Merge pull request #689 from MRXLT/encryption

support encryption model 

cmake/paddlepaddle.cmake

@@ -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

@@ -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;
 }

paddle_inference/inferencer-fluid-cpu/include/fluid_cpu_engine.h

@@ -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

@@ -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

@@ -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

@@ -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

+# 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

+# 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

+# 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

+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/uci_housing_example/encrypt.tar.gz
+tar -xzf encrypt.tar.gz

python/examples/encryption/test_client.py

+# 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.
+# pylint: disable=doc-string-missing
+
+from paddle_serving_client import Client
+import sys
+
+client = Client()
+client.load_client_config(sys.argv[1])
+client.use_key("./key")
+client.connect(["127.0.0.1:9300"], encryption=True)
+
+import paddle
+test_reader = paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.uci_housing.test(), buf_size=500),
+    batch_size=1)
+
+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

@@ -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

@@ -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)
-
-    save_inference_model(
-        server_model_folder,
-        feed_var_names,
-        target_vars,
-        executor,
-        main_program=main_program)
-
+    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

@@ -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.")
@@ -232,9 +236,15 @@ class Server(object):
             engine.force_update_static_cache = False
 
             if device == "cpu":
-                engine.type = "FLUID_CPU_ANALYSIS_DIR"
+                if self.encryption_model:
+                    engine.type = "FLUID_CPU_ANALYSIS_ENCRYPT"
+                else:
+                    engine.type = "FLUID_CPU_ANALYSIS_DIR"
             elif device == "gpu":
-                engine.type = "FLUID_GPU_ANALYSIS_DIR"
+                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

@@ -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

@@ -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

@@ -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()
 
@@ -299,9 +305,15 @@ class Server(object):
             engine.use_trt = self.use_trt
 
             if device == "cpu":
-                engine.type = "FLUID_CPU_ANALYSIS_DIR"
+                if use_encryption_model:
+                    engine.type = "FLUID_CPU_ANALYSIS_ENCRPT"
+                else:
+                    engine.type = "FLUID_CPU_ANALYSIS_DIR"
             elif device == "gpu":
-                engine.type = "FLUID_GPU_ANALYSIS_DIR"
+                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

@@ -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:
@@ -113,10 +123,89 @@ 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":
-        start_multi_card(args)
+        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
         web_service = WebService(name=args.name)

python/paddle_serving_server_gpu/web_service.py

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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
@@ -529,7 +563,7 @@ function java_run_test() {
             cd examples # pwd: /Serving/java/examples
             mvn compile > /dev/null
             mvn install > /dev/null
-            
+
             # fit_a_line (general, asyn_predict, batch_predict)
             cd ../../python/examples/grpc_impl_example/fit_a_line # pwd: /Serving/python/examples/grpc_impl_example/fit_a_line
             sh get_data.sh
@@ -786,7 +820,7 @@ function python_test_pipeline(){
             python -m paddle_serving_server.serve --model imdb_cnn_model --port 9292 --workdir test9292 &> cnn.log &
             python -m paddle_serving_server.serve --model imdb_bow_model --port 9393 --workdir test9393 &> bow.log &
             sleep 5
-            
+
             # test: thread servicer & thread op
             cat << EOF > config.yml
 rpc_port: 18080
@@ -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