From 4d035eb35fa905df74cf43aab77092ae8c16a11f Mon Sep 17 00:00:00 2001 From: TeslaZhao Date: Wed, 18 May 2022 13:12:40 +0800 Subject: [PATCH] Update doc --- cmake/paddlepaddle.cmake | 17 +- .../proto/general_model_service.proto | 7 +- core/configure/proto/server_configure.proto | 14 + core/general-server/op/general_remote_op.cpp | 126 +++ core/general-server/op/general_remote_op.h | 58 + .../proto/general_model_service.proto | 2 +- core/predictor/common/constant.cpp | 2 +- core/predictor/framework/bsf-inl.h | 2 +- .../sdk-cpp/proto/general_model_service.proto | 2 +- .../proto/load_general_model_service.proto | 1 + doc/Offical_Docs/10-0_Terminology.md | 1 + doc/Offical_Docs/11-0_Contributors.md | 97 ++ doc/Offical_Docs/11_Lookup_CN.md | 0 doc/Offical_Docs/12-0_FAQ_CN.md | 328 ++++++ doc/Offical_Docs/3-0_QuickStart_Int_CN.md | 9 +- .../3-1_QuickStart_Cpp_Resnet_CN.md | 111 ++ .../3-2_QuickStart_Pipeline_OCR_CN.md | 93 +- doc/Offical_Docs/4-0_ModelZoo_CN.md | 67 +- doc/Offical_Docs/5-1_Save_Model_Params_CN.md | 188 ++++ .../6-1_Cpp_Asynchronous_Framwork_CN.md | 167 +++ .../6-2_Cpp_Serving_Protocols_CN.md | 290 +++++ doc/Offical_Docs/6-5_Cpp_ABTest_CN.md | 198 ++++ .../7-0_Python_Pipeline_Int_CN.md | 3 +- .../7-1_Python_Pipeline_Design_CN.md | 117 +- .../7-2_Python_Pipeline_Senior_CN.md | 402 +++++-- .../7-3_Python_Pipeline_Optimize_CN.md | 213 +++- .../7-4_Python_Pipeline_Benchmark_CN.md | 59 + doc/Offical_Docs/9-0_Kubernetes_Int_CN.md | 3 +- doc/Offical_Docs/Home_Page_CN.md | 88 ++ .../6-1_Cpp_Asynchronous_Framwork_CN_1.png | Bin 0 -> 366826 bytes .../images/6-5_Cpp_ABTest_CN_1.png | Bin 0 -> 302218 bytes .../C++/PaddleClas/resnet_50_vd/README_CN.md | 69 ++ .../C++/PaddleClas/resnet_50_vd/daisy.jpg | Bin 0 -> 39696 bytes .../PaddleClas/resnet_50_vd/http_client.py | 82 ++ .../PaddleClas/resnet_50_vd/imagenet.label | 1000 +++++++++++++++++ .../C++/PaddleClas/resnet_50_vd/rpc_client.py | 51 + examples/C++/imdb/README_CN.md | 20 +- examples/C++/imdb/abtest_client.py | 50 +- examples/C++/imdb/test_http_client.py | 2 +- .../main/proto/general_model_service.proto | 8 +- .../paddle/include/paddle_engine.h | 16 +- python/paddle_serving_server/serve.py | 25 + python/paddle_serving_server/server.py | 20 + .../proto/general_model_service.proto | 7 + 44 files changed, 3824 insertions(+), 191 deletions(-) create mode 100644 core/general-server/op/general_remote_op.cpp create mode 100644 core/general-server/op/general_remote_op.h create mode 100644 doc/Offical_Docs/10-0_Terminology.md create mode 100644 doc/Offical_Docs/11-0_Contributors.md delete mode 100644 doc/Offical_Docs/11_Lookup_CN.md create mode 100644 doc/Offical_Docs/12-0_FAQ_CN.md create mode 100644 doc/Offical_Docs/3-1_QuickStart_Cpp_Resnet_CN.md create mode 100644 doc/Offical_Docs/5-1_Save_Model_Params_CN.md create mode 100644 doc/Offical_Docs/6-1_Cpp_Asynchronous_Framwork_CN.md create mode 100644 doc/Offical_Docs/6-2_Cpp_Serving_Protocols_CN.md create mode 100644 doc/Offical_Docs/6-5_Cpp_ABTest_CN.md create mode 100644 doc/Offical_Docs/7-4_Python_Pipeline_Benchmark_CN.md create mode 100644 doc/Offical_Docs/Home_Page_CN.md create mode 100644 doc/Offical_Docs/images/6-1_Cpp_Asynchronous_Framwork_CN_1.png create mode 100644 doc/Offical_Docs/images/6-5_Cpp_ABTest_CN_1.png create mode 100644 examples/C++/PaddleClas/resnet_50_vd/README_CN.md create mode 100644 examples/C++/PaddleClas/resnet_50_vd/daisy.jpg create mode 100644 examples/C++/PaddleClas/resnet_50_vd/http_client.py create mode 100644 examples/C++/PaddleClas/resnet_50_vd/imagenet.label create mode 100644 examples/C++/PaddleClas/resnet_50_vd/rpc_client.py diff --git a/cmake/paddlepaddle.cmake b/cmake/paddlepaddle.cmake index 4f345c163..f6548478a 100644 --- a/cmake/paddlepaddle.cmake +++ b/cmake/paddlepaddle.cmake @@ -171,14 +171,27 @@ LINK_DIRECTORIES(${PADDLE_INSTALL_DIR}/third_party/install/mklml/lib) SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PADDLE_INSTALL_DIR}/third_party/install/mkldnn/lib") LINK_DIRECTORIES(${PADDLE_INSTALL_DIR}/third_party/install/mkldnn/lib) +#SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PADDLE_INSTALL_DIR}/third_party/install/paddle2onnx/lib") +#LINK_DIRECTORIES(${PADDLE_INSTALL_DIR}/third_party/install/paddle2onnx/lib) + +#SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${PADDLE_INSTALL_DIR}/third_party/install/onnxruntime/lib") +#LINK_DIRECTORIES(${PADDLE_INSTALL_DIR}/third_party/install/onnxruntime/lib) + if (NOT WITH_MKLML) ADD_LIBRARY(openblas STATIC IMPORTED GLOBAL) SET_PROPERTY(TARGET openblas PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/openblas/lib/libopenblas.a) endif() +#ADD_LIBRARY(paddle2onnx STATIC IMPORTED GLOBAL) +#SET_PROPERTY(TARGET paddle2onnx PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/paddle2onnx/lib/libpaddle2onnx.so) + +#ADD_LIBRARY(onnxruntime STATIC IMPORTED GLOBAL) +#SET_PROPERTY(TARGET onnxruntime PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/third_party/install/onnxruntime/lib/libonnxruntime.so.1.10.0) + ADD_LIBRARY(paddle_inference STATIC IMPORTED GLOBAL) -SET_PROPERTY(TARGET paddle_inference PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_inference.so) -if (WITH_ASCEND_CL) + +SET_PROPERTY(TARGET paddle_inference PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_inference.a) +if (WITH_ASCEND_CL OR WITH_XPU) SET_PROPERTY(TARGET paddle_inference PROPERTY IMPORTED_LOCATION ${PADDLE_INSTALL_DIR}/lib/libpaddle_inference.so) endif() diff --git a/core/configure/proto/general_model_service.proto b/core/configure/proto/general_model_service.proto index c2deab2f6..b4f1ce0cf 100644 --- a/core/configure/proto/general_model_service.proto +++ b/core/configure/proto/general_model_service.proto @@ -90,11 +90,12 @@ message Request { message Response { repeated ModelOutput outputs = 1; repeated int64 profile_time = 2; + bool profile_server = 3; + uint64 log_id = 4; // Error code - int32 err_no = 3; - + int32 err_no = 5; // Error messages - string err_msg = 4; + string err_msg = 6; }; message ModelOutput { diff --git a/core/configure/proto/server_configure.proto b/core/configure/proto/server_configure.proto index d1d9ee256..f5edd23fd 100644 --- a/core/configure/proto/server_configure.proto +++ b/core/configure/proto/server_configure.proto @@ -49,6 +49,20 @@ message EngineDesc { optional bool gpu_multi_stream = 20; optional bool use_ascend_cl = 21; + /* + * "gpu_memory_mb": allocate gpu memory by config.EnableUseGpu() + * "cpu_math_thread_num": set thread numbers of cpu math by + * config.SetCpuMathLibraryNumThreads() + * "trt_workspace_size": set TensorRT workspace size by + * config.EnableTensorRtEngine(), 1 << 25 default + * "trt_use_static": If true, save the optimization information of the TRT + * serialized to the disk, and load from the disk. + */ + optional int32 gpu_memory_mb = 22 [ default = 100 ]; + optional int32 cpu_math_thread_num = 23 [ default = 1 ]; + optional int32 trt_workspace_size = 24 [ default = 33554432 ]; + optional bool trt_use_static = 25 [ default = false ]; + /* * "runtime_thread_num": n == 0 means don`t use Asynchronous task scheduling * mode. diff --git a/core/general-server/op/general_remote_op.cpp b/core/general-server/op/general_remote_op.cpp new file mode 100644 index 000000000..2e77067dd --- /dev/null +++ b/core/general-server/op/general_remote_op.cpp @@ -0,0 +1,126 @@ +// Copyright (c) 2019 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. + +#include "core/general-server/op/general_remote_op.h" +#include +#include +#include "core/util/include/timer.h" + +// paddle inference 2.1 support: FLOAT32, INT64, INT32, UINT8, INT8 +// will support: FLOAT16 +#define BRPC_MAX_BODY_SIZE 2 * 1024 * 1024 * 1024 +const std::string LODABALANCE = ""; + +namespace baidu { +namespace paddle_serving { +namespace serving { + +using baidu::paddle_serving::Timer; +using baidu::paddle_serving::predictor::general_model::Tensor; +using baidu::paddle_serving::predictor::general_model::Request; +using baidu::paddle_serving::predictor::general_model::Response; + +brpc::Channel BRPCStub::brpc_channels[MAX_MP_NUM]; + +brpc::ChannelOptions BRPCStub::options; +std::atomic BRPCStub::inited(0); + +int GeneralRemoteOp::inference() { + LOG(INFO) << "Enter GeneralRemoteOp:inference()"; + int expected = 0; + std::vector op_address = address(); + if (BRPCStub::inited.compare_exchange_strong(expected, 1)) { + BRPCStub::options.protocol = "baidu_std"; + BRPCStub::options.connection_type = "short"; + BRPCStub::options.timeout_ms = 80000 /*milliseconds*/; + BRPCStub::options.max_retry = 100; + brpc::fLU64::FLAGS_max_body_size = BRPC_MAX_BODY_SIZE; + + LOG(ERROR) << "address size: " << op_address.size(); + for (int i = 0; i < op_address.size(); ++i) { + LOG(INFO) << i + 1 << " address is " << op_address[i].c_str(); + BRPCStub::brpc_channels[i].Init( + op_address[i].c_str(), LODABALANCE.c_str(), &BRPCStub::options); + } + + BRPCStub::inited++; + } + while (BRPCStub::inited < 2) { + } + + Timer timeline; + int64_t start = timeline.TimeStampUS(); + timeline.Start(); + VLOG(2) << "Going to run Remote inference"; + + Request* req = (Request*)(get_request_message()); + Response* res = mutable_data(); + uint64_t log_id = req->log_id(); + + brpc::Controller brpc_controllers[MAX_MP_NUM]; + brpc::CallId brpc_callids[MAX_MP_NUM]; + Response brpc_response_tmp; + + size_t i = 0; + // Init BRPC controllers, callids and stubs + for (i = 0; i < op_address.size(); ++i) { + brpc_controllers[i].set_log_id(log_id); + brpc_callids[i] = brpc_controllers[i].call_id(); + } + for (i = 0; i < op_address.size(); ++i) { + baidu::paddle_serving::predictor::general_model::GeneralModelService_Stub + stub(&BRPCStub::brpc_channels[i]); + LOG(INFO) << "Sended 1 request to Slave Sever " << i; + if (0 == i) { + stub.inference(&brpc_controllers[i], req, res, brpc::DoNothing()); + continue; + } + stub.inference( + &brpc_controllers[i], req, &brpc_response_tmp, brpc::DoNothing()); + } + + LOG(INFO) << "All request are sended, waiting for all responses."; + + // Wait RPC done. + for (i = 0; i < op_address.size(); ++i) { + brpc::Join(brpc_callids[i]); + } + + // Print RPC Results + for (i = 0; i < op_address.size(); ++i) { + LOG(INFO) << "brpc_controller_" << i + << " status:" << brpc_controllers[i].Failed(); + if (!brpc_controllers[i].Failed()) { + LOG(INFO) << "Received response from " + << brpc_controllers[i].remote_side() + << " Latency=" << brpc_controllers[i].latency_us() << "us"; + } else { + LOG(ERROR) << brpc_controllers[i].ErrorText(); + } + } + LOG(INFO) << "All brpc remote stubs joined done."; + + res->set_log_id(log_id); + res->set_profile_server(req->profile_server()); + int64_t end = timeline.TimeStampUS(); + res->add_profile_time(start); + res->add_profile_time(end); + + return 0; +} + +DEFINE_OP(GeneralRemoteOp); +} // namespace serving +} // namespace paddle_serving +} // namespace baidu diff --git a/core/general-server/op/general_remote_op.h b/core/general-server/op/general_remote_op.h new file mode 100644 index 000000000..94bfcb9f6 --- /dev/null +++ b/core/general-server/op/general_remote_op.h @@ -0,0 +1,58 @@ +// Copyright (c) 2019 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. + +#pragma once +#include +#include +#include +#include +#include +#include +#include +#include + +#include "core/general-server/general_model_service.pb.h" + +#include "core/sdk-cpp/builtin_format.pb.h" +#include "core/sdk-cpp/general_model_service.pb.h" +#include "core/sdk-cpp/include/common.h" +#include "core/sdk-cpp/include/predictor_sdk.h" + +#define MAX_MP_NUM 16 + +namespace baidu { +namespace paddle_serving { +namespace serving { + +using baidu::paddle_serving::predictor::general_model::Request; +using baidu::paddle_serving::predictor::general_model::Response; + +class GeneralRemoteOp + : public baidu::paddle_serving::predictor::OpWithChannel< + baidu::paddle_serving::predictor::general_model::Response> { + public: + DECLARE_OP(GeneralRemoteOp); + int inference(); +}; + +class BRPCStub { + public: + static brpc::Channel brpc_channels[MAX_MP_NUM]; + static brpc::ChannelOptions options; + static std::atomic inited; +}; + +} // namespace serving +} // namespace paddle_serving +} // namespace baidu diff --git a/core/general-server/proto/general_model_service.proto b/core/general-server/proto/general_model_service.proto index 904ffb97f..a5adeeb95 100755 --- a/core/general-server/proto/general_model_service.proto +++ b/core/general-server/proto/general_model_service.proto @@ -94,9 +94,9 @@ message Response { repeated int64 profile_time = 2; bool profile_server = 3; uint64 log_id = 4; + // Error code int32 err_no = 5; - // Error messages string err_msg = 6; }; diff --git a/core/predictor/common/constant.cpp b/core/predictor/common/constant.cpp index b0acb8869..5df873017 100644 --- a/core/predictor/common/constant.cpp +++ b/core/predictor/common/constant.cpp @@ -20,7 +20,7 @@ namespace predictor { DEFINE_bool(use_parallel_infer_service, false, ""); DEFINE_int32(el_log_level, 16, ""); -DEFINE_int32(idle_timeout_s, 16, ""); +DEFINE_int32(idle_timeout_s, 80, ""); DEFINE_int32(port, 8010, ""); DEFINE_string(workflow_path, "./conf", ""); DEFINE_string(workflow_file, "workflow.prototxt", ""); diff --git a/core/predictor/framework/bsf-inl.h b/core/predictor/framework/bsf-inl.h index fc468bfe9..f1885ae35 100644 --- a/core/predictor/framework/bsf-inl.h +++ b/core/predictor/framework/bsf-inl.h @@ -341,7 +341,7 @@ bool TaskExecutor::move_task_to_batch( LOG(INFO) << "Hit auto padding, merge " << padding_task_count << " tasks into 1 batch."; } - LOG(INFO) << "Number of tasks remaining in _task_queue is" + LOG(INFO) << "Number of tasks remaining in _task_queue is " << _task_queue.size(); return true; } diff --git a/core/sdk-cpp/proto/general_model_service.proto b/core/sdk-cpp/proto/general_model_service.proto index de9516259..5c17f955f 100755 --- a/core/sdk-cpp/proto/general_model_service.proto +++ b/core/sdk-cpp/proto/general_model_service.proto @@ -94,9 +94,9 @@ message Response { repeated int64 profile_time = 2; bool profile_server = 3; uint64 log_id = 4; + // Error code int32 err_no = 5; - // Error messages string err_msg = 6; }; diff --git a/core/sdk-cpp/proto/load_general_model_service.proto b/core/sdk-cpp/proto/load_general_model_service.proto index c58f79ecd..da731589c 100644 --- a/core/sdk-cpp/proto/load_general_model_service.proto +++ b/core/sdk-cpp/proto/load_general_model_service.proto @@ -21,6 +21,7 @@ option cc_generic_services = true; message RequestAndResponse { required int32 a = 1; required float b = 2; + required uint64 log_id = 3 [ default = 0 ]; }; service LoadGeneralModelService { diff --git a/doc/Offical_Docs/10-0_Terminology.md b/doc/Offical_Docs/10-0_Terminology.md new file mode 100644 index 000000000..329002b0a --- /dev/null +++ b/doc/Offical_Docs/10-0_Terminology.md @@ -0,0 +1 @@ +# 名词术语解释 diff --git a/doc/Offical_Docs/11-0_Contributors.md b/doc/Offical_Docs/11-0_Contributors.md new file mode 100644 index 000000000..905da9793 --- /dev/null +++ b/doc/Offical_Docs/11-0_Contributors.md @@ -0,0 +1,97 @@ +# 开发者贡献 + +- [贡献代码流程](#1) + - [创建个人仓库](#1.1) + - [本地克隆仓库和分支](#1.2) + - [提交代码](#1.3) + - [通过 CI 验证](#1.4) + - [Code Review](#1.5) + - [代码合入](#1.6) +- [致谢开发者](#2) + + + +## 贡献代码流程 + +Paddle Serving 使用 Git 分支模式。通常，按以下步骤贡献代码： + + + +**一.创建个人仓库** + +Paddle Serving 社区一直在快速发展，每个人都写到官方回购中是没有意义的。所以，请先 `fork` 出个人仓库，并提交 `Pull Requests`。`fork` 个人仓库，只需前往 [Serving](https://github.com/PaddlePaddle/Serving) 页面并单击右上角 ["Fork"](https://github.com/PaddlePaddle/Serving/fork)。 + + + +**二.本地克隆仓库和分支** + +创建个人仓库后，`clone` 个人仓库到本地计算机，默认创建本地 `develop` 分支。 +```bash +git clone https://github.com/your-github-account/Serving +``` + + + +**三.提交代码** + +本地修改代码并验证后，准备提交代码。在提交代码前请安装 [`pre-commit`](http://pre-commit.com/)、cpplint 和 pylint。 +```bash +pip3 install pre-commit +pre-commit install + +pip3 install cpplint pylint +``` +在提交代码时，会进行代码格式检查和修正，待所有检查都通过后，方可提交。 +```shell + $ git commit + CRLF end-lines remover...............................(no files to check)Skipped + yapf.....................................................................Passed + Check for added large files..............................................Passed + Check for merge conflicts................................................Passed + Check for broken symlinks................................................Passed + Detect Private Key...................................(no files to check)Skipped + Fix End of Files.........................................................Passed + clang-format.............................................................Passed + cpplint..................................................................Passed + pylint...................................................................Passed + copyright_checker........................................................Passed + [my-cool-stuff c703c041] add test file + 1 file changed, 0 insertions(+), 0 deletions(-) + create mode 100644 233 +``` + +运行代码提交命令，提交到个人仓库，再通过 Github 页面创建一个 `pull request` 提交到 Paddel Serving 主仓库。 +```bash +git push origin develop +``` + + + +**四.通过 CI 验证** + +所有提交到 Paddle Serving 主仓库的 `pull request` 都会运行 `py36`、`py38`、`py39`的所有 CI 测试用例。全部通过后才能合入。 + + + +**五.Code Review** + +所有提交的代码要经过管理员的评审，至少通过2人评审后方可合入。 + + + +**六.代码合入** + +待通过全部 CI 验证，并且完成 Code Review 和修改后，由仓库管理员合入代码。 + + + +## 致谢开发者 + +- 感谢 [@loveululu](https://github.com/loveululu) 提供 Cube python API +- 感谢 [@EtachGu](https://github.com/EtachGu) 更新 docker 使用命令 +- 感谢 [@BeyondYourself](https://github.com/BeyondYourself) 提供grpc教程，更新FAQ教程，整理文件目录。 +- 感谢 [@mcl-stone](https://github.com/mcl-stone) 提供faster rcnn benchmark脚本 +- 感谢 [@cg82616424](https://github.com/cg82616424) 提供unet benchmark脚本和修改部分注释错误 +- 感谢 [@cuicheng01](https://github.com/cuicheng01) 提供PaddleClas的11个模型 +- 感谢 [@Jiaqi Liu](https://github.com/LiuChiachi) 新增list[str]类型输入的预测支持 +- 感谢 [@Bin Lu](https://github.com/Intsigstephon) 提供PP-Shitu C++模型示例 diff --git a/doc/Offical_Docs/11_Lookup_CN.md b/doc/Offical_Docs/11_Lookup_CN.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/doc/Offical_Docs/12-0_FAQ_CN.md b/doc/Offical_Docs/12-0_FAQ_CN.md new file mode 100644 index 000000000..59455a77c --- /dev/null +++ b/doc/Offical_Docs/12-0_FAQ_CN.md @@ -0,0 +1,328 @@ +# 常见问题与解答 + +常见问题解答分为8大类问题： +- [版本升级问题](#1) +- [基础知识](#2) +- [安装问题](#3) +- [编译问题](#4) +- [环境问题](#5) +- [部署问题](#6) +- [预测问题](#7) +- [日志排查](#8) + + + +## 版本升级问题 + +#### Q: 从 `v0.6.x` 升级到 `v0.7.0` 版本时，运行 Python Pipeline 程序时报错信息如下： +``` +Failed to predict: (data_id=1 log_id=0) [det|0] Failed to postprocess: postprocess() takes 4 positional arguments but 5 were given +``` +**A:** 在服务端程序（例如 web_service.py)的postprocess函数定义中增加参数data_id，改为 def postprocess(self, input_dicts, fetch_dict, **data_id**, log_id) 即可。 + + + +## 基础知识 + +#### Q: Paddle Serving 、Paddle Inference、PaddleHub Serving 三者的区别及联系？ + +**A:** Paddle Serving 是远程服务，即发起预测的设备（手机、浏览器、客户端等）与实际预测的硬件不在一起。 paddle inference 是一个 library，适合嵌入到一个大系统中保证预测效率，Paddle Serving 调用 paddle inference 做远程服务。paddlehub serving 可以认为是一个示例，都会使用 Paddle Serving 作为统一预测服务入口。如果在 web 端交互，一般是调用远程服务的形式，可以使用 Paddle Serving 的 web service 搭建。 + +#### Q: Paddle Serving 支持哪些数据类型? + +**A:** 在 protobuf 定义中 `feed_type` 和 `fetch_type` 编号与数据类型对应如下，完整信息可参考[保存用于 Serving 部署的模型参数](./5-1_Save_Model_Params_CN.md) + +| 类型 | 类型值 | +|------|------| +| int64 | 0 | +| float32 |1 | +| int32 | 2 | +| float64 | 3 | +| int16 | 4 | +| float16 | 5 | +| bfloat16 | 6 | +| uint8 | 7 | +| int8 | 8 | +| bool | 9 | +| complex64 | 10 +| complex128 | 11 | + +#### Q: Paddle Serving 是否支持 Windows 和 Linux 原生环境部署？ + +**A:** 安装 `Linux Docker`，在 Docker 中部署 Paddle Serving，参考[安装指南](./2-0_Index_CN.md) + +#### Q: Paddle Serving 如何修改消息大小限制 + +**A:** Server 和 Client 通过修改 `FLAGS_max_body_size` 参数来扩大数据量限制，单位为字节，默认为64MB + +#### Q: Paddle Serving 客户端目前支持哪些开发语言？ + +**A:** 提供 Python、C++ 和 Java SDK + +#### Q: Paddle Serving 支持哪些网络协议？ + +**A:** C++ Serving 同时支持 HTTP、gRPC 和 bRPC 协议。其中 HTTP 协议既支持 HTTP + Json 格式，同时支持 HTTP + proto 格式。完整信息请阅读[C++ Serving 通讯协议](./6-2_Cpp_Serving_Protocols_CN.md)；Python Pipeline 支持 HTTP 和 gRPC 协议，更多信息请阅读[Python Pipeline 框架设计](./6-2_Cpp_Serving_Protocols_CN.md) + + + +## 安装问题 + +#### Q: `pip install` 安装 `python wheel` 过程中，报错信息如何修复？ + +``` +Collecting opencv-python + Getting requirements to build wheel ... error + ERROR: Command errored out with exit status 1: + command: /home/work/Python-2.7.17/build/bin/python /home/work/Python-2.7.17/build/lib/python2.7/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmpLiweA9 + cwd: /tmp/pip-install-_w6AUI/opencv-python + Complete output (22 lines): + Traceback (most recent call last): + File "setup.py", line 99, in main + % {"ext": re.escape(sysconfig.get_config_var("EXT_SUFFIX"))} + File "/home/work/Python-2.7.17/build/lib/python2.7/re.py", line 210, in escape + s = list(pattern) + TypeError: 'NoneType' object is not iterable +``` + +**A:** 指定 `opencv-python` 安装版本4.2.0.32，运行 `pip3 install opencv-python==4.2.0.32` + +#### Q: pip3 install wheel包过程报错，详细信息如下： + +``` + Complete output from command python setup.py egg_info: + Found cython-generated files... + error in grpcio setup command: 'install_requires' must be a string or list of strings containing valid project/version requirement specifiers; Expected ',' or end-of-list in futures>=2.2.0; python_version<'3.2' at ; python_version<'3.2' + + ---------------------------------------- +Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-install-taoxz02y/grpcio/ +``` + +**A:** 需要升级 pip3 版本，再重新执行安装命令。 + +``` +pip3 install --upgrade pip +pip3 install --upgrade setuptools +``` + +#### Q: 运行过程中出现 `No module named xxx` 错误，信息如下： + +``` +Traceback (most recent call last): + File "../../deploy/serving/test_client.py", line 18, in + from paddle_serving_app.reader import * + File "/usr/local/python2.7.15/lib/python2.7/site-packages/paddle_serving_app/reader/__init__.py", line 15, in + from .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, Base64ToImage + File "/usr/local/python2.7.15/lib/python2.7/site-packages/paddle_serving_app/reader/image_reader.py", line 24, in + from shapely.geometry import Polygon +ImportError: No module named shapely.geometry +``` + +**A:** 有2种方法，第一种通过 pip3 安装shapely，第二种通过 pip3 安装所有依赖组件[requirements.txt](https://github.com/PaddlePaddle/Serving/blob/develop/python/requirements.txt)。 + +``` +方法1： +pip3 install shapely==1.7.0 + +方法2： +pip3 install -r python/requirements.txt +``` + + + +## 编译问题 + +#### Q: 如何使用自己编译的 Paddle Serving 进行预测？ + +**A:** 编译 Paddle Serving 请阅读[编译 Serving](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Compile_CN.md)。 + +#### Q: 使用 Java 客户端，mvn compile 过程出现 "No compiler is provided in this environment. Perhaps you are running on a JRE rather than a JDK?" 错误 + +**A:** 没有安装 JDK，或者 `JAVA_HOME` 路径配置错误（正确配置是 JDK 路径，常见错误配置成 JRE 路径，例如正确路径参考 `JAVA_HOME="/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.262.b10-0.el7_8.x86_64/"`）。Java JDK 安装参考 https://segmentfault.com/a/1190000015389941。 + +#### Q: 编译过程报错 /usr/local/bin/ld: cannot find -lbz2 +``` +/usr/local/bin/ld: cannot find -lbz2 +collect2: error: ld returned 1 exit status +core/general-server/CMakeFiles/serving.dir/build.make:276: recipe for target 'core/general-server/serving' failed +make[2]: *** [core/general-server/serving] Error 1 +CMakeFiles/Makefile2:1181: recipe for target 'core/general-server/CMakeFiles/serving.dir/all' failed +make[1]: *** [core/general-server/CMakeFiles/serving.dir/all] Error 2 +Makefile:129: recipe for target 'all' failed +make: *** [all] Error 2 +``` + +**A:** Ubuntu 系统运行命令安装 libbz2: `apt install libbz2-dev`， + + + +## 环境问题 + +#### Q：程序运行出现 `CXXABI` 相关错误。 + +错误原因是编译 Python 使用的 GCC 版本和编译 Serving 的 GCC 版本不一致。对于 Docker 用户，推荐使用[Docker容器](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Docker_Images_CN.md)，由于 Docker 容器内的 Python 版本与 Serving 在发布前都做过适配，这样就不会出现类似的错误。 + +推荐使用 GCC 8.2 预编译包 [Python3.6](https://paddle-serving.bj.bcebos.com/others/Python3.6.10-gcc82.tar) 。下载解压后，需要将对应的目录设置为 `PYTHONROOT`，并设置 `PATH` 和 `LD_LIBRARY_PATH`。 + +```bash +export PYTHONROOT=/path/of/python # 对应解压后的Python目录 +export PATH=$PYTHONROOT/bin:$PATH +export LD_LIBRARY_PATH=$PYTHONROOT/lib:$LD_LIBRARY_PATH +``` + +#### Q：遇到 `libstdc++.so.6` 的版本不够的问题 + +触发该问题的原因在于，编译 Paddle Serving 相关可执行程序和动态库，所采用的是 GCC 8.2(Cuda 9.0 和 10.0 的 Server 可执行程序受限 CUDA 兼容性采用 GCC 4.8编译)。Python 在调用的过程中，有可能链接到了其他 GCC 版本的 `libstdc++.so`。 需要做的就是受限确保所在环境具备 GCC 8.2，其次将 GCC8.2 的`libstdc++.so.*`拷贝到某个目录例如`/home/libstdcpp` 下。最后 `export LD_LIBRARY_PATH=/home/libstdcpp:$LD_LIBRARY_PATH` 即可。 + +#### Q: 遇到 `OPENSSL_1.0.1EC` 符号找不到的问题。 + +目前 Serving 的可执行程序和客户端动态库需要链接 `1.0.2k` 版本的 `openssl` 动态库。如果环境当中没有，可以执行 + +```bash +wget https://paddle-serving.bj.bcebos.com/others/centos_ssl.tar && \ + tar xf centos_ssl.tar && rm -rf centos_ssl.tar && \ + mv libcrypto.so.1.0.2k /usr/lib/libcrypto.so.1.0.2k && mv libssl.so.1.0.2k /usr/lib/libssl.so.1.0.2k && \ + ln -sf /usr/lib/libcrypto.so.1.0.2k /usr/lib/libcrypto.so.10 && \ + ln -sf /usr/lib/libssl.so.1.0.2k /usr/lib/libssl.so.10 && \ + ln -sf /usr/lib/libcrypto.so.10 /usr/lib/libcrypto.so && \ + ln -sf /usr/lib/libssl.so.10 /usr/lib/libssl.so +``` + +其中 `/usr/lib` 可以换成其他目录，并确保该目录在 `LD_LIBRARY_PATH` 下。 + +### GPU相关环境问题 + +#### Q：需要做哪些检查确保 Serving 可以运行在 GPU 环境 + +**注：如果是使用 Serving 提供的镜像不需要做下列检查，如果是其他开发环境可以参考以下指导。** + +首先需要确保`nvidia-smi`可用，其次需要确保所需的动态库so文件在`LD_LIBRARY_PATH`所在的目录（包括系统lib库）。 + +（1）CUDA 显卡驱动：文件名通常为 `libcuda.so.$DRIVER_VERSION` 例如驱动版本为440.10.15，文件名就是 `libcuda.so.440.10.15`。 + +（2）CUDA 和 cuDNN 动态库：文件名通常为 `libcudart.so.$CUDA_VERSION`，和 `libcudnn.so.$CUDNN_VERSION`。例如 CUDA9 就是 `libcudart.so.9.0`，Cudnn7就是 `libcudnn.so.7`。CUDA 和 cuDNN 与 Serving 的版本匹配参见[Serving所有镜像列表](Docker_Images_CN.md#%E9%99%84%E5%BD%95%E6%89%80%E6%9C%89%E9%95%9C%E5%83%8F%E5%88%97%E8%A1%A8). + + (3) CUDA 10.1及更高版本需要 TensorRT。安装 TensorRT 相关文件的脚本参考 [install_trt.sh](../tools/dockerfiles/build_scripts/install_trt.sh). + + + +## 部署问题 + +#### Q: GPU 环境运行 Serving 报错，GPU count is: 0。 + +``` +terminate called after throwing an instance of 'paddle::platform::EnforceNotMet' +what(): +-------------------------------------------- +C++ Call Stacks (More useful to developers): +-------------------------------------------- +0 std::string paddle::platform::GetTraceBackString(std::string const&, char const*, int) +1 paddle::platform::SetDeviceId(int) +2 paddle::AnalysisConfig::fraction_of_gpu_memory_for_pool() const +3 std::unique_ptr > paddle::CreatePaddlePredictor(paddle::AnalysisConfig const&) +4 std::unique_ptr > paddle::CreatePaddlePredictor(paddle::AnalysisConfig const&) +---------------------- +Error Message Summary: +---------------------- +InvalidArgumentError: Device id must be less than GPU count, but received id is: 0. GPU count is: 0. +[Hint: Expected id < GetCUDADeviceCount(), but received id:0 >= GetCUDADeviceCount():0.] at (/home/scmbuild/workspaces_cluster.dev/baidu.lib.paddlepaddle/baidu/lib/paddlepaddle/Paddle/paddle/fluid/platform/gpu_info.cc:211) +``` + +**A:** 原因是 `libcuda.so` 没有链接成功。首先在机器上找到 `libcuda.so`，使用 `ldd` 命令检查 libnvidia 版本与 nvidia-smi 中版本是否一致（libnvidia-fatbinaryloader.so.418.39，与NVIDIA-SMI 418.39 Driver Version: 418.39），然后用 export 导出 `libcuda.so` 的路径即可（例如 libcuda.so 在 /usr/lib64/，export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib64/） + +#### Q: 遇到 GPU not found, please check your environment or use cpu version by "pip install paddle_serving_server" + +**A:** 检查环境中是否有N卡：`ls /dev/ | grep nvidia` + +#### Q: Paddle Serving 支持哪些镜像环境？ + +**A:** 支持 CentOS 和 Ubuntu 环境镜像 ，完整列表查阅[这里](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Docker_Images_CN.md) + +#### Q: Paddle Serving 是否支持本地离线安装 + +**A:** 支持离线部署，需要把一些相关的[依赖包](https://github.com/PaddlePaddle/Serving/blob/develop/doc/Compile_CN.md) 提前准备安装好 + +#### Q: Docker 中启动 Server IP地址 127.0.0.1 与 0.0.0.0 差异 +**A:** 必须将容器的主进程设置为绑定到特殊的 `0.0.0.0` 表示“所有接口”地址，否则它将无法从容器外部访问。在 Docker 中 `127.0.0.1` 仅代表“这个容器”，而不是“这台机器”。如果您从容器建立到 `127.0.0.1` 的出站连接，它将返回到同一个容器；如果您将服务器绑定到 `127.0.0.1`，接收不到来自外部的连接。 + + + +## 预测问题 + +#### Q: 使用 GPU 第一次预测时特别慢，如何调整 RPC 服务的等待时间避免超时？ + +**A:** GPU 第一次预测需要初始化。使用 `set_rpc_timeout_ms` 设置更长的等待时间，单位为毫秒，默认时间为20秒。 + +示例： + +``` +from paddle_serving_client import Client + +client = Client() +client.load_client_config(sys.argv[1]) +client.set_rpc_timeout_ms(100000) +client.connect(["127.0.0.1:9393"]) +``` +#### Q: 执行 GPU 预测时遇到 `ExternalError: Cudnn error, CUDNN_STATUS_BAD_PARAM at (../batch_norm_op.cu:198)`错误 + +**A:** 将 cuDNN 的 lib64路径添加到 `LD_LIBRARY_PATH`，安装自 `pypi` 的 Paddle Serving 中 `post9` 版本使用的是 `cuDNN 7.3,post10` 使用的是 `cuDNN 7.5。如果是使用自己编译的 Paddle Serving，可以在 `log/serving.INFO` 日志文件中查看对应的 cuDNN 版本。 + +#### Q: 执行 GPU 预测时遇到 `Error: Failed to find dynamic library: libcublas.so` + +**A:** 将 CUDA 的 lib64路径添加到 `LD_LIBRARY_PATH`, post9 版本的 Paddle Serving 使用的是 `cuda 9.0，post10` 版本使用的 `cuda 10.0`。 + +#### Q: Client 的 `fetch var`变量名如何设置 + +**A:** 通过[保存用于 Serving 部署的模型参数](https://github.com/PaddlePaddle/Serving/blob/v0.8.3/doc/Save_EN.md) 生成配置文件 `serving_server_conf.prototxt`，获取需要的变量名。 + +#### Q: 如何使用多语言客户端 + +**A:** 多语言客户端要与多语言服务端配套使用。当前版本下（0.8.3） + +#### Q: 如何在 Windows 下使用 Paddle Serving + +**A:** 在 Windows 上可以运行多语言 RPC 客户端，或使用 HTTP 方式访问。 + +#### Q: 报错信息 `libnvinfer.so: cannot open shared object file: No such file or directory)` + + **A:** 没有安装 TensorRT，安装 TensorRT 请参考链接: https://blog.csdn.net/hesongzefairy/article/details/105343525 + + + +## 日志排查 + +#### Q: 部署和预测中的日志信息在哪里查看？ + +**A:** Server 的日志分为两部分，一部分打印到标准输出，一部分打印到启动服务时的目录下的 `log/serving.INFO` 文件中。 +Client 的日志直接打印到标准输出。 +通过在部署服务之前 'export GLOG_v=3'可以输出更为详细的日志信息。 + +#### Q: C++ Serving 启动成功后，日志文件在哪里，在哪里设置日志级别？ + +**A:** C++ Serving 服务的所有日志在程序运行的当前目录的`log/`目录下，分为 serving.INFO、serving.WARNING 和 serving.ERROR 文件。 +1)警告是 `glog` 组件打印的，告知 `glog` 初始化之前日志打印在 STDERR； +2)一般采用 `GLOG_v` 方式启动服务同时设置日志级别。 + +例如： +``` +GLOG_v=2 python -m paddle_serving_server.serve --model xxx_conf/ --port 9999 +``` + +#### Q: Python Pipeline 启动成功后，日志文件在哪里，在哪里设置日志级别？ + +**A:** Python Pipeline 服务的日志信息请阅读[Python Pipeline 设计](./7-1_Python_Pipeline_Design_CN.md) 第三节服务日志。 + +#### Q: （GLOG_v=2下）Server 日志一切正常，但 Client 始终得不到正确的预测结果 + +**A:** 可能是配置文件有问题，检查下配置文件（is_load_tensor，fetch_type等有没有问题） + +#### Q: 如何给 Server 传递 Logid + +**A:** Logid 默认为0，Client 通过在 predict 函数中指定 log_id 参数 + +#### Q: C++ Serving 出现问题如何调试和定位 + +**A:** 推荐您使用 GDB 进行定位和调试，如果您使用 Serving 的 Docker，在启动容器时候，需要加上 `docker run --privileged `参数，开启特权模式，这样才能在 docker 容器中使用 GDB 定位和调试 +如果 C++ Serving 出现 `core dump`，一般会生成 core 文件，若没有，运行 `ulimit -c unlimited`命令开启core dump。 +使用 GDB 调试 core 文件的方法为：`gdb <可执行文件> `，进入后输入 `bt` 指令显示栈信息。 + +注意：可执行文件路径是 C++ bin 文件的路径，而不是 python 命令，一般为类似下面的这种 `/usr/local/lib/python3.6/site-packages/paddle_serving_server/serving-gpu-102-0.7.0/serving` diff --git a/doc/Offical_Docs/3-0_QuickStart_Int_CN.md b/doc/Offical_Docs/3-0_QuickStart_Int_CN.md index 9562c363e..827982e0c 100644 --- a/doc/Offical_Docs/3-0_QuickStart_Int_CN.md +++ b/doc/Offical_Docs/3-0_QuickStart_Int_CN.md @@ -1,10 +1,9 @@ # 快速开始案例部署 您可以通过以下 Paddle Serving 快速开始案例，分别了解到 C++ Serving 与 Python Pipeline 2种框架的部署方法。 -- [使用 C++ Serving 部署 Resnet50 模型案例]() -- [使用 Python Pipeline 部署 OCR 模型案例]() +- [使用 C++ Serving 部署 Resnet50 模型案例](./3-1_QuickStart_Cpp_Resnet_CN.md) +- [使用 Python Pipeline 部署 OCR 模型案例](./3-2_QuickStart_Pipeline_OCR_CN.md) 通过阅读以下内容掌握 Paddle Serving 基础功能以及2种框架特性和使用指南： -- [基础功能]() -- [进阶 C++ Serving 介绍]() -- [进阶 Python Pipeline 介绍]() +- [进阶 C++ Serving 介绍](./doc/Offical_Docs/6-0_C++_Serving_Advanced_Introduction_CN.md) +- [进阶 Python Pipeline 介绍](./7-0_Python_Pipeline_Int_CN.md) diff --git a/doc/Offical_Docs/3-1_QuickStart_Cpp_Resnet_CN.md b/doc/Offical_Docs/3-1_QuickStart_Cpp_Resnet_CN.md new file mode 100644 index 000000000..115df510d --- /dev/null +++ b/doc/Offical_Docs/3-1_QuickStart_Cpp_Resnet_CN.md @@ -0,0 +1,111 @@ +# C++ Serving 快速部署案例 + +- [模型介绍](#1) +- [部署步骤](#2) + - [2.1 保存模型](#2.1) + - [2.2 保存 Serving 部署的模型参数](#2.2) + - [2.3 启动服务](#2.3) + - [2.4 启动客户端](#2.4) + + + +## 模型介绍 +残差网络（ResNet)于2015年被提出，摘得 ImageNet 榜单5项第一，成绩大幅领先第二名，是 CNN 图像史上的一个里程碑。 + +从经验上看，网络结构层数越多，有利于复杂特征的提取，从理论上讲会取得更好的结果。但是，随着网络层数的增加，准确率会趋于饱和甚至会下降，称为退化问题（Degradation problem）。其根本原因是深层网络出现梯度消失或者梯度爆炸的问题。残差网络利用短路机制加入了残差单元，解决了退化问题。 + +ResNet 网络是参考了 VGG19 网络，加入残差单元，ResNet50 有50层网络。 + + + +## 部署步骤 + +前提条件是你已完成[环境安装](./2-0_Index_CN.md)步骤，并已验证环境安装成功，此处不在赘述。 + +克隆 Serving 仓库后，进入 `examples/C++/PaddleClas/resnet_50_vd` 目录下，已提供程序、配置和性能测试脚本。 +``` +git clone https://github.com/PaddlePaddle/Serving +``` + +按以下5个步骤操作即可实现模型部署。 +- 一.获取模型 +- 二.保存 Serving 部署的模型参数 +- 三.启动服务 +- 四.启动客户端 + + + +**一.获取模型** + +下载 `ResNet50_vd` 的 推理模型，更多模型信息请阅读[ImageNet 预训练模型库](https://github.com/PaddlePaddle/PaddleClas/blob/8fa820f5c81edb1e7a2b222306a307bc27bff90f/docs/zh_CN/algorithm_introduction/ImageNet_models.md) +``` +wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar && tar xf ResNet50_vd_infer.tar + +``` + + + +**二.保存 Serving 部署的模型参数** + +用 `paddle_serving_client` 把下载的推理模型转换成易于 Serving 部署的模型格式，完整信息请参考 [保存用于 Serving 部署的模型参数](./5-1_Save_Model_Params_CN.md)。 + +``` +python3 -m paddle_serving_client.convert --dirname ./ResNet50_vd_infer/ \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --serving_server ./ResNet50_vd_serving/ \ + --serving_client ./ResNet50_vd_client/ +``` + +保存参数后，会在当前文件夹多出 `ResNet50_vd_serving` 和 `ResNet50_vd_client` 的文件夹，分别用户服务端和客户端。 +``` +├── daisy.jpg +├── http_client.py +├── imagenet.label +├── ResNet50_vd_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ResNet50_vd_infer +│   ├── inference.pdiparams +│   ├── inference.pdiparams.info +│   └── inference.pdmodel +├── ResNet50_vd_serving +│   ├── fluid_time_file +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── rpc_client.py +``` + + + +**三.启动服务** + +C++ Serving 服务可以指定一个网络端口同时接收 HTTP、gRPC 和 bRPC 请求。命令参数 `--model` 指定模型路径，`--gpu_ids` 指定 GPU 卡，`--port` 指定端口。 + +``` +python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --gpu_ids 0 --port 9394 +``` + + + +**四.启动客户端** + +HTTP 客户端程序 `http_client.py` 创建请求参数，向服务端发起 HTTP 请求。 + +``` +python3 http_client.py +``` + +RPC 客户端程序 `rpc_client.py` 创建请求参数，向服务端发起 gRPC 请求。 + +``` +python3 rpc_client.py +``` + +成功运行后，模型预测的结果会打印如下： + +``` +prediction: daisy, probability: 0.9341399073600769 +``` diff --git a/doc/Offical_Docs/3-2_QuickStart_Pipeline_OCR_CN.md b/doc/Offical_Docs/3-2_QuickStart_Pipeline_OCR_CN.md index 3180780a3..d7772a2be 100644 --- a/doc/Offical_Docs/3-2_QuickStart_Pipeline_OCR_CN.md +++ b/doc/Offical_Docs/3-2_QuickStart_Pipeline_OCR_CN.md @@ -1,7 +1,19 @@ # Python Pipeline 快速部署案例 +- [模型介绍](#1) +- [部署步骤](#2) + - [获取模型与保存模型参数](#2.1) + - [保存 Serving 部署的模型参数](#2.2) + - [下载测试数据集（可选）](#2.3) + - [修改配置文件（可选）](#2.4) + - [代码与配置信息绑定](#2.5) + - [启动服务与验证](#2.6) + + Python Pipeline 框架使用 Python 语言开发，是一套端到端多模型组合服务编程框架，旨在降低编程门槛，提高资源使用率（尤其是GPU设备），提升整体服务的预估效率。详细设计参考[ Python Pipeline 设计与使用]() + + ## 模型介绍 OCR 技术一般指光学字符识别。 OCR（Optical Character Recognition，光学字符识别）是指电子设备（例如扫描仪或数码相机）检查纸上打印的字符，通过检测暗、亮的模式确定其形状，然后用字符识别方法将形状翻译成计算机文字的过程。 @@ -16,8 +28,9 @@ PaddleOCR 提供的 PP-OCR 系列模型覆盖轻量级服务端、轻量级移 | 中英文超轻量移动端模型 | 9.4M | ch_ppocr_mobile_v2.0_xx | 移动端| | 中英文通用服务端模型 | 143.4M | ch_ppocr_server_v2.0_xx | 服务器端 | + -## 模型步骤 +## 部署步骤 前提条件是你已完成[环境安装]()步骤，并已验证环境安装成功，此处不在赘述。 @@ -25,13 +38,20 @@ PaddleOCR 提供的 PP-OCR 系列模型覆盖轻量级服务端、轻量级移 ``` git clone https://github.com/PaddlePaddle/Serving ``` -按以下5个步骤操作即可实现 OCR 示例部署。 +通过6个步骤操作即可实现 OCR 示例部署。 +- 一.获取模型 +- 二.保存 Serving 部署的模型参数 +- 三.下载测试数据集（可选） +- 四.修改 `config.yml` 配置（可选） +- 五.代码与配置信息绑定 +- 六.启动服务与验证 + + -**一.获取模型** +**一.获取模型与保存模型参数** 本章节选用中英文超轻量模型 ch_PP-OCRv2_xx 制作部署案例，模型体积小，效果很好，属于性价比很高的选择。 -为了节省大家的时间，已将预训练模型使用[保存模型服务化参数]()方法打包成压缩包，下载并解压即可使用。如你自训练的模型需经过保存模型服务化参数步骤才能服务化部署。 ``` python3 -m paddle_serving_app.package --get_model ocr_rec tar -xzvf ocr_rec.tar.gz @@ -39,15 +59,27 @@ python3 -m paddle_serving_app.package --get_model ocr_det tar -xzvf ocr_det.tar.gz ``` -**二.下载测试数据集（可选）** -第二步，下载测试图片集，如使用自有测试数据集，可忽略此步骤。 + + +**二.保存 Serving 部署的模型参数** + +为了节省大家的时间，已将预训练模型使用[保存用于 Serving 部署的模型参数](./5-1_Save_Model_Params_CN.md)方法打包成压缩包，下载并解压即可使用。如你自训练的模型需经过保存模型服务化参数步骤才能服务化部署。 + + + +**三.下载测试数据集（可选）** + +下载测试图片集，如使用自有测试数据集，可忽略此步骤。 ``` wget --no-check-certificate https://paddle-serving.bj.bcebos.com/ocr/test_imgs.tar tar xf test_imgs.tar ``` -**三.修改 Config.yml 配置（可选）** -第三步，通过修改配置文件设置服务、图、OP 级别属性。如果使用默认配置，此步骤可忽略。 + + +**四.修改配置文件（可选）** + +修改配置文件 `config.yml` 设置服务、图、OP 级别属性。如果使用默认配置，此步骤可忽略。 由于配置项较多，仅重点介绍部分核心选项的使用，完整配置选项说明可参考[ 配置说明]() ``` @@ -155,8 +187,10 @@ op: #min_subgraph_size: 3 ``` + + +**五.代码与配置信息绑定** -**四.代码与配置信息绑定 ** 第四步，实现代码和配置文件 Config.yml 绑定，以及设置多模型组合关系。具体包括： 1. 重写模型前后处理： @@ -199,15 +233,52 @@ ocr_service.prepare_pipeline_config("config.yml") ocr_service.run_service() ``` + -**五.启动服务与验证** +**六.启动服务与验证** -运行程序 `web_service.py` 启动服务端 +启动服务前，可看到程序路径下所有文件路径如下： +``` +. +├── 7.jpg +├── benchmark.py +├── benchmark.sh +├── config.yml +├── imgs +│   └── ggg.png +├── ocr_det_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ocr_det_model +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── ocr_rec_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ocr_rec_model +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── pipeline_http_client.py +├── pipeline_rpc_client.py +├── ppocr_keys_v1.txt +├── ProcessInfo.json +├── README_CN.md +├── README.md +└── web_service.py +``` + +运行服务程序 `web_service.py` 启动服务端，接收客户端请求，采用图执行引擎执行推理预测。 ``` # Run Server python3 web_service.py &>log.txt & ``` +客户端程序 `pipeline_http_client.py` 注册服务端地址，并发送客户端请求。 + 启动客户端前，要确认 URL://{ip}:{port}/{name}/{method} 。本项目中 {name} 即是 web_service.py 中 OcrService name 参数 "ocr"。 {method} 默认为 "prediction" ``` diff --git a/doc/Offical_Docs/4-0_ModelZoo_CN.md b/doc/Offical_Docs/4-0_ModelZoo_CN.md index b16103530..80f4b31e8 100644 --- a/doc/Offical_Docs/4-0_ModelZoo_CN.md +++ b/doc/Offical_Docs/4-0_ModelZoo_CN.md @@ -1,8 +1,28 @@ # 模型库 +- [模型分类](#1) + - [1.1 图像分类与识别](#1.1) + - [1.2 文本类](#1.2) + - [1.3 推荐系统](#1.3) + - [1.4 人脸识别](#1.4) + - [1.5 目标检测](#1.5) + - [1.6 文字识别](#1.6) + - [1.7 图像分割](#1.7) + - [1.8 关键点检测](#1.8) + - [1.9 视频理解](#1.9) +- [模型示例库](#2) + Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例。 -**一.图像分类与识别**模型部署示例请参阅下表： + + +## 模型分类 + + + +**一.图像分类与识别** + +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 图像识别 |pp_shitu | PaddleClas | [C++ Serving](../examples/C++/PaddleClas/pp_shitu) | [.tar.gz](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/serving/pp_shitu.tar.gz) | @@ -22,7 +42,11 @@ Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例 --- -**二.文本类**模型部署示例请参阅下表： + + +**二.文本类** + +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 文本生成 | bert_chinese_L-12_H-768_A-12 | PaddleNLP | [C++ Serving](../examples/C++/PaddleNLP/bert)
[Pipeline Serving](../examples/Pipeline/PaddleNLP/bert) | [.tar.gz](https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticModel/bert_chinese_L-12_H-768_A-12.tar.gz) | @@ -33,8 +57,11 @@ Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例 | 抽取文本向量| In-batch Negatives | PaddleNLP | [Pipeline Serving](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/applications/neural_search/recall/in_batch_negative) | [model](https://bj.bcebos.com/v1/paddlenlp/models/inbatch_model.zip) | --- + + +**三.推荐系统** -**三.推荐系统**模型部署示例请参阅下表： +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | CTR预估 | criteo_ctr | PaddleRec | [C++ Serving](../examples/C++/PaddleRec/criteo_ctr) | [.tar.gz](https://paddle-serving.bj.bcebos.com/criteo_ctr_example/criteo_ctr_demo_model.tar.gz) | @@ -42,15 +69,21 @@ Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例 | 内容推荐 | wide&deep | PaddleRec | [C++ Serving](https://github.com/PaddlePaddle/PaddleRec/blob/release/2.1.0/doc/serving.md) | [model](https://github.com/PaddlePaddle/PaddleRec/blob/release/2.1.0/models/rank/wide_deep/README.md) | --- + -**四.人脸识别**模型部署示例请参阅下表： +**四.人脸识别** + +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 人脸识别|blazeface | PaddleDetection | [C++ Serving](../examples/C++/PaddleDetection/blazeface) | [.tar.gz](https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ObjectDetection/blazeface.tar.gz) |C++ Serving| --- + + +**五.目标检测** -**五.目标检测**模型部署示例请参阅下表： +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 目标检测 |cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco | PaddleDetection | [C++ Serving](../examples/C++/PaddleDetection/cascade_rcnn) | [.tar.gz](https://paddle-serving.bj.bcebos.com/pddet_demo/cascade_mask_rcnn_r50_vd_fpn_ssld_2x_coco_serving.tar.gz) | @@ -65,8 +98,11 @@ Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例 | 目标检测 |YOLOv3-DarkNet | PaddleDetection | [C++ Serving](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/deploy/serving) | [.pdparams](https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams)
[.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/yolov3/yolov3_darknet53_270e_coco.yml) | --- + -**六.文字识别**模型部署示例请参阅下表： +**六.文字识别** + +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 文字识别 |ocr_rec | PaddleOCR | [C++ Serving](../examples/C++/PaddleOCR/ocr)
[Pipeline Serving](../examples/Pipeline/PaddleOCR/ocr) | [.tar.gz](https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/OCR/ocr_rec.tar.gz) | @@ -79,27 +115,40 @@ Paddle Serving 已实现9个类别，共计46个模型的服务化部署示例 | 文字识别 |ch_ppocr_server_v2.0 | PaddleOCR | [Pipeline Serving](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/deploy/pdserving/README.md) | [model](https://github.com/PaddlePaddle/PaddleOCR) | --- + + +**七.图像分割** -**七.图像分割**模型部署示例请参阅下表： +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 图像分割 | deeplabv3 | PaddleSeg | [C++ Serving](../examples/C++/PaddleSeg/deeplabv3) | [.tar.gz](https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageSegmentation/deeplabv3.tar.gz) | | 图像分割 | unet | PaddleSeg | [C++ Serving](../examples/C++/PaddleSeg/unet_for_image_seg) | [.tar.gz](https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageSegmentation/unet.tar.gz) | --- + -**八.关键点检测**模型部署示例请参阅下表： +**八.关键点检测** + +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 关键点检测 |faster_rcnn_hrnetv2p_w18_1x | PaddleDetection | [C++ Serving](../examples/C++/PaddleDetection/faster_rcnn_hrnetv2p_w18_1x) | [.tar.gz](https://paddle-serving.bj.bcebos.com/pddet_demo/faster_rcnn_hrnetv2p_w18_1x.tar.gz) | --- + + +**九.视频理解** -**九.视频理解**模型部署示例请参阅下表： +模型部署示例请参阅下表： | 场景| 模型 | 类型 | 示例使用的框架 | 下载 | | --- | --- | --- | --- | ---- | | 视频理解 |PPTSN_K400 | PaddleVideo | [Pipeline Serving](../examples/Pipeline/PaddleVideo/PPTSN_K400) | [model](https://paddle-serving.bj.bcebos.com/model/PaddleVideo/PPTSN_K400.tar) | +--- + + +## 模型示例库 Paddle Serving 代码库下模型部署示例请参考 [examples](../examples) 目录。更多 Paddle Serving 部署模型请参考 [wholechain](https://www.paddlepaddle.org.cn/wholechain)。 diff --git a/doc/Offical_Docs/5-1_Save_Model_Params_CN.md b/doc/Offical_Docs/5-1_Save_Model_Params_CN.md new file mode 100644 index 000000000..2faaae7b4 --- /dev/null +++ b/doc/Offical_Docs/5-1_Save_Model_Params_CN.md @@ -0,0 +1,188 @@ +# 保存用于 Serving 部署的模型参数 + +- [背景介绍](#1) +- [功能设计](#2) +- [功能使用](#3) + - [PYTHON 命令执行](#3.1) + - [代码引入执行](#3.2) +- [Serving 部署](#4) + - [服务端部署示例](#4.1) + - [客户端部署示例](#4.2) + + + +## 背景介绍 + +模型参数信息保存在模型文件中，为什么还要保存用于 Paddle Serving 部署的模型参数呢，原因有3个： + +1. 服务化场景分为客户端和服务端，服务端加载模型，而在客户端没有模型信息，但需要在客户端需实现数据拼装和类型转换。 +2. 模型升级过程中 `feed vars` 和 `fetch vars` 的名称变化会导致代码升级，通过增加一个 `alias_name` 字段映射名称，代码无需升级。 +3. 部署 `Web` 服务，并使用 `URL` 方式访问时，请求信息中缺少类型和维度信息，在服务端推理前需要进行转换。 + + + +## 功能设计 + +飞桨训推一体框架中，从动态图模型训练到静态图推理部署，一体化流程如下所示 +``` +①动态图训练 → ②模型动转静 → ③静态模型 → ④模型保存 → ⑤Serving 部署 +``` +在飞桨框架2.1对模型与参数的保存与载入相关接口进行了梳理，完整文档参考[模型保存与载入](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/02_paddle2.0_develop/08_model_save_load_cn.html) +- 对于训练调优场景，我们推荐使用 `paddle.save/load` 保存和载入模型； +- 对于推理部署场景，我们推荐使用 `paddle.jit.save/load`（动态图）和 `paddle.static.save/load_inference_model` （静态图）保存载入模型； + +Paddle Serving 模型参数保存接口定位是在 `②模型动转静` 导出 `③静态模型`后，使用 `paddle.static.load_inference_model` 接口加载模型，和 `paddle.static.save_vars` 接口保存模型参数。 + +生成的模型参数信息保存在 `paddle_serving_server/client.prototxt` 文件中，其格式如下 +``` +feed_var { + name: "x" + alias_name: "image" + is_lod_tensor: false + feed_type: 1 + shape: 3 + shape: 960 + shape: 960 +} +fetch_var { + name: "save_infer_model/scale_0.tmp_1" + alias_name: "save_infer_model/scale_0.tmp_1" + is_lod_tensor: false + fetch_type: 1 + shape: 1 + shape: 960 + shape: 960 +} +``` + +| 参数 | 描述 | +|------|---------| +| name | 实际变量名 | +| alias_name | 变量别名，与 name 的关联业务场景中变量名 | +| is_lod_tensor | 是否为 LOD Tensor | +| feed_type | feed 变量类型| +| fetch_type | fetch 变量类型| +| shape 数组 | 变量的 Shape 信息 | + +feed 与 fetch 变量的类型列表如下: +| 类型 | 类型值 | +|------|------| +| int64 | 0 | +| float32 |1 | +| int32 | 2 | +| float64 | 3 | +| int16 | 4 | +| float16 | 5 | +| bfloat16 | 6 | +| uint8 | 7 | +| int8 | 8 | +| bool | 9 | +| complex64 | 10 +| complex128 | 11 | + + + +## 功能使用 + +Paddle 推理模型有3种形式，每种形式的读模型的方式都不同，散列方式必须以路径方式加载，其余2种采用目录或文件方式均可。 +1) Paddle 2.0前版本：`__model__`, `__params__` +2) Paddle 2.0后版本：`*.pdmodel`, `*.pdiparams` +3) 散列：`__model__`, `conv2d_1.w_0`, `conv2d_2.w_0`, `fc_1.w_0`, `conv2d_1.b_0`, ... + +`paddle_serving_client.convert` 接口既支持 PYTHON 命令方式执行，又支持 代码中引入运行。 + + +| 参数 | 类型 | 默认值 | 描述 | +|--------------|------|-----------|--------------------------------| +| `dirname` | str | - | 需要转换的模型文件存储路径，Program结构文件和参数文件均保存在此目录。| +| `serving_server` | str | `"serving_server"` | 转换后的模型文件和配置文件的存储路径。默认值为serving_server | +| `serving_client` | str | `"serving_client"` | 转换后的客户端配置文件存储路径。默认值为serving_client | +| `model_filename` | str | None | 存储需要转换的模型Inference Program结构的文件名称。如果设置为None，则使用 `__model__` 作为默认的文件名 | +| `params_filename` | str | None | 存储需要转换的模型所有参数的文件名称。当且仅当所有模型参数被保>存在一个单独的二进制文件中，它才需要被指定。如果模型参数是存储在各自分离的文件中，设置它的值为None | + + + +**一.PYTHON 命令执行** + +首先需要安装 `paddle_serivng_client` 包，以目录方式加载模型。 + +示例一，是以模型路径方式加载模型，适用于全部3种类型。 +```python +python3 -m paddle_serving_client.convert --dirname ./your_inference_model_dir +``` + +示例二，以指定加载 `当前路径` 下模型 `dygraph_model.pdmodel` 和 `dygraph_model.pdiparams`，保存结果在 `serving_server` 和 `serving_client` 目录。 +```python +python3 -m paddle_serving_client.convert --dirname . --model_filename dygraph_model.pdmodel --params_filename dygraph_model.pdiparams --serving_server serving_server --serving_client serving_client +``` + + + +**二.代码引入执行** + +代码引入执行方式，通过 `import io` 包并调用 `inference_model_to_serving` 实现模型参数保存。 +```python +import paddle_serving_client.io as serving_io +serving_io.inference_model_to_serving(dirname, serving_server="serving_server", serving_client="serving_client", model_filename=None, params_filename=None) +``` + + + +## Serving 部署 +生成完的模型可直接用于服务化推理，服务端使用和客户端使用。 + + + +**一.服务端部署示例** + +示例一：C++ Serving 启动服务 +``` +python3 -m paddle_serving_server.serve --model serving_server --port 9393 --gpu_id 0 +``` + +示例二：Python Pipeline 启动服务，在 `config.yml` 中指定模型路径 +``` +op: + det: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 6 + + #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置 + local_service_conf: + #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测 + client_type: local_predictor + + #det模型路径 + model_config: ocr_det_model + + #Fetch结果列表，以client_config中fetch_var的alias_name为准 + fetch_list: ["save_infer_model/scale_0.tmp_1"] + + # device_type, 0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu + device_type: 0 +``` + + + +**二.客户端部署示例** + +通过 `client` 对象的 `load_client_config` 接口加载模型配置信息 +``` +from paddle_serving_client import Client +from paddle_serving_app.reader import Sequential, File2Image, Resize, CenterCrop +from paddle_serving_app.reader import RGB2BGR, Transpose, Div, Normalize + +client = Client() +client.load_client_config( + "serving_client/serving_client_conf.prototxt") +client.connect(["127.0.0.1:9393"]) + +seq = Sequential([ + File2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)), + Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True) +]) + +image_file = "daisy.jpg" +img = seq(image_file) +fetch_map = client.predict(feed={"inputs": img}, fetch=["save_infer_model/scale_0.tmp_0"]) +``` diff --git a/doc/Offical_Docs/6-1_Cpp_Asynchronous_Framwork_CN.md b/doc/Offical_Docs/6-1_Cpp_Asynchronous_Framwork_CN.md new file mode 100644 index 000000000..7e9c4ed77 --- /dev/null +++ b/doc/Offical_Docs/6-1_Cpp_Asynchronous_Framwork_CN.md @@ -0,0 +1,167 @@ +# C++ Serving 异步模式 + +- [设计方案](#1) + - [网络同步线程](#1.1) + - [异步调度线程](#1.2) + - [动态批量](#1.3) +- [使用案例](#2) + - [开启同步模式](#2.1) + - [开启异步模式](#2.2) +- [性能测试](#3) + - [测试结果](#3.1) + - [测试数据](#3.2) + + + +## 设计方案 + + + +**一.同步网络线程** + +Paddle Serving 的网络框架层面是同步处理模式，即 bRPC 网络处理线程从系统内核拿到完整请求数据后( epoll 模式)，在同一线程内完成业务处理，C++ Serving 默认使用同步模式。同步模式比较简单直接，适用于模型预测时间短，或单个 Request 请求批量较大的情况。 + +

+ +

+ +Server 端线程数 N = 模型预测引擎数 N = 同时处理 Request 请求数 N，超发的 Request 请求需要等待当前线程处理结束后才能得到响应和处理。 + + + +**二.异步调度线程** + +为了提高计算芯片吞吐和计算资源利用率，C++ Serving 在调度层实现异步多线程并发合并请求，实现动态批量推理。异步模型主要适用于模型支持批量，单个 Request 请求的无批量或较小，单次预测时间较长的情况。 + +

+ +

+ +异步模式下，Server 端 N 个线程只负责接收 Request 请求，实际调用预测引擎是在异步框架的线程池中，异步框架的线程数可以由配置选项来指定。为了方便理解，我们假设每个 Request 请求批量均为1，此时异步框架会尽可能多得从请求池中取 n(n≤M)个 Request 并将其拼装为1个 Request(batch=n)，调用1次预测引擎，得到1个 Response(batch = n)，再将其对应拆分为 n 个 Response 作为返回结果。 + + + +**三.动态批量** + +通常，异步框架合并多个请求的前提是所有请求的 `feed var` 的维度除 batch 维度外必须是相同的。例如，以 OCR 文字识别案例中检测模型为例，A 请求的 `x` 变量的 shape 是 [1, 3, 960, 960]，B 请求的 `x` 变量的 shape 是 [2, 3, 960, 960]，虽然第一个维度值不相同，但第一个维度属于 `batch` 维度，因此，请求 A 和 请求 B 可以合并。C 请求的 `x` 变量的 shape 是 [1, 3, 640, 480]，由于除了 `batch` 维度外还有2个维度值不同，A 和 C 不能直接合并。 + +从经验来看，当2个请求的同一个变量 shape 维度的数量相等时，通过 `padding` 补0的方式按最大 shape 值对齐即可。即 C 请求的 shape 补齐到 [1, 3, 960, 960]，那么就可以与 A 和 B 请求合并了。Paddle Serving 框架实现了动态 Padding 功能补齐 shape。 + +当多个将要合并的请求中有一个 shape 值很大时，所有请求的 shape 都要按最大补齐，导致计算量成倍增长。Paddle Serving 设计了一套合并策略，满足任何一个条件均可合并: + +- 条件 1：绝对值差的字节数小于 **1024** 字节，评估补齐绝对长度 +- 条件 2：相似度的乘积大于 **50%**，评估相似度，评估补齐绝对值整体数据量比例 + +场景1：`Shape-1 = [batch, 500, 500], Shape-2 = [batch, 400, 400]`。此时，`绝对值差 = 500*500 - 400*400 = 90000` 字节，`相对误差= (400/500) * (400/500) = 0.8*0.8 = 0.64`，满足条件1，不满足条件2，触发动态 Padding。 + +场景2：`Shape-1 = [batch, 1, 1], Shape-2 = [batch, 2, 2]`。此时，`绝对值差 = 2*2 - 1*1 = 3`字节，`相对误差 = (1/2) * (1/2) = 0.5*0.5 = 0.25`，满足条件2，不满足条件1，触发动态 Padding。 + +场景3：`Shape-1 = [batch, 3, 320, 320], Shape-2 = [batch, 3, 960, 960]`。此时，`绝对值差 = 3*960*960 - 3*320*320 = 2457600`字节，`相对误差 = (3/3) * (320/960) * (320/960) = 0.3*0.3 = 0.09`，条件1和条件2均不满足，未触发动态 Padding。 + + + +## 使用案例 + + + +**一.开启同步模式** + +启动命令不使用 `--runtime_thread_num` 和 `--batch_infer_size` 时，属于同步处理模式，未开启异步模式。`--thread 16` 表示启动16个同步网络处理线程。 +``` +python3 -m paddle_serving_server.serve --model uci_housing_model --thread 16 --port 9292 +``` + + + +**二.开启异步模式** + +启动命令使用 `--runtime_thread_num 2` 和 `--batch_infer_size 32` 开启异步模式，Serving 框架会启动2个异步线程，单次合并最大批量为32，自动开启动态 Padding。 +``` +python3 -m paddle_serving_server.serve --model uci_housing_model --thread 16 --port 9292 --runtime_thread_num 4 --batch_infer_size 32 --ir_optim --gpu_multi_stream --gpu_ids 0 +``` + + + +## 性能测试 + + +- GPU：Tesla P4 7611 MiB +- CUDA：cuda11.2-cudnn8-trt8 +- Python 版本：python3.7 +- 模型：ResNet_v2_50 +- 测试数据：构造全1输入，单client请求100次，shape 范围(1, 224 ± 50, 224 ± 50) + +同步模式启动命令: +``` +python3 -m paddle_serving_server.serve --model resnet_v2_50_imagenet_model --port 9393 --thread 8 --ir_optim --gpu_multi_stream --gpu_ids 1 --enable_prometheus --prometheus_port 1939 +``` + +异步模式启动命令: +``` +python3 -m paddle_serving_server.serve --model resnet_v2_50_imagenet_model --port 9393 --thread 64 --runtime_thread_num 8 --ir_optim --gpu_multi_stream --gpu_ids 1 --enable_prometheus --prometheus_port 19393 +``` + + + +**一.测试结果** + +使用异步模式，并开启动态批量后，并发测试不同 shape 数据时，吞吐性能大幅提升。 +

+ +
+ +**二.测试数据** + +1. 同步模式 + +| client_num | batch_size |CPU_util_pre(%) |CPU_util(%) |GPU_memory(mb) |GPU_util(%) |qps(samples/s) |total count |mean(ms) |median(ms) |80 percent(ms) |90 percent(ms) |99 percent(ms) |total cost(s) |each cost(s)|infer_count_total|infer_cost_total(ms)|infer_cost_avg(ms)| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 1 |1 |1.30 |18.90 |2066 |71.56 |22.938 |100 |43.594 |23.516 |78.118 |78.323 |133.544 |4.4262 |4.3596 |7100.0000 |1666392.70 | 41.1081 | +| 5 |1 |2.00 |28.20 |3668 |92.57 |33.630 |500 |148.673 |39.531 |373.231 |396.306 |419.088 |15.0606 |14.8676 |7600.0000 |1739372.7480| 145.9601 | +|10 |1 |1.90 |29.80 |4202 |91.98 |34.303 |1000 |291.512 |76.728 |613.963 |632.736 |1217.863 |29.8004 |29.1516 |8600.0000 |1974147.7420| 234.7750 | +|20 |1 |4.70 |49.60 |4736 |92.63 |34.359 |2000 |582.089 |154.952 |1239.115 |1813.371 |1858.128 |59.7303 |58.2093 |12100.0000 |2798459.6330 |235.6248 | +|30 |1 |5.70 |65.70 |4736 |92.60 |34.162 |3000 |878.164 |231.121 |2391.687 |2442.744 |2499.963 |89.6546 |87.8168 |17600.0000 |4100408.9560 |236.6877 | +|40 |1 |5.40 |74.40 |5270 |92.44 |34.090 |4000 |1173.373 |306.244 |3037.038 |3070.198 |3134.894 |119.4162 |117.3377 |21600.0000 |5048139.2170 |236.9326| +|50 |1 |1.40 |64.70 |5270 |92.37 |34.031 |5000 |1469.250 |384.327 |3676.812 |3784.330 |4366.862 |149.7041 |146.9254 |26600.0000 |6236269.4230 |237.6260| +|70 |1 |3.70 |79.70 |5270 |91.89 |33.976 |7000 |2060.246 |533.439 |5429.255 |5552.704 |5661.492 |210.1008 |206.0250 |33600.0000 |7905005.9940 |238.3909| + + +2. 异步模式 - 未开启动态批量 + +| client_num | batch_size |CPU_util_pre(%) |CPU_util(%) |GPU_memory(mb) |GPU_util(%) |qps(samples/s) |total count |mean(ms) |median(ms) |80 percent(ms) |90 percent(ms) |99 percent(ms) |total cost(s) |each cost(s)|infer_count_total|infer_cost_total(ms)|infer_cost_avg(ms)| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 1 |1 |6.20 |13.60 |5170 |71.11 |22.894 |100 |43.677 |23.992 |78.285 |78.788 |123.542 |4.4253 |4.3679 |3695.0000 |745061.9120 |40.6655 | +| 5 |1 |6.10 |32.20 |7306 |89.54 |33.532 |500 |149.109 |43.906 |376.889 |401.999 |422.753 |15.1623 |14.9113 |4184.0000 |816834.2250 |146.7736| +|10 |1 |4.90 |43.60 |7306 |91.55 |38.136 |1000 |262.216 |75.393 |575.788 |632.016 |1247.775 |27.1019 |26.2220 |5107.0000 |1026490.3950 |227.1464| +|20 |1 |5.70 |39.60 |7306 |91.36 |58.601 |2000 |341.287 |145.774 |646.824 |994.748 |1132.979 |38.3915 |34.1291 |7461.0000 |1555234.6260 |229.9113| +|30 |1 |1.30 |45.40 |7484 |91.10 |69.008 |3000 |434.728 |204.347 |959.184 |1092.181 |1661.289 |46.3822 |43.4732 |10289.0000 |2269499.9730 |249.4257| +|40 |1 |3.10 |73.00 |7562 |91.83 |80.956 |4000 |494.091 |272.889 |966.072 |1310.011 |1851.887 |52.0609 |49.4095 |12102.0000 |2678878.2010 |225.8016| +|50 |1 |0.80 |68.00 |7522 |91.10 |83.018 |5000 |602.276 |364.064 |1058.261 |1473.051 |1671.025 |72.9869 |60.2280 |14225.0000 |3256628.2820 |272.1385| +|70 |1 |6.10 |78.40 |7584 |92.02 |65.069 |7000 |1075.777 |474.014 |2411.296 |2705.863 |3409.085 |111.6653 |107.5781 |17974.0000 |4139377.4050 |235.4626 + + + +3. 异步模式 - 开启动态批量 + + +| client_num | batch_size |CPU_util_pre(%) |CPU_util(%) |GPU_memory(mb) |GPU_util(%) |qps(samples/s) |total count |mean(ms) |median(ms) |80 percent(ms) |90 percent(ms) |99 percent(ms) |total cost(s) |each cost(s)|infer_count_total|infer_cost_total(ms)|infer_cost_avg(ms)| +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| 1 |1 |1.20 |13.30 |6048 |70.07 |22.417 |100 |44.606 |24.486 |78.365 |78.707 |139.349 |4.5201 |4.4608 |1569.0000 |462418.6390 |41.7646 | +| 5 |1 |1.20 |50.80 |7116 |87.37 |31.106 |500 |160.740 |42.506 |414.903 |458.841 |481.112 |16.3525 |16.0743 |2059.0000 |539439.3300 |157.1851 +|10 |1 |0.80 |26.20 |7264 |88.74 |37.417 |1000 |267.254 |79.452 |604.451 |686.477 |1345.528 |27.9848 |26.7258 |2950.0000 |752428.0570 |239.0446| +|20 |1 |1.50 |32.80 |7264 |89.52 |70.641 |2000 |283.117 |133.441 |516.066 |652.089 |1274.957 |33.0280 |28.3121 |4805.0000 |1210814.5610 |260.5873| +|30 |1 |0.90 |59.10 |7348 |89.57 |84.894 |3000 |353.380 |217.385 |613.587 |757.829 |1277.283 |40.7093 |35.3384 |6924.0000 |1817515.1710 |276.3695| +|40 |1 |1.30 |57.30 |7356 |89.30 |99.853 |4000 |400.584 |204.425 |666.015 |1031.186 |1380.650 |49.4807 |40.0588 |8104.0000 |2200137.0060 |324.2558| +|50 |1 |1.50 |50.60 |7578 |89.04 |121.545 |5000 |411.364 |331.118 |605.809 |874.543 |1285.650 |48.2343 |41.1369 |9350.0000 |2568777.6400 |295.8593| +|70 |1 |3.80 |83.20 |7602 |89.59 |133.568 |7000 |524.073 |382.653 |799.463 |1202.179 |1576.809 |57.2885 |52.4077 |10761.0000 |3013600.9670 |315.2540| diff --git a/doc/Offical_Docs/6-2_Cpp_Serving_Protocols_CN.md b/doc/Offical_Docs/6-2_Cpp_Serving_Protocols_CN.md new file mode 100644 index 000000000..4b31be083 --- /dev/null +++ b/doc/Offical_Docs/6-2_Cpp_Serving_Protocols_CN.md @@ -0,0 +1,290 @@ +# C++ Serving 通讯协议 + +- [网络框架](#0) +- [Tensor](#1) + - [1.1 构建 FLOAT32 Tensor](#1.1) + - [1.2 构建 STRING Tensor](#1.2) +- [Request](#2) + - [2.1 构建 Protobuf Request](#2.1) + - [2.2 构建 Json Request](#2.2) +- [Response](#3) + - [3.1 读取 Response 数据](#3.1) + + + +## 网络框架 + +C++ Serving 基于 [bRPC](https://github.com/apache/incubator-brpc) 网络框架构建服务，支持 bRPC、gRPC 和 RESTful 协议请求。不限于开发语言和框架，甚至 `curl` 方式，只要按照上述协议封装数据并发送，Server 就能够接收、处理和返回结果。 + +对于支持的各种协议我们提供了部分的 Client SDK 示例供用户参考和使用，用户也可以根据自己的需求去开发新的 Client SDK，也欢迎用户添加其他语言/协议（例如 GRPC-Go、GRPC-C++ HTTP2-Go、HTTP2-Java 等）Client SDK 到我们的仓库供其他开发者借鉴和参考。 + +| 通信协议 | 速度 | 是否支持 | 是否提供Client SDK | +|-------------|-----|---------|-------------------| +| bRPC | 最快 | 支持 | [C++]、[Python(Pybind方式)] | +| HTTP 2.0 + Proto | 快 | 不支持 | | +| gRPC | 较快 | 支持 | [Java]、[Python] | +| HTTP 1.1 + Proto | 较快 | 支持 | [Java]、[Python] | +| HTTP 1.1 + Json | 慢 | 支持 | [Java]、[Python]、[Curl] | + + +C++ Serving 请求和应答的数据格式为 protobuf，重要的结构有以下3个： + + + +## Tensor + +[Tensor](https://github.com/PaddlePaddle/Serving/blob/develop/core/general-server/proto/general_model_service.proto#L22) 可以装载多种类型的数据，是 Request 和 Response 的基础单元。Tensor 的定义如下： + +```protobuf +message Tensor { + // VarType: INT64 + repeated int64 int64_data = 1; + + // VarType: FP32 + repeated float float_data = 2; + + // VarType: INT32 + repeated int32 int_data = 3; + + // VarType: FP64 + repeated double float64_data = 4; + + // VarType: UINT32 + repeated uint32 uint32_data = 5; + + // VarType: BOOL + repeated bool bool_data = 6; + + // (No support)VarType: COMPLEX64, 2x represents the real part, 2x+1 + // represents the imaginary part + repeated float complex64_data = 7; + + // (No support)VarType: COMPLEX128, 2x represents the real part, 2x+1 + // represents the imaginary part + repeated double complex128_data = 8; + + // VarType: STRING + repeated string data = 9; + + // Element types: + // 0 => INT64 + // 1 => FP32 + // 2 => INT32 + // 3 => FP64 + // 4 => INT16 + // 5 => FP16 + // 6 => BF16 + // 7 => UINT8 + // 8 => INT8 + // 9 => BOOL + // 10 => COMPLEX64 + // 11 => COMPLEX128 + // 20 => STRING + int32 elem_type = 10; + + // Shape of the tensor, including batch dimensions. + repeated int32 shape = 11; + + // Level of data(LOD), support variable length data, only for fetch tensor + // currently. + repeated int32 lod = 12; + + // Correspond to the variable 'name' in the model description prototxt. + string name = 13; + + // Correspond to the variable 'alias_name' in the model description prototxt. + string alias_name = 14; // get from the Model prototxt + + // VarType: FP16, INT16, INT8, BF16, UINT8 + bytes tensor_content = 15; +}; +``` + +Tensor 结构中重要成员 `elem_type`、`shape`、`lod` 和 `name/alias_name`。 +- name/alias_name: 名称及别名，与模型配置对应 +- elem_type：数据类型，当前支持FLOAT32, INT64, INT32, UINT8, INT8, FLOAT16 +- shape：数据维度 +- lod：变长结构 LoD(Level-of-Detail) Tensor 是 Paddle 的高级特性，是对 Tensor 的一种扩充，用于支持更自由的数据输入。详见[LOD](../LOD_CN.md) + +|elem_type|类型| +|---------|----| +|0|INT64| +|1|FLOAT32| +|2|INT32| +|3|FP64| +|4|INT16| +|5|FP16| +|6|BF16| +|7|UINT8| +|8|INT8| +|9|BOOL| +|10|COMPLEX64| +|11|COMPLEX128 +|20|STRING| + + + +**一.构建 FLOAT32 Tensor** + +创建 Tensor 对象，通过 `mutable_float_data::Resize()` 设置 FLOAT32 类型数据长度，通过 memcpy 函数拷贝数据。 +```C +// 原始数据 +std::vector float_data; +Tensor *tensor = new Tensor; +// 设置维度，可以设置多维 +for (uint32_t j = 0; j < float_shape.size(); ++j) { + tensor->add_shape(float_shape[j]); +} +// 设置LOD信息 +for (uint32_t j = 0; j < float_lod.size(); ++j) { + tensor->add_lod(float_lod[j]); +} +// 设置类型、名称及别名 +tensor->set_elem_type(1); +tensor->set_name(name); +tensor->set_alias_name(alias_name); +// 拷贝数据 +int total_number = float_data.size(); +tensor->mutable_float_data()->Resize(total_number, 0); +memcpy(tensor->mutable_float_data()->mutable_data(), float_data.data(), total_number * sizeof(float)); +``` + + + +**二.构建 STRING Tensor** + +创建 Tensor 对象，通过 `set_tensor_content` 设置 string 类型数据。 +```C +// 原始数据 +std::string string_data; +Tensor *tensor = new Tensor; +for (uint32_t j = 0; j < string_shape.size(); ++j) { + tensor->add_shape(string_shape[j]); +} +for (uint32_t j = 0; j < string_lod.size(); ++j) { + tensor->add_lod(string_lod[j]); +} +tensor->set_elem_type(8); +tensor->set_name(name); +tensor->set_alias_name(alias_name); +tensor->set_tensor_content(string_data); +``` + + + +## Request + +Request 为客户端需要发送的请求数据，其以 Tensor 为基础数据单元，并包含了额外的请求信息。定义如下： + +```protobuf +message Request { + repeated Tensor tensor = 1; + repeated string fetch_var_names = 2; + bool profile_server = 3; + uint64 log_id = 4; +}; +``` + +- fetch_vat_names: 需要获取的输出数据名称，在 `GeneralResponseOP` 会根据该列表进行过滤.请参考模型文件 `serving_client_conf.prototxt` 中的 `fetch_var` 字段下的 `alias_name`。 +- profile_server: 调试参数，打开时会输出性能信息 +- log_id: 请求ID + +当使用 bRPC 或 gRPC 进行请求时，使用 protobuf 或 Json 格式请求数据。 + + + +**一.构建 Protobuf Request** + +创建 Request 对象，通过 `add_tensor` 接口来设置 Tensor。 +```C +Request req; +req.set_log_id(log_id); +for (auto &name : fetch_name) { + req.add_fetch_var_names(name); +} +// 添加Tensor +Tensor *tensor = req.add_tensor(); +... +``` + + +**二.构建 Json Request** + +当使用 RESTful 请求时，可以使用 Json 格式数据，示例如下： + +```JSON +{"tensor":[{"float_data":[0.0137,-0.1136,0.2553,-0.0692,0.0582,-0.0727,-0.1583,-0.0584,0.6283,0.4919,0.1856,0.0795,-0.0332],"elem_type":1,"name":"x","alias_name":"x","shape":[1,13]}],"fetch_var_names":["price"],"log_id":0} +``` + +可参考示例，不用修改整体结构，仅需修改数据类型和数据。 + + + +## Response + +Response 为服务端返回给客户端的结果，包含了 Tensor 数据、错误码、错误信息等。定义如下： + +```protobuf +message Response { + repeated ModelOutput outputs = 1; + repeated int64 profile_time = 2; + // Error code + int32 err_no = 3; + + // Error messages + string err_msg = 4; +}; + +message ModelOutput { + repeated Tensor tensor = 1; + string engine_name = 2; +} +``` + +Response 结构中核心成员： +- profile_time：当设置 `request->set_profile_server(true)` 时，会返回性能信息 +- err_no：错误码 +- err_msg：错误信息 +- engine_name：输出节点名称 + +|err_no|err_msg| +|---------|----| +|0|OK| +|-5000|"Paddle Serving Framework Internal Error."| +|-5001|"Paddle Serving Memory Alloc Error."| +|-5002|"Paddle Serving Array Overflow Error."| +|-5100|"Paddle Serving Op Inference Error."| + + + +**一.读取 Response 数据** + +读取 Response 对象中 Tensor 数据示例如下 +```C +Response res; +uint32_t model_num = res.outputs_size(); +for (uint32_t m_idx = 0; m_idx < model_num; ++m_idx) { + std::string engine_name = output.engine_name(); + int idx = 0; + // 读取tensor维度 + int shape_size = output.tensor(idx).shape_size(); + for (int i = 0; i < shape_size; ++i) { + shape[i] = output.tensor(idx).shape(i); + } + // 读取LOD信息 + int lod_size = output.tensor(idx).lod_size(); + if (lod_size > 0) { + lod.resize(lod_size); + for (int i = 0; i < lod_size; ++i) { + lod[i] = output.tensor(idx).lod(i); + } + } + // 读取float数据 + int size = output.tensor(idx).float_data_size(); + float_data = std::vector( + output.tensor(idx).float_data().begin(), + output.tensor(idx).float_data().begin() + size); + // 读取int8数据 + string_data = output.tensor(idx).tensor_content(); +} +``` diff --git a/doc/Offical_Docs/6-5_Cpp_ABTest_CN.md b/doc/Offical_Docs/6-5_Cpp_ABTest_CN.md new file mode 100644 index 000000000..ff5c7145f --- /dev/null +++ b/doc/Offical_Docs/6-5_Cpp_ABTest_CN.md @@ -0,0 +1,198 @@ +# C++ Serving ABTest + +- [功能设计](#1) +- [使用案例](#2) + - [1.1 安装 Paddle Serving Wheels](#2.1) + - [1.2 下载多个模型并保存模型参数](#2.2) + - [1.3 启动 A，B，C 3个服务](#2.3) + - [1.4 客户端注册 A，B，C 服务端地址](#2.4) + - [1.5 启动客户端并验证结果](#2.5) + +ABTest 是一种功能测试方案，一般是为同一个产品目标制定多种方案，让一部分用户使用 A 方案，另一部分用户使用 B 或 C 方案，根据测试效果，如点击率、转化率等来评价方案的优劣。 + +模型服务化部署框架中，ABTest 属于一个重要的基础功能，为模型迭代升级提供实验环境。Paddle Serving 的 PYTHON SDK 中实现 ABTest 功能，为用户提供简单易用功能测试环境。 + + + +## 功能设计 + +Paddle Serving 的 ABTest 功能是基于 PYTHON SDK 和 多个服务端构成。每个服务端加载不同模型，在客户端上注册多个服务端地址和访问比例，最终确定访问。 + +
+ +
+ +## 使用案例 + +以 [imdb](https://github.com/PaddlePaddle/Serving/tree/develop/examples/C%2B%2B/imdb) 示例为例，介绍 ABTest 的使用，部署有5个步骤： + +1. 安装 Paddle Serving Wheels +2. 下载多个模型并保存模型参数 +3. 启动 A，B，C 3个服务 +4. 客户端注册 A，B，C 服务端地址 +5. 启动客户端并验证结果 + + + +**一.安装 Paddle Serving Wheels** + +使用 ABTest 功能的前提是使用 PYTHON SDK，因此需要安装 `paddle_serving_client` 的 wheel 包。[安装方法](./2-1_Docker_Images_CN.md) 如下： + +``` +pip3 install paddle-serving-client==0.8.3 -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + + + +**二.下载多个模型并保存模型参数** + +本示例已提供了一键下载脚本 `sh get_data.sh`，下载自训练的模型 `bow`、`cnn`和`lstm` 3种不同方式训练的模型。 + +``` +sh get_data.sh +``` + +3种模型的所有文件如下所示，已为用户提前保存模型参数，无需执行保存操作。 +``` +├── imdb_bow_client_conf +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── imdb_bow_model +│   ├── embedding_0.w_0 +│   ├── fc_0.b_0 +│   ├── fc_0.w_0 +│   ├── fc_1.b_0 +│   ├── fc_1.w_0 +│   ├── fc_2.b_0 +│   ├── fc_2.w_0 +│   ├── fluid_time_file +│   ├── __model__ +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── imdb_cnn_client_conf +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── imdb_cnn_model +│   ├── embedding_0.w_0 +│   ├── fc_0.b_0 +│   ├── fc_0.w_0 +│   ├── fc_1.b_0 +│   ├── fc_1.w_0 +│   ├── fluid_time_file +│   ├── __model__ +│   ├── sequence_conv_0.b_0 +│   ├── sequence_conv_0.w_0 +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── imdb_lstm_client_conf +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── imdb_lstm_model +│   ├── embedding_0.w_0 +│   ├── fc_0.b_0 +│   ├── fc_0.w_0 +│   ├── fc_1.b_0 +│   ├── fc_1.w_0 +│   ├── fc_2.b_0 +│   ├── fc_2.w_0 +│   ├── lstm_0.b_0 +│   ├── lstm_0.w_0 +│   ├── __model__ +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +``` + +虽然3个模型的网络结构不同，但是 `feed var` 和 `fetch_var` 都是相同的便于做 ABTest。 +``` +feed_var { + name: "words" + alias_name: "words" + is_lod_tensor: true + feed_type: 0 + shape: -1 +} +fetch_var { + name: "fc_2.tmp_2" + alias_name: "prediction" + is_lod_tensor: false + fetch_type: 1 + shape: 2 +} +``` + + + +**三.启动 A，B，C 3个服务** + +后台启动 `bow`、`cnn` 和 `lstm` 模型服务: + +```python +## 启动 bow 模型服务 +python3 -m paddle_serving_server.serve --model imdb_bow_model/ --port 9297 >/dev/null 2>&1 & + +## 启动 cnn 模型服务 +python3 -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9298 >/dev/null 2>&1 & + +## 启动 lstm 模型服务 +python3 -m paddle_serving_server.serve --model imdb_lstm_model/ --port 9299 >/dev/null 2>&1 & +``` + + + +**四.客户端注册 A，B，C 服务端地址** + +使用 `paddle_serving_client` 中 `Client::add_variant(self, tag, cluster, variant_weight)` 接口注册服务标签、服务地址和权重。框架会将所有权重求和后计算每个服务的比例。本示例中，bow 服务的权重是10，cnn 服务的权重是30, lstm的权重是60，每次请求分别请求到3个服务的比例是10%、30%和60%。 + +``` +from paddle_serving_client import Client +from paddle_serving_app.reader.imdb_reader import IMDBDataset +import sys +import numpy as np + +client = Client() +client.load_client_config(sys.argv[1]) +client.add_variant("bow", ["127.0.0.1:9297"], 10) +client.add_variant("cnn", ["127.0.0.1:9298"], 30) +client.add_variant("lstm", ["127.0.0.1:9299"], 60) +client.connect() +``` +如要在结果中打印请求到了哪个服务，在 `client.predict(feed, fetch, batch, need_variant_tag, logid)` 中设置 `need_variant_tag=True`。 + + + +**五.启动客户端并验证结果** + +运行命令: +``` +head test_data/part-0 | python3.7 abtest_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab +``` + +运行结果如下，10次请求中，bow 服务2次，cnn 服务3次，lstm 服务5次，与设置的比例基本相近。 +``` +I0506 04:02:46.720135 44567 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9297"): added 1 +I0506 04:02:46.722630 44567 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9298"): added 1 +I0506 04:02:46.723577 44567 naming_service_thread.cpp:202] brpc::policy::ListNamingService("127.0.0.1:9299"): added 1 +I0506 04:02:46.814075 44567 general_model.cpp:490] [client]logid=0,client_cost=9.889ms,server_cost=6.283ms. +server_tag=lstm prediction=[0.500398 0.49960205] +I0506 04:02:46.826339 44567 general_model.cpp:490] [client]logid=0,client_cost=10.261ms,server_cost=9.503ms. +server_tag=lstm prediction=[0.5007235 0.49927652] +I0506 04:02:46.828992 44567 general_model.cpp:490] [client]logid=0,client_cost=1.667ms,server_cost=0.741ms. +server_tag=bow prediction=[0.25859657 0.74140346] +I0506 04:02:46.843299 44567 general_model.cpp:490] [client]logid=0,client_cost=13.402ms,server_cost=12.827ms. +server_tag=lstm prediction=[0.50039905 0.4996009 ] +I0506 04:02:46.850219 44567 general_model.cpp:490] [client]logid=0,client_cost=5.129ms,server_cost=4.332ms. +server_tag=cnn prediction=[0.6369219 0.36307803] +I0506 04:02:46.854203 44567 general_model.cpp:490] [client]logid=0,client_cost=2.804ms,server_cost=0.782ms. +server_tag=bow prediction=[0.15088597 0.849114 ] +I0506 04:02:46.858268 44567 general_model.cpp:490] [client]logid=0,client_cost=3.292ms,server_cost=2.677ms. +server_tag=cnn prediction=[0.4608788 0.5391212] +I0506 04:02:46.869217 44567 general_model.cpp:490] [client]logid=0,client_cost=10.13ms,server_cost=9.556ms. +server_tag=lstm prediction=[0.5000269 0.49997318] +I0506 04:02:46.883790 44567 general_model.cpp:490] [client]logid=0,client_cost=13.312ms,server_cost=12.822ms. +server_tag=lstm prediction=[0.50083774 0.49916226] +I0506 04:02:46.887256 44567 general_model.cpp:490] [client]logid=0,client_cost=2.432ms,server_cost=1.812ms. +server_tag=cnn prediction=[0.47895813 0.52104187] + +``` diff --git a/doc/Offical_Docs/7-0_Python_Pipeline_Int_CN.md b/doc/Offical_Docs/7-0_Python_Pipeline_Int_CN.md index 98bfb0efe..71df7d608 100644 --- a/doc/Offical_Docs/7-0_Python_Pipeline_Int_CN.md +++ b/doc/Offical_Docs/7-0_Python_Pipeline_Int_CN.md @@ -8,5 +8,6 @@ Python Pipeline 使用案例请阅读[Python Pipeline 快速部署案例](./3-2_ 通过阅读以下内容掌握 Python Pipeline 核心功能和使用方法、高阶功能用法和性能优化指南等。 - [Python Pipeline 框架设计](7-1_Python_Pipeline_Design_CN.md) -- [Python Pipeline 高阶用法](7-2_Python_Pipeline_Senior_CN.md) +- [Python Pipeline 核心功能](7-2_Python_Pipeline_Senior_CN.md) - [Python Pipeline 优化指南](7-3_Python_Pipeline_Optimize_CN.md) +- [Python Pipeline 性能指标](7-4_Python_Pipeline_Benchmark_CN.md) diff --git a/doc/Offical_Docs/7-1_Python_Pipeline_Design_CN.md b/doc/Offical_Docs/7-1_Python_Pipeline_Design_CN.md index 6372ec6af..24cbbf0d0 100644 --- a/doc/Offical_Docs/7-1_Python_Pipeline_Design_CN.md +++ b/doc/Offical_Docs/7-1_Python_Pipeline_Design_CN.md @@ -1,5 +1,22 @@ -# Python Pipeline 核心功能 +# Python Pipeline 框架设计 +- [目标](#1) +- [框架设计](#2) + - [2.1 网络层设计](#2.1) + - [2.2 图执行引擎层](#2.2) + - [2.3 服务日志](#2.3) + - [2.4 错误信息](#2.4) +- [自定义信息](#3) + - [3.1 自定义 Web 服务 URL](#3.1) + - [3.2 自定义服务输入和输出结构](#3.2) + - [3.3 自定义服务并发和模型配置](#3.3) + - [3.4 自定义推理过程](#3.4) + - [3.5 自定义业务错误类型](#3.5) + + + + +## 目标 为了解决多个深度学习模型组合的复杂问题，Paddle Serving 团队设计了一个通用端到端多模型组合框架，其核心特点包括: 1. 通用性：框架既要满足通用模型的输入类型，又要满足模型组合的复杂拓扑关系。 @@ -7,6 +24,7 @@ 3. 高可用性：高可用的架构依赖每个服务的健壮性，服务状态可查询、异常可监控和管理是必备条件。 4. 易于开发与调试：使用 Python 语言开发可大幅提升研发效率，运行的错误信息准确帮助开发者快速定位问题。 + ## 框架设计 Python Pipeline 框架分为网络服务层和图执行引擎2部分，网络服务层处理多种网络协议请求和通用输入参数问题，图执行引擎层解决复杂拓扑关系。如下图所示 @@ -15,6 +33,8 @@ Python Pipeline 框架分为网络服务层和图执行引擎2部分，网络服 + + **一.网络服务层** 网络服务层包括了 gRPC-gateway 和 gRPC Server。gPRC gateway 接收 HTTP 请求，打包成 proto 格式后转发给 gRPC Server，一套处理程序可同时处理 HTTP、gRPC 2种类型请求。 @@ -58,6 +78,18 @@ ocr_service.prepare_pipeline_config("config.yml") ocr_service.run_service() ``` +与网络框架相关的配置在 `config.yml` 中设置。其中 `worker_num` 表示框架主线程 gRPC 线程池工作线程数，可理解成网络同步线程并发数。 + +其次，`rpc_port` 和 `http_port` 是服务端口，可同时开启，不允许同时为空。 +``` +worker_num: 10 + +# http 和 gRPC 服务端口 +rpc_port: 9988 +http_port: 18089 +``` + + **二.图执行引擎层** @@ -126,15 +158,37 @@ Channel的设计原则： + - -**三. 服务日志** +**三.服务日志** Pipeline 服务日志在当前目录的 `PipelineServingLogs` 目录下，有3种类型日志，分别是 `pipeline.log`、`pipeline.log.wf`、`pipeline.tracer`。 - `pipeline.log` : 记录 debug & info日志信息 - `pipeline.log.wf` : 记录 warning & error日志 - `pipeline.tracer` : 统计各个阶段耗时、channel 堆积信息 +``` +├── config.yml +├── get_data.sh +├── PipelineServingLogs +│   ├── pipeline.log +│   ├── pipeline.log.wf +│   └── pipeline.tracer +├── README_CN.md +├── README.md +├── uci_housing_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── uci_housing_model +│   ├── fc_0.b_0 +│   ├── fc_0.w_0 +│   ├── __model__ +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── web_service_java.py +└── web_service.py +``` + 在服务发生异常时，错误信息会记录在 pipeline.log.wf 日志中。打印 tracer 日志要求在 config.yml 的 DAG 属性中添加 tracer 配置。 1. 日志与请求的唯一标识 @@ -177,9 +231,46 @@ Pipeline 的日志模块在 `logger.py` 中定义，使用了 `logging.handlers. ``` -**四. 服务超时与重试** + + +**四. 错误信息** +框架提供的错误信息如下所示， 完整信息在 `error_catch.py` 中 `CustomExceptionCode` 类中定义。 +| 错误码 | 说明 | +| :---: | :-------------: | +| 0 | 成功 | +| 50 ~ 999 | 产品错误 | +| 3000 ~ 3999 | 框架内部服务错误 | +| 4000 ~ 4999 | 配置错误 | +| 5000 ~ 5999 | 用户输入错误 | +| 6000 ~ 6999 | 超时错误 | +| 7000 ~ 7999 | 类型检查错误 | +| 8000 ~ 8999 | 内部通讯错误 | +| 9000 ~ 9999 | 推理错误 | +| 10000 ~ | 其他错误 | + +具体错误信息如下： + +``` +class CustomExceptionCode(enum.Enum): + OK = 0 + PRODUCT_ERROR = 50 + + NOT_IMPLEMENTED = 3000 + CLOSED_ERROR = 3001 + NO_SERVICE = 3002 + INIT_ERROR = 3003 + CONF_ERROR = 4000 + INPUT_PARAMS_ERROR = 5000 + TIMEOUT = 6000 + TYPE_ERROR = 7000 + RPC_PACKAGE_ERROR = 8000 + CLIENT_ERROR = 9000 + UNKNOW = 10000 +``` + + ## 自定义信息 @@ -190,8 +281,9 @@ Pipeline 的日志模块在 `logger.py` 中定义，使用了 `logging.handlers. - 自定义推理过程 - 自定义业务错误类型 + -1. 自定义 Web 服务 URL +**一.自定义 Web 服务 URL** 在 Web 服务中自定义服务名称是常见操作，尤其是将已有服务迁移到新框架。URL 中核心字段包括 `ip`、`port`、`name` 和 `method`，根据最新部署的环境信息设置前2个字段，重点介绍如何设置 `name` 和 `method`，框架提供默认的 `methon` 是 `prediciton`，如 `http://127.0.0.1:9999/ocr/prediction` 。 @@ -230,8 +322,9 @@ service PipelineService { } }; ``` + -2. 自定义服务输入和输出结构 +**二.自定义服务输入和输出结构** 输入和输出结构包括 proto 中 Request 和 Response 结构，以及 Op 前后处理返回。 @@ -241,10 +334,15 @@ service PipelineService { 修改后，需要[重新编译]() -3. 自定义服务并发和模型配置 + + +**三.自定义服务并发和模型配置** + 完整的配置信息可参考[配置信息]() -4. 自定义推理过程 + + +**四.自定义推理过程** 推理 Op 为开发者提供3个外部函数接口： @@ -369,8 +467,9 @@ class ResponseOp(Op): return resp ``` + -5. 自定义业务错误类型 +**五.自定义业务错误类型** 用户可根据业务场景自定义错误码，继承 ProductErrCode，在 Op 的 preprocess 或 postprocess 中返回列表中返回，下一阶段处理会根据自定义错误码跳过后置OP处理。 ```python diff --git a/doc/Offical_Docs/7-2_Python_Pipeline_Senior_CN.md b/doc/Offical_Docs/7-2_Python_Pipeline_Senior_CN.md index 8b611c803..c372d0800 100644 --- a/doc/Offical_Docs/7-2_Python_Pipeline_Senior_CN.md +++ b/doc/Offical_Docs/7-2_Python_Pipeline_Senior_CN.md @@ -1,75 +1,222 @@ -# Python Pipeline 高阶用法 +# Python Pipeline 核心功能 + +从设计上，Python Pipeline 框架实现轻量级的服务化部署，提供了丰富的核心功能，既能满足服务基本使用，又能满足特性需求。 + +- [安装与环境检查](#1) +- [服务启动与关闭](#2) +- [本地与远程推理](#3) +- [批量推理](#4) + - [4.1 客户端打包批量数据](#4.1) + - [4.2 服务端合并多个请求动态合并批量](#4.2) + - [4.3 Mini-Batch](#4.3) +- [单机多卡推理](#5) +- [多种计算芯片上推理](#6) +- [TensorRT 推理加速](#7) +- [MKLDNN 推理加速](#8) +- [低精度推理](#9) + - [9.1 CPU 低精度推理](#9.1) + - [9.2 GPU 和 TensorRT 低精度推理](#9.2) + - [9.3 性能测试](#9.3) +- [复杂图结构 DAG 跳过某个 Op 运行](#10) + + + +## 安装与环境检查 + +在运行 Python Pipeline 服务前，确保当前环境下可部署且通过[安装指南](./2-0_Index_CN.md)已完成安装。其次，`v0.8.0`及以上版本提供了环境检查功能，检验环境是否安装正确。 + +输入以下命令，进入环境检查程序。 +```python +python3 -m paddle_serving_server.serve check +``` -在复杂业务场景中使用常规功能无法满足需求，本文介绍一些高阶用法。 -- DAG 结构跳过某个 Op 运行 -- 批量推理 -- 单机多卡推理 -- 多种计算芯片上推理 -- 低精度推理 -- TensorRT 推理加速 -- MKLDNN 推理加速 +在环境检验程序中输入多条指令来检查，例如 `check_pipeline`，`check_all`等，完整指令列表如下。 +| 指令 | 描述| +|---------|----| +|check_all | 检查 Paddle Inference、Pipeline Serving、C++ Serving。只打印检测结果，不记录日志| +|check_pipeline | 检查 Pipeline Serving，只打印检测结果，不记录日志| +|check_cpp | 检查 C++ Serving，只打印检测结果，不记录日志| +|check_inference | 检查 Paddle Inference 是否安装正确，只打印检测结果，不记录日志| +|debug | 发生报错后，该命令将打印提示日志到屏幕，并记录详细日志文件| +|exit | 退出| -**一.DAG 结构跳过某个 Op 运行** -此应用场景一般在 Op 前后处理中有 if 条件判断时，不满足条件时，跳过后面处理。实际做法是在跳过此 Op 的 process 阶段，只要在 preprocess 做好判断，跳过 process 阶段，在和 postprocess 后直接返回即可。 -preprocess 返回结果列表的第二个结果是 `is_skip_process=True` 表示是否跳过当前 Op 的 process 阶段，直接进入 postprocess 处理。 +程序会分别运行 cpu 和 gpu 示例。运行成功则打印 `Pipeline cpu environment running success +` 和 `Pipeline gpu environment running success`。 -```python -## Op::preprocess() 函数实现 -def preprocess(self, input_dicts, data_id, log_id): - """ - In preprocess stage, assembling data for process stage. users can - override this function for model feed features. - Args: - input_dicts: input data to be preprocessed - data_id: inner unique id - log_id: global unique id for RTT - Return: - input_dict: data for process stage - is_skip_process: skip process stage or not, False default - prod_errcode: None default, otherwise, product errores occured. - It is handled in the same way as exception. - prod_errinfo: "" default - """ - # multiple previous Op - if len(input_dicts) != 1: - _LOGGER.critical( - self._log( - "Failed to run preprocess: this Op has multiple previous " - "inputs. Please override this func.")) - os._exit(-1) - (_, input_dict), = input_dicts.items() - return input_dict, False, None, "" +``` +/usr/local/lib/python3.7/runpy.py:125: RuntimeWarning: 'paddle_serving_server.serve' found in sys.modules after import of package 'paddle_serving_server', but prior to execution of 'paddle_serving_server.serve'; this may result in unpredictable behaviour + warn(RuntimeWarning(msg)) +Welcome to the check env shell.Type help to list commands. +(Cmd) check_pipeline +Pipeline cpu environment running success +Pipeline gpu environment running success ``` -以下示例 Jump::preprocess() 重载了原函数，返回了 True 字段 + +运行失败时，错误信息会记录到当前目录下 `stderr.log` 文件 和 `Pipeline_test_cpu/PipelineServingLogs` 目录下。用户可根据错误信息调试。 + +``` +(Cmd) check_all +PaddlePaddle inference environment running success +C++ cpu environment running success +C++ gpu environment running failure, if you need this environment, please refer to https://github.com/PaddlePaddle/Serving/blob/develop/doc/Install_CN.md +Traceback (most recent call last): + File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main + "__main__", mod_spec) + File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code + exec(code, run_globals) + File "/usr/local/lib/python3.7/site-packages/paddle_serving_server/serve.py", line 541, in + Check_Env_Shell().cmdloop() + File "/usr/local/lib/python3.7/cmd.py", line 138, in cmdloop + stop = self.onecmd(line) + File "/usr/local/lib/python3.7/cmd.py", line 217, in onecmd + return func(arg) + File "/usr/local/lib/python3.7/site-packages/paddle_serving_server/serve.py", line 501, in do_check_all + check_env("all") + File "/usr/local/lib/python3.7/site-packages/paddle_serving_server/env_check/run.py", line 94, in check_env + run_test_cases(pipeline_test_cases, "Pipeline", is_open_std) + File "/usr/local/lib/python3.7/site-packages/paddle_serving_server/env_check/run.py", line 66, in run_test_cases + mv_log_to_new_dir(new_dir_path) + File "/usr/local/lib/python3.7/site-packages/paddle_serving_server/env_check/run.py", line 48, in mv_log_to_new_dir + shutil.move(file_path, dir_path) + File "/usr/local/lib/python3.7/shutil.py", line 555, in move + raise Error("Destination path '%s' already exists" % real_dst) +shutil.Error: Destination path '/home/work/Pipeline_test_cpu/PipelineServingLogs' already exists + +``` + + + +## 服务启动与关闭 + +服务启动需要三类文件，PYTHON 程序、模型文件和配置文件。以[Python Pipeline 快速部署案例](./3-2_QuickStart_Pipeline_OCR_CN.md)为例， +``` +. +├── config.yml +├── imgs +│   └── ggg.png +├── ocr_det_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ocr_det_model +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── ocr_det.tar.gz +├── ocr_rec_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ocr_rec_model +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── pipeline_http_client.py +├── pipeline_rpc_client.py +├── ppocr_keys_v1.txt +└── web_service.py +``` + +启动服务端程序运行 `web_service.py`，启动客户端程序运行 `pipeline_http_client.py` 或 `pipeline_rpc_client.py`。服务端启动的日志信息在 `PipelineServingLogs` 目录下可用于调试。 +``` +├── PipelineServingLogs +│   ├── pipeline.log +│   ├── pipeline.log.wf +│   └── pipeline.tracer +``` + +关闭程序可使用2种方式， +- 前台关闭程序：`Ctrl+C` 关停服务 +- 后台关闭程序： ```python -class JumpOp(Op): - ## Overload func JumpOp::preprocess - def preprocess(self, input_dicts, data_id, log_id): - (_, input_dict), = input_dicts.items() - if input_dict.has_key("jump"): - return input_dict, True, None, "" - else - return input_dict, False, None, "" +python3 -m paddle_serving_server.serve stop # 触发 SIGINT 信号 +python3 -m paddle_serving_server.serve kill # 触发 SIGKILL 信号，强制关闭 +``` + + + +## 本地与远程推理 + +本地推理是指在服务所在机器环境下开启多进程推理，而远程推理是指本地服务请求远程 C++ Serving 推理服务。 + +本地推理的优势是实现简单，一般本地处理相比于远程推理耗时更低。而远程推理的优势是可实现 Python Pipeline 较难实现的功能，如部署加密模型，大模型推理。 + +Python Pipeline 的本地推理可参考如下配置，在 `uci` op 中 增加 `local_service_conf` 配置，并设置 `client_type: local_predictor`。 +``` +op: + uci: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 10 + + #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置 + local_service_conf: + + #uci模型路径 + model_config: uci_housing_model + + #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu + device_type: 0 + + #计算硬件ID，优先由device_type决定硬件类型。devices为""或空缺时为CPU预测；当为"0", "0,1,2"时为GPU预测，表示使用的GPU卡 + devices: "" # "0,1" + + #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测 + client_type: local_predictor + + #Fetch结果列表，以client_config中fetch_var的alias_name为准 + fetch_list: ["price"] +``` + +Python Pipeline 的远程推理可参考如下配置，设置 `client_type: brpc`，`server_endpoints`，`timeout` 和本地 `client_config`。 + +``` +op: + bow: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 1 + + #client连接类型，brpc + client_type: brpc + + #Serving交互重试次数，默认不重试 + retry: 1 + + #Serving交互超时时间, 单位ms + timeout: 3000 + + #Serving IPs + server_endpoints: ["127.0.0.1:9393"] + + #bow模型client端配置 + client_config: "imdb_bow_client_conf/serving_client_conf.prototxt" + + #Fetch结果列表，以client_config中fetch_var的alias_name为准 + fetch_list: ["prediction"] ``` + -**二. 批量推理** +## 批量推理 Pipeline 支持批量推理，通过增大 batch size 可以提高 GPU 利用率。Python Pipeline 支持3种 batch 形式以及适用的场景如下： - 场景1：客户端打包批量数据(Client Batch) - 场景2：服务端合并多个请求动态合并批量(Server auto-batching) -- 场景3：服务端拆分一个批量数据推理请求成为多个小块推理(Server mini-batch) +- 场景3：拆分一个大批量的推理请求为多个小批量推理请求(Server mini-batch) + -1. 客户端打包批量数据 +**一.客户端打包批量数据** 当输入数据是 numpy 类型，如shape 为[4, 3, 512, 512]的 numpy 数据，即4张图片，可直接作为输入数据。 当输入数据的 shape 不同时，需要按最大的shape的尺寸 Padding 对齐后发送给服务端 -2. 服务端合并多个请求动态合并批量 + + +**二.服务端合并多个请求动态合并批量** + 有助于提升吞吐和计算资源的利用率，当多个请求的 shape 尺寸不相同时，不支持合并。当前有2种合并策略，分别是： - 等待时间与最大批量结合（推荐）：结合`batch_size`和`auto_batching_timeout`配合使用，实际请求的批量条数超过`batch_size`时会立即执行，不超过时会等待`auto_batching_timeout`时间再执行 @@ -119,9 +266,11 @@ op: ``` + +**三.Mini-Batch** -3.服务端拆分一个批量数据推理请求成为多个小块推理：会降低批量数据 Padding 对齐的大小，从而提升速度。可参考 [OCR 示例]()，核心思路是拆分数据成多个小批量，放入 list 对象 feed_list 并返回 +拆分一个批量数据推理请求成为多个小块推理：会降低批量数据 Padding 对齐的大小，从而提升速度。可参考 [OCR 示例]()，核心思路是拆分数据成多个小批量，放入 list 对象 feed_list 并返回 ``` def preprocess(self, input_dicts, data_id, log_id): @@ -181,8 +330,9 @@ def preprocess(self, input_dicts, data_id, log_id): return feed_list, False, None, "" ``` + -**三. 单机多卡推理** +## 单机多卡推理 单机多卡推理与 `config.yml` 中配置4个参数关系紧密，`is_thread_op`、`concurrency`、`device_type` 和 `devices`，必须在进程模型和 GPU 模式，每张卡上可分配多个进程，即 M 个 Op 进程与 N 个 GPU 卡绑定。 ``` @@ -218,8 +368,9 @@ op: 对于更灵活的进程与 GPU 卡绑定方式，会持续开发。 + -**四. 多种计算芯片上推理** +## 多种计算芯片上推理 除了支持 CPU、GPU 芯片推理之外，Python Pipeline 还支持在多种计算硬件上推理。根据 `config.yml` 中的 `device_type` 和 `devices`来设置推理硬件和加速库如下： - CPU(Intel) : 0 @@ -232,27 +383,99 @@ op: 当不设置`device_type`时，根据 `devices` 来设置，即当 `device_type` 为 "" 或空缺时为 CPU 推理；当有设定如"0,1,2"时，为 GPU 推理，并指定 GPU 卡。 -以使用 GPU 的编号为0和1号卡并开启 TensorRT 为例，TensorRT 要配合 `ir_optim` 一同开启，`config.yml`详细配置如下： +以使用 XPU 的编号为0卡为例，配合 `ir_optim` 一同开启，`config.yml`详细配置如下： ``` # 计算硬件类型 -device_type: 2 +device_type: 4 # 计算硬件ID，优先由device_type决定硬件类型 -devices: "0,1" +devices: "0" # 开启ir优化 ir_optim: True ``` - -**五. 低精度推理** -Pipeline Serving支持低精度推理，CPU、GPU和TensoRT支持的精度类型如下图所示： + + +## TensorRT 推理加速 +TensorRT 是一个高性能的深度学习推理优化器，在 Nvdia 的 GPU 硬件平台运行的推理框架，为深度学习应用提供低延迟、高吞吐率的部署推理。 +通过设置`device_type`、`devices`和`ir_optim` 字段即可实现 TensorRT 高性能推理。必须同时设置 `ir_optim: True` 才能开启 TensorRT。 + +``` +op: + imagenet: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 1 + + #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置 + local_service_conf: + + #uci模型路径 + model_config: serving_server/ + + #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu + device_type: 2 + + #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡 + devices: "1" # "0,1" + + #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测 + client_type: local_predictor + + #Fetch结果列表，以client_config中fetch_var的alias_name为准 + fetch_list: ["score"] + + #开启 ir_optim + ir_optim: True +``` + + +## MKL-DNN 推理加速 + +MKL-DNN 针对 Intel CPU 和 GPU 的数学核心库，对深度学习网络进行算子和指令集的性能优化，从而提升执行速度。Paddle 框架已集成了 MKL-DNN。 + +目前仅支持 Intel CPU 推理加速，通过设置`device_type` 和 `devices` 和 `use_mkldnn` 字段使用 MKL-DNN。 + +``` +op: + imagenet: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 1 + + #当op配置没有server_endpoints时，从local_service_conf读取本地服务配置 + local_service_conf: + + #uci模型路径 + model_config: serving_server/ + + #计算硬件类型: 空缺时由devices决定(CPU/GPU)，0=cpu, 1=gpu, 2=tensorRT, 3=arm cpu, 4=kunlun xpu + device_type: 0 + + #计算硬件ID，当devices为""或不写时为CPU预测；当devices为"0", "0,1,2"时为GPU预测，表示使用的GPU卡 + devices: "" + + #client类型，包括brpc, grpc和local_predictor.local_predictor不启动Serving服务，进程内预测 + client_type: local_predictor + + #Fetch结果列表，以client_config中fetch_var的alias_name为准 + fetch_list: ["score"] + + #开启 MKLDNN + use_mkldnn: True +``` + + +## 低精度推理 + +Pipeline Serving支持低精度推理，CPU、GPU和TensoRT支持的精度类型如下图所示： 低精度推理需要有量化模型，配合`config.yml`配置一起使用，以[低精度示例]() 为例 -1. CPU 低精度推理配置 + + +**一.CPU 低精度推理** 通过设置，`device_type` 和 `devices` 字段使用 CPU 推理，通过调整`precision`、`thread_num`和`use_mkldnn`参数选择低精度和性能调优。 @@ -290,9 +513,11 @@ op: use_mkldnn: True ``` -2. GPU + TensorRT 低精度推理 + -通过设置，`device_type` 和 `devices` 字段使用原生 GPU 或 TensorRT 推理，通过调整`precision`、`ir_optim`和`use_calib`参数选择低精度和性能调优，如开启 TensorRT，必须一同开启`ir_optim`，`use_calib`仅配合 int8 使用。 +**二.GPU 和 TensorRT 低精度推理** + +通过设置`device_type` 和 `devices` 字段使用原生 GPU 或 TensorRT 推理，通过调整`precision`、`ir_optim`和`use_calib`参数选择低精度和性能调优，如开启 TensorRT，必须一同开启`ir_optim`，`use_calib`仅配合 int8 使用。 ``` op: imagenet: @@ -327,8 +552,9 @@ op: ir_optim: True ``` + -3. 性能测试 +**三.性能测试** 测试环境如下： - GPU 型号: A100-40GB @@ -345,7 +571,6 @@ op: - GPU + int8 + ir_optim + TensorRT + use_calib : 15.1 ms - GPU + fp16 + ir_optim + TensorRT : 17.2 ms - CPU 推理性能较好的配置是 - CPU + bf16 + MKLDNN : 18.2 ms - CPU + fp32 + thread_num=10 : 18.4 ms @@ -354,3 +579,50 @@ CPU 推理性能较好的配置是
+ +## 复杂图结构 DAG 跳过某个 Op 运行 + +此应用场景一般在 Op 前后处理中有 if 条件判断时，不满足条件时，跳过后面处理。实际做法是在跳过此 Op 的 process 阶段，只要在 preprocess 做好判断，跳过 process 阶段，在和 postprocess 后直接返回即可。 +preprocess 返回结果列表的第二个结果是 `is_skip_process=True` 表示是否跳过当前 Op 的 process 阶段，直接进入 postprocess 处理。 + +```python +## Op::preprocess() 函数实现 +def preprocess(self, input_dicts, data_id, log_id): + """ + In preprocess stage, assembling data for process stage. users can + override this function for model feed features. + Args: + input_dicts: input data to be preprocessed + data_id: inner unique id + log_id: global unique id for RTT + Return: + input_dict: data for process stage + is_skip_process: skip process stage or not, False default + prod_errcode: None default, otherwise, product errores occured. + It is handled in the same way as exception. + prod_errinfo: "" default + """ + # multiple previous Op + if len(input_dicts) != 1: + _LOGGER.critical( + self._log( + "Failed to run preprocess: this Op has multiple previous " + "inputs. Please override this func.")) + os._exit(-1) + (_, input_dict), = input_dicts.items() + return input_dict, False, None, "" + +``` +以下示例 Jump::preprocess() 重载了原函数，返回了 True 字段 +```python +class JumpOp(Op): + ## Overload func JumpOp::preprocess + def preprocess(self, input_dicts, data_id, log_id): + (_, input_dict), = input_dicts.items() + if input_dict.has_key("jump"): + return input_dict, True, None, "" + else + return input_dict, False, None, "" +``` diff --git a/doc/Offical_Docs/7-3_Python_Pipeline_Optimize_CN.md b/doc/Offical_Docs/7-3_Python_Pipeline_Optimize_CN.md index bc49f23cb..ef2380193 100644 --- a/doc/Offical_Docs/7-3_Python_Pipeline_Optimize_CN.md +++ b/doc/Offical_Docs/7-3_Python_Pipeline_Optimize_CN.md @@ -1,24 +1,105 @@ # Python Pipeline 优化指南 +- [优化响应时长](#1) + - [1.1 分析响应时长](#1.1) + - [Pipeline Trace Tool](#1.1.1) + - [Pipeline Profile Tool](#1.1.2) + - [1.2 优化思路](#1.2) +- [优化服务吞吐](#2) + - [2.1 分析吞吐瓶颈](#2.1) + - [2.2 优化思路](#2.2) + - [增加 Op 并发](#2.2.1) + - [动态批量](#2.2.2) + - [CPU 与 GPU 处理分离](#2.2.3) -## 如何通过 Timeline 工具进行优化 -为了更好地对性能进行优化，Python Pipeline 提供了 Timeline 工具，对整个服务的各个阶段时间进行打点。 +通常，服务的性能优化是基于耗时分析，首先要掌握服务运行的各阶段耗时信息，从中找到耗时最长的性能瓶颈再做针对性优化。对于模型推理服务化不仅要关注耗时，由于 GPU 芯片昂贵，更要关注服务吞吐，从而提升 GPU 利用率实现降本增效。因此，模型推理服务化可总结为： +- 优化响应时长 +- 优化服务吞吐 -## 在 Server 端输出 Profile 信息 +经过分析和调优后，各个阶段实现整体服务的性能最优。 -Server 端用 yaml 中的 `use_profile` 字段进行控制： + -```yaml +## 优化响应时长 + +首先，优化响应时长的主要思路首先要掌握各阶段耗时，并分析出性能瓶颈或者耗时占比较高的阶段，再针对性能瓶颈做专项优化。 + +Paddle Serving 提供2种耗时分析工具，`Pipeline Trace Tool` 和 `Pipeline Profile Tool`。2个工具的特点如下： +- Pipeline Trace Tool : 统计服务端所有进程各个阶段的平均耗时，包括每个 `Op` 和 `Channel`，用于定量分析。 +- Pipeline Profile Tool : 是可视化 Trace View 工具，生成多进程并发效果图，用定性和定量分析执行和并发效果。 + + + +**一.耗时分析** + + + +1.Pipeline Trace Tool + +`Pipeline Trace Tool` 统计每个 `Op` 和 `Channel` 中各阶段的处理耗时， + +开启方法在配置文件 `config.yml` 的 `dag` 区段内添加 `tracer` 字段，框架会每隔 `interval_s` 时间生成 Trace 信息。 +``` dag: - use_profile: true + #op资源类型, True, 为线程模型；False，为进程模型 + is_thread_op: True + + #tracer, 跟踪框架吞吐，每个OP和channel的工作情况。无tracer时不生成数据 + tracer: + #每次trace的时间间隔，单位秒/s + interval_s: 10 +``` + +生成的 Trace 信息保存在 `./PipelineServingLogs/pipeline.tracer` 日志中。如下图所示 ``` +==================== TRACER ====================== + Op(uci): + in[8473.507333333333 ms]： # 等待前置 Channel 中数据放入 Op 的耗时，如长时间无请求，此值会变大 + prep[0.6753333333333333 ms] # 推理前处理 preprocess 阶段耗时 + midp[26.476333333333333 ms] # 推理 process 阶段耗时 + postp[1.8616666666666666 ms] # 推理后处理 postprocess 阶段耗时 + out[1.3236666666666668 ms] # 后处理结果放入后置 channel 耗时 + idle[0.9965882097324374] # 框架自循环耗时，间隔 1 ms，如此值很大说明系统负载高，调度变慢 + DAGExecutor: + Query count[30] # interval_s 间隔时间内请求数量 + QPS[27.35 q/s] # interval_s 间隔时间内服务 QPS + Succ[1.0] # interval_s 间隔时间内请求成功率 + Error req[] # 异常请求信息 + Latency: + ave[36.55233333333334 ms] # 平均延时 + .50[8.702 ms] # 50分位延时 + .60[8.702 ms] # 60分位延时 + .70[92.346 ms] # 70分位延时 + .80[92.346 ms] # 70分位延时 + .90[92.346 ms] # 90分位延时 + .95[92.346 ms] # 95分位延时 + .99[92.346 ms] # 99分位延时 + Channel (server worker num[1]): + chl0(In: ['@DAGExecutor'], Out: ['uci']) size[0/0] # 框架 RequestOp 与 uci Op 之间 Channel 中堆积请求数。此值较大，说明下游 uci Op 消费能力不足。 + chl1(In: ['uci'], Out: ['@DAGExecutor']) size[0/0] # uci Op 与 框架 ResponseOp 之间 Channel 中堆积的请求数。此值较大，说明下游 ReponseOp 消费能力不足。 + ==================== TRACER ====================== +``` + + +2.Pipeline Profile Tool -开启该功能后，Server 端在预测的过程中会将对应的日志信息打印到标准输出，为了更直观地展现各阶段的耗时，提供 Analyst 模块对日志文件做进一步的分析处理。 +``` +dag: + #op资源类型, True, 为线程模型；False，为进程模型 + is_thread_op: True + + #使用性能分析, 默认为 False，imeline性能数据，对性能有一定影响 + use_profile: True, +``` -使用时先将 Server 的输出保存到文件，以 `profile.txt` 为例，脚本将日志中的时间打点信息转换成 json 格式保存到 `trace` 文件，`trace` 文件可以通过 chrome 浏览器的 tracing 功能进行可视化。 +开启后，Server 端在预测的过程中会将对应的日志信息打印到`标准输出`，为了更直观地展现各阶段的耗时，因此服务启动要使用如下命令： +``` +python3.7 web_service.py > profile.txt 2>&1 +``` -```python +服务接收请求后，输出 Profile 信息到 `profile.txt` 文件中。再粘贴如下代码到 `trace.py`， 使用框架提供 Analyst 模块对日志文件做进一步的分析处理。 +``` from paddle_serving_server.pipeline import Analyst import json import sys @@ -30,50 +111,104 @@ if __name__ == "__main__": analyst.save_trace(trace_filename) ``` -具体操作：打开 chrome 浏览器，在地址栏输入 `chrome://tracing/` ，跳转至 tracing 页面，点击 load 按钮，打开保存的 `trace` 文件，即可将预测服务的各阶段时间信息可视化。 +运行命令，脚本将日志中的时间打点信息转换成 json 格式保存到 `trace` 文件。 +``` +python3.7 trace.py +``` + +`trace` 文件可以通过 `chrome` 浏览器的 `tracing` 功能进行可视化。 +``` +打开 chrome 浏览器，在地址栏输入 chrome://tracing/ ，跳转至 tracing 页面，点击 load 按钮，打开保存的 trace 文件，即可将预测服务的各阶段时间信息可视化。 +``` + +通过图示中并发请求的处理流程可观测到推理阶段的流水线状态，以及多个请求在推理阶段的`间隔`信息，进行优化。 + + + +**二.降低响应时长优化思路** + +根据 `Pipeline Trace Tool` 输出结果在不同阶段耗时长的问题，常见场景的优化方法如下： +- Op 推理阶段(midp) 耗时长: + - 增加 Op 并发度 + - 开启 auto-batching (前提是多个请求的 shape 一致) + - 若批量数据中某条数据的 shape 很大，padding 很大导致推理很慢，可参考 OCR 示例中 mini-batch 方法。 + - 开启 TensorRT/MKL-DNN 优化 + - 开启低精度推理 +- Op 前处理阶段(prep) 或 后处理阶段耗时长: + - 增加 OP 并发度 + - 优化前后处理逻辑 +- in/out 耗时长（channel 堆积>5） + - 检查 channel 传递的数据大小，可能为传输的数据大导致延迟大。 + - 优化传入数据，不传递数据或压缩后再传入 + - 增加 Op 并发度 + - 减少上游 Op 并发度 + +根据 `Pipeline Profile Tool` 输出结果优化流水行并发的效果 +- 增加 Op 并发度，或调整不同 Op 的并发度 +- 开启 auto-batching -## 在 Client 端输出 Profile 信息 +此外，还有一些优化思路，如将 CPU 处理较慢的过程转换到 GPU 上处理等，客户端与服务端传输较大数据时，可使用共享内存方式传递内存或显存地址等。 -Client 端在 `predict` 接口设置 `profile=True`，即可开启 Profile 功能。 + -开启该功能后，Client 端在预测的过程中会将该次预测对应的日志信息打印到标准输出，后续分析处理同 Server。 +## 优化服务吞吐 -## 分析方法 -根据 `pipeline.tracer` 日志中的各个阶段耗时，按以下公式逐步分析出主要耗时在哪个阶段。 + + +**一.分析吞吐瓶颈** + +服务的吞吐量受到多种多因素条件制约，如 Op 处理时长、传输数据耗时、并发数和 DAG 图结构等，可以将这些因素进一步拆解，当传输数据不是极端庞大的时候，最重要因素是流水线中`最慢 Op 的处理时长和并发数`。 ``` -单 OP 耗时： +Op 处理时长： op_cost = process(pre + mid + post) -OP 期望并发数： -op_concurrency = 单OP耗时(s) * 期望QPS - 服务吞吐量： -service_throughput = 1 / 最慢OP的耗时 * 并发数 +service_throughput = 1 / 最慢 op_cost * 并发数 服务平响： service_avg_cost = ∑op_concurrency 【关键路径】 -Channel 堆积： -channel_acc_size = QPS(down - up) * time - 批量预测平均耗时： avg_batch_cost = (N * pre + mid + post) / N ``` + -## 优化思路 +**二.优化思路** -根据长耗时在不同阶段，采用不同的优化方法. -- OP 推理阶段(mid-process): - - 增加 OP 并发度 - - 开启 auto-batching (前提是多个请求的 shape 一致) - - 若批量数据中某条数据的 shape 很大，padding 很大导致推理很慢，可使用 mini-batch - - 开启 TensorRT/MKL-DNN 优化 - - 开启低精度推理 -- OP 前处理阶段(pre-process): - - 增加 OP 并发度 - - 优化前处理逻辑 -- in/out 耗时长（channel 堆积>5） - - 检查 channel 传递的数据大小和延迟 - - 优化传入数据，不传递数据或压缩后再传入 - - 增加 OP 并发度 - - 减少上游 OP 并发度 +优化吞吐的主要方法是 `增大 Op 并发数`、`自动批量` 和 `CPU 与 GPU 处理分离` + + + +1.增加 Op 并发** + +调整 Op 的并发数量通过设置 `is_thread_op: False` 进程类型 Op 和 `uci` Op 的 `concurrency` 字段 +``` +dag: + #op资源类型, True, 为线程模型；False，为进程模型 + is_thread_op: False +op: + uci: + #并发数，is_thread_op=True时，为线程并发；否则为进程并发 + concurrency: 10 +``` +Op 的进程数量不是越大越好，受到机器 CPU 核数、内存和显存大小的限制，推荐设置 Op 的并发数不超过系统 CPU 核数。 + + + +2.动态批量 + +动态批量是增加吞吐的有一种方法，开启方式可参考[Python Pipeline 核心功能](./7-2_Python_Pipeline_Senior_CN.md#批量推理) + + + +3.CPU 与 GPU 处理分离 + +在 `CV` 模型中，对图片或视频的前后处理成为主要瓶颈时，可考虑此方案，即将前后处理过程独立成一个 Op 并独立设置并发度。 + +将 CPU 前后处理和 GPU 推理过程比例调整到服务最佳配比。以 OCR 为例，原有流水线设计为 `RequestOp -> DetOp -> RecOp -> ResponseOp`。 + +根据耗时分析，`DetOp` 和 `RecOp` 的前处理耗时很长，因此，将2个模型前处理分离成独立 Op，最新的流水线设计为: + +`RequestOp -> PreDetOp -> DetOp -> PreRecOp -> RecOp -> ResponseOp`，并调大 `PreDetOp` 和 `PreRecOp`的并发度，从而获得 20% 的性能提升。 + +由于增加了2次数据传递，单条请求的处理延时会增加。 diff --git a/doc/Offical_Docs/7-4_Python_Pipeline_Benchmark_CN.md b/doc/Offical_Docs/7-4_Python_Pipeline_Benchmark_CN.md new file mode 100644 index 000000000..9d29abf82 --- /dev/null +++ b/doc/Offical_Docs/7-4_Python_Pipeline_Benchmark_CN.md @@ -0,0 +1,59 @@ +# Python Pipeline 性能测试 + +- [测试环境](#1) +- [性能指标与结论](#2) + + + +## 测试环境 + +测试环境如下表所示： +| | GPU | 显存 | CPU | 内存 | +|----------|---------|----------|----------------------------------------------|------| +| Serving端 | 4x Tesla P4-8GB | 7611MiB | Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz 48核 | 216G | +| Client端 | 4x Tesla P4-8GB | 7611MiB | Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz 48核 | 216G | + +使用单卡GPU，未开启TensorRT。 +模型：ResNet_v2_50 + + + +## 性能指标与结论 + +通过测试，使用 Python Pipeline 模式通过多进程并发，充分利用 GPU 显卡，具有较好的吞吐性能。 + + +测试数据如下： + +|model_name |thread_num |batch_size |CPU_util(%) |GPU_memory(mb) |GPU_util(%) |qps(samples/s) |total count |mean(ms) |median(ms) |80 percent(ms) |90 percent(ms) |99 percent(ms) |total cost(s) |each cost(s)| +|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:--|:-- +|ResNet_v2_50 |1 |1 |2.2 |3327 |17.25 |17.633658869240787 |355 |56.428481238996476 |38.646728515625 |39.496826171875 |39.98369140625 |1273.1911083984373 |20.131953477859497 |20.033540725708008| +|ResNet_v2_50 |1 |4 |2.7 |3617 |28.122 |53.50748430453522 |268 |74.71539215543378 |74.6181640625 |75.3138671875 |75.6051025390625 |77.85322998046874 |20.03458046913147 |20.024930953979492| +|ResNet_v2_50 |1 |8 |1.7 |3877 |25.7869 |59.60582783086999 |150 |133.5897119140625 |132.7503662109375 |134.968310546875 |136.470703125 |140.79039062499996 |20.132259607315063 |20.03933620452881| +|ResNet_v2_50 |1 |16 |7.0 |4777 |27.0175 |63.2627646819339 |80 |252.30162048339844 |251.8448486328125 |253.046630859375 |253.91142578125 |263.361640625 |20.233070850372314 |20.18476152420044| +|ResNet_v2_50 |1 |32 |7.5 |6567 |38.532 |62.945314687348024 |40 |506.8969482421875 |507.3531494140625 |510.562353515625 |511.421240234375 |536.8068920898437 |20.335111618041992 |20.276386737823486| +|ResNet_v2_50 |2 |1 |4.7 |6567 |49.4828 |50.40600094376044 |1010 |39.63352195815285 |39.5345458984375 |40.452880859375 |41.1375 |42.940522460937494 |20.037296772003174 |20.01696753501892| +|ResNet_v2_50 |2 |4 |2.7 |6567 |44.4744 |83.4255836891382 |420 |95.38548002697172 |95.7069091796875 |97.599951171875 |98.098583984375 |102.39680908203125 |20.137707471847534 |20.03199553489685| +|ResNet_v2_50 |2 |8 |2.2 |6567 |42.898 |91.3727510505176 |230 |174.89108568274457 |175.0452880859375 |175.82001953125 |176.7634033203125 |178.64064453125002 |20.13729453086853 |20.1132071018219| +|ResNet_v2_50 |2 |16 |2.2 |6567 |45 |97.5591285698611 |124 |327.16720088835683 |328.6126708984375 |329.75185546875 |330.386962890625 |336.86397460937496 |20.336385011672974 |20.284939169883728| +|ResNet_v2_50 |2 |32 |3.2 |6567 |59.5714 |100.70765418116333 |64 |633.9812698364258 |637.8568115234375 |648.103515625 |650.7439697265625 |659.2212915039062 |20.336090803146362 |20.28787398338318| +|ResNet_v2_50 |4 |1 |3.1 |6567 |64.3333 |80.27845081929433 |1617 |49.56464230756223 |49.4873046875 |51.5537109375 |52.693408203125 |55.207568359374996 |20.142391681671143 |20.038144528865814| +|ResNet_v2_50 |4 |4 |3.3 |6567 |70.4563 |136.62061939701394 |688 |116.51574919944586 |121.8629150390625 |129.8181640625 |133.384423828125 |142.69500732421875 |20.143372297286987 |20.041599333286285| +|ResNet_v2_50 |4 |8 |3.0 |6567 |70.896 |158.46554975132275 |399 |201.30669079926378 |210.69775390625 |228.51748046875 |236.427294921875 |252.24822753906233 |20.143179416656494 |20.081032752990723| +|ResNet_v2_50 |4 |16 |3.2 |6567 |66.3832 |156.4935247130092 |197 |407.6668608224937 |423.974609375 |450.368212890625 |464.45986328125 |482.93658203125 |20.141408443450928 |20.078101694583893| +|ResNet_v2_50 |4 |32 |3.3 |6567 |72.4791 |162.01742190796557 |104 |785.5079204852765 |813.0341796875 |887.107958984375 |909.6556640625 |935.3334838867188 |20.541000843048096 |20.423666059970856| +|ResNet_v2_50 |8 |1 |3.5 |6567 |93.977 |115.9749228558386 |2337 |68.5580409078145 |65.45849609375 |76.13930664062501 |83.542041015625 |91.45666015624998 |20.15090799331665 |20.028797417879105| +|ResNet_v2_50 |8 |4 |4.2 |6567 |90.0952 |175.58748591910316 |889 |180.7330482920592 |170.5810546875 |218.99931640625 |240.06337890625002 |254.413759765625 |20.252012729644775 |20.084695398807526| +|ResNet_v2_50 |8 |8 |2.6 |6567 |93.8693 |206.76595246418208 |526 |306.52158695119414 |303.043212890625 |321.0791015625 |350.5477294921875 |400.32452392578125 |20.351513147354126 |20.15437400341034| +|ResNet_v2_50 |8 |16 |3.2 |6567 |85.7273 |205.31850043117367 |265 |614.1745522553066 |552.372314453125 |775.89169921875 |802.022607421875 |902.2763183593761 |20.650842428207397 |20.345011442899704| +|ResNet_v2_50 |8 |32 |5.0 |6567 |89.8717 |219.8410273718835 |146 |1138.4533474020761 |1039.640869140625 |1364.289794921875 |1474.6744384765625 |1788.2614379882834 |21.251720190048218 |20.777225106954575| +|ResNet_v2_50 |12 |1 |5.0 |6567 |89.4762 |110.00858327847862 |2218 |108.50048552943953 |103.015625 |121.09404296875003 |137.1392333984375 |151.80401123046872 |20.162063121795654 |20.055511037508648| +|ResNet_v2_50 |12 |4 |4.1 |6567 |77.7619 |153.7824464757549 |779 |309.68895575507463 |285.585205078125 |378.07421875 |413.481640625 |424.70853515625 |20.262390613555908 |20.104551911354065| +|ResNet_v2_50 |12 |8 |3.6 |6567 |72.6977 |165.36021780846013 |425 |571.1991590073529 |510.995849609375 |731.9383300781251 |747.6568359375 |757.304716796875 |20.56117272377014 |20.230452219645183| +|ResNet_v2_50 |12 |16 |1.5 |6567 |76.2222 |189.6414991568285 |252 |987.7153136238219 |926.00390625 |1080.99130859375 |1249.4956298828126 |1434.4802392578124 |21.26116919517517 |20.74245794614156| +|ResNet_v2_50 |12 |32 |2.8 |6567 |84.25 |203.868228281784 |138 |1811.640237559443 |1764.2760009765625 |1855.28046875 |2023.56826171875 |2586.8038134765625 |21.66105055809021 |20.834286351998646| +|ResNet_v2_50 |16 |1 |4.8 |6567 |94.3333 |116.34927733312234 |2347 |136.7957122373642 |135.959716796875 |144.1568359375 |146.105517578125 |175.05707519531248 |20.172020435333252 |20.067057371139526| +|ResNet_v2_50 |16 |4 |15.4 |6567 |83.6364 |160.59012047270738 |822 |393.3079394412447 |396.446533203125 |426.272216796875 |429.777734375 |564.1119360351562 |20.47448492050171 |20.206754431128502| +|ResNet_v2_50 |16 |8 |6.8 |6567 |81.0233 |169.95774070621547 |437 |741.5512622684854 |751.521484375 |763.199169921875 |948.8041992187501 |1001.156142578125 |20.56981921195984 |20.254074171185493| +|ResNet_v2_50 |16 |16 |3.5 |6567 |77.8706 |186.56600081516 |248 |1332.1007946383568 |1365.2745361328125 |1399.212255859375 |1432.4037353515625 |1771.4374853515626 |21.26861262321472 |20.64799252152443| +|ResNet_v2_50 |16 |32 |4.3 |6567 |83.6371 |201.1293408638195 |140 |2419.3400198800223 |2561.09228515625 |2616.081103515625 |2642.0835205078124 |2883.8197412109366 |22.274224042892456 |21.169659316539764| diff --git a/doc/Offical_Docs/9-0_Kubernetes_Int_CN.md b/doc/Offical_Docs/9-0_Kubernetes_Int_CN.md index 16559a58f..cd0668096 100644 --- a/doc/Offical_Docs/9-0_Kubernetes_Int_CN.md +++ b/doc/Offical_Docs/9-0_Kubernetes_Int_CN.md @@ -3,6 +3,5 @@ Kubernetes 集群部署 服务部署经历从物理机、虚拟机、容器化、云原生4个阶段。云原生，提供集装箱组合模式的乐高生态，Docker、Kubernetes 已称为云原生时代基础设施，推动应用程序大发展。Kubernetes 的可扩展性和分布式架构一直是人工智能和机器学习的绝佳选择，随着解决方案不断成熟，推动机器学习大规模工程落地。 本章节介绍 Kubernetes 上集群化部署 Paddle Serving 方案以及企业级安全网关部署案例。 -- [Kubernetes 集群部署方案]() +- [Kubernetes 集群部署方案](./9-1_Kubernetes_CN.md) - [Kubernetes 安全网关部署案例]() - diff --git a/doc/Offical_Docs/Home_Page_CN.md b/doc/Offical_Docs/Home_Page_CN.md new file mode 100644 index 000000000..183e060b3 --- /dev/null +++ b/doc/Offical_Docs/Home_Page_CN.md @@ -0,0 +1,88 @@ +# Paddle Serving - 端到端服务化推理框架 + +## 1.Paddle Serving 介绍 +面向模型服务化部署场景的端到端服务化推理框架 Paddle Serving，可以实现飞桨模型在 X86、ARM 平台多种硬件上高性能服务化部署，支持5种以上的 GPU、NPU 硬件推理加速；此外，Paddle Serving 提供 Docker 和 Kubernetes 的云端部署方案。 + +## 2.快速上手-代码模块 + +进入到 Serving 的 git 目录下，进入到 [fit_a_line](https://github.com/PaddlePaddle/Serving/tree/v0.8.3/examples/C%2B%2B/fit_a_line) 示例 +``` +## 下载模型 +sh get_data.sh + +## 启动服务 +python3 -m paddle_serving_server.serve --model uci_housing_model --thread 10 --port 9393 + +## HTTP curl +curl -XPOST http://0.0.0.0:9393/GeneralModelService/inference -d ' {"tensor":[{"float_data":[0.0137,-0.1136,0.2553,-0.0692,0.0582,-0.0727,-0.1583,-0.0584,0.6283,0.4919,0.1856,0.0795,-0.0332],"elem_type":1,"name":"x","alias_name":"x","shape":[1,13]}],"fetch_var_names":["price"],"log_id":0}' +``` + +## 3.部署流程图 + +开发流程：①准备部署环境；②准备部署模型；③Serving程序开发；④服务启动与优化 + +**①准备部署环境** +docker 是一个开源的应用容器引擎，可以让应用程序更加方便地被打包和移植。Paddle Serving 容器化部署建议在 docker 中进行Serving服务化部署。在 Serving Docker 环境中安装 PYTHON Wheel 包 + +**②准备部署模型** + +下载推理模型后，为了便于模型服务化部署，需要将推理模型保存成用于 Serving 部署的参数形式 + +**③Serving程序开发** + +修改服务端和客户端代码适配模型的前后处理，通过修改配置或命令行参数，如端口、指定硬件和并发数量等指定部署参数。 + +**④服务启动与优化** + +命令方式启动服务端和客户端，根据输出结果和性能指标做进一步的性能优化。 + +## 4.Demo 展示区 + +参考 [模型库](./4-0_ModelZoo_CN.md) + +## 5.核心优势 + +Paddle Serving 具备工业级功能、高性能等优势。 + +**一.工业级** + +- 支持 HTTP、gRPC、bRPC 等多种协议；提供 C++、Python、Java 语言 SDK +- 设计并实现基于有向无环图(DAG)的异步流水线高性能推理框架，具有多模型组合、异步调度、并发推理、动态批量、多卡多流推理、请求缓存等特性 +- 适配 x86(Intel) CPU、ARM CPU、Nvidia GPU、昆仑 XPU、华为昇腾310/910、海光 DCU、Nvidia Jetson 等多种硬件 +- 集成 Intel MKLDNN、Nvidia TensorRT 加速库，以及低精度和量化推理 +- 提供一套模型安全部署解决方案，包括加密模型部署、鉴权校验、HTTPs 安全网关，并在实际项目中应用 +- 支持云端部署，提供百度云智能云 kubernetes 集群部署 Paddle Serving 案例 +- 提供丰富的经典模型部署示例，如 PaddleOCR、PaddleClas、PaddleDetection、PaddleSeg、PaddleNLP 和 PaddleRec等套件，共计40多个预训练精品模型 + +**二.高性能** + +# 1. 测试环境和说明 +1) GPU型号：Tesla P4(7611 Mib) +2) Cuda版本：11.0 +3) 模型：ResNet_v2_50 +4) 为了测试异步合并batch的效果，测试数据中batch=1 +5) [使用的测试代码和使用的数据集](../../examples/C++/PaddleClas/resnet_v2_50) +6) 下图中蓝色是C++ Serving，灰色为TF-Serving。 +7) 折线图为QPS，数值越大表示每秒钟处理的请求数量越大，性能就越好。 +8) 柱状图为平均处理时延，数值越大表示单个请求处理时间越长，性能就越差。 + +同步模型默认参数配置情况下，C++ Serving QPS 和平均时延指标均优于 TF-Serving。 +

+
+ +
+

+ +异步模式情况下，两者性能接近，但当 Client 并发数达到70的时候，TF-Serving 服务直接超时，而 C++ Serving 能够正常返回结果。 +

+
+ +
+

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./ResNet50_vd_client/ +``` +会在当前文件夹多出 `ResNet50_vd_serving` 和 `ResNet50_vd_client` 的文件夹 + + +保存参数后，会在当前文件夹多出 `ResNet50_vd_serving` 和 `ResNet50_vd_client` 的文件夹： +``` +├── daisy.jpg +├── http_client.py +├── imagenet.label +├── ResNet50_vd_client +│   ├── serving_client_conf.prototxt +│   └── serving_client_conf.stream.prototxt +├── ResNet50_vd_infer +│   ├── inference.pdiparams +│   ├── inference.pdiparams.info +│   └── inference.pdmodel +├── ResNet50_vd_serving +│   ├── fluid_time_file +│   ├── inference.pdiparams +│   ├── inference.pdmodel +│   ├── serving_server_conf.prototxt +│   └── serving_server_conf.stream.prototxt +├── rpc_client.py +``` + +**三.启动服务** + +C++ Serving 服务可以指定一个网络端口同时接收 HTTP、gRPC 和 bRPC 请求。命令参数 `--model` 指定模型路径，`--gpu_ids` 指定 GPU 卡，`--port` 指定端口。 + +``` +python3 -m paddle_serving_server.serve --model ResNet50_vd_serving --gpu_ids 0 --port 9394 +``` + +**四.启动客户端** + +1. `rpc_client.py` 封装了 HTTP 请求客户端 + +``` +python3 http_client.py +``` + +2. `http_client.py` 封装了 gRPC 请求客户端 + +``` +python3 rpc_client.py +``` + +成功运行后，模型预测的结果会打印如下： + +``` +prediction: daisy, probability: 0.9341399073600769 +``` diff --git a/examples/C++/PaddleClas/resnet_50_vd/daisy.jpg b/examples/C++/PaddleClas/resnet_50_vd/daisy.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7edeca63e5f32e68550ef720d81f59df58a8eabc GIT binary patch literal 39696 zcmb4qRa9I}(Cy$u&>=vO!3US%FgU^8T>`;mHi?MmxWj zgyCK9G#`)@BD2NFuj3JRE+88YFSwA?t$P+k`Ai1J{}|@~3BtsBt`qe zQWKgje+xzTlNXedwxj@_CrJ@Xq5DZm0Rds$Nud!|aA1!VEsz$pMQ07{Ba=_sWB^(M z2<@c{Wq?c{f#3VMiGa^-82V5#LHQA=Z~iO&q&zHu-+`8N*8CRqyQxHAGI?koD*v?g zvnX^+Oyb^o7TPeG6jV)FP{i~wKjv_s7%godzcd93U_Uj6LYkN;Oi>w#fx-dVV)0b7 zhuQZ*DlPdvEF<#ZL@OaDQK=wHGASrAsJ&Ot2!e!`tGpuMy{fq%U4xKv@XhHjogis0qv_xssmL8TSlL7XqpbVq( zbH{l|L2-aR(qxK8cz_V#_Z|;)OI+G?jHEv9kBnOapXg{&#b`r3o(W=v5GG+OS^`2C zWPlKGEI4_KP+5%-pb!Cy2>Y*3hzxqUV9o?Xl=0ijq=^ zUc3wY53{PKJP6%i&7L(1S{x6@;_qOJ&}8XTQ~@V}Gc8%mSY4gkulB4!n6rRC6kXLd~SjY>_Z*qiLke% 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(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. + +import sys +from paddle_serving_client import HttpClient + +#app +from paddle_serving_app.reader import Sequential, URL2Image, Resize +from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize +import time + +client = HttpClient() +client.load_client_config("./ResNet50_vd_client/serving_client_conf.prototxt") +''' +if you want use GRPC-client, set_use_grpc_client(True) +or you can directly use client.grpc_client_predict(...) +as for HTTP-client,set_use_grpc_client(False)(which is default) +or you can directly use client.http_client_predict(...) +''' +#client.set_use_grpc_client(True) +''' +if you want to enable Encrypt Module,uncommenting the following line +''' +#client.use_key("./key") +''' +if you want to compress,uncommenting the following line +''' +#client.set_response_compress(True) +#client.set_request_compress(True) +''' +we recommend use Proto data format in HTTP-body, set True(which is default) +if you want use JSON data format in HTTP-body, set False +''' +#client.set_http_proto(True) +client.connect(["127.0.0.1:9394"]) + +label_dict = {} +label_idx = 0 +with open("imagenet.label") as fin: + for line in fin: + label_dict[label_idx] = line.strip() + label_idx += 1 + +#preprocess +seq = Sequential([ + URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)), + Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True) +]) + +start = time.time() +image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg" +for i in range(1): + img = seq(image_file) + res = client.predict(feed={"inputs": img}, fetch=[], batch=False) + + if res is None: + raise ValueError("predict error") + + if res.err_no != 0: + raise ValueError("predict error. Response : {}".format(res)) + + max_val = res.outputs[0].tensor[0].float_data[0] + max_idx = 0 + for one_data in enumerate(res.outputs[0].tensor[0].float_data): + if one_data[1] > max_val: + max_val = one_data[1] + max_idx = one_data[0] + label = label_dict[max_idx].strip().replace(",", "") + print("prediction: {}, probability: {}".format(label, max_val)) +end = time.time() +print(end - start) diff --git a/examples/C++/PaddleClas/resnet_50_vd/imagenet.label b/examples/C++/PaddleClas/resnet_50_vd/imagenet.label new file mode 100644 index 000000000..d71467351 --- /dev/null +++ b/examples/C++/PaddleClas/resnet_50_vd/imagenet.label @@ -0,0 +1,1000 @@ +tench, Tinca tinca, +goldfish, Carassius auratus, +great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias, +tiger shark, Galeocerdo cuvieri, +hammerhead, hammerhead shark, +electric ray, crampfish, numbfish, torpedo, +stingray, +cock, +hen, +ostrich, Struthio camelus, +brambling, Fringilla montifringilla, +goldfinch, Carduelis carduelis, +house finch, linnet, Carpodacus mexicanus, +junco, snowbird, +indigo bunting, indigo finch, indigo bird, Passerina cyanea, +robin, American robin, Turdus migratorius, +bulbul, +jay, +magpie, +chickadee, +water ouzel, dipper, +kite, +bald eagle, American eagle, Haliaeetus leucocephalus, +vulture, +great grey owl, great gray owl, Strix nebulosa, +European fire salamander, Salamandra salamandra, +common newt, Triturus vulgaris, +eft, +spotted salamander, Ambystoma maculatum, +axolotl, mud puppy, Ambystoma mexicanum, +bullfrog, Rana catesbeiana, +tree frog, tree-frog, +tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui, +loggerhead, loggerhead turtle, Caretta caretta, +leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea, +mud turtle, +terrapin, +box turtle, box tortoise, +banded gecko, +common iguana, iguana, Iguana iguana, +American chameleon, anole, Anolis carolinensis, +whiptail, whiptail lizard, +agama, +frilled lizard, Chlamydosaurus kingi, +alligator lizard, +Gila monster, Heloderma suspectum, +green lizard, Lacerta viridis, +African chameleon, Chamaeleo chamaeleon, +Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis, +African crocodile, Nile crocodile, Crocodylus niloticus, +American alligator, Alligator mississipiensis, +triceratops, +thunder snake, worm snake, Carphophis amoenus, +ringneck snake, ring-necked snake, ring snake, +hognose snake, puff adder, sand viper, +green snake, grass snake, +king snake, kingsnake, +garter snake, grass snake, +water snake, +vine snake, +night snake, Hypsiglena torquata, +boa constrictor, Constrictor constrictor, +rock python, rock snake, Python sebae, +Indian cobra, Naja naja, +green mamba, +sea snake, +horned viper, cerastes, sand viper, horned asp, Cerastes cornutus, +diamondback, diamondback rattlesnake, Crotalus adamanteus, +sidewinder, horned rattlesnake, Crotalus cerastes, +trilobite, +harvestman, daddy longlegs, Phalangium opilio, +scorpion, +black and gold garden spider, Argiope aurantia, +barn spider, Araneus cavaticus, +garden spider, Aranea diademata, +black widow, Latrodectus mactans, +tarantula, +wolf spider, hunting spider, +tick, +centipede, +black grouse, +ptarmigan, +ruffed grouse, partridge, Bonasa umbellus, +prairie chicken, prairie grouse, prairie fowl, +peacock, +quail, +partridge, +African grey, African gray, Psittacus erithacus, +macaw, +sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita, +lorikeet, +coucal, +bee eater, +hornbill, +hummingbird, +jacamar, +toucan, +drake, +red-breasted merganser, Mergus serrator, +goose, +black swan, Cygnus atratus, +tusker, +echidna, spiny anteater, anteater, +platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus, +wallaby, brush kangaroo, +koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus, +wombat, +jellyfish, +sea anemone, anemone, +brain coral, +flatworm, platyhelminth, +nematode, nematode worm, roundworm, +conch, +snail, +slug, +sea slug, nudibranch, +chiton, coat-of-mail shell, sea cradle, polyplacophore, +chambered nautilus, pearly nautilus, nautilus, +Dungeness crab, Cancer magister, +rock crab, Cancer irroratus, +fiddler crab, +king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica, +American lobster, Northern lobster, Maine lobster, Homarus americanus, +spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish, +crayfish, crawfish, crawdad, crawdaddy, +hermit crab, +isopod, +white stork, Ciconia ciconia, +black stork, Ciconia nigra, +spoonbill, +flamingo, +little blue heron, Egretta caerulea, +American egret, great white heron, Egretta albus, +bittern, +crane, +limpkin, Aramus pictus, +European gallinule, Porphyrio porphyrio, +American coot, marsh hen, mud hen, water hen, Fulica americana, +bustard, +ruddy turnstone, Arenaria interpres, +red-backed sandpiper, dunlin, Erolia alpina, +redshank, Tringa totanus, +dowitcher, +oystercatcher, oyster catcher, +pelican, +king penguin, Aptenodytes patagonica, +albatross, mollymawk, +grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus, +killer whale, killer, orca, grampus, sea wolf, Orcinus orca, +dugong, Dugong dugon, +sea lion, +Chihuahua, +Japanese spaniel, +Maltese dog, Maltese terrier, Maltese, +Pekinese, Pekingese, Peke, +Shih-Tzu, +Blenheim spaniel, +papillon, +toy terrier, +Rhodesian ridgeback, +Afghan hound, Afghan, +basset, basset hound, +beagle, +bloodhound, sleuthhound, +bluetick, +black-and-tan coonhound, +Walker hound, Walker foxhound, +English foxhound, +redbone, +borzoi, Russian wolfhound, +Irish wolfhound, +Italian greyhound, +whippet, +Ibizan hound, Ibizan Podenco, +Norwegian elkhound, elkhound, +otterhound, otter hound, +Saluki, gazelle hound, +Scottish deerhound, deerhound, +Weimaraner, +Staffordshire bullterrier, Staffordshire bull terrier, +American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier, +Bedlington terrier, +Border terrier, +Kerry blue terrier, +Irish terrier, +Norfolk terrier, +Norwich terrier, +Yorkshire terrier, +wire-haired fox terrier, +Lakeland terrier, +Sealyham terrier, Sealyham, +Airedale, Airedale terrier, +cairn, cairn terrier, +Australian terrier, +Dandie Dinmont, Dandie Dinmont terrier, +Boston bull, Boston terrier, +miniature schnauzer, +giant schnauzer, +standard schnauzer, +Scotch terrier, Scottish terrier, Scottie, +Tibetan terrier, chrysanthemum dog, +silky terrier, Sydney silky, +soft-coated wheaten terrier, +West Highland white terrier, +Lhasa, Lhasa apso, +flat-coated retriever, +curly-coated retriever, +golden retriever, +Labrador retriever, +Chesapeake Bay retriever, +German short-haired pointer, +vizsla, Hungarian pointer, +English setter, +Irish setter, red setter, +Gordon setter, +Brittany spaniel, +clumber, clumber spaniel, +English springer, English springer spaniel, +Welsh springer spaniel, +cocker spaniel, English cocker spaniel, cocker, +Sussex spaniel, +Irish water spaniel, +kuvasz, +schipperke, +groenendael, +malinois, +briard, +kelpie, +komondor, +Old English sheepdog, bobtail, +Shetland sheepdog, Shetland sheep dog, Shetland, +collie, +Border collie, +Bouvier des Flandres, Bouviers des Flandres, +Rottweiler, +German shepherd, German shepherd dog, German police dog, alsatian, +Doberman, Doberman pinscher, +miniature pinscher, +Greater Swiss Mountain dog, +Bernese mountain dog, +Appenzeller, +EntleBucher, +boxer, +bull mastiff, +Tibetan mastiff, +French bulldog, +Great Dane, +Saint Bernard, St Bernard, +Eskimo dog, husky, +malamute, malemute, Alaskan malamute, +Siberian husky, +dalmatian, coach dog, carriage dog, +affenpinscher, monkey pinscher, monkey dog, +basenji, +pug, pug-dog, +Leonberg, +Newfoundland, Newfoundland dog, +Great Pyrenees, +Samoyed, Samoyede, +Pomeranian, +chow, chow chow, +keeshond, +Brabancon griffon, +Pembroke, Pembroke Welsh corgi, +Cardigan, Cardigan Welsh corgi, +toy poodle, +miniature poodle, +standard poodle, +Mexican hairless, +timber wolf, grey wolf, gray wolf, Canis lupus, +white wolf, Arctic wolf, Canis lupus tundrarum, +red wolf, maned wolf, Canis rufus, Canis niger, +coyote, prairie wolf, brush wolf, Canis latrans, +dingo, warrigal, warragal, Canis dingo, +dhole, Cuon alpinus, +African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus, +hyena, hyaena, +red fox, Vulpes vulpes, +kit fox, Vulpes macrotis, +Arctic fox, white fox, Alopex lagopus, +grey fox, gray fox, Urocyon cinereoargenteus, +tabby, tabby cat, +tiger cat, +Persian cat, +Siamese cat, Siamese, +Egyptian cat, +cougar, puma, catamount, mountain lion, painter, panther, Felis concolor, +lynx, catamount, +leopard, Panthera pardus, +snow leopard, ounce, Panthera uncia, +jaguar, panther, Panthera onca, Felis onca, +lion, king of beasts, Panthera leo, +tiger, Panthera tigris, +cheetah, chetah, Acinonyx jubatus, +brown bear, bruin, Ursus arctos, +American black bear, black bear, Ursus americanus, Euarctos americanus, +ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus, +sloth bear, Melursus ursinus, Ursus ursinus, +mongoose, +meerkat, mierkat, +tiger beetle, +ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle, +ground beetle, carabid beetle, +long-horned beetle, longicorn, longicorn beetle, +leaf beetle, chrysomelid, +dung beetle, +rhinoceros beetle, +weevil, +fly, +bee, +ant, emmet, pismire, +grasshopper, hopper, +cricket, +walking stick, walkingstick, stick insect, +cockroach, roach, +mantis, mantid, +cicada, cicala, +leafhopper, +lacewing, lacewing fly, +"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", +damselfly, +admiral, +ringlet, ringlet butterfly, +monarch, monarch butterfly, milkweed butterfly, Danaus plexippus, +cabbage butterfly, +sulphur butterfly, sulfur butterfly, +lycaenid, lycaenid butterfly, +starfish, sea star, +sea urchin, +sea cucumber, holothurian, +wood rabbit, cottontail, cottontail rabbit, +hare, +Angora, Angora rabbit, +hamster, +porcupine, hedgehog, +fox squirrel, eastern fox squirrel, Sciurus niger, +marmot, +beaver, +guinea pig, Cavia cobaya, +sorrel, +zebra, +hog, pig, grunter, squealer, Sus scrofa, +wild boar, boar, Sus scrofa, +warthog, +hippopotamus, hippo, river horse, Hippopotamus amphibius, +ox, +water buffalo, water ox, Asiatic buffalo, Bubalus bubalis, +bison, +ram, tup, +bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis, +ibex, Capra ibex, +hartebeest, +impala, Aepyceros melampus, +gazelle, +Arabian camel, dromedary, Camelus dromedarius, +llama, +weasel, +mink, +polecat, fitch, foulmart, foumart, Mustela putorius, +black-footed ferret, ferret, Mustela nigripes, +otter, +skunk, polecat, wood pussy, +badger, +armadillo, +three-toed sloth, ai, Bradypus tridactylus, +orangutan, orang, orangutang, Pongo pygmaeus, +gorilla, Gorilla gorilla, +chimpanzee, chimp, Pan troglodytes, +gibbon, Hylobates lar, +siamang, Hylobates syndactylus, Symphalangus syndactylus, +guenon, guenon monkey, +patas, hussar monkey, Erythrocebus patas, +baboon, +macaque, +langur, +colobus, colobus monkey, +proboscis monkey, Nasalis larvatus, +marmoset, +capuchin, ringtail, Cebus capucinus, +howler monkey, howler, +titi, titi monkey, +spider monkey, Ateles geoffroyi, +squirrel monkey, Saimiri sciureus, +Madagascar cat, ring-tailed lemur, Lemur catta, +indri, indris, Indri indri, Indri brevicaudatus, +Indian elephant, Elephas maximus, +African elephant, Loxodonta africana, +lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens, +giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca, +barracouta, snoek, +eel, +coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch, +rock beauty, Holocanthus tricolor, +anemone fish, +sturgeon, +gar, garfish, garpike, billfish, Lepisosteus osseus, +lionfish, +puffer, pufferfish, blowfish, globefish, +abacus, +abaya, +"academic gown, academic robe, judges robe", +accordion, piano accordion, squeeze box, +acoustic guitar, +aircraft carrier, carrier, flattop, attack aircraft carrier, +airliner, +airship, dirigible, +altar, +ambulance, +amphibian, amphibious vehicle, +analog clock, +apiary, bee house, +apron, +ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin, +assault rifle, assault gun, +backpack, back pack, knapsack, packsack, rucksack, haversack, +bakery, bakeshop, bakehouse, +balance beam, beam, +balloon, +ballpoint, ballpoint pen, ballpen, Biro, +Band Aid, +banjo, +bannister, banister, balustrade, balusters, handrail, +barbell, +barber chair, +barbershop, +barn, +barometer, +barrel, cask, +barrow, garden cart, lawn cart, wheelbarrow, +baseball, +basketball, +bassinet, +bassoon, +bathing cap, swimming cap, +bath towel, +bathtub, bathing tub, bath, tub, +beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon, +beacon, lighthouse, beacon light, pharos, +beaker, +bearskin, busby, shako, +beer bottle, +beer glass, +bell cote, bell cot, +bib, +bicycle-built-for-two, tandem bicycle, tandem, +bikini, two-piece, +binder, ring-binder, +binoculars, field glasses, opera glasses, +birdhouse, +boathouse, +bobsled, bobsleigh, bob, +bolo tie, bolo, bola tie, bola, +bonnet, poke bonnet, +bookcase, +bookshop, bookstore, bookstall, +bottlecap, +bow, +bow tie, bow-tie, bowtie, +brass, memorial tablet, plaque, +brassiere, bra, bandeau, +breakwater, groin, groyne, mole, bulwark, seawall, jetty, +breastplate, aegis, egis, +broom, +bucket, pail, +buckle, +bulletproof vest, +bullet train, bullet, +butcher shop, meat market, +cab, hack, taxi, taxicab, +caldron, cauldron, +candle, taper, wax light, +cannon, +canoe, +can opener, tin opener, +cardigan, +car mirror, +carousel, carrousel, merry-go-round, roundabout, whirligig, +"carpenters kit, tool kit", +carton, +car wheel, +cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM, +cassette, +cassette player, +castle, +catamaran, +CD player, +cello, violoncello, +cellular telephone, cellular phone, cellphone, cell, mobile phone, +chain, +chainlink fence, +chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour, +chain saw, chainsaw, +chest, +chiffonier, commode, +chime, bell, gong, +china cabinet, china closet, +Christmas stocking, +church, church building, +cinema, movie theater, movie theatre, movie house, picture palace, +cleaver, meat cleaver, chopper, +cliff dwelling, +cloak, +clog, geta, patten, sabot, +cocktail shaker, +coffee mug, +coffeepot, +coil, spiral, volute, whorl, helix, +combination lock, +computer keyboard, keypad, +confectionery, confectionary, candy store, +container ship, containership, container vessel, +convertible, +corkscrew, bottle screw, +cornet, horn, trumpet, trump, +cowboy boot, +cowboy hat, ten-gallon hat, +cradle, +crane, +crash helmet, +crate, +crib, cot, +Crock Pot, +croquet ball, +crutch, +cuirass, +dam, dike, dyke, +desk, +desktop computer, +dial telephone, dial phone, +diaper, nappy, napkin, +digital clock, +digital watch, +dining table, board, +dishrag, dishcloth, +dishwasher, dish washer, dishwashing machine, +disk brake, disc brake, +dock, dockage, docking facility, +dogsled, dog sled, dog sleigh, +dome, +doormat, welcome mat, +drilling platform, offshore rig, +drum, membranophone, tympan, +drumstick, +dumbbell, +Dutch oven, +electric fan, blower, +electric guitar, +electric locomotive, +entertainment center, +envelope, +espresso maker, +face powder, +feather boa, boa, +file, file cabinet, filing cabinet, +fireboat, +fire engine, fire truck, +fire screen, fireguard, +flagpole, flagstaff, +flute, transverse flute, +folding chair, +football helmet, +forklift, +fountain, +fountain pen, +four-poster, +freight car, +French horn, horn, +frying pan, frypan, skillet, +fur coat, +garbage truck, dustcart, +gasmask, respirator, gas helmet, +gas pump, gasoline pump, petrol pump, island dispenser, +goblet, +go-kart, +golf ball, +golfcart, golf cart, +gondola, +gong, tam-tam, +gown, +grand piano, grand, +greenhouse, nursery, glasshouse, +grille, radiator grille, +grocery store, grocery, food market, market, +guillotine, +hair slide, +hair spray, +half track, +hammer, +hamper, +hand blower, blow dryer, blow drier, hair dryer, hair drier, +hand-held computer, hand-held microcomputer, +handkerchief, hankie, hanky, hankey, +hard disc, hard disk, fixed disk, +harmonica, mouth organ, harp, mouth harp, +harp, +harvester, reaper, +hatchet, +holster, +home theater, home theatre, +honeycomb, +hook, claw, +hoopskirt, crinoline, +horizontal bar, high bar, +horse cart, horse-cart, +hourglass, +iPod, +iron, smoothing iron, +"jack-o-lantern", +jean, blue jean, denim, +jeep, landrover, +jersey, T-shirt, tee shirt, +jigsaw puzzle, +jinrikisha, ricksha, rickshaw, +joystick, +kimono, +knee pad, +knot, +lab coat, laboratory coat, +ladle, +lampshade, lamp shade, +laptop, laptop computer, +lawn mower, mower, +lens cap, lens cover, +letter opener, paper knife, paperknife, +library, +lifeboat, +lighter, light, igniter, ignitor, +limousine, limo, +liner, ocean liner, +lipstick, lip rouge, +Loafer, +lotion, +loudspeaker, speaker, speaker unit, loudspeaker system, speaker system, +"loupe, jewelers loupe", +lumbermill, sawmill, +magnetic compass, +mailbag, postbag, +mailbox, letter box, +maillot, +maillot, tank suit, +manhole cover, +maraca, +marimba, xylophone, +mask, +matchstick, +maypole, +maze, labyrinth, +measuring cup, +medicine chest, medicine cabinet, +megalith, megalithic structure, +microphone, mike, +microwave, microwave oven, +military uniform, +milk can, +minibus, +miniskirt, mini, +minivan, +missile, +mitten, +mixing bowl, +mobile home, manufactured home, +Model T, +modem, +monastery, +monitor, +moped, +mortar, +mortarboard, +mosque, +mosquito net, +motor scooter, scooter, +mountain bike, all-terrain bike, off-roader, +mountain tent, +mouse, computer mouse, +mousetrap, +moving van, +muzzle, +nail, +neck brace, +necklace, +nipple, +notebook, notebook computer, +obelisk, +oboe, hautboy, hautbois, +ocarina, sweet potato, +odometer, hodometer, mileometer, milometer, +oil filter, +organ, pipe organ, +oscilloscope, scope, cathode-ray oscilloscope, CRO, +overskirt, +oxcart, +oxygen mask, +packet, +paddle, boat paddle, +paddlewheel, paddle wheel, +padlock, +paintbrush, +"pajama, pyjama, pjs, jammies", +palace, +panpipe, pandean pipe, syrinx, +paper towel, +parachute, chute, +parallel bars, bars, +park bench, +parking meter, +passenger car, coach, carriage, +patio, terrace, +pay-phone, pay-station, +pedestal, plinth, footstall, +pencil box, pencil case, +pencil sharpener, +perfume, essence, +Petri dish, +photocopier, +pick, plectrum, plectron, +pickelhaube, +picket fence, paling, +pickup, pickup truck, +pier, +piggy bank, penny bank, +pill bottle, +pillow, +ping-pong ball, +pinwheel, +pirate, pirate ship, +pitcher, ewer, +"plane, carpenters plane, woodworking plane", +planetarium, +plastic bag, +plate rack, +plow, plough, +"plunger, plumbers helper", +Polaroid camera, Polaroid Land camera, +pole, +police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria, +poncho, +pool table, billiard table, snooker table, +pop bottle, soda bottle, +pot, flowerpot, +"potters wheel", +power drill, +prayer rug, prayer mat, +printer, +prison, prison house, +projectile, missile, +projector, +puck, hockey puck, +punching bag, punch bag, punching ball, punchball, +purse, +quill, quill pen, +quilt, comforter, comfort, puff, +racer, race car, racing car, +racket, racquet, +radiator, +radio, wireless, +radio telescope, radio reflector, +rain barrel, +recreational vehicle, RV, R.V., +reel, +reflex camera, +refrigerator, icebox, +remote control, remote, +restaurant, eating house, eating place, eatery, +revolver, six-gun, six-shooter, +rifle, +rocking chair, rocker, +rotisserie, +rubber eraser, rubber, pencil eraser, +rugby ball, +rule, ruler, +running shoe, +safe, +safety pin, +saltshaker, salt shaker, +sandal, +sarong, +sax, saxophone, +scabbard, +scale, weighing machine, +school bus, +schooner, +scoreboard, +screen, CRT screen, +screw, +screwdriver, +seat belt, seatbelt, +sewing machine, +shield, buckler, +shoe shop, shoe-shop, shoe store, +shoji, +shopping basket, +shopping cart, +shovel, +shower cap, +shower curtain, +ski, +ski mask, +sleeping bag, +slide rule, slipstick, +sliding door, +slot, one-armed bandit, +snorkel, +snowmobile, +snowplow, snowplough, +soap dispenser, +soccer ball, +sock, +solar dish, solar collector, solar furnace, +sombrero, +soup bowl, +space bar, +space heater, +space shuttle, +spatula, +speedboat, +"spider web, spiders web", +spindle, +sports car, sport car, +spotlight, spot, +stage, +steam locomotive, +steel arch bridge, +steel drum, +stethoscope, +stole, +stone wall, +stopwatch, stop watch, +stove, +strainer, +streetcar, tram, tramcar, trolley, trolley car, +stretcher, +studio couch, day bed, +stupa, tope, +submarine, pigboat, sub, U-boat, +suit, suit of clothes, +sundial, +sunglass, +sunglasses, dark glasses, shades, +sunscreen, sunblock, sun blocker, +suspension bridge, +swab, swob, mop, +sweatshirt, +swimming trunks, bathing trunks, +swing, +switch, electric switch, electrical switch, +syringe, +table lamp, +tank, army tank, armored combat vehicle, armoured combat vehicle, +tape player, +teapot, +teddy, teddy bear, +television, television system, +tennis ball, +thatch, thatched roof, +theater curtain, theatre curtain, +thimble, +thresher, thrasher, threshing machine, +throne, +tile roof, +toaster, +tobacco shop, tobacconist shop, tobacconist, +toilet seat, +torch, +totem pole, +tow truck, tow car, wrecker, +toyshop, +tractor, +trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi, +tray, +trench coat, +tricycle, trike, velocipede, +trimaran, +tripod, +triumphal arch, +trolleybus, trolley coach, trackless trolley, +trombone, +tub, vat, +turnstile, +typewriter keyboard, +umbrella, +unicycle, monocycle, +upright, upright piano, +vacuum, vacuum cleaner, +vase, +vault, +velvet, +vending machine, +vestment, +viaduct, +violin, fiddle, +volleyball, +waffle iron, +wall clock, +wallet, billfold, notecase, pocketbook, +wardrobe, closet, press, +warplane, military plane, +washbasin, handbasin, washbowl, lavabo, wash-hand basin, +washer, automatic washer, washing machine, +water bottle, +water jug, +water tower, +whiskey jug, +whistle, +wig, +window screen, +window shade, +Windsor tie, +wine bottle, +wing, +wok, +wooden spoon, +wool, woolen, woollen, +worm fence, snake fence, snake-rail fence, Virginia fence, +wreck, +yawl, +yurt, +web site, website, internet site, site, +comic book, +crossword puzzle, crossword, +street sign, +traffic light, traffic signal, stoplight, +book jacket, dust cover, dust jacket, dust wrapper, +menu, +plate, +guacamole, +consomme, +hot pot, hotpot, +trifle, +ice cream, icecream, +ice lolly, lolly, lollipop, popsicle, +French loaf, +bagel, beigel, +pretzel, +cheeseburger, +hotdog, hot dog, red hot, +mashed potato, +head cabbage, +broccoli, +cauliflower, +zucchini, courgette, +spaghetti squash, +acorn squash, +butternut squash, +cucumber, cuke, +artichoke, globe artichoke, +bell pepper, +cardoon, +mushroom, +Granny Smith, +strawberry, +orange, +lemon, +fig, +pineapple, ananas, +banana, +jackfruit, jak, jack, +custard apple, +pomegranate, +hay, +carbonara, +chocolate sauce, chocolate syrup, +dough, +meat loaf, meatloaf, +pizza, pizza pie, +potpie, +burrito, +red wine, +espresso, +cup, +eggnog, +alp, +bubble, +cliff, drop, drop-off, +coral reef, +geyser, +lakeside, lakeshore, +promontory, headland, head, foreland, +sandbar, sand bar, +seashore, coast, seacoast, sea-coast, +valley, vale, +volcano, +ballplayer, baseball player, +groom, bridegroom, +scuba diver, +rapeseed, +daisy, +"yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", +corn, +acorn, +hip, rose hip, rosehip, +buckeye, horse chestnut, conker, +coral fungus, +agaric, +gyromitra, +stinkhorn, carrion fungus, +earthstar, +hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa, +bolete, +ear, spike, capitulum, +toilet tissue, toilet paper, bathroom tissue diff --git a/examples/C++/PaddleClas/resnet_50_vd/rpc_client.py b/examples/C++/PaddleClas/resnet_50_vd/rpc_client.py new file mode 100644 index 000000000..5e4c42841 --- /dev/null +++ b/examples/C++/PaddleClas/resnet_50_vd/rpc_client.py @@ -0,0 +1,51 @@ +# 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. + +import sys +from paddle_serving_client import Client + +#app +from paddle_serving_app.reader import Sequential, URL2Image, Resize +from paddle_serving_app.reader import CenterCrop, RGB2BGR, Transpose, Div, Normalize +import time + +client = Client() +client.load_client_config("./ResNet50_vd_client/serving_client_conf.prototxt") +client.connect(["127.0.0.1:9394"]) + +label_dict = {} +label_idx = 0 +with open("imagenet.label") as fin: + for line in fin: + label_dict[label_idx] = line.strip() + label_idx += 1 + +#preprocess +seq = Sequential([ + URL2Image(), Resize(256), CenterCrop(224), RGB2BGR(), Transpose((2, 0, 1)), + Div(255), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True) +]) + +start = time.time() +image_file = "https://paddle-serving.bj.bcebos.com/imagenet-example/daisy.jpg" +for i in range(1): + img = seq(image_file) + fetch_map = client.predict(feed={"inputs": img}, fetch=[], batch=False) + + prob = max(fetch_map["save_infer_model/scale_0.tmp_1"][0]) + label = label_dict[fetch_map["save_infer_model/scale_0.tmp_1"][0].tolist() + .index(prob)].strip().replace(",", "") + print("prediction: {}, probability: {}".format(label, prob)) +end = time.time() +print(end - start) diff --git a/examples/C++/imdb/README_CN.md b/examples/C++/imdb/README_CN.md index a1fecc8af..42841c6b8 100755 --- a/examples/C++/imdb/README_CN.md +++ b/examples/C++/imdb/README_CN.md @@ -1,4 +1,4 @@ -## IMDB评论情绪预测服务 +## IMDB 评论情绪预测 ABTest 服务 (简体中文|[English](./README.md)) @@ -11,16 +11,24 @@ sh get_data.sh ### 启动预测服务(支持BRPC-Client/GRPC-Client/Http-Client) +```python +## 启动 bow 模型服务 +python3 -m paddle_serving_server.serve --model imdb_bow_model/ --port 9297 >/dev/null 2>&1 & + +## 启动 cnn 模型服务 +python3 -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9298 >/dev/null 2>&1 & + +## 启动 lstm 模型服务 +python3 -m paddle_serving_server.serve --model imdb_lstm_model/ --port 9299 >/dev/null 2>&1 & ``` -python3 -m paddle_serving_server.serve --model imdb_cnn_model/ --port 9292 -``` -### BRPC-Client预测 + +### ABTest 预测 ``` -head test_data/part-0 | python3 test_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab +head test_data/part-0 | python3 abtest_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab ``` 预测test_data/part-0的前十个样例。 -### BRPC-Client预测 +### http预测 ``` head test_data/part-0 | python3 test_http_client.py imdb_cnn_client_conf/serving_client_conf.prototxt imdb.vocab ``` diff --git a/examples/C++/imdb/abtest_client.py b/examples/C++/imdb/abtest_client.py index 1a14c87c3..e0f910e37 100644 --- a/examples/C++/imdb/abtest_client.py +++ b/examples/C++/imdb/abtest_client.py @@ -11,35 +11,35 @@ # 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 +from paddle_serving_app.reader.imdb_reader import IMDBDataset +import sys import numpy as np client = Client() -client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt') -client.add_variant("bow", ["127.0.0.1:8000"], 10) -client.add_variant("lstm", ["127.0.0.1:9000"], 90) +client.load_client_config(sys.argv[1]) +client.add_variant("bow", ["127.0.0.1:9297"], 10) +client.add_variant("cnn", ["127.0.0.1:9298"], 30) +client.add_variant("lstm", ["127.0.0.1:9299"], 60) client.connect() -print('please wait for about 10s') -with open('processed.data') as f: - cnt = {"bow": {'acc': 0, 'total': 0}, "lstm": {'acc': 0, 'total': 0}} - for line in f: - word_ids, label = line.split(';') - word_ids = [int(x) for x in word_ids.split(',')] - word_len = len(word_ids) - feed = { - "words": np.array(word_ids).reshape(word_len, 1), - "words.lod": [0, word_len] - } - fetch = ["acc", "cost", "prediction"] - [fetch_map, tag] = client.predict( - feed=feed, fetch=fetch, need_variant_tag=True, batch=True) - if (float(fetch_map["prediction"][0][1]) - 0.5) * (float(label[0]) - 0.5 - ) > 0: - cnt[tag]['acc'] += 1 - cnt[tag]['total'] += 1 +# you can define any english sentence or dataset here +# This example reuses imdb reader in training, you +# can define your own data preprocessing easily. +imdb_dataset = IMDBDataset() +imdb_dataset.load_resource(sys.argv[2]) - for tag, data in cnt.items(): - print('[{}](total: {}) acc: {}'.format(tag, data[ - 'total'], float(data['acc']) / float(data['total']))) +for line in sys.stdin: + word_ids, label = imdb_dataset.get_words_and_label(line) + word_len = len(word_ids) + feed = { + "words": np.array(word_ids).reshape(word_len, 1), + "words.lod": [0, word_len] + } + #print(feed) + fetch = ["prediction"] + fetch_map = client.predict( + feed=feed, fetch=fetch, batch=True, need_variant_tag=True) + print("server_tag={} prediction={} ".format(fetch_map[1], fetch_map[0][ + "prediction"][0])) diff --git a/examples/C++/imdb/test_http_client.py b/examples/C++/imdb/test_http_client.py index e3cc70515..d22b92d78 100755 --- a/examples/C++/imdb/test_http_client.py +++ b/examples/C++/imdb/test_http_client.py @@ -40,7 +40,7 @@ if you want use JSON data format in HTTP-body, set False ''' #client.set_http_proto(True) -client.connect(["127.0.0.1:9292"]) +client.connect(["127.0.0.1:9297"]) # you can define any english sentence or dataset here # This example reuses imdb reader in training, you diff --git a/java/src/main/proto/general_model_service.proto b/java/src/main/proto/general_model_service.proto index aa06d388a..6b0fc7e0d 100644 --- a/java/src/main/proto/general_model_service.proto +++ b/java/src/main/proto/general_model_service.proto @@ -89,11 +89,13 @@ message Request { message Response { repeated ModelOutput outputs = 1; repeated int64 profile_time = 2; - // Error code - int32 err_no = 3; + bool profile_server = 3; + uint64 log_id = 4; + // Error code + int32 err_no = 5; // Error messages - string err_msg = 4; + string err_msg = 6; }; message ModelOutput { diff --git a/paddle_inference/paddle/include/paddle_engine.h b/paddle_inference/paddle/include/paddle_engine.h index 1fbb7222c..2d7673055 100644 --- a/paddle_inference/paddle/include/paddle_engine.h +++ b/paddle_inference/paddle/include/paddle_engine.h @@ -241,10 +241,10 @@ class PaddleInferenceEngine : public EngineCore { } config.SwitchSpecifyInputNames(true); - config.SetCpuMathLibraryNumThreads(1); + config.SetCpuMathLibraryNumThreads(engine_conf.cpu_math_thread_num()); if (engine_conf.has_use_gpu() && engine_conf.use_gpu()) { // 2000MB GPU memory - config.EnableUseGpu(50, gpu_id); + config.EnableUseGpu(engine_conf.gpu_memory_mb(), gpu_id); if (engine_conf.has_gpu_multi_stream() && engine_conf.gpu_multi_stream()) { config.EnableGpuMultiStream(); @@ -267,17 +267,17 @@ class PaddleInferenceEngine : public EngineCore { if (engine_conf.has_use_trt() && engine_conf.use_trt()) { config.SwitchIrOptim(true); if (!engine_conf.has_use_gpu() || !engine_conf.use_gpu()) { - config.EnableUseGpu(50, gpu_id); + config.EnableUseGpu(engine_conf.gpu_memory_mb(), gpu_id); if (engine_conf.has_gpu_multi_stream() && engine_conf.gpu_multi_stream()) { config.EnableGpuMultiStream(); } } - config.EnableTensorRtEngine(1 << 25, + config.EnableTensorRtEngine(engine_conf.trt_workspace_size(), max_batch, local_min_subgraph_size, precision_type, - false, + engine_conf.trt_use_static(), FLAGS_use_calib); std::map> min_input_shape; std::map> max_input_shape; @@ -413,7 +413,11 @@ class PaddleInferenceEngine : public EngineCore { << ", use_ascend_cl: " << engine_conf.has_use_ascend_cl() << ", use_xpu: " << engine_conf.use_xpu() << ", enable_memory_optimization: " - << engine_conf.enable_memory_optimization(); + << engine_conf.enable_memory_optimization() + << ", gpu_memory_mb: " << engine_conf.gpu_memory_mb() + << ", cpu_math_thread_num: " << engine_conf.cpu_math_thread_num() + << ", trt_workspace_size: " << engine_conf.trt_workspace_size() + << ", trt_use_static: " << engine_conf.trt_use_static(); VLOG(2) << "create paddle predictor sucess, path: " << model_path; return 0; diff --git a/python/paddle_serving_server/serve.py b/python/paddle_serving_server/serve.py index 13f3739d1..8ac473d36 100755 --- a/python/paddle_serving_server/serve.py +++ b/python/paddle_serving_server/serve.py @@ -280,6 +280,27 @@ def serve_args(): default="", nargs="+", help="min_subgraph_size") + parser.add_argument( + "--gpu_memory_mb", + type=int, + default=50, + help="Initially allocate GPU storage size") + parser.add_argument( + "--cpu_math_thread_num", + type=int, + default=1, + help="Initialize the number of CPU computing threads") + parser.add_argument( + "--trt_workspace_size", + type=int, + default=33554432, + help="Initialize allocation 1 << 25 GPU storage size") + parser.add_argument( + "--trt_use_static", + default=False, + action="store_true", + help="Initialize TRT with static data") + return parser.parse_args() @@ -396,10 +417,14 @@ def start_gpu_card_model(gpu_mode, port, args): # pylint: disable=doc-string-mi server.set_dist_endpoints(args.dist_endpoints.split(",")) server.set_dist_subgraph_index(args.dist_subgraph_index) server.set_min_subgraph_size(args.min_subgraph_size) + server.set_gpu_memory_mb(args.gpu_memory_mb) + server.set_cpu_math_thread_num(args.cpu_math_thread_num) if args.use_trt and device == "gpu": server.set_trt() server.set_ir_optimize(True) + server.set_trt_workspace_size(args.trt_workspace_size) + server.set_trt_use_static(args.trt_use_static) if is_ocr: info = set_ocr_dynamic_shape_info() server.set_trt_dynamic_shape_info(info) diff --git a/python/paddle_serving_server/server.py b/python/paddle_serving_server/server.py index 266efc3e2..4edc69352 100755 --- a/python/paddle_serving_server/server.py +++ b/python/paddle_serving_server/server.py @@ -119,6 +119,10 @@ def __init__(self): self.dist_master_serving = False self.min_subgraph_size = [] self.trt_dynamic_shape_info = [] + self.gpu_memory_mb = 50 + self.cpu_math_thread_num = 1 + self.trt_workspace_size = 33554432 # 1 << 25 + self.trt_use_static = False def get_fetch_list(self, infer_node_idx=-1): fetch_names = [ @@ -289,6 +293,18 @@ def set_min_subgraph_size(self, min_subgraph_size): def set_trt_dynamic_shape_info(self, info): self.trt_dynamic_shape_info = info + def set_gpu_memory_mb(self, gpu_memory_mb): + self.gpu_memory_mb = gpu_memory_mb + + def set_cpu_math_thread_num(self, cpu_math_thread_num): + self.cpu_math_thread_num = cpu_math_thread_num + + def set_trt_workspace_size(self, trt_workspace_size): + self.trt_workspace_size = trt_workspace_size + + def set_trt_use_static(self, trt_use_static): + self.trt_use_static = trt_use_static + def _prepare_engine(self, model_config_paths, device, use_encryption_model): self.device = device if self.model_toolkit_conf == None: @@ -342,6 +358,10 @@ def _prepare_engine(self, model_config_paths, device, use_encryption_model): engine.use_xpu = self.use_xpu engine.use_ascend_cl = self.use_ascend_cl engine.use_gpu = False + #engine.gpu_memory_mb = self.gpu_memory_mb + #engine.cpu_math_thread_num = self.cpu_math_thread_num + #engine.trt_workspace_size = self.trt_workspace_size + #engine.trt_use_static = self.trt_use_static # use distributed model. if self.dist_subgraph_index >= 0: diff --git a/tools/cpp_examples/demo-serving/proto/general_model_service.proto b/tools/cpp_examples/demo-serving/proto/general_model_service.proto index 8fedb60e9..3a1cba2c7 100755 --- a/tools/cpp_examples/demo-serving/proto/general_model_service.proto +++ b/tools/cpp_examples/demo-serving/proto/general_model_service.proto @@ -42,6 +42,13 @@ message Request { message Response { repeated ModelOutput outputs = 1; repeated int64 profile_time = 2; + bool profile_server = 3; + uint64 log_id = 4; + + // Error code + int32 err_no = 5; + // Error messages + string err_msg = 6; }; message ModelOutput {