1.3 RKNPU2 Usage

RKNPU2 provides C/C++ programming interfaces for model inference on the board-side Rockchip NPU platform.

1.3.1 Environment Installation

If the RKNPU2 Runtime library file librknnrt.so is missing or needs updating on the board, for Linux systems, you can push rknpu2/runtime/Linux/librknn_api/aarch64/librknnrt.so to the /usr/lib directory using scp.

1.3.2 Board-Side Inference

The RKNN SDK directory rknpu2/examples provides many model inference demos. Users can refer to these examples to develop and deploy their own AI applications.The rknn_model_zoo repository also provides C/C++ examples.

1.4 RKNN-Toolkit Lite2 Introduction

RKNN-Toolkit Lite2 provides a Python interface for board-side model inference, making it convenient for users to develop AI applications using Python. Note: RKNN-Toolkit Lite2 is installed and run on the board-side, and it is used only for inference; it does not support model conversion.

1.4.1 RKNN-Toolkit Lite2 Installation

# Install python3/pip3
sudo apt-get update
sudo apt-get install -y python3 python3-dev python3-pip gcc python3-opencv python3-numpy
# Install RKNN-Toolkit Lite2; find the installation package in rknn-toolkit-lite2/packages and install the corresponding version according to the system python version
pip3 install rknn_toolkit_lite2-2.0.0b0-cp310-cp310-linux_aarch64.whl

1.4.2 RKNN-Toolkit Lite2 Usage

In the RKNN SDK/rknn-toolkit-lite2/examples directory, there are applications developed based on RKNN-Toolkit Lite2. Although the number of provided examples is limited, in practice, the interfaces of RKNN-Toolkit Lite2 and RKNN-Toolkit2 are quite similar. Users can refer to the RKNN-Toolkit2 examples for porting to RKNN-Toolkit Lite2.

1.5 Detailed Development Documentation

For detailed usage of NPU and Toolkit, please refer to the doc documentation under RKNN SDK.