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@ -1,3 +1,175 @@
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## Install and Build
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TBD
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### Download & Install
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Download the latest C-API development package from CI system and install. You can find the required version in the table below:
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<table>
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<thead>
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<tr>
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<th>Version Tips</th>
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<th>C-API</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>cpu_avx_mkl</td>
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<td><a href="https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz" rel="nofollow">paddle.tgz</a></td>
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</tr>
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<tr>
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<td>cpu_avx_openblas</td>
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<td>-</td>
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</tr>
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<tr>
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<td>cpu_noavx_openblas</td>
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<td><a href="https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddle.tgz" rel="nofollow">paddle.tgz</a></td>
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</tr>
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<tr>
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<td>cuda7.5_cudnn5_avx_mkl</td>
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<td><a href="https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz" rel="nofollow">paddle.tgz</a></td>
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</tr>
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<tr>
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<td>cuda8.0_cudnn5_avx_mkl</td>
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<td><a href="https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz" rel="nofollow">paddle.tgz</a></td>
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</tr>
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<tr>
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<td>cuda8.0_cudnn7_avx_mkl</td>
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<td><a href="https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz" rel="nofollow">paddle.tgz</a></td>
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</tr></tbody></table>
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### From source
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Users can also compile the C-API library from PaddlePaddle source code by compiling with the following compilation options:
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<table>
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<thead>
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<tr>
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<th>Options</th>
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<th>Value</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>WITH_C_API</td>
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<td>ON</td>
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</tr>
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<tr>
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<td>WITH_PYTHON</td>
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<td>OFF（recommended）</td>
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</tr>
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<tr>
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<td>WITH_SWIG_PY</td>
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<td>OFF（recommended）</td>
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</tr>
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<tr>
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<td>WITH_GOLANG</td>
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<td>OFF（recommended）</td>
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</tr>
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<tr>
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<td>WITH_GPU</td>
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<td>ON/OFF</td>
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</tr>
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<tr>
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<td>WITH_MKL</td>
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<td>ON/OFF</td>
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</tr></tbody></table>
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It is best to set up with recommended values to avoid linking with unnecessary libraries. Set other compilation options as you need.
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Pull the latest following code snippet from github, and configure compilation options(replace PADDLE_ROOT with the installation path of the PaddlePaddle C-API inference library):
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```shell
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PADDLE_ROOT=/path/of/capi
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git clone https://github.com/PaddlePaddle/Paddle.git
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cd Paddle
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mkdir build
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cd build
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cmake -DCMAKE_INSTALL_PREFIX=$PADDLE_ROOT \
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-DCMAKE_BUILD_TYPE=Release \
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-DWITH_C_API=ON \
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-DWITH_SWIG_PY=OFF \
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-DWITH_GOLANG=OFF \
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-DWITH_PYTHON=OFF \
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-DWITH_MKL=OFF \
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-DWITH_GPU=OFF \
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..
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```
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After running the above code to generate Makefile , run: `make && make install`. After successful compilation, the dependencies required by C-API(includes: (1)PaddlePaddle inference library and header files; (2) Third-party libraries and header files) will be stored in the `PADDLE_ROOT` directory.
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If the compilation is successful, see the following directory structure under `PADDLE_ROOT`(includes PaddlePaddle header files and libraries, and third-party libraries and header files(determined by the link methods if necessary)):
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```text
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├── include
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│ └── paddle
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│ ├── arguments.h
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│ ├── capi.h
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│ ├── capi_private.h
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│ ├── config.h
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│ ├── error.h
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│ ├── gradient_machine.h
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│ ├── main.h
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│ ├── matrix.h
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│ ├── paddle_capi.map
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│ └── vector.h
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├── lib
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│ ├── libpaddle_capi_engine.a
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│ ├── libpaddle_capi_layers.a
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│ ├── libpaddle_capi_shared.so
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│ └── libpaddle_capi_whole.a
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└── third_party
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├── gflags
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│ ├── include
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│ │ └── gflags
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│ │ ├── gflags_completions.h
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│ │ ├── gflags_declare.h
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│ │ ...
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│ └── lib
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│ └── libgflags.a
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├── glog
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│ ├── include
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│ │ └── glog
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│ │ ├── config.h
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│ │ ...
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│ └── lib
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│ └── libglog.a
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├── openblas
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│ ├── include
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│ │ ├── cblas.h
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│ │ ...
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│ └── lib
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│ ...
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├── protobuf
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│ ├── include
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│ │ └── google
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│ │ └── protobuf
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│ │ ...
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│ └── lib
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│ └── libprotobuf-lite.a
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└── zlib
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├── include
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│ ...
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└── lib
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...
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```
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### Linking Description:
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There are three kinds of linking methods:
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1. Linking with dynamic library `libpaddle_capi_shared.so`（This way is much more convenient and easier, **Without special requirements, it is recommended**）, refer to the following：
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1. Compiling with CPU version and using `OpenBLAS`; only need to link one library named `libpaddle_capi_shared.so` to develop prediction program through C-API.
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1. Compiling with CPU version and using `MKL` lib, you need to link MKL library directly to develop prediction program through PaddlePaddle C-API, due to `MKL` has its own dynamic library.
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1. Compiling with GPU version, CUDA library will be loaded dynamically on prediction program run-time, and also set CUDA library to `LD_LIBRARY_PATH` environment variable.
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2. Linking with static library `libpaddle_capi_whole.a`，refer to the following：
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1. Specify `-Wl,--whole-archive` linking options.
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1. Explicitly link third-party libraries such as `gflags`、`glog`、`libz`、`protobuf` .etc, you can find them under `PADDLE_ROOT/third_party` directory.
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1. Use OpenBLAS library if compiling C-API，must explicitly link `libopenblas.a`.
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1. Use MKL when compiling C-API, must explicitly link MKL dynamic library.
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3. Linking with static library `libpaddle_capi_layers.a` and `libpaddle_capi_engine.a`，refer to the following：
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1. This linking methods is mainly used for mobile prediction.
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1. Split `libpaddle_capi_whole.a` into two static linking library at least to reduce the size of linking libraries.
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1. Specify `-Wl,--whole-archive -lpaddle_capi_layers` and `-Wl,--no-whole-archive -lpaddle_capi_engine` for linking.
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1. The third-party dependencies need explicitly link same as method 2 above.
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Yu Yang
7 years ago