PaddlePaddle FastDeploy


FastDeploy is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with out-of-the-box and unified experience, end-to-end optimization for over 150+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, image segmentation, face detection, face recognition, keypoint detection, matting, OCR, NLP, TTS and other tasks to meet developers’ industrial deployment needs for multi-scenario, multi-hardware and multi-platform.

Currently FastDeploy initially supports rknpu2, it can run some AI models on RK3588. Other models are still being adapted. For detailed support list and progress please visit github

On RK3588

Compilation and Installation

Notice: This manual is based on RK3588 Firefly Ubuntu20.04 v1.0.4a firmware and compiling with python 3.8. Firmware comes with rknpu2 v1.4.0 so here we will skip rknpu2 installation part. If you need C++ compilation or rknpu2 installaion please read Official Document:


sudo apt update
sudo apt install -y python3 python3-dev python3-pip gcc python3-opencv python3-numpy

Here we offering a pre-build python whl for fast deploy: Google Drive.

If you decide to use this pre-build package then you can skip the building part (Preparation above is still needed). This pre-build package is based on FastDeploy v1.0.0 (commit id c4bb83ee)

Notice: This project is updated very frequently, the pre-build whl here may be outdated, only for quickly understand and test. For develop and deploy please build from the latest source:

Building on RK3588

sudo apt update
sudo apt install -y gcc cmake

git clone

cd FastDeploy/python

export RKNN2_TARGET_SOC=RK3588
python3 build
python3 bdist_wheel

If your RK3588 has insufficient RAM, OOM error may occur, you can add -j N after python3 build to control jobs.

Installation on RK3588

You can find whl under FastDeploy/python/dist after building, or use pre-build package above. Use pip3 to install:

pip3 install fastdeploy_python-*-linux_aarch64.whl


Here are 3 tuned demos: Google Drive

Notice: This project is updated very frequently, demos here may be outdated, only for quickly understand and test. For develop and deploy please get latest models from official github mentioned at the beginning of this article.

decompress and run:

  • Picodet object detection

cd demos/vision/detection/paddledetection/rknpu2/python

python3 --model_file ./picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet_rk3588.rknn \
                 --config_file ./picodet_s_416_coco_lcnet/infer_cfg.yml \
                 --image 000000014439.jpg
  • Scrfd face detection

cd demos/vision/facedet/scrfd/rknpu2/python

python3 --model_file ./scrfd_500m_bnkps_shape640x640_rk3588.rknn \
                 --image test_lite_face_detector_3.jpg
  • PaddleSeg portrait segmentaion

cd demos/vision/segmentation/paddleseg/rknpu2/python

python3 --model_file ./Portrait_PP_HumanSegV2_Lite_256x144_infer/Portrait_PP_HumanSegV2_Lite_256x144_infer_rk3588.rknn \
                 --config_file ./Portrait_PP_HumanSegV2_Lite_256x144_infer/deploy.yaml \
                 --image images/portrait_heng.jpg


In the previous chapter, only the running environment was deployed on RK3588. Demo was converted and adjusted in advance on the PC. This means that further development such as model transformations, parameter adjustments, etc., will need to be done on the PC, so FastDeploy will also need to be installed on the x86_64 Linux PC.

Compilation and Installation

Needs: Ubuntu18 or above, python 3.6 or 3.8

Install rknn-toolkit2

Use conda or virtualenv to create a virtual environment for installation, for usage of conda or virtualenv please google it.

First go download the rknn_toolkit2 whl: github

sudo apt install -y libxslt1-dev zlib1g zlib1g-dev libglib2.0-0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc g++

# Create virtual env
conda create -n rknn2 python=3.6
conda activate rknn2

# rknn_toolkit2 needs numpy 1.16.6
pip3 install numpy==1.16.6

# Install rknn_toolkit2
pip3 install rknn_toolkit2-1.4.0-*cp36*-linux_x86_64.whl

Build and Install FastDeploy

pip3 install wheel
sudo apt install -y cmake

git clone
cd FastDeploy/python
# OPENCV_DIRECTORY is optional, it will download pre-build OpenCV if this option is empty
export OPENCV_DIRECTORY=/usr/lib/x86_64-linux-gnu/cmake/opencv4

python build
python bdist_wheel

pip3 install dist/fastdeploy_python-*-linux_x86_64.whl
pip3 install paddlepaddle

Examples and Tutorial

Many examples are under FastDeploy/examples, and tutorial can be found in in every directory level.