Add cpp trainer lib and demo #10681
Add cpp trainer lib and demo #10681
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… add-cpp-trainer-demo
… add-cpp-trainer-demo
… add-cpp-trainer-demo
jacquesqiao
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jacquesqiao
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… add-cpp-trainer-demo
…addle into add-cpp-trainer-demo
… add-cpp-trainer-demo
…ao/Paddle into add-cpp-trainer-demo
… add-cpp-trainer-demo
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| cmake_minimum_required(VERSION 3.0) | |||
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demo的话，是不是放到fluid/demo下比较好呢？或者doc/fluid下呢？这样以后官网可以展示demo。
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done, 放到 fluid/train/demo下了，这个和c++训练有关，就先放到train目录下了
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| set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11") | ||
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| set(PADDLE_LIB "${PROJECT_SOURCE_DIR}/lib") |
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PROJECT_SOURCE_DIR请问是什么呢？inference_dist按照后就只有一个目录：PADDLE_LIB就够了。
| set(PADDLE_LIB "${PROJECT_SOURCE_DIR}/lib") | ||
| set(MATH_TYPE $ENV{LIB_TYPE} CACHE STRING "Choose the Math library type: openblas mkl") | ||
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| option(WITH_MKLDNN "Compile PaddlePaddle with MKLDNN" OFF) |
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10行可以先去掉，mkldnn的现在效果不好，以后加如何？
| add_executable(demo_trainer demo_trainer.cc) | ||
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| if(MATH_TYPE STREQUAL "mklml") | ||
| include_directories("${PADDLE_LIB}/third_party/install/mklml/include") |
paddle/fluid/train/test/README.md
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| ### step 1. build paddle lib | ||
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| ``` | ||
| # option MATH_TYPE=mklml |
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第5行还要加openblas
第5-6行不是编译paddle lib需要的，是第四步的时候才需要，需要移动下位置。
paddle/fluid/train/test/README.md
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| ### step 2. copy lib to this dir | ||
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| ``` | ||
| cp -r /paddle/src/dir/paddle/fluid/train/lib . |
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第2步可以去掉，库安装完不用拷贝，只要在cmake里面加上库的路径即可。
paddle/fluid/train/test/README.md
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| This will generate two files: | ||
| - startup_program: used to init all parameters | ||
| - main_program: main logic of the network |
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- 第三步，需要安装完paddlepaddle whl包的。才能跑。这里也需要说明一下。
- 这一步跑完有什么打印的信息么？可以写一下，这样用户知道这一步成功了。
paddle/fluid/train/test/README.md
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| mkdir build | ||
| cd build | ||
| cmake .. | ||
| make |
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这一步的例子中，cmake选项没有实例，可以参考https://github.com/luotao1/fluid_inference_example#inference-example-project
paddle/fluid/train/test/README.md
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| make | ||
| cp ../startup_program . | ||
| cp ../main_program . | ||
| ./demo_trainer |
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这里跑完有什么结果么？可以写一下结果，这样用户知道例子跑通了。
paddle/fluid/train/test/README.md
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| ### step 4. build demo_trainer and run it. | ||
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| ``` | ||
| mkdir build |
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这里需要说明下是在当前目录mkdir build，用户不知道去哪儿新建目录。
也需要说明整个demo目录可以放在任意一个地方。
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| } // namespace train | ||
| } // namespace paddle |
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ReadBinaryFile和load函数既然在paddle/train的namespace里面，是否应该放在paddle代码里，而不是demo代码里。
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需要注明是使用CPU静态库的版本。GPU/动态库有点区别，但类似。
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后面统一整理？目前业务方只需要用CPU的部分，可以先给他们用起来
| -DWITH_MKL=OFF \ | ||
| -DWITH_MKLDNN=OFF | ||
| make -j8 | ||
| make -j8 inference_lib_dist |
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@Superjomn 训练也使用的话，叫inference_lib_dist好么？改成fluid_lib_dist?
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打算后面统一整理一下，这个demo可以尽快merge
| ``` | ||
| step: 0 loss: 1069.02 | ||
| step: 1 loss: 1069.02 | ||
| step: 2 loss: 1069.02 |
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WARNING: Logging before InitGoogleLogging() is written to STDERR
W0521 20:55:43.756713 24692 init.cc:84] 'CUDA' is not supported, Please re-compile with WITH_GPU option
W0521 20:55:43.756901 24692 init.cc:100] 'CUDA' is not supported, Please re-compile with WITH_GPU option
step: 0 loss: 58.8651
step: 1 loss: 58.8651
step: 2 loss: 58.8651
我打出来是这样，每个阶段的loss都一样？
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对，因为没有加入optimize op，有一个参数控制，https://github.com/PaddlePaddle/Paddle/pull/10681/files#diff-7e8d0736b2aff0b2bc699d05f454e0a3R19
这是业务的需求
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| auto loss_var = scope.Var(loss_name); | ||
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| for (int i = 0; i < 100; ++i) { |
task list: #10574