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How to Build GPU Deployment Environment

Build Options

Please do not modify other cmake paramters exclude the following options.

Option Supported Platform Description
ENABLE_ORT_BACKEND Linux(x64)/Windows(x64) Default OFF, whether to intergrate ONNX Runtime backend
ENABLE_PADDLE_BACKEND Linux(x64)/Windows(x64) Default OFF, whether to intergrate Paddle Inference backend
ENABLE_TRT_BACKEND Linux(x64)/Windows(x64) Default OFF, whether to intergrate TensorRT backend
ENABLE_OPENVINO_BACKEND Linux(x64)/Windows(x64) Default OFF, whether to intergrate OpenVINO backend(Only CPU is supported)
ENABLE_VISION Linux(x64)/Windows(x64) Default OFF, whether to intergrate vision models
ENABLE_TEXT Linux(x64/Windows(x64) Default OFF, whether to intergrate text models
CUDA_DIRECTORY Linux(x64/Windows(x64) Default /usr/local/cuda，require CUDA>=11.2
TRT_DIRECTORY Linux(x64/Windows(x64) Default empty，require TensorRT>=8.4， e.g. /Download/TensorRT-8.5
WITH_CAPI Linux(x64)/Windows(x64)/Mac OSX(x86) Default OFF, whether to intergrate C API
WITH_CSHARPAPI Windows(x64) Default OFF, whether to intergrate C# API

The configuration for third libraries(Optional, if the following option is not defined, the prebuilt third libraries will download automaticly while building FastDeploy).

Option Description
ORT_DIRECTORY While ENABLE_ORT_BACKEND=ON, use ORT_DIRECTORY to specify your own ONNX Runtime library path.
OPENCV_DIRECTORY While ENABLE_VISION=ON, use OPENCV_DIRECTORY to specify your own OpenCV library path.
OPENVINO_DIRECTORY While ENABLE_OPENVINO_BACKEND=ON, use OPENVINO_DIRECTORY to specify your own OpenVINO library path.

How to Build and Install C++ SDK

Linux

Prerequisite for Compiling on Linux:

  • gcc/g++ >= 5.4 (8.2 is recommended)
  • cmake >= 3.18.0
  • cuda >= 11.2
  • cudnn >= 8.2

It it recommend install OpenCV library manually, and define -DOPENCV_DIRECTORY to set path of OpenCV library(If the flag is not defined, a prebuilt OpenCV library will be downloaded automaticly while building FastDeploy, but the prebuilt OpenCV cannot support reading video file or other function e.g imshow)

sudo apt-get install libopencv-dev

git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
mkdir build && cd build
cmake .. -DENABLE_ORT_BACKEND=ON \
         -DENABLE_PADDLE_BACKEND=ON \
         -DENABLE_OPENVINO_BACKEND=ON \
         -DENABLE_TRT_BACKEND=ON \
         -DWITH_GPU=ON \
         -DTRT_DIRECTORY=/Paddle/TensorRT-8.4.1.5 \
         -DCUDA_DIRECTORY=/usr/local/cuda \
         -DCMAKE_INSTALL_PREFIX=${PWD}/compiled_fastdeploy_sdk \
         -DENABLE_VISION=ON \
         -DOPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4
make -j12
make install

Windows

Prerequisite for Compiling on Windows:

  • Windows 10/11 x64
  • Visual Studio 2019
  • cuda >= 11.2
  • cudnn >= 8.2

Notice: Make sure Visual Studio Integration is installed during CUDA installation, or manually copy the 4 files under C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\extras\visual_studio_integration\MSBuildExtensions\ into C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\MSBuild\Microsoft\VC\v160\BuildCustomizations\. Otherwise, you may run into No CUDA toolset found error during cmake.

Launch the x64 Native Tools Command Prompt for VS 2019 from the Windows Start Menu and run the following commands:

git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
mkdir build && cd build
cmake .. -G "Visual Studio 16 2019" -A x64 \
         -DENABLE_ORT_BACKEND=ON \
         -DENABLE_PADDLE_BACKEND=ON \
         -DENABLE_OPENVINO_BACKEND=ON \
         -DENABLE_TRT_BACKEND=ON
         -DENABLE_VISION=ON \
         -DWITH_GPU=ON \
         -DTRT_DIRECTORY="D:\Paddle\TensorRT-8.4.1.5" \
         -DCUDA_DIRECTORY="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2" \
         -DCMAKE_INSTALL_PREFIX="D:\Paddle\compiled_fastdeploy"
% nuget restore  （please execute it when WITH_CSHARPAPI=ON to prepare dependencies in C#)
msbuild fastdeploy.sln /m /p:Configuration=Release /p:Platform=x64
msbuild INSTALL.vcxproj /m /p:Configuration=Release /p:Platform=x64

Once compiled, the C++ inference library is generated in the directory specified by CMAKE_INSTALL_PREFIX

If you use CMake GUI, please refer to How to Compile with CMakeGUI + Visual Studio 2019 IDE on Windows

How to Build and Install Python SDK

Notice the wheel is required if you need to pack a wheel, execute pip install wheel first.

Linux

Prerequisite for Compiling on Linux:

  • gcc/g++ >= 5.4 (8.2 is recommended)

  • cmake >= 3.18.0

  • python >= 3.6

  • cuda >= 11.2

  • cudnn >= 8.2

All compilation options are imported via environment variables

It it recommend install OpenCV library manually, and define -DOPENCV_DIRECTORY to set path of OpenCV library(If the flag is not defined, a prebuilt OpenCV library will be downloaded automaticly while building FastDeploy, but the prebuilt OpenCV cannot support reading video file or other function e.g imshow)

sudo apt-get install libopencv-dev

git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/python
export ENABLE_ORT_BACKEND=ON
export ENABLE_PADDLE_BACKEND=ON
export ENABLE_OPENVINO_BACKEND=ON
export ENABLE_VISION=ON
export ENABLE_TRT_BACKEND=ON
export WITH_GPU=ON
export TRT_DIRECTORY=/Paddle/TensorRT-8.4.1.5
export CUDA_DIRECTORY=/usr/local/cuda
# The OPENCV_DIRECTORY is optional, if not exported, a prebuilt OpenCV library will be downloaded
export OPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4

python setup.py build
python setup.py bdist_wheel

Windows

Prerequisite for Compiling on Windows:

  • Windows 10/11 x64
  • Visual Studio 2019
  • python >= 3.6
  • cuda >= 11.2
  • cudnn >= 8.2

Launch the x64 Native Tools Command Prompt for VS 2019 from the Windows Start Menu and run the following commands:

git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/python
set ENABLE_ORT_BACKEND=ON
set ENABLE_PADDLE_BACKEND=ON
set ENABLE_OPENVINO_BACKEND=ON
set ENABLE_VISION=ON
set ENABLE_TRT_BACKEND=ON
set WITH_GPU=ON
set TRT_DIRECTORY=D:\Paddle\TensorRT-8.4.1.5
set CUDA_DIRECTORY=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2

python setup.py build
python setup.py bdist_wheel

The compiled wheel package will be generated in the FastDeploy/python/dist directory once finished. Users can pip-install it directly.

During the compilation, if developers want to change the compilation parameters, it is advisable to delete the build and .setuptools-cmake-build subdirectories in the FastDeploy/python to avoid the possible impact from cache, and then recompile.