nvidia本地环境部署以及jetson交叉编译环境部署

本文旨在处理本地电脑nvidia环境部署，以及nvidia orin jenson交叉编译的环境部署。

1. 部署电脑环境

https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html

cuda版本下载
https://developer.nvidia.com/cuda-12-6-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=runfile_local)

cudnn下载
https://developer.nvidia.com/cudnn-9-5-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=Ubuntu&target_version=20.04&target_type=deb_local

cudnn版本
https://docs.nvidia.com/deeplearning/cudnn/v9.5.1/release-notes.html

onnxruntime依赖
https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html

电脑部署版本

2. 部署交叉编译环境

2.1 安装交叉编译环境

1. uname 命令可以显示操作系统和内核的相关信息，这在某些情况下也包含了板子的架构信息
(myenv) qwer@qwer-Z790-EAGLE-AX:/mnt/data/docker_images$ uname -a
Linux qwer-Z790-EAGLE-AX 5.15.0-124-generic #134~20.04.1-Ubuntu SMP Tue Oct 1 15:27:33 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
2. cat /proc/device-tree  # Jetson 板子的结果类似 NVIDIA Jetson Nano Developer Kit
3. lsb_release -a # 该命令显示操作系统的版本信息
4. 对于 NVIDIA Jetson 板子，可以使用以下命令查看详细的 Jetson 版本信息
    head -n 1 /etc/nv_tegra_release  # 详细的 Jetson 版本信息 R32 (release), REVISION: 4.4
5. dmesg | grep -i board
6. cat /proc/cpuinfo

安装交叉编译环境

sudo apt-get install g+±aarch64-linux-gnu

# 选择合适版本的cuda交叉编译镜像
https://catalog.ngc.nvidia.com/orgs/nvidia/containers/l4t-cuda/tags

# 创建docker环境
docker run -it \
    --runtime=nvidia \
    --privileged \
    --network host \
    --gpus all \
    -e DISPLAY=$DISPLAY \
    -e LIBGL_ALWAYS_SOFTWARE=0 \
    -e LIBGL_ALWAYS_INDIRECT=1 \
    -e NVIDIA_VISIBLE_DEVICES=all \
    -e NVIDIA_DRIVER_CAPABILITIES=all \
    -v /tmp/.X11-unix:/tmp/.X11-unix \
    --device=/dev/bus/usb:/dev/bus/usb \
    --device=/dev/input/js0:/dev/input/js0 \
    -v /mnt/data:/mnt/data \
    --name cross_compiler_12_2_arm64 nvcr.io/nvidia/l4t-cuda:12.2.2-devel-arm64-ubuntu22.04 

# 安装cudnn
https://developer.nvidia.com/cudnn-9-5-1-download-archive?target_os=Linux&target_arch=aarch64-jetson&Compilation=Native&Distribution=Ubuntu&target_version=22.04&target_type=deb_local

2.2 编译onnxruntime

cd onnxruntime

python tools/ci_build/build.py --config Release --use_cuda --build_shared_lib --enable_pybind \
--parallel --skip_tests --build_dir ./build --cuda_home ${CUDA_HOME} \
--cudnn_home ${CUDNN_HOME}
# config Release：指定构建为发布版本。
# use_cuda：启用 CUDA 支持。
# build_shared_lib：构建共享库（onnxruntime 库）。
# enable_pybind：启用 Python 绑定。
# skip_tests：跳过测试（如果你不需要运行测试）。
# 构建 .whl 文件：
# 构建过程完成后，生成 .whl 文件。你可以通过 setup.py 来完成打包：
python setup.py bdist_wheel

1. 编译遇到报错

CMake Error at onnxruntime_providers_cuda.cmake:200 (target_link_libraries):
  Target "onnxruntime_providers_cuda" links to:
    CUDA::cublasLt

修改文件 onnxruntime/cmake/external/onnxruntime_providers_cuda.cmake

###start
set(CUDA_LIB_DIR "/usr/local/cuda-12.2/targets/sbsa-linux/lib")
# 检查是否能找到 CUDA::cublasLt，如果找不到则手动添加路径
if (NOT TARGET CUDA::cublasLt)
    add_library(CUDA::cublasLt SHARED IMPORTED)
    set_target_properties(CUDA::cublasLt PROPERTIES IMPORTED_LOCATION "${CUDA_LIB_DIR}/libcublasLt.so")
endif()

# 检查其他 CUDA 库
if (NOT TARGET CUDA::cublas)
    add_library(CUDA::cublas SHARED IMPORTED)
    set_target_properties(CUDA::cublas PROPERTIES IMPORTED_LOCATION "${CUDA_LIB_DIR}/libcublas.so")
endif()

if (NOT TARGET CUDA::cudart)
    add_library(CUDA::cudart SHARED IMPORTED)
    set_target_properties(CUDA::cudart PROPERTIES IMPORTED_LOCATION "${CUDA_LIB_DIR}/libcudart.so")
endif()

if (NOT TARGET CUDA::curand)
    add_library(CUDA::curand SHARED IMPORTED)
    set_target_properties(CUDA::curand PROPERTIES IMPORTED_LOCATION "${CUDA_LIB_DIR}/libcurand.so")
endif()

if (NOT TARGET CUDA::cufft)
    add_library(CUDA::cufft SHARED IMPORTED)
    set_target_properties(CUDA::cufft PROPERTIES IMPORTED_LOCATION "${CUDA_LIB_DIR}/libcufft.so")
endif()

# 链接库
target_link_libraries(${target} PRIVATE CUDA::cublasLt CUDA::cublas cudnn CUDA::curand CUDA::cufft CUDA::cudart
    ${ABSEIL_LIBS} ${ONNXRUNTIME_PROVIDERS_SHARED} Boost::mp11 safeint_interface)