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[English](../../en/build_and_install/rknpu2.md) | 简体中文
# FastDeploy RKNPU2资源导航
## 写在前面
# FastDeploy RKNPU2 导航文档
RKNPU2指的是Rockchip推出的RK356X以及RK3588系列芯片的NPU。
目前FastDeploy已经初步支持使用RKNPU2来部署模型。
如果您在使用的过程中出现问题，请附带上您的运行环境，在Issues中反馈。
## FastDeploy RKNPU2 环境安装简介
如果您想在FastDeploy中使用RKNPU2推理引擎，你需要配置以下几个环境。
| 工具名 | 是否必须 | 安装设备 | 用途 |
|--------------|------|-------|---------------------------------|
| Paddle2ONNX | 必装 | PC | 用于转换PaddleInference模型到ONNX模型 |
| RKNNToolkit2 | 必装 | PC | 用于转换ONNX模型到rknn模型 |
| RKNPU2 | 选装 | Board | RKNPU2的基础驱动，FastDeploy已经集成，可以不装 |
## 安装模型转换环境
模型转换环境需要在Ubuntu下完成，我们建议您使用conda作为python控制器，并使用python3.6作为您的模型转换环境。
例如您可以输入以下命令行完成对python3.6环境的创建
```bash
conda create -n rknn2 python=3.6
conda activate rknn2
```
### 安装必备的依赖软件包
在安装RKNNtoolkit2之前我们需要安装一下必备的软件包
```bash
sudo apt-get install libxslt1-dev zlib1g zlib1g-dev libglib2.0-0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc g++
```
### 安装RKNNtoolkit2
目前，FastDeploy使用的转化工具版本号为1.4.2b3。如果你有使用最新版本的转换工具的需求，你可以在Rockchip提供的[百度网盘(提取码为rknn)](https://eyun.baidu.com/s/3eTDMk6Y)
中找到最新版本的模型转换工具。
```bash
# rknn_toolkit2对numpy存在特定依赖,因此需要先安装numpy==1.16.6
pip install numpy==1.16.6
# 安装rknn_toolkit2-1.3.0_11912b58-cp38-cp38-linux_x86_64.whl
wget https://bj.bcebos.com/fastdeploy/third_libs/rknn_toolkit2-1.4.2b3+0bdd72ff-cp36-cp36m-linux_x86_64.whl
pip install rknn_toolkit2-1.4.2b3+0bdd72ff-cp36-cp36m-linux_x86_64.whl
```
## 安装FastDeploy C++ SDK
针对RK356X和RK3588的性能差异，我们提供了两种编译FastDeploy的方式。
### 板端编译FastDeploy C++ SDK
针对RK3588，其CPU性能较强，板端编译的速度还是可以接受的，我们推荐在板端上进行编译。以下教程在RK356X(debian10),RK3588(debian 11) 环境下完成。
```bash
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
# 如果您使用的是develop分支输入以下命令
git checkout develop
mkdir build && cd build
cmake .. -DENABLE_ORT_BACKEND=ON \
-DENABLE_RKNPU2_BACKEND=ON \
-DENABLE_VISION=ON \
-DRKNN2_TARGET_SOC=RK3588 \
-DCMAKE_INSTALL_PREFIX=${PWD}/fastdeploy-0.0.0
make -j8
make install
```
### 交叉编译FastDeploy C++ SDK
针对RK356X，其CPU性能较弱，我们推荐使用交叉编译进行编译。以下教程在Ubuntu 22.04环境下完成。
```bash
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
# 如果您使用的是develop分支输入以下命令
git checkout develop
mkdir build && cd build
cmake .. -DCMAKE_C_COMPILER=/home/zbc/opt/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-gcc \
-DCMAKE_CXX_COMPILER=/home/zbc/opt/gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu/bin/aarch64-linux-gnu-g++ \
-DCMAKE_TOOLCHAIN_FILE=./../cmake/toolchain.cmake \
-DTARGET_ABI=arm64 \
-DENABLE_ORT_BACKEND=OFF \
-DENABLE_RKNPU2_BACKEND=ON \
-DENABLE_VISION=ON \
-DRKNN2_TARGET_SOC=RK356X \
-DCMAKE_INSTALL_PREFIX=${PWD}/fastdeploy-0.0.0
make -j8
make install
```
如果你找不到编译工具，你可以复制[交叉编译工具](https://bj.bcebos.com/paddle2onnx/libs/gcc-linaro-6.3.1-2017.zip)进行下载。
### 配置环境变量
为了方便大家配置环境变量，FastDeploy提供了一键配置环境变量的脚本，在运行程序前，你需要执行以下命令
```bash
# 临时配置
source PathToFastDeploySDK/fastdeploy_init.sh
# 永久配置
source PathToFastDeploySDK/fastdeploy_init.sh
sudo cp PathToFastDeploySDK/fastdeploy_libs.conf /etc/ld.so.conf.d/
sudo ldconfig
```
## 编译FastDeploy Python SDK
除了NPU，Rockchip的芯片还有其他的一些功能。
这些功能大部分都是需要C/C++进行编程，因此如果您使用到了这些模块，我们不推荐您使用Python SDK.
Python SDK的编译暂时仅支持板端编译, 以下教程在RK3568(debian 10)、RK3588(debian 11) 环境下完成。Python打包依赖`wheel`，编译前请先执行`pip install wheel`
```bash
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
# 如果您使用的是develop分支输入以下命令
git checkout develop
cd python
export ENABLE_ORT_BACKEND=ON
export ENABLE_RKNPU2_BACKEND=ON
export ENABLE_VISION=ON
# 请根据你的开发版的不同，选择RK3588和RK356X
export RKNN2_TARGET_SOC=RK3588
# 如果你的核心板的运行内存大于等于8G，我们建议您执行以下命令进行编译。
python3 setup.py build
# 值得注意的是，如果你的核心板的运行内存小于8G，我们建议您执行以下命令进行编译。
python3 setup.py build -j1
python3 setup.py bdist_wheel
cd dist
pip3 install fastdeploy_python-0.0.0-cp39-cp39-linux_aarch64.whl
```
## 导航目录
* [RKNPU2开发环境搭建](../faq/rknpu2/environment.md)

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Two steps before deployment
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
@@ -49,7 +49,7 @@ The visualized result after running is as follows
## Other Documents
- [C++ API Reference](https://baidu-paddle.github.io/fastdeploy-api/cpp/html/)
- [PPOCR Model Description](../../)
- [PPOCR Model Description](../README.md)
- [PPOCRv3 Python Deployment](../python)
- [Model Prediction Results](../../../../../../docs/en/faq/how_to_change_backend.md)
- [How to switch the model inference backend engine](../../../../../../docs/en/faq/how_to_change_backend.md)

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Two steps before deployment
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
This directory provides examples that `infer.py` fast finishes the deployment of PPOCRv3 on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
@@ -43,7 +43,7 @@ The visualized result after running is as follows
## Other Documents
- [Python API reference](https://baidu-paddle.github.io/fastdeploy-api/python/html/)
- [PPOCR Model Description](../../)
- [PPOCR Model Description](../README.md)
- [PPOCRv3 C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../../docs/api/vision_results/)
- [Model Prediction Results](../../../../../../docs/api/vision_results/README.md)
- [How to switch the model inference backend engine](../../../../../../docs/en/faq/how_to_change_backend.md)

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在部署前，需确认以下两个步骤
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`infer.py`快速完成PPOCRv3在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
@@ -56,7 +56,7 @@ python3 infer_static_shape.py \
## 其它文档
- [Python API文档查阅](https://baidu-paddle.github.io/fastdeploy-api/python/html/)
- [PPOCR 系列模型介绍](../../)
- [PPOCR 系列模型介绍](../README.md)
- [PPOCRv3 C++部署](../cpp)
- [模型预测结果说明](../../../../../../docs/api/vision_results/)
- [模型预测结果说明](../../../../../../docs/api/vision_results/README_CN.md)
- [如何切换模型推理后端引擎](../../../../../../docs/cn/faq/how_to_change_backend.md)