server_gpu import pipeline
from paddle_serving_server_gpu.pipeline import Op
def port_is_available(port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
sock.settimeout(2)
result = sock.connect_ex(('0.0.0.0', port))
if result != 0:
return True
else:
return False
class WebService(object):
def __init__(self, name="default_service"):
self.name = name
......@@ -136,7 +146,7 @@ class WebService(object):
self.port_list = []
default_port = 12000
for i in range(1000):
if self.port_is_available(default_port + i):
if port_is_available(default_port + i):
self.port_list.append(default_port + i)
if len(self.port_list) > len(self.gpus):
break
......
tools/Dockerfile.centos6.cuda9.0-cudnn7.devel
浏览文件 @ 3cefebf8
......@@ -39,6 +39,8 @@ RUN yum -y install wget && \
make clean && \
echo 'export PATH=/usr/local/python3.6/bin:$PATH' >> /root/.bashrc && \
echo 'export LD_LIBRARY_PATH=/usr/local/python3.6/lib:$LD_LIBRARY_PATH' >> /root/.bashrc && \
pip install requests && \
pip3 install requests && \
source /root/.bashrc && \
cd .. && rm -rf Python-3.6.8* && \
wget https://github.com/protocolbuffers/protobuf/releases/download/v3.11.2/protobuf-all-3.11.2.tar.gz && \
......
tools/Dockerfile.centos6.devel
浏览文件 @ 3cefebf8
......@@ -49,6 +49,8 @@ RUN yum -y install wget && \
cd .. && rm -rf protobuf-* && \
yum -y install epel-release && yum -y install patchelf libXext libSM libXrender && \
yum clean all && \
pip install requests && \
pip3 install requests && \
localedef -c -i en_US -f UTF-8 en_US.UTF-8 && \
echo "export LANG=en_US.utf8" >> /root/.bashrc && \
echo "export LANGUAGE=en_US.utf8" >> /root/.bashrc
tools/Dockerfile.ci
浏览文件 @ 3cefebf8
......@@ -23,7 +23,8 @@ RUN wget https://dl.google.com/go/go1.14.linux-amd64.tar.gz >/dev/null \
RUN yum -y install python-devel sqlite-devel >/dev/null \
&& curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py >/dev/null \
&& python get-pip.py >/dev/null \
&& rm get-pip.py
&& rm get-pip.py \
&& pip install requests
RUN wget http://nixos.org/releases/patchelf/patchelf-0.10/patchelf-0.10.tar.bz2 \
&& yum -y install bzip2 >/dev/null \
......@@ -34,6 +35,9 @@ RUN wget http://nixos.org/releases/patchelf/patchelf-0.10/patchelf-0.10.tar.bz2
&& cd .. \
&& rm -rf patchelf-0.10*
RUN yum install -y python3 python3-devel \
&& pip3 install requests
RUN wget https://github.com/protocolbuffers/protobuf/releases/download/v3.11.2/protobuf-all-3.11.2.tar.gz && \
tar zxf protobuf-all-3.11.2.tar.gz && \
cd protobuf-3.11.2 && \
......@@ -41,8 +45,6 @@ RUN wget https://github.com/protocolbuffers/protobuf/releases/download/v3.11.2/p
make clean && \
cd .. && rm -rf protobuf-*
RUN yum install -y python3 python3-devel
RUN yum -y update >/dev/null \
&& yum -y install dnf >/dev/null \
&& yum -y install dnf-plugins-core >/dev/null \
......
tools/Dockerfile.cuda10.0-cudnn7.devel
浏览文件 @ 3cefebf8
......@@ -30,11 +30,13 @@ RUN wget https://dl.google.com/go/go1.14.linux-amd64.tar.gz >/dev/null \
RUN yum -y install python-devel sqlite-devel \
&& curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py >/dev/null \
&& python get-pip.py >/dev/null \
&& rm get-pip.py
&& rm get-pip.py \
&& pip install requests
RUN yum install -y python3 python3-devel \
&& yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
&& yum clean all
&& yum clean all \
&& pip3 install requests
RUN localedef -c -i en_US -f UTF-8 en_US.UTF-8 \
&& echo "export LANG=en_US.utf8" >> /root/.bashrc \
......
tools/Dockerfile.cuda9.0-cudnn7.devel
浏览文件 @ 3cefebf8
......@@ -29,11 +29,13 @@ RUN wget https://dl.google.com/go/go1.14.linux-amd64.tar.gz >/dev/null \
RUN yum -y install python-devel sqlite-devel \
&& curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py >/dev/null \
&& python get-pip.py >/dev/null \
&& rm get-pip.py
&& rm get-pip.py \
&& pip install requests
RUN yum install -y python3 python3-devel \
&& yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
&& yum clean all
&& yum clean all \
&& pip3 install requests
RUN localedef -c -i en_US -f UTF-8 en_US.UTF-8 \
&& echo "export LANG=en_US.utf8" >> /root/.bashrc \
......
tools/Dockerfile.devel
浏览文件 @ 3cefebf8
......@@ -19,11 +19,13 @@ RUN wget https://dl.google.com/go/go1.14.linux-amd64.tar.gz >/dev/null \
RUN yum -y install python-devel sqlite-devel \
&& curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py >/dev/null \
&& python get-pip.py >/dev/null \
&& rm get-pip.py
&& rm get-pip.py \
&& pip install requests
RUN yum install -y python3 python3-devel \
&& yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
&& yum clean all
&& yum clean all \
&& pip3 install requests
RUN localedef -c -i en_US -f UTF-8 en_US.UTF-8 \
&& echo "export LANG=en_US.utf8" >> /root/.bashrc \
......
tools/serving_build.sh
浏览文件 @ 3cefebf8
......@@ -514,6 +514,40 @@ function python_test_lac() {
cd ..
}
function python_test_encryption(){
#pwd: /Serving/python/examples
cd encryption
sh get_data.sh
local TYPE=$1
export SERVING_BIN=${SERIVNG_WORKDIR}/build-server-${TYPE}/core/general-server/serving
case $TYPE in
CPU)
#check_cmd "python encrypt.py"
#sleep 5
check_cmd "python -m paddle_serving_server.serve --model encrypt_server/ --port 9300 --use_encryption_model > /dev/null &"
sleep 5
check_cmd "python test_client.py encrypt_client/serving_client_conf.prototxt"
kill_server_process
;;
GPU)
#check_cmd "python encrypt.py"
#sleep 5
check_cmd "python -m paddle_serving_server_gpu.serve --model encrypt_server/ --port 9300 --use_encryption_model --gpu_ids 0"
sleep 5
check_cmd "python test_client.py encrypt_client/serving_client_conf.prototxt"
kill_servere_process
;;
*)
echo "error type"
exit 1
;;
esac
echo "encryption $TYPE test finished as expected"
setproxy
unset SERVING_BIN
cd ..
}
function java_run_test() {
# pwd: /Serving
local TYPE=$1
......@@ -960,6 +994,7 @@ function python_run_test() {
python_test_lac $TYPE # pwd: /Serving/python/examples
python_test_multi_process $TYPE # pwd: /Serving/python/examples
python_test_multi_fetch $TYPE # pwd: /Serving/python/examples
python_test_encryption $TYPE # pwd: /Serving/python/examples
python_test_yolov4 $TYPE # pwd: /Serving/python/examples
python_test_grpc_impl $TYPE # pwd: /Serving/python/examples
python_test_resnet50 $TYPE # pwd: /Serving/python/examples
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或