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RKNN Installation

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This document aims to demonstrate how to install the RKNN SDK. For more information, please refer to the RKNN Toolkit2 repository doc directory.

Introduction to RKNN​

Rockchip RK3566/RK3568 series, RK3588 series, K3562 series, RV1103/RV1106 series chips are equipped with a neural network processor (NPU). Using RKNN, users can quickly deploy AI models to Rockchip chips for NPU hardware-accelerated inference. To use RKNPU, users need to first use the RKNN-Toolkit2 tool on an x86 computer to convert the trained model into the RKNN format, and then use the RKNN C API or Python API for inference on the development board.

Required Tools:

  • RKNN-Toolkit2 is a software development kit for users to perform model conversion, inference, and performance evaluation on PC and Rockchip NPU platforms.
  • RKNN-Toolkit-Lite2 provides a Python programming interface for Rockchip NPU platforms, helping users deploy RKNN models and accelerate AI applications.
  • RKNN Runtime provides C/C++ programming interfaces for Rockchip NPU platforms, helping users deploy RKNN models and accelerate AI applications.
  • RKNPU kernel driver is responsible for interacting with the NPU hardware.

The overall framework is as follows:

framework.png

Set up the RKNN Environment​

Configure RKNN-Toolkit2 Environment on PC​

  • Download the RKNN Repository

    It is recommended to create a directory to store the RKNN repository. For example, create a folder named Projects and place the RKNN-Toolkit2 and RKNN Model Zoo repositories under this directory. The commands are as follows:

    # Create Projects folder
    mkdir Projects
    cd Projects

    # Download RKNN-Toolkit2 repository
    git clone https://github.com/airockchip/rknn-toolkit2.git

    # Download RKNN Model Zoo repository
    git clone https://github.com/airockchip/rknn_model_zoo.git

  • (Optional) Install Anaconda

    If Python 3.8 (recommended version) is not installed in the system, or if there are multiple Python environments installed simultaneously, it is recommended to use Anaconda to create a new Python 3.8 environment.

    • Install Anaconda

      Execute the following command in the computer's terminal window to check if Anaconda is installed. If Anaconda is already installed, this step can be skipped.

      $ conda --version
      conda 23.10.0

      If "conda: command not found" appears, it means Anaconda is not installed. Please refer to the Anaconda official website for installation.

    • Create a conda environment

      conda create -n rknn python=3.8

    • Activate the conda environment

      conda activate rknn

    • Deactivate the environment

      conda deactivate

Install Dependencies and RKNN-Toolkit2 on PC​

  • After activating the conda rknn environment, navigate to the rknn-toolkit2 directory and install dependencies libraries based on your Python version by selecting the corresponding requirements_cpXX.txt file. Then install RKNN-Toolkit2 using the wheel package. The commands are as follows:

    # Navigate to the rknn-toolkit2 directory
    cd Projects/rknn-toolkit2/rknn-toolkit2
    # Choose the appropriate requirements file according to your python version
    pip install -r packages/requirements_cp38-1.6.0.txt -i https://mirror.baidu.com/pypi/simple
    # Choose the appropriate wheel package file according to your python version and processor architecture:
    pip install packages/rknn_toolkit2-1.6.0+81f21f4d-cp38-cp38-linux_x86_64.whl

  • Verify if the installation is successful

    Execute the following command. If no errors occur, it means that the RKNN-Toolkit2 environment is successfully installed.

    $ python3
    >>> from rknn.api import RKNN

Install RKNN Toolkit Lite2 and Its Dependencies on the Board​

info

Radxa official image has RKNPU2 and its dependencies installed by default. Only python3-rknnlite2 needs to be installed. If it doesn't work, try to comment out the command.

sudo apt update
sudo apt install python3-rknnlite2
# sudo apt install rknpu2-rk3588   # For SOC RK3588 series
# sudo apt install rknpu2-rk356x   # For SOC RK356X series

If you are using the CLI version, you can visit the RKNN Toolkit Lite2 deb package download page.

(Optional) Install RKNN Model Zoo on the Board​

# Download the RKNN Model Zoo repository
git clone https://github.com/airockchip/rknn_model_zoo.git