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
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: https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/rknpu2.md
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Notice: This project is updated very frequently, the pre-build whl here may be outdated, only for quickly understand and test.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 https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy/python export ENABLE_ORT_BACKEND=ON export ENABLE_RKNPU2_BACKEND=ON export ENABLE_VISION=ON export RKNN2_TARGET_SOC=RK3588 python3 setup.py build python3 setup.py bdist_wheel
If your RK3588 has insufficient RAM, OOM error may occur, you can add
-j N after
python3 setup.py 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Notice: This project is updated very frequently, demos here may be outdated, only for quickly understand and test.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 infer.py --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 infer.py --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 infer.py --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
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 https://github.com/PaddlePaddle/FastDeploy.git cd FastDeploy/python export ENABLE_ORT_BACKEND=ON export ENABLE_PADDLE_BACKEND=ON export ENABLE_OPENVINO_BACKEND=ON export ENABLE_VISION=ON export ENABLE_TEXT=ON # 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 setup.py build python setup.py 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 README.md in every directory level.