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⚡️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 🔥160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform.

• ✨✨✨ In 2023.01.17 we released YOLOv8 for deployment on FastDeploy series hardware, which includes Paddle YOLOv8 and ultralytics YOLOv8

  • You can deploy Paddle YOLOv8 on Intel CPU, NVIDIA GPU, Jetson, Phytium, Kunlunxin, HUAWEI Ascend ,ARM CPU RK3588 and Sophgo TPU. Both Python deployments and C++ deployments are included.
  • You can deploy ultralytics YOLOv8 on Intel CPU, NVIDIA GPU, Jetson. Both Python deployments and C++ deployments are included
  • Fastdeploy supports quick deployment of multiple models, including YOLOv8, PP-YOLOE+, YOLOv5 and other models

• Serving deployment combined with VisualDL supports visual deployment. After the VDL service is started in the FastDeploy container, you can modify the model configuration, start/manage the model service, view performance data, and send requests on the VDL interface. For details, see related documents

  • Serving deployment visualization
  • Serving request visualization

• ✨👥✨ Community

  • Slack：Join our Slack community and chat with other community members about ideas
  • Wechat：Scan the QR code below using WeChat, follow the PaddlePaddle official account and fill out the questionnaire to join the WeChat group, and share the deployment industry implementation pain points with the community developers

X86_64 CPU
NVDIA GPU
Phytium CPU
KunlunXin XPU
Huawei Ascend NPU
Graphcore IPU
Sophgo
Intel graphics card
Jetson
ARM CPU
RK3588 etc.
RV1126 etc.
Amlogic
NXP

• ✴️ A Quick Start for Python SDK
• ✴️ A Quick Start for C++ SDK
• Installation
  • How to Install Prebuilt Library
  • How to Build GPU Deployment Environment
  • How to Build CPU Deployment Environment
  • How to Build IPU Deployment Environment
  • How to Build KunlunXin XPU Deployment Environment
  • How to Build RV1126 Deployment Environment
  • How to Build RKNPU2 Deployment Environment
  • How to Build A311D Deployment Environment
  • How to build Huawei Ascend Deployment Environment
  • How to Build FastDeploy Library on Nvidia Jetson Platform
  • How to Build FastDeploy Android C++ SDK
• Quick Start
  • PP-YOLOE Python Deployment Example
  • PP-YOLOE C++ Deployment Example
• Demos on Different Backends
  • Runtime Python Inference
  • Runtime C++ Inference
  • How to Change Model Inference Backend
• Serving Deployment
  • FastDeploy Serving Deployment Image Compilation
  • Serving Deployment
• API Documents
  • Python API
  • C++ API
  • Android Java API
• Performance Tune-up
  • Quantization Acceleration
  • Multi thread
• FAQ
  • 1. Using the FastDeploy C++ SDK on Windows Platform
  • 2. FastDeploy to deploy on Android Platform
  • 3. TensorRT Q&As
• More FastDeploy Deploy Modules
  • Benchmark Testing
• Model list
  • 🖥️ Supported Server-side and Cloud Model List
  • 📳 Supported Mobile and Edge Model List
  • ⚛️ Supported Web and Mini Program Model List
• 💕 Developer Contributions
  • Develop a new model

pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2

pip install fastdeploy-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# For deployment of GPU/TensorRT, please refer to examples/vision/detection/paddledetection/python
import cv2
import fastdeploy.vision as vision

im = cv2.imread("000000014439.jpg")
model = vision.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                 "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                 "ppyoloe_crn_l_300e_coco/infer_cfg.yml")

result = model.predict(im)
print(result)

vis_im = vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("vis_image.jpg", vis_im)

wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

// For GPU/TensorRT deployment, please refer to examples/vision/detection/paddledetection/cpp
#include "fastdeploy/vision.h"

int main(int argc, char* argv[]) {
  namespace vision = fastdeploy::vision;
  auto im = cv::imread("000000014439.jpg");
  auto model = vision::detection::PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
                                          "ppyoloe_crn_l_300e_coco/model.pdiparams",
                                          "ppyoloe_crn_l_300e_coco/infer_cfg.yml");

  vision::DetectionResult res;
  model.Predict(&im, &res);

  auto vis_im = vision::VisDetection(im, res, 0.5);
  cv::imwrite("vis_image.jpg", vis_im);
  return 0;
 }

For more deployment models, please refer to Vision Model Deployment Examples .

Notes: ✅: already supported; ❔: to be supported in the future; N/A: Not Available;

Server-side and cloud model list(click to fold)

Task	Model	Linux	Linux	Win	Win	Mac	Mac	Linux	Linux	Linux	Linux	Linux	Linux	Linux
---	---	X86 CPU	NVIDIA GPU	X86 CPU	NVIDIA GPU	X86 CPU	Arm CPU	AArch64 CPU	Phytium D2000 aarch64	NVIDIA Jetson	Graphcore IPU	kunlunxin XPU	Huawei Ascend	Serving
Classification	PaddleClas/ResNet50	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	TorchVison/ResNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Classification	ltralytics/YOLOv5Cls	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔
Classification	PaddleClas/PP-LCNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/PP-LCNetv2	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/EfficientNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/GhostNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/MobileNetV1	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/MobileNetV2	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/MobileNetV3	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/ShuffleNetV2	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/SqueeezeNetV1.1	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Classification	PaddleClas/Inceptionv3	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Classification	PaddleClas/PP-HGNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅
Detection	🔥🔥PaddleDetection/PP-YOLOE+	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	🔥PaddleDetection/YOLOv8	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Detection	🔥ultralytics/YOLOv8	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Detection	PaddleDetection/PicoDet	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅
Detection	PaddleDetection/YOLOX	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	PaddleDetection/YOLOv3	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	PaddleDetection/PP-YOLO	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	PaddleDetection/PP-YOLOv2	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	PaddleDetection/Faster-RCNN	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅
Detection	PaddleDetection/Mask-RCNN	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅
Detection	Megvii-BaseDetection/YOLOX	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Detection	WongKinYiu/YOLOv7	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Detection	WongKinYiu/YOLOv7end2end_trt	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Detection	WongKinYiu/YOLOv7end2end_ort	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
Detection	meituan/YOLOv6	✅	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔
Detection	ultralytics/YOLOv5	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Detection	WongKinYiu/YOLOR	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	✅	❔
Detection	WongKinYiu/ScaledYOLOv4	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
Detection	ppogg/YOLOv5Lite	✅	✅	✅	✅	✅	✅	✅	✅	✅	?	❔	❔	❔
Detection	RangiLyu/NanoDetPlus	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
Perception	Paddle3D/Smoke	❔	✅	❔	✅	❔	❔	❔	❔	❔	❔	❔	❔	✅
KeyPoint	PaddleDetection/TinyPose	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔
KeyPoint	PaddleDetection/PicoDet + TinyPose	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔
HeadPose	omasaht/headpose	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Tracking	PaddleDetection/PP-Tracking	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
OCR	PaddleOCR/PP-OCRv2	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	✅	❔
OCR	PaddleOCR/PP-OCRv3	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅
Segmentation	PaddleSeg/PP-LiteSeg	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	❔	❔
Segmentation	PaddleSeg/PP-HumanSegLite	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	✅	❔
Segmentation	PaddleSeg/HRNet	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	✅	❔
Segmentation	PaddleSeg/PP-HumanSegServer	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	✅	❔
Segmentation	PaddleSeg/Unet	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	✅	✅	❔
Segmentation	PaddleSeg/Deeplabv3	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	✅	✅	❔
FaceDetection	biubug6/RetinaFace	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceDetection	Linzaer/UltraFace	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceDetection	deepcam-cn/YOLOv5Face	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceDetection	insightface/SCRFD	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceAlign	Hsintao/PFLD	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceAlign	Single430/FaceLandmark1000	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
FaceAlign	jhb86253817/PIPNet	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
FaceRecognition	insightface/ArcFace	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceRecognition	insightface/CosFace	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceRecognition	insightface/PartialFC	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
FaceRecognition	insightface/VPL	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
Matting	ZHKKKe/MODNet	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Matting	PeterL1n/RobustVideoMatting	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Matting	PaddleSeg/PP-Matting	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Matting	PaddleSeg/PP-HumanMatting	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	✅	✅	❔
Matting	PaddleSeg/ModNet	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
Video Super-Resolution	PaddleGAN/BasicVSR	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Video Super-Resolution	PaddleGAN/EDVR	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Video Super-Resolution	PaddleGAN/PP-MSVSR	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔	❔
Information Extraction	PaddleNLP/UIE	✅	✅	✅	✅	✅	✅	✅	❔	✅	❔	❔	❔
NLP	PaddleNLP/ERNIE-3.0	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	✅	❔	✅
Speech	PaddleSpeech/PP-TTS	✅	✅	✅	✅	✅	✅	✅	❔	❔	--	❔	❔	✅

Mobile and Edge Model List（click to fold）

Task	Model	Size(MB)	Linux	Android	Linux	Linux	Linux	Linux	Linux	TBD ...
---	---	---	ARM CPU	ARM CPU	Rockchip NPU RK3588/RK3568/RK3566	Rockchip NPU RV1109/RV1126/RK1808	Amlogic NPU A311D/S905D/C308X	NXP NPU i.MX 8M Plus	TBD...
Classification	PaddleClas/ResNet50	98	✅	✅	✅	✅
Classification	PaddleClas/PP-LCNet	11.9	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/PP-LCNetv2	26.6	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/EfficientNet	31.4	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/GhostNet	20.8	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/MobileNetV1	17	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/MobileNetV2	14.2	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/MobileNetV3	22	✅	✅	❔	✅	❔	❔	--
Classification	PaddleClas/ShuffleNetV2	9.2	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/SqueezeNetV1.1	5	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/Inceptionv3	95.5	✅	✅	❔	✅	--	--	--
Classification	PaddleClas/PP-HGNet	59	✅	✅	❔	✅	--	--	--
Detection	PaddleDetection/PicoDet_s	4.9	✅	✅	✅	✅	✅	✅	--
Detection	YOLOv5		❔	❔	✅	❔	❔	❔	--
Face Detection	deepinsight/SCRFD	2.5	✅	✅	✅	--	--	--	--
Keypoint Detection	PaddleDetection/PP-TinyPose	5.5	✅	✅	❔	❔	❔	❔	--
Segmentation	PaddleSeg/PP-LiteSeg(STDC1)	32.2	✅	✅	✅	--	--	--	--
Segmentation	PaddleSeg/PP-HumanSeg-Lite	0.556	✅	✅	✅	--	--	--	--
Segmentation	PaddleSeg/HRNet-w18	38.7	✅	✅	✅	--	--	--	--
Segmentation	PaddleSeg/PP-HumanSeg	107.2	✅	✅	✅	--	--	--	--
Segmentation	PaddleSeg/Unet	53.7	✅	✅	✅	--	--	--	--
Segmentation	PaddleSeg/Deeplabv3	150	❔	✅	✅
OCR	PaddleOCR/PP-OCRv2	2.3+4.4	✅	✅	❔	--	--	--	--
OCR	PaddleOCR/PP-OCRv3	2.4+10.6	✅	❔	❔	❔	❔	❔	--

We sincerely appreciate the open-sourced capabilities in EasyEdge as we adopt it for the SDK generation and download in this project.

FastDeploy is provided under the Apache-2.0.