Installation | Documents | Quick Start | API Docs | Release Notes

⚡️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.

🌠 Recent updates

✨✨✨ 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

🌌 Inference Backend and Abilities


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

🔮 Contents

✴️ A Quick Start for Python SDK
✴️ A Quick Start for C++ SDK
Installation
Quick Start
- PP-YOLOE Python Deployment Example
- PP-YOLOE C++ Deployment Example
Demos on Different Backends
Serving Deployment
- FastDeploy Serving Deployment Image Compilation
- Serving Deployment
API Documents
Performance Tune-up
- Quantization Acceleration
- Multi thread
FAQ
More FastDeploy Deploy Modules
- Benchmark Testing
Model list
💕 Developer Contributions
- Develop a new model

Quick Start💨

A Quick Start for Python SDK(click to fold)

🎆 Installation

🔸 Prerequisites

CUDA >= 11.2 、cuDNN >= 8.0 、 Python >= 3.6
OS: Linux x86_64/macOS/Windows 10

🔸 Install FastDeploy SDK with both CPU and GPU support

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

🔸 Conda Installation (Recommended✨)

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

🔸 Install FastDeploy SDK with only CPU support

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

🎇 Python Inference Example

Prepare model and picture

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

Test inference results

# 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)

A Quick Start for C++ SDK(click to expand)

🎆 Installation

Please refer to C++ Prebuilt Libraries Download

🎇 C++ Inference Example

Prepare models and pictures

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

Test inference results

// 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 .

✴️ ✴️ Server-side and Cloud Model List ✴️ ✴️

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	✅	✅	✅	✅	✅	✅	✅	✅	✅	❔	❔	❔	❔
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 Device Deployment

Mobile and Edge Model List（click to fold）

Task	Model	Size(MB)	Linux	Android	Linux	Linux	Linux	Linux	Linux
---	---	---	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	✅	❔	❔	❔	❔	❔	--

⚛️ Web and Mini Program Model List

Web and mini program model list(click to fold)

Task	Model	web_demo
---	---	Paddle.js
Detection	FaceDetection	✅
Detection	ScrewDetection	✅
Segmentation	PaddleSeg/HumanSeg	✅
Object Recognition	GestureRecognition	✅
Object Recognition	ItemIdentification	✅
OCR	PaddleOCR/PP-OCRv3	✅

💐 Acknowledge

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

©️ License

FastDeploy is provided under the Apache-2.0.

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README_EN.md

README_EN.md

🌠 Recent updates

🌌 Inference Backend and Abilities

🔮 Contents

Quick Start💨

🎆 Installation

🔸 Prerequisites

🔸 Install FastDeploy SDK with both CPU and GPU support

🔸 Conda Installation (Recommended✨)

🔸 Install FastDeploy SDK with only CPU support

🎇 Python Inference Example

🎆 Installation

🎇 C++ Inference Example

✴️ ✴️ Server-side and Cloud Model List ✴️ ✴️

📳 Mobile and Edge Device Deployment

⚛️ Web and Mini Program Model List

💐 Acknowledge

©️ License

Files

README_EN.md

Latest commit

History

README_EN.md

File metadata and controls

🌠 Recent updates

🌌 Inference Backend and Abilities

🔮 Contents

Quick Start💨

🎆 Installation

🔸 Prerequisites

🔸 Install FastDeploy SDK with both CPU and GPU support

🔸 Conda Installation (Recommended✨)

🔸 Install FastDeploy SDK with only CPU support

🎇 Python Inference Example

🎆 Installation

🎇 C++ Inference Example

✴️ ✴️ Server-side and Cloud Model List ✴️ ✴️

📳 Mobile and Edge Device Deployment

⚛️ Web and Mini Program Model List

💐 Acknowledge

©️ License