クイックスタート：RK3588 で CNN モデルを実行する

sudo apt update
sudo apt install openssh-server
sudo systemctl enable --now ssh
hostname -I

ssh rk3588@BOARD_IP

uname -r
sudo dmesg | grep -i rknpu
sudo cat /sys/kernel/debug/rknpu/version
sudo cat /sys/kernel/debug/rknpu/load
ls -l /sys/class/drm/renderD*/device/driver

RKNPU driver: v0.9.8
NPU load:  Core0:  0%, Core1:  0%, Core2:  0%,

/sys/class/drm/renderD130/device/driver -> .../bus/platform/drivers/RKNPU

mountpoint -q /sys/kernel/debug || sudo mount -t debugfs debugfs /sys/kernel/debug

grep '^CONFIG_ROCKCHIP_RKNPU=' /boot/config-$(uname -r)

CONFIG_ROCKCHIP_RKNPU=y

sudo apt update
apt-cache policy linux-image-vendor-seeed-rk3588 linux-dtb-vendor-seeed-rk3588

sudo apt install --reinstall linux-image-vendor-seeed-rk3588 linux-dtb-vendor-seeed-rk3588
sudo reboot

sudo apt update
sudo apt install git wget cmake make gcc g++ openssh-client
mkdir -p ~/RKNN2_Project
cd ~/RKNN2_Project

git clone https://github.com/airockchip/rknn-toolkit2.git
git -C rknn-toolkit2 checkout 59a913d172e7f5ff03c9076e2ec7b1b1288ffd08

git clone https://github.com/airockchip/rknn_model_zoo.git
git -C rknn_model_zoo checkout bad6c7334531becaf90a561988519b7bec34d0ab

ssh rk3588@BOARD_IP 'mkdir -p ~/RKNN2_Project/packages'

scp ~/RKNN2_Project/rknn-toolkit2/rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so \
  rk3588@BOARD_IP:~/RKNN2_Project/packages/

sudo apt update
sudo apt install binutils

if [ -e /usr/lib/librknnrt.so ]; then
  sudo cp -a /usr/lib/librknnrt.so "/usr/lib/librknnrt.so.bak-$(date +%Y%m%d-%H%M%S)"
fi
sudo install -m 0644 ~/RKNN2_Project/packages/librknnrt.so /usr/lib/librknnrt.so
sudo ldconfig
strings /usr/lib/librknnrt.so | grep 'librknnrt version'

librknnrt version: 2.3.2 (429f97ae6b@2025-04-09T09:09:27)

scp ~/RKNN2_Project/rknn-toolkit2/rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl \
  rk3588@BOARD_IP:~/RKNN2_Project/packages/

sudo apt install python3.11-venv
python3 -m venv ~/venvs/rknn-lite2
source ~/venvs/rknn-lite2/bin/activate

python -m pip install \
  ~/RKNN2_Project/packages/rknn_toolkit_lite2-2.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
python -m pip check
python -c "from rknnlite.api import RKNNLite; print('RKNN Toolkit Lite2 import OK')"

No broken requirements found.
RKNN Toolkit Lite2 import OK

mkdir -p ~/Downloads
cd ~/Downloads
wget -c https://github.com/conda-forge/miniforge/releases/download/25.3.0-1/Miniforge3-25.3.0-1-Linux-x86_64.sh
bash Miniforge3-25.3.0-1-Linux-x86_64.sh -b -p "$HOME/miniforge3"
source ~/miniforge3/etc/profile.d/conda.sh
conda create -n rknn2 python=3.11 -y
conda activate rknn2

cd ~/RKNN2_Project/rknn-toolkit2/rknn-toolkit2
python -m pip install \
  -r packages/x86_64/requirements_cp311-2.3.2.txt \
  packages/x86_64/rknn_toolkit2-2.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl \
  'onnx==1.16.1' 'protobuf==4.25.4'

python -m pip check
python -m pip show rknn-toolkit2
python -c "from rknn.api import RKNN; print('RKNN Toolkit2 import OK')"

cd ~/RKNN2_Project/rknn_model_zoo/examples/yolo11/model
bash download_model.sh
ls -lh yolo11n.onnx

conda activate rknn2
cd ~/RKNN2_Project/rknn_model_zoo
export PYTHONPATH="$PWD${PYTHONPATH:+:$PYTHONPATH}"
ls datasets/COCO/coco_subset_20.txt
ls datasets/COCO/subset

cd examples/yolo11/python
python convert.py ../model/yolo11n.onnx rk3588 i8 ../model/yolo11n_rk3588.rknn
ls -lh ../model/yolo11n_rk3588.rknn

I rknn-toolkit2 version: 2.3.2
...
--> Building model
...
I rknn building ...
I rknn building done.
done
--> Export rknn model
done

cd ~/RKNN2_Project
wget -c https://dn.odroid.com/compiler/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu.tar
tar -xf gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu.tar

export GCC_COMPILER="$HOME/RKNN2_Project/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu"
"${GCC_COMPILER}-gcc" --version

cd ~/RKNN2_Project/rknn_model_zoo
bash build-linux.sh -t rk3588 -a aarch64 -b Release -d yolo11

install/rk3588_linux_aarch64/rknn_yolo11_demo/
├── rknn_yolo11_demo
├── lib/
│   ├── librknnrt.so
│   └── librga.so
└── model/
    ├── yolo11n_rk3588.rknn
    ├── bus.jpg
    └── coco_80_labels_list.txt

ssh rk3588@BOARD_IP 'mkdir -p ~/RKNN2_Project'
cd ~/RKNN2_Project/rknn_model_zoo
scp -r install/rk3588_linux_aarch64/rknn_yolo11_demo \
  rk3588@BOARD_IP:~/RKNN2_Project/

cd ~/RKNN2_Project/rknn_yolo11_demo
sudo env LD_LIBRARY_PATH="$PWD/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" \
  ./rknn_yolo11_demo model/yolo11n_rk3588.rknn model/bus.jpg

model input num: 1, output num: 9
model is NHWC input fmt
model input height=640, width=640, channel=3
...
rknn_run
bus @ (91 135 552 435) 0.948
person @ (109 236 223 536) 0.898
person @ (212 240 285 509) 0.843
person @ (477 230 559 521) 0.827
person @ (79 359 116 515) 0.448
write_image path: out.png width=640 height=640 channel=3 ...

mkdir -p ~/RKNN2_Project/results
scp rk3588@BOARD_IP:~/RKNN2_Project/rknn_yolo11_demo/out.png \
  ~/RKNN2_Project/results/yolo11n-rk3588-out.png

cd ~/RKNN2_Project/rknn_yolo11_demo
sudo "$HOME/venvs/rknn-lite2/bin/python" - <<'PYCODE'
import numpy as np
from rknnlite.api import RKNNLite

rknn = RKNNLite()
try:
    ret = rknn.load_rknn('model/yolo11n_rk3588.rknn')
    if ret != 0:
        raise RuntimeError(f'load_rknn failed: {ret}')
    ret = rknn.init_runtime()
    if ret != 0:
        raise RuntimeError(f'init_runtime failed: {ret}')
    outputs = rknn.inference(inputs=[np.zeros((1, 640, 640, 3), dtype=np.uint8)])
    if outputs is None:
        raise RuntimeError('inference failed')
    print('Lite2 inference OK; output count:', len(outputs))
finally:
    rknn.release()
PYCODE

Lite2 inference OK; output count: 9