## 0. 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. FastDeploy Supports AI model deployment on **X86 CPU、NVIDIA GPU、ARM CPU、XPU、NPU、IPU** etc. You can switch different inference backends and hardware with a single line of code. Deploying AI model in 3 steps with FastDeploy: (1)Install FastDeploy SDK; (2)Use FastDeploy's API to implement the deployment code; (3) Deploy. **Notes** : This document downloads FastDeploy examples to complete the high performance deployment experience; only X86 CPUs, NVIDIA GPUs are shown for reasoning and GPU environments are ready by default (e.g. CUDA >= 11.2, etc.), if you need to deploy AI model on other hardware or learn about FastDeploy's full capabilities, please refer to [FastDeploy GitHub](https://github.com/PaddlePaddle/FastDeploy). ## 1. Install FastDeploy SDK ``` pip install fastdeploy-gpu-python==0.0.0 -f https://www.paddlepaddle.org.cn/whl/fastdeploy_nightly_build.html ``` ## 2. Run Deployment Example ``` # download model, image and dictionary files wget https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar tar -xvf ch_PP-OCRv2_det_infer.tar wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar wgethttps://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar tar -xvf ch_PP-OCRv2_rec_infer.tar wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt # download deployment example git clone https://github.com/PaddlePaddle/FastDeploy.git cd examples/vison/ocr/PP-OCRv2/python/ # CPU deployment python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device cpu # GPU deployment python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu # TensorRT inference on GPU (note: if you run TensorRT inference the first time, there is a serialization of the model, which is time-consuming and requires patience) python infer.py --det_model ch_PP-OCRv2_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv2_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg --device gpu --backend trt ``` The results of the completed visualisation are shown below