fastdeploy_en.md

## 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 deployment example
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/detection/paddledetection/python/

#  download PPYOLOE model and test image
wget https://bj.bcebos.com/fastdeploy/models/ppyoloe_plus_crn_m_80e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
tar xvf ppyoloe_plus_crn_m_80e_coco.tgz

# CPU deployment
python infer_ppyoloe.py --model_dir ppyoloe_plus_crn_m_80e_coco --image 000000014439.jpg --device cpu
# GPU deployment
python infer_ppyoloe.py --model_dir ppyoloe_plus_crn_m_80e_coco --image 000000014439.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_ppyoloe.py --model_dir ppyoloe_plus_crn_m_80e_coco --image 000000014439.jpg --device gpu --use_trt True
```