# Start with Jetson AGX Orin Developer Kit.

## 1. SDK-Manager

#### Flash the last version of jetpack to NVME
22.04 Ubuntu is compatible with JetPack 6.0.
**Taking account NVME must be in GPT format and ext4 partition.**
With JetPack 6 install complete installation. Optional: DeepStream

## Build your kernel
### install-path is your path, check it with pwd

sudo apt-get install git fakeroot build-essential ncurses-dev xz-utils libssl-dev bc flex libelf-dev bison

cd ~/
mkdir Projects && cd Projects
wget https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v3.0/release/jetson_linux_r36.3.0_aarch64.tbz2
wget https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v3.0/release/tegra_linux_sample-root-filesystem_r36.3.0_aarch64.tbz2
tar -xvjf jetson_linux_r36.3.0_aarch64.tbz2
wget https://cdn.kernel.org/pub/linux/kernel/v6.x/linux-6.8.9.tar.xz
wget https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v3.0/release/tegra_linux_sample-root-filesystem_r36.3.0_aarch64.tbz2
tar -xvjf tegra_linux_sample-root-filesystem_r36.3.0_aarch64.tbz2

 # -C ./Linux_for_Tegra/
xz -d linux-6.8.9.tar.xz
tar -xf linux-6.8.9.tar
mv linux-6.8.9 kernel-jammy-src
cd Linux_for_Tegra/source/
./source_sync.sh -k -t jetson_36.3
cd kernel && rm -rf kernel-jammy-src
cd ~/Projects
mv kernel-jammy-src Linux_for_Tegra/source/kernel/

cd Linux_for_Tegra/source/

# modify defconfig
cd ~/Projects/Linux_for_Tegra/source/kernel/kernel-jammy-src/configs/aarch
# Edit defconfig
CONFIG_ARM64_PMEM=y
CONFIG_PCIE_TEGRA194=y
CONFIG_PCIE_TEGRA194_HOST=y
CONFIG_BLK_DEV_NVME=y
CONFIG_NVME_CORE=y
CONFIG_FB_SIMPLE=y
or
make defconfig
scripts/config --file .config --enable ARM64_PMEM
scripts/config --file .config --enable PCIE_TEGRA194
scripts/config --file .config --enable PCIE_TEGRA194_HOST
scripts/config --file .config --enable BLK_DEV_NVME
scripts/config --file .config --enable NVME_CORE
scripts/config --file .config --enable FB_SIMPLE

# Build kernel
make -j $(nproc) -C kernel
export INSTALL_MOD_PATH=/home/johnny/Projects/Linux_for_Tegra/rootfs
sudo -E make install -C kernel
cp kernel/kernel-jammy-src/arch/arm64/boot/Image \
  ~/Projects/Linux_for_Tegra/kernel/Image
  
# make modules
export KERNEL_HEADERS=$PWD/kernel/kernel-jammy-src
export INSTALL_MOD_PATH=~/Projects/Linux_for_Tegra/rootfs
make modules
sudo -E make modules_install

# OUT TREE MODULES
cd ~/Projects/Linux_for_Tegra/source
KERNEL_HEADERS=$PWD/kernel/kernel-jammy-src
make modules
sudo -E make modules_install

# edit boot
sudo gedit /boot/extlinux/exlinux.conf

# make dtbs
export KERNEL_HEADERS=$PWD/kernel/kernel-jammy-src
make dtbs
sudo cp nvidia-oot/device-tree/platform/generic-dts/dtbs/* /boot/dtb

## Recommended libraries

# Update package lists
sudo apt-get -y update 

# MANDATORY Install a set of tools and libraries required for building and development
    
sudo apt-get install -f -y --no-install-recommends \
    ninja-build \
    libopenblas-dev \
    libopenmpi-dev \
    openmpi-bin \
    openmpi-common \
    libomp-dev \
    autoconf \
    bc \
    build-essential \
    cmake \
    ffmpeg \
    g++ \
    gcc \
    gettext-base \
    git \
    gfortran \
    hdf5-tools \
    iputils-ping \
    libatlas-base-dev \
    libavcodec-dev \
    libavdevice-dev \
    libavfilter-dev \
    libavformat-dev \
    libavutil-dev \
    libblas-dev \
    libbz2-dev \
    libc++-dev \
    libcgal-dev \
    libeigen3-dev \
    libffi-dev \
    libfreeimage-dev \
    libfreetype6-dev \
    libglew-dev \
    libgflags-dev \
    libgoogle-glog-dev \
    libgtk-3-dev \
    libgtk2.0-dev \
    libhdf5-dev \
    libjpeg-dev \
    libjpeg-turbo8-dev \
    libjpeg8-dev \
    liblapack-dev \
    liblapacke-dev \
    liblzma-dev \
    libncurses5-dev \
    libncursesw5-dev \
    libomp-dev \
    libopenblas-dev \
    libopenblas-base \
    libopenexr-dev \
    libopenjp2-7 \
    libopenjp2-7-dev \
    libopenmpi-dev \
    libpng-dev \
    libprotobuf-dev \
    libreadline-dev \
    libsndfile1 \
    libsqlite3-dev \
    libssl-dev \
    libswresample-dev \
    libswscale-dev \
    libtbb-dev \
    libtbb2 \
    libtesseract-dev \
    libtiff-dev \
    libv4l-dev \
    libx264-dev \
    libxine2-dev \
    libxslt1-dev \
    libxvidcore-dev \
    libxml2-dev \
    locales \
    moreutils \
    openssl \
    pkg-config \
    python3-dev \
    python3-numpy \
    python3-pip \
    python3-matplotlib \
    qv4l2 \
    rsync \
    scons \
    v4l-utils \
    zlib1g-dev \
    zip \
    nvidia-l4t-gstreamer \
    ubuntu-restricted-extras \
    libsoup2.4-dev \
    libjson-glib-dev
    

#    libwebp-dev
#    libpostproc-dev
    
    
# GStreamer    
sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio gstreamer1.0-rtsp libgstrtspserver-1.0-dev

# Install package
sudo apt install -y ccache

# Update symlinks
sudo /usr/sbin/update-ccache-symlinks

# Prepend ccache into the PATH
echo 'export PATH="/usr/lib/ccache:$PATH"' | tee -a ~/.bashrc

# Source bashrc to test the new PATH
source ~/.bashrc && echo $PATH

# Install last version of CUDA, Cudnn, TensorRT (Optional)

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/arm64/cuda-keyring_1.1-1_all.deb
sudo dpkg -i cuda-keyring_1.1-1_all.deb
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-8 cuda-compat-12-8
sudo apt-get install cudnn python3-libnvinfer python3-libnvinfer-dev tensorrt

## Install cmake

VERSION="3.31.7"
sudo apt remove cmake
wget https://cmake.org/files/v3.31/cmake-${VERSION}.tar.gz
tar xf cmake-${VERSION}.tar.gz
cd cmake-${VERSION}
./configure
make -j $(nproc)
sudo make install
cmake --version

## Install clang18

wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
sudo add-apt-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
sudo apt update
sudo apt install clang-18 lldb-18 lld-18
sudo update-alternatives --install /usr/bin/clang clang /usr/bin/clang-18 100
sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-18 100

## Build OpenBlas

git clone https://github.com/xianyi/OpenBLAS.git
cd OpenBLAS/
export OMP_NUM_THREADS=12
make TARGET=ARMV8 USE_OPENMP=1
sudo make PREFIX=/usr/local install
sudo ldconfig

grep OPENBLAS_VERSION /usr/local/include/openblas_config.h

## 3.1 Install Pytorch Native

export TORCH_INSTALL=https://developer.download.nvidia.com/compute/redist/jp/v60dp/pytorch/torch-2.2.0a0+81ea7a4.nv24.01-cp310-cp310-linux_aarch64.whl

python3 -m pip install --upgrade pip
python3 -m pip install --upgrade --no-cache $TORCH_INSTALL

## 3.2 Install Tensorflow Native

sudo apt-get -y update 

sudo pip3 install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v60dp/tensorflow/tensorflow-2.14.0+nv24.01-cp310-cp310-linux_aarch64.whl

## 4. Install Miniconda ARM

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh

chmod +x Miniconda3-latest-Linux-aarch64.sh

bash Miniconda3-latest-Linux-aarch64.sh

After install Miniconda, I recommend create a Python 3.12 Environment.

conda create -n py312 python=3.12

## Build from Source Pytorch 2.4 with Miniconda and Cuda 12.6, Python 3.12 or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/ESIIS5vgF21KtSxeDb9uUs0Bsnbphv9qPwxd-rSHzH6ipQ?e=2YSvaL)

### Required upgrade CUDA

sudo nvpmodel -m 0 # this reboot system
sudo jetson_clocks

git clone --recursive --branch v2.6.0 http://github.com/pytorch/pytorch

export PYTORCH_BUILD_NUMBER=1 \
export TORCH_CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=0" \
export USE_NCCL=0 && \
export USE_QNNPACK=0 && \
export USE_PYTORCH_QNNPACK=0 && \
export USE_NATIVE_ARCH=1 && \
export USE_DISTRIBUTED=1 && \
export USE_TENSORRT=0 && \
export TORCH_CUDA_ARCH_LIST="8.7"

export PYTORCH_BUILD_VERSION=2.6.0 && \
export MAX_JOBS="-j$(nproc)"

cd pytorch
pip3 install --no-cache-dir -r requirements.txt
pip3 install --no-cache-dir scikit-build ninja
conda install -c conda-forge libstdcxx-ng=12
sudo apt remove cmake -y
pip install cmake --upgrade
python setup.py bdist_wheel

## Build TorchVision or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/Ea29cyNJlLxDnrahZa-FZ8oBVyvCpghVcq6uqPPnp8-JJQ?e=kpRk38)

git clone --branch v0.21.0 https://github.com/pytorch/vision torchvision   # see below for version of torchvision to download
cd torchvision
export BUILD_VERSION=0.21.0 # where 0.x.0 is the torchvision version  
export TORCH_CUDA_ARCH_LIST="8.7"
python setup.py bdist_wheel

## Build TorchAudio or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/EW_ayOzz2PlJqdry6QUeoL4BmkbBZNo1MMvptfbDXD7JSg?e=qjIxXv)

git clone --branch v2.6.0 https://github.com/pytorch/audio torchaudio   # see below for version of torchvision to download
cd torchaudio
export BUILD_VERSION=2.6.0 # where 0.x.0 is the torchvision version  
export TORCH_CUDA_ARCH_LIST="8.7"
python setup.py bdist_wheel

## Build TorchText or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/EWiYlWdgZRxFm0MA3UtW5lEBg340Ge2Spdu6kyiblQAc4Q?e=SmEBWt)

git clone --branch v0.19.1 https://github.com/pytorch/text.git torchtext  # see below for version of torchvision to download
cd torchtext
export BUILD_VERSION=0.19.1 # where 0.x.0 is the torchvision version  
export TORCH_CUDA_ARCH_LIST="8.7"
python setup.py build_ext -j $(nproc) bdist_wheel 

## Built TensorRT or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/EccP4FiKIUFKsix8xRFywSoBpBF1mFFEfk_mWC4cie5ZFA?e=41dBcK) or for C++ [here](https://ubarcelona-my.sharepoint.com/:f:/g/personal/jnunezca11_alumnes_ub_edu/EivHsg55wClBsmL0EOSjgsMBim_CiE68i5IO7JMo_M9_hA?e=48lVqb)

#### Python 3.12

export EXT_PATH=~/external
export TRT_OSSPATH=~/external/TensorRT

mkdir -p $EXT_PATH && cd $EXT_PATH
git clone https://github.com/pybind/pybind11.git

wget https://www.python.org/ftp/python/3.12.8/Python-3.12.8.tgz
tar -xvf Python-3.12.8.tgz
mkdir -p $EXT_PATH/python3.12/include
cp -r Python-3.12.2/Include/* $EXT_PATH/python3.12/include

wget http://http.us.debian.org/debian/pool/main/p/python3.12/libpython3.12-dev_3.12.8-5_arm64.deb
ar x libpython3.12-dev*.deb
mkdir debian && tar -xf data.tar.zst -C debian
cp debian/usr/include/aarch64-linux-gnu/python3.12/pyconfig.h python3.12/include/

git clone --branch release/10.7 --recursive https://github.com/NVIDIA/TensorRT.git
cd TensorRT
mkdir -p build && cd build
export TRT_LIBPATH=/usr/lib/aarch64-linux-gnu/
cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=12.6 -DCUDNN_VERSION=9.6 -DGPU_ARCHS="87"
CC=/usr/bin/gcc make -j$(nproc)

cd ../python
TENSORRT_MODULE=tensorrt PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=12 TARGET_ARCHITECTURE=aarch64 TRT_OSSPATH=~/external/TensorRT ./build.sh
pip install ./build/bindings_wheel/dist/tensorrt-*.whl

#### Python 3.11

export EXT_PATH=~/external
export TRT_OSSPATH=~/external/TensorRT

mkdir -p $EXT_PATH && cd $EXT_PATH
git clone https://github.com/pybind/pybind11.git

wget https://www.python.org/ftp/python/3.11.9/Python-3.11.9.tgz
tar -xvf Python-3.11.9.tgz
mkdir -p $EXT_PATH/python3.11/include
cp -r Python-3.11.9/Include/* $EXT_PATH/python3.11/include

wget http://http.us.debian.org/debian/pool/main/p/python3.11/libpython3.11-dev_3.11.9-1_arm64.deb
ar x libpython3.11-dev*.deb
mkdir debian && tar -xf data.tar.xz -C debian
cp debian/usr/include/aarch64-linux-gnu/python3.11/pyconfig.h python3.11/include/

git clone --branch release/10.7 --recursive https://github.com/NVIDIA/TensorRT.git
cd TensorRT
mkdir -p build && cd build
export TRT_LIBPATH=/usr/lib/aarch64-linux-gnu/
cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=12.6 -DCUDNN_VERSION=9.6 -DGPU_ARCHS="87"
CC=/usr/bin/gcc make -j$(nproc)

cd ../python
TENSORRT_MODULE=tensorrt PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=11 TARGET_ARCHITECTURE=aarch64 TRT_OSSPATH=~/external/TensorRT ./build.sh
pip install ./build/bindings_wheel/dist/tensorrt-*.whl

pip3 install -U tensorrt-10.7-cp311-none-linux_aarch64.whl

#### Python 3.10

export EXT_PATH=~/external
export TRT_OSSPATH=~/external/TensorRT

mkdir -p $EXT_PATH && cd $EXT_PATH
git clone https://github.com/pybind/pybind11.git

wget https://www.python.org/ftp/python/3.10.11/Python-3.10.11.tgz
tar -xvf Python-3.10.11.tgz
mkdir -p $EXT_PATH/python3.10/include
cp -r Python-3.10.11/Include/* $EXT_PATH/python3.11/include

wget http://http.us.debian.org/debian/pool/main/p/python3.10/libpython3.10-dev_3.10.12-1_amd64.deb
ar x libpython3.10-dev*.deb
mkdir debian && tar -xf data.tar.xz -C debian
cp debian/usr/include/aarch64-linux-gnu/python3.10/pyconfig.h python3.10/include/

git clone --branch release/10.7 --recursive https://github.com/NVIDIA/TensorRT.git
cd TensorRT
mkdir -p build && cd build
export TRT_LIBPATH=/usr/lib/aarch64-linux-gnu/
cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out -DTRT_PLATFORM_ID=aarch64 -DCUDA_VERSION=12.6 -DCUDNN_VERSION=9.6 -DGPU_ARCHS="87"
CC=/usr/bin/gcc make -j$(nproc)

cd ../python
TENSORRT_MODULE=tensorrt PYTHON_MAJOR_VERSION=3 PYTHON_MINOR_VERSION=10 TARGET_ARCHITECTURE=aarch64 TRT_OSSPATH=~/external/TensorRT ./build.sh
pip install ./build/bindings_wheel/dist/tensorrt-*.whl

pip3 install -U tensorrt-10.4-cp311-none-linux_aarch64.whl

## Build OpenCV CUDA

conda create -n py311 python=3.11
conda activate py311
conda install cmake numpy --yes

cd Projects
wget -O opencv.zip https://github.com/opencv/opencv/archive/4.x.zip
wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.x.zip
unzip opencv.zip
unzip opencv_contrib.zip
# -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_PREFIX_PATH="/usr/lib/aarch64-linux-gnu;/usr/include" \
mkdir -p build && cd build
export ENABLE_CONTRIB=1
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
cmake -DCMAKE_BUILD_TYPE=RELEASE \
      -DCMAKE_PREFIX_PATH="/usr/lib/aarch64-linux-gnu;/usr/include" \
      -DCPACK_BINARY_DEB=ON \
      -DBUILD_EXAMPLES=OFF \
      -DBUILD_opencv_python2=OFF \
      -DBUILD_opencv_python3=ON \
      -DBUILD_opencv_java=OFF \
      -DCMAKE_INSTALL_PREFIX=/usr/local \
      -DCUDA_ARCH_BIN=8.7 \
      -DCUDA_ARCH_PTX= \
      -DCUDA_FAST_MATH=ON \
      -DCUDNN_INCLUDE_DIR=/usr/include/ \
      -DEIGEN_INCLUDE_PATH=/usr/include/eigen3 \
      -DWITH_EIGEN=ON \
      -DENABLE_NEON=ON \
      -DOPENCV_DNN_CUDA=ON \
      -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-12.6 \
      -DOPENCV_ENABLE_NONFREE=ON \
      -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib-4.x/modules \
      -DOPENCV_GENERATE_PKGCONFIG=ON \
      -DOpenGL_GL_PREFERENCE=GLVND \
      -DWITH_CUBLAS=ON \
      -DWITH_CUDA=ON \
      -DWITH_CUDNN=ON \
      -DWITH_GSTREAMER=ON \
      -DWITH_LIBV4L=ON \
      -DWITH_GTK=ON \
      -DWITH_OPENGL=OFF \
      -DWITH_OPENCL=OFF \
      -DWITH_IPP=OFF \
      -DWITH_TBB=ON \
      -DBUILD_TIFF=ON \
      -DBUILD_PERF_TESTS=OFF \
      -DBUILD_TESTS=OFF \
      -DBUILD_NEW_PYTHON_SUPPORT=ON \
      -DBUILD_opencv_python3=ON \
      -DHAVE_opencv_python3=ON \
      ../opencv-4.x
       
cmake --build .
sudo make install

## Built OpenCV Python or download it [here](https://ubarcelona-my.sharepoint.com/:u:/g/personal/jnunezca11_alumnes_ub_edu/EeYJgPoinetLo6VAxIlyfA8BJC-uI9EsLQm0xeGz897wWA?e=UrwiUa)

git clone --recursive https://github.com/opencv/opencv-python.git
cd opencv-python

wget https://raw.githubusercontent.com/dusty-nv/jetson-containers/master/packages/opencv/patches.diff

git apply patches.diff || echo "failed to apply git patches"


sed -i 's|weight != 1.0|(float)weight != 1.0f|' opencv/modules/dnn/src/cuda4dnn/primitives/normalize_bbox.hpp
sed -i 's|nms_iou_threshold > 0|(float)nms_iou_threshold > 0.0f|' opencv/modules/dnn/src/cuda4dnn/primitives/region.hpp
grep 'weight' opencv/modules/dnn/src/cuda4dnn/primitives/normalize_bbox.hpp
grep 'nms_iou_threshold' opencv/modules/dnn/src/cuda4dnn/primitives/region.hpp

export export ENABLE_CONTRIB=1
export CMAKE_BUILD_PARALLEL_LEVEL=$(nproc)
export CMAKE_ARGS="\
       -DCPACK_BINARY_DEB=ON \
       -DBUILD_EXAMPLES=OFF \
       -DBUILD_opencv_python2=OFF \
       -DBUILD_opencv_python3=ON \
       -DBUILD_opencv_java=OFF \
       -DCMAKE_BUILD_TYPE=RELEASE \
       -DCMAKE_INSTALL_PREFIX=/usr/local \
       -DCUDA_ARCH_BIN=8.7 \
       -DCUDA_ARCH_PTX= \
       -DCUDA_FAST_MATH=ON \
       -DCUDNN_INCLUDE_DIR=/usr/include/ \
       -DEIGEN_INCLUDE_PATH=/usr/include/eigen3 \
       -DWITH_EIGEN=ON \
       -DENABLE_NEON=ON \
       -DOPENCV_DNN_CUDA=ON \
       -DOPENCV_ENABLE_NONFREE=ON \
       -DOPENCV_EXTRA_MODULES_PATH=$(pwd)/opencv_contrib/modules \
       -DOPENCV_GENERATE_PKGCONFIG=ON \
       -DOpenGL_GL_PREFERENCE=GLVND \
       -DWITH_CUBLAS=ON \
       -DWITH_CUDA=ON \
       -DWITH_CUDNN=ON \
       -DWITH_GSTREAMER=ON \
       -DWITH_LIBV4L=ON \
       -DWITH_GTK=ON \
       -DWITH_OPENGL=OFF \
       -DWITH_OPENCL=OFF \
       -DWITH_IPP=OFF \
       -DWITH_TBB=ON \
       -DBUILD_TIFF=ON \
       -DBUILD_PERF_TESTS=OFF \
       -DBUILD_TESTS=OFF"
pip3 wheel --verbose .

Install OpenCV.

pip install -U opencv_contrib_python-4.9.0.80-cp312-cp312-linux_aarch64.whl

## Build OnnxRuntime-GPU

git clone --recursive https://github.com/microsoft/onnxruntime
cd onnxruntime 

export PATH="/usr/local/cuda/bin:${PATH}"
export CUDACXX="/usr/local/cuda/bin/nvcc"

pip3 install -U packaging
./build.sh --config Release --update --parallel --build --build_wheel --build_shared_lib --skip_tests \
--use_tensorrt --cuda_home /usr/local/cuda --cudnn_home /usr/lib/aarch64-linux-gnu \
--tensorrt_home /usr/lib/aarch64-linux-gnu --cmake_extra_defines CMAKE_CXX_FLAGS="-Wno-unused-variable -I/usr/local/cuda/include" --cmake_extra_defines CMAKE_CUDA_ARCHITECTURES="87"

# Install torch2trt

git clone --recursive https://github.com/NVIDIA-AI-IOT/torch2trt
cd torch2trt
python setup.py install

# cmake -B build . && cmake --build build --target install && sudo ldconfig

cd scripts    
bash build_contrib.sh   

# Install trt-Pose

git clone --recursive https://github.com/NVIDIA-AI-IOT/trt_pose
cd trt_pose
python3 setup.py develop --user

# Example nanoSAM

git clone https://github.com/NVIDIA-AI-IOT/nanosam.git
cd nanosam

Download the folder data(models) and unzip in nanosam folder. [Data Folder](https://drive.google.com/file/d/1CHaxy_4eP3mSjkgb9ViStjszMQkj_jrN/view?usp=sharing)

pip install -U pillow transformers

RUN

python3 examples/demo_click_segment_track.py

If your external camera doesn't work.

sudo apt update
sudo apt upgrade
sudo apt install libffi-dev
sudo apt install libglib2.0-0
sudo apt install --reinstall libffi7
sudo apt install v4l-utils
export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libffi.so.7 # recommented to put in bashrc
# LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libgomp.so.1
# export LD_PRELOAD='/home/jetson/miniconda3/lib/python3.12/site-packages/scikit_learn.libs/libgomp-d22c30c5.so.1.0.0'

v4l2-ctl --list-devices 


Check if your camera works.

sudo apt install guvcview

Now update the code in demo_click_segment_track.py:
Original code:

cap = cv2.VideoCapture(0)

to

camera_id = "/dev/video0"
cap = cv2.VideoCapture(camera_id, cv2.CAP_V4L2)


# Example llamaspeak

1. **Open a terminal** and Configure ngc cli
2. Download [riva](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/resources/riva_quickstart_arm64)
3. Add "default-runtime": "nvidia" in /etc/docker/daemon.json.

sudo gedit /etc/docker/daemon.json

{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "args": [],
            "path": "nvidia-container-runtime"
        }
    }
}

Save it and execute.

sudo systemctl restart docker

4. Init riva to download models

bash riva_init.sh
bash riva_start.sh

5. **Open another terminal** and clone jetson-containers

git clone --recursive https://github.com/dusty-nv/jetson-containers.git
cd jetson-containers

6. Create a folder and download the model(.bin are deprecated)

mkdir data/models/text-generation-webui && cd $_
wget https://huggingface.co/TheBloke/Llama-2-13B-GGUF/resolve/main/llama-2-13b.Q4_0.gguf

7. Move to jetson containers folders

cd /path/to/your/jetson-containers/

9. Run text-web-ui

./run.sh --workdir /opt/text-generation-webui $(./autotag text-generation-webui) \
   python3 server.py --listen --verbose --api \
	--model-dir=/data/models/text-generation-webui \
	--model=llama-2-13b.Q4_0.gguf \
	--loader=llamacpp \
	--n-gpu-layers=128 \
	--n_ctx=4096 \
	--n_batch=4096 \
	--threads=$(($(nproc) - 2))

9. **Open another terminal** and move on jetson-containers folder

cd /path/to/your/jetson-containers/data
openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 365 -nodes -subj '/CN=localhost'

10. Run and enjoy it!

cd /path/to/your/jetson-containers
./run.sh --workdir=/opt/llamaspeak \
  --env SSL_CERT=/data/cert.pem \
  --env SSL_KEY=/data/key.pem \
  $(./autotag llamaspeak) \
  python3 chat.py --verbose

11. Open navigator

Optional:
You can try anothers model like codellama, mistral etc.


## 6. Install Docker with CUDA Support for use [Jetson Generative AI Playground](http://jetson-ai-playground.com/)

sudo apt install -y curl
sudo apt-get update

curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

sudo nvidia-ctk runtime configure --runtime=containerd
sudo systemctl restart containerd

sudo nvidia-ctk runtime configure --runtime=crio
sudo systemctl restart crio

### Add permission user for not use "sudo docker" all time

sudo chmod 666 /var/run/docker.sock
sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker

### Add "default-runtime": "nvidia" in /etc/docker/daemon.json.

sudo gedit /etc/docker/daemon.json

{
    "default-runtime": "nvidia",
    "runtimes": {
        "nvidia": {
            "args": [],
            "path": "nvidia-container-runtime"
        }
    }
}

sudo systemctl restart docker

# Build FFMPEG

wget https://www.ffmpeg.org/releases/ffmpeg-7.0.2.tar.gz && \
tar -xvzf ffmpeg-7.0.2.tar.gz

cd ffmpeg-7.0.2
mkdir -p ./libaom && \
cd ./libaom && \
git clone https://aomedia.googlesource.com/aom && \
mkdir -p aom_build && \
cd aom_build && \
PATH="$HOME/bin:$PATH" cmake -G "Unix Makefiles" -DCMAKE_INSTALL_PREFIX="$HOME/ffmpeg_build" -DENABLE_TESTS=OFF -DENABLE_NASM=on ../aom && \
PATH="$HOME/bin:$PATH" make && \
sudo make install

cd ffmpeg-7.0.2
git -C SVT-AV1 pull 2> /dev/null || git clone https://gitlab.com/AOMediaCodec/SVT-AV1.git && \
mkdir -p SVT-AV1/build && \
cd SVT-AV1/build && \
PATH="$HOME/bin:$PATH" cmake -G "Unix Makefiles" -DCMAKE_INSTALL_PREFIX="$HOME/ffmpeg_build" -DCMAKE_BUILD_TYPE=Release -DBUILD_DEC=OFF -DBUILD_SHARED_LIBS=OFF .. && \
PATH="$HOME/bin:$PATH" make && \
sudo make install

cd ffmpeg-7.0.2
sudo apt-get update && sudo apt-get install -y --no-install-recommends libdav1d-dev

cd ffmpeg-7.0.2
PATH="$HOME/bin:$PATH" PKG_CONFIG_PATH="$HOME/ffmpeg_build/lib/pkgconfig" ./configure \
    --prefix="$HOME/ffmpeg_build" \
    # --pkg-config-flags="--static" \
    --extra-cflags="-I$HOME/ffmpeg_build/include" \
    --extra-cflags="-I/usr/src/jetson_multimedia_api/include/" \
    --extra-ldflags="-L$HOME/ffmpeg_build/lib -L/usr/lib/aarch64-linux-gnu/tegra" \
    --extra-libs="-lpthread -lm -lnvbufsurface -lnvbufsurftransform" \
    --ld="g++" \
    --bindir="$HOME/bin" \
    --enable-shared --disable-doc \
    --enable-libaom --enable-libsvtav1 --enable-libdav1d \
    --enable-nvv4l2dec --enable-libv4l2 && \
make && \
make install && \
ldconfig

ffmpeg -version
ffmpeg -decoders
ffmpeg -decoders | grep av1 || true
ffmpeg -decoders | grep h264_nvv4l2dec || true 

# Extra
## To join more models, please go to Jetson-Containes, thanks to Dusty and JetsonHacks.

https://github.com/dusty-nv/jetson-containers.git