Revert "Merge pull request #689 from MRXLT/encryption"

This reverts commit cb37efc0.

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.a)
+SET_PROPERTY(TARGET paddle_fluid PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_fluid.so)
 
 if (WITH_TRT)
 ADD_LIBRARY(nvinfer SHARED IMPORTED GLOBAL)
@@ -127,17 +127,12 @@ 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 cryptopp)
-
+    xxhash)
 
 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>  //NOLINT
+#include <thread>
 
 #include "core/predictor/framework.pb.h"
-#include "quant.h"     // NOLINT
-#include "seq_file.h"  // NOLINT
+#include "quant.h"
+#include "seq_file.h"
 
 inline uint64_t time_diff(const struct timeval &start_time,
                           const struct timeval &end_time) {
@@ -113,15 +113,13 @@ 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(
-        std::to_string(i).c_str(), sizeof(i), value_buf, value_buf_len);
+    seq_file_writer.write((char *)&i, 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) {  // NOLINT
+float *read_embedding_table(const char *file1, std::vector<int64_t> &dims) {
   std::ifstream is(file1);
   // Step 1: is read version, os write version
   uint32_t version;
@@ -244,7 +242,7 @@ 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(static_cast<int>(pow2bits) - 1, val);
+          val = std::min((int)pow2bits - 1, val);
           *(tensor_temp + 2 * sizeof(float) + e) = val;
         }
         result[k] = tensor_temp;
@@ -264,8 +262,7 @@ 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(
-        std::to_string(i).c_str(), sizeof(i), result[i], per_line_size);
+    seq_file_writer.write((char *)&i, sizeof(i), result[i], per_line_size);
   }
   return 0;
 }

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

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #pragma once
+
 #include <pthread.h>
 #include <fstream>
 #include <map>
@@ -28,6 +29,7 @@ namespace paddle_serving {
 namespace fluid_cpu {
 
 using configure::SigmoidConf;
+
 class AutoLock {
  public:
   explicit AutoLock(pthread_mutex_t& mutex) : _mut(mutex) {
@@ -528,60 +530,7 @@ 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

@@ -52,13 +52,6 @@ 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

@@ -25,6 +25,7 @@
 #include "core/configure/inferencer_configure.pb.h"
 #include "core/predictor/framework/infer.h"
 #include "paddle_inference_api.h"  // NOLINT
+
 DECLARE_int32(gpuid);
 
 namespace baidu {
@@ -590,60 +591,6 @@ 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

@@ -54,12 +54,6 @@ 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

@@ -13,19 +13,16 @@
 # 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'))
@@ -164,7 +161,6 @@ 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
@@ -197,28 +193,7 @@ class Client(object):
         else:
             self.rpc_timeout_ms = rpc_timeout
 
-    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):
+    def connect(self, endpoints=None):
         # check whether current endpoint is available
         # init from client config
         # create predictor here
@@ -228,8 +203,6 @@ 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

@@ -21,9 +21,6 @@ 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
 
@@ -32,10 +29,7 @@ def save_model(server_model_folder,
                client_config_folder,
                feed_var_dict,
                fetch_var_dict,
-               main_program=None,
-               encryption=False,
-               key_len=128,
-               encrypt_conf=None):
+               main_program=None):
     executor = Executor(place=CPUPlace())
 
     feed_var_names = [feed_var_dict[x].name for x in feed_var_dict]
@@ -44,29 +38,14 @@ 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("..")
+
+    save_inference_model(
+        server_model_folder,
+        feed_var_names,
+        target_vars,
+        executor,
+        main_program=main_program)
+
     config = model_conf.GeneralModelConfig()
 
     #int64 = 0; float32 = 1; int32 = 2;
@@ -134,10 +113,7 @@ def inference_model_to_serving(dirname,
                                serving_server="serving_server",
                                serving_client="serving_client",
                                model_filename=None,
-                               params_filename=None,
-                               encryption=False,
-                               key_len=128,
-                               encrypt_conf=None):
+                               params_filename=None):
     place = fluid.CPUPlace()
     exe = fluid.Executor(place)
     inference_program, feed_target_names, fetch_targets = \
@@ -148,7 +124,7 @@ 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, encryption, key_len, encrypt_conf)
+               inference_program)
     feed_names = feed_dict.keys()
     fetch_names = fetch_dict.keys()
     return feed_names, fetch_names

python/paddle_serving_server/__init__.py

@@ -157,7 +157,6 @@ 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
@@ -198,9 +197,6 @@ 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.")
@@ -236,15 +232,9 @@ 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"
+                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"
+                engine.type = "FLUID_GPU_ANALYSIS_DIR"
 
             self.model_toolkit_conf.engines.extend([engine])
 

python/paddle_serving_server/serve.py

@@ -18,14 +18,8 @@ Usage:
         python -m paddle_serving_server.serve --model ./serving_server_model --port 9292
 """
 import argparse
-import sys
-import json
-import base64
-import time
-from multiprocessing import Process
-from web_service import WebService, port_is_available
+from .web_service import WebService
 from flask import Flask, request
-from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
 
 
 def parse_args():  # pylint: disable=doc-string-missing
@@ -59,11 +53,6 @@ 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,
@@ -82,18 +71,17 @@ def parse_args():  # pylint: disable=doc-string-missing
     return parser.parse_args()
 
 
-def start_standard_model(serving_port):  # pylint: disable=doc-string-missing
+def start_standard_model():  # pylint: disable=doc-string-missing
     args = parse_args()
     thread_num = args.thread
     model = args.model
-    port = serving_port
+    port = args.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 == "":
@@ -123,7 +111,6 @@ def start_standard_model(serving_port):  # 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:
@@ -134,88 +121,11 @@ def start_standard_model(serving_port):  # pylint: disable=doc-string-missing
     server.run_server()
 
 
-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":
-        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)
+        start_standard_model()
     else:
         service = WebService(name=args.name)
         service.load_model_config(args.model)

python/paddle_serving_server/web_service.py

@@ -25,16 +25,6 @@ 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
@@ -120,7 +110,7 @@ class WebService(object):
         self.mem_optim = mem_optim
         self.ir_optim = ir_optim
         for i in range(1000):
-            if port_is_available(default_port + i):
+            if self.port_is_available(default_port + i):
                 self.port_list.append(default_port + i)
                 break
 

python/paddle_serving_server_gpu/__init__.py

@@ -68,11 +68,6 @@ 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,
@@ -282,8 +277,7 @@ class Server(object):
     def set_trt(self):
         self.use_trt = True
 
-    def _prepare_engine(self, model_config_paths, device, use_encryption_model):
-
+    def _prepare_engine(self, model_config_paths, device):
         if self.model_toolkit_conf == None:
             self.model_toolkit_conf = server_sdk.ModelToolkitConf()
 
@@ -305,15 +299,9 @@ 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"
+                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"
+                engine.type = "FLUID_GPU_ANALYSIS_DIR"
 
             self.model_toolkit_conf.engines.extend([engine])
 
@@ -470,7 +458,6 @@ class Server(object):
                        workdir=None,
                        port=9292,
                        device="cpu",
-                       use_encryption_model=False,
                        cube_conf=None):
         if workdir == None:
             workdir = "./tmp"
@@ -484,8 +471,7 @@ class Server(object):
 
         self.set_port(port)
         self._prepare_resource(workdir, cube_conf)
-        self._prepare_engine(self.model_config_paths, device,
-                             use_encryption_model)
+        self._prepare_engine(self.model_config_paths, device)
         self._prepare_infer_service(port)
         self.workdir = workdir
 

python/paddle_serving_server_gpu/serve.py

@@ -19,21 +19,19 @@ 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, port, args):  # pylint: disable=doc-string-missing
+def start_gpu_card_model(index, gpuid, args):  # pylint: disable=doc-string-missing
     gpuid = int(gpuid)
     device = "gpu"
+    port = args.port
     if gpuid == -1:
         device = "cpu"
     elif gpuid >= 0:
-        port = port + index
+        port = args.port + index
     thread_num = args.thread
     model = args.model
     mem_optim = args.mem_optim_off is False
@@ -75,20 +73,14 @@ def start_gpu_card_model(index, gpuid, port, args):  # pylint: disable=doc-strin
         server.set_container_id(args.container_id)
 
     server.load_model_config(model)
-    server.prepare_server(
-        workdir=workdir,
-        port=port,
-        device=device,
-        use_encryption_model=args.use_encryption_model)
+    server.prepare_server(workdir=workdir, port=port, device=device)
     if gpuid >= 0:
         server.set_gpuid(gpuid)
     server.run_server()
 
 
-def start_multi_card(args, serving_port=None):  # pylint: disable=doc-string-missing
+def start_multi_card(args):  # pylint: disable=doc-string-missing
     gpus = ""
-    if serving_port == None:
-        serving_port = args.port
     if args.gpu_ids == "":
         gpus = []
     else:
@@ -105,16 +97,14 @@ def start_multi_card(args, serving_port=None):  # pylint: disable=doc-string-mis
             env_gpus = []
     if len(gpus) <= 0:
         print("gpu_ids not set, going to run cpu service.")
-        start_gpu_card_model(-1, -1, serving_port, args)
+        start_gpu_card_model(-1, -1, 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:
@@ -123,89 +113,10 @@ def start_multi_card(args, serving_port=None):  # pylint: disable=doc-string-mis
             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)
+        start_multi_card(args)
     else:
         from .web_service import WebService
         web_service = WebService(name=args.name)

python/paddle_serving_server_gpu/web_service.py

@@ -28,16 +28,6 @@ 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
@@ -146,7 +136,7 @@ class WebService(object):
         self.port_list = []
         default_port = 12000
         for i in range(1000):
-            if port_is_available(default_port + i):
+            if self.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

@@ -39,8 +39,6 @@ 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

@@ -49,8 +49,6 @@ 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

@@ -23,8 +23,7 @@ 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 \
-    && pip install requests  
+    && rm get-pip.py
 
 RUN wget http://nixos.org/releases/patchelf/patchelf-0.10/patchelf-0.10.tar.bz2 \
     && yum -y install bzip2 >/dev/null \
@@ -35,9 +34,6 @@ 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 && \
@@ -45,6 +41,8 @@ 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

@@ -30,13 +30,11 @@ 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 \
-    && pip install requests
+    && rm get-pip.py 
 
 RUN yum install -y python3 python3-devel \
     && yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
-    && yum clean all \
-    && pip3 install requests
+    && yum clean all 
 
 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

@@ -29,13 +29,11 @@ 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 \
-    && pip install requests
+    && rm get-pip.py 
 
 RUN yum install -y python3 python3-devel \
     && yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
-    && yum clean all \
-    && pip3 install requests
+    && yum clean all 
 
 RUN localedef -c -i en_US -f UTF-8 en_US.UTF-8 \
     && echo "export LANG=en_US.utf8" >> /root/.bashrc \

tools/Dockerfile.devel

@@ -19,13 +19,11 @@ 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 \
-    && pip install requests 
+    && rm get-pip.py 
 
 RUN yum install -y python3 python3-devel \
     && yum -y install epel-release && yum -y install patchelf libXext libSM libXrender\
-    && yum clean all \
-    && pip3 install requests
+    && yum clean all 
 
 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

@@ -514,40 +514,6 @@ 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
@@ -563,7 +529,7 @@ 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
@@ -820,7 +786,7 @@ 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
@@ -994,7 +960,6 @@ 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