mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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53 lines
1.6 KiB
Markdown
53 lines
1.6 KiB
Markdown
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# How to Build FastDeploy Library on Nvidia Jetson Platform
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FastDeploy supports CPU inference with ONNX Runtime and GPU inference with Nvidia TensorRT on Nvidia Jetson platform
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## How to Build and Install FastDeploy C++ Library
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Prerequisite for Compiling on NVIDIA Jetson:
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- gcc/g++ >= 5.4 (8.2 is recommended)
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- cmake >= 3.10.0
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- jetpack >= 4.6.1
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```
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy
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mkdir build && cd build
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cmake .. -DBUILD_ON_JETSON=ON \
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-DENABLE_VISION=ON \
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-DCMAKE_INSTALL_PREFIX=${PWD}/installed_fastdeploy
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make -j8
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make install
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```
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Once compiled, the C++ inference library is generated in the directory specified by `CMAKE_INSTALL_PREFIX`
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## How to Build and Install FastDeploy Python Library
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Prerequisite for Compiling on NVIDIA Jetson:
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- gcc/g++ >= 5.4 (8.2 is recommended)
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- cmake >= 3.10.0
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- jetpack >= 4.6.1
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- python >= 3.6
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Notice the `wheel` is required if you need to pack a wheel, execute `pip install wheel` first.
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All compilation options are imported via environment variables
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```
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/python
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export BUILD_ON_JETSON=ON
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export ENABLE_VISION=ON
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python setup.py build
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python setup.py bdist_wheel
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```
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The compiled `wheel` package will be generated in the `FastDeploy/python/dist` directory once finished. Users can pip-install it directly.
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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.
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