[Docs] DeepStream documentations

projects/easydeploy/deepstream/README.md

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+# Inference MMYOLO Models with DeepStream
+
+This project demonstrates how to inference MMYOLO models with customized parsers in [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).
+
+## Pre-requisites
+
+### 1. Install Nvidia Driver and CUDA
+
+First, please follow the official documents and instructions to install dedicated Nvidia graphic driver and CUDA matched to your gpu and target Nvidia AIoT devices.
+
+### 2. Install DeepStream SDK
+
+Second, please follow the official instruction to download and install DeepStream SDK. Currently stable version of DeepStream is v6.2.
+
+### 3. Generate TensorRT Engine
+
+As DeepStream builds on top of several NVIDIA libraries, you need to first convert your trained MMYOLO models to TensorRT engine files. We strongly recommend you to try the supported TensorRT deployment solution in [EasyDeploy](../../easydeploy/).
+
+## Build and Run
+
+Please make sure that your converted TensorRT engine is already located in the `deepstream` folder as the config shows. Create your own model config files and change the `config-file` parameter in [deepstream_app_config.txt](deepstream_app_config.txt) to the model you want to run with.
+
+```bash
+mkdir build && cd build
+cmake ..
+make -j$(nproc) && make install
+```
+
+Then you can run the inference with this command.
+
+```bash
+deepstream-app -c deepstream_app_config.txt
+```
+
+## Code Structure
+
+```bash
+├── deepstream
+│   ├── configs                   # config file for MMYOLO models
+│   │   └── config_infer_rtmdet.txt
+│   ├── custom_mmyolo_bbox_parser # customized parser for MMYOLO models to DeepStream formats
+│   │   └── nvdsparsebbox_mmyolo.cpp
+|   ├── CMakeLists.txt
+│   ├── coco_labels.txt           # labels for coco detection
+│   ├── deepstream_app_config.txt # deepStream reference app configs for MMYOLO models
+│   ├── README_zh-CN.md
+│   └── README.md
+```

projects/easydeploy/deepstream/README_zh-CN.md

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+# 使用 DeepStream SDK 推理 MMYOLO 模型
+
+本项目演示了如何使用 [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) 配合改写的 parser 来推理 MMYOLO 的模型。
+
+## 预先准备
+
+### 1. 安装 Nidia 驱动和 CUDA
+
+首先请根据当前的显卡驱动和目标使用设备的驱动完成显卡驱动和 CUDA 的安装。
+
+### 2. 安装 DeepStream SDK
+
+目前 DeepStream SDK 稳定版本已经更新到 v6.2，官方推荐使用这个版本。
+
+### 3. 将 MMYOLO 模型转换为 TensorRT Engine
+
+推荐使用 EasyDeploy 中的 TensorRT 方案完成目标模型的转换部署，具体可参考 [此文档](../../easydeploy/docs/model_convert.md) 。
+
+## 编译使用
+
+当前项目使用的是 MMYOLO 的 rtmdet 模型，若想使用其他的模型，请参照目录下的配置文件进行改写。然后将转换完的 TensorRT engine 放在当前目录下并执行如下命令：
+
+```bash
+mkdir build && cd build
+cmake ..
+make -j$(nproc) && make install
+```
+
+完成编译后可使用如下命令进行推理：
+
+```bash
+deepstream-app -c deepstream_app_config.txt
+```
+
+## 项目代码结构
+
+```bash
+├── deepstream
+│   ├── configs                   # MMYOLO 模型对应的 DeepStream 配置
+│   │   └── config_infer_rtmdet.txt
+│   ├── custom_mmyolo_bbox_parser # 适配 DeepStream formats 的 parser
+│   │   └── nvdsparsebbox_mmyolo.cpp
+|   ├── CMakeLists.txt
+│   ├── coco_labels.txt           # coco labels
+│   ├── deepstream_app_config.txt # DeepStream app 配置
+│   ├── README_zh-CN.md
+│   └── README.md
+```