[Doc] Add runtime demo in quick_start (#524)

* add paddle_trt in benchmark

* update benchmark in device

* update benchmark

* update result doc

* fixed for CI

* update python api_docs

* update index.rst

* add runtime cpp examples

* deal with comments

* Update infer_paddle_tensorrt.py

* Add runtime quick start

* deal with comments

Co-authored-by: Jason <928090362@qq.com>

docs/cn/quick_start/runtime/cpp.md

@@ -1 +1,119 @@
 # C++推理
+
+确认开发环境已准备FastDeploy C++部署库，参考[FastDeploy安装](../../build_and_install/)安装预编译的FastDeploy，或根据自己需求进行编译安装。
+
+本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
+
+## 1. 获取模型
+
+```bash
+wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
+tar xvf mobilenetv2.tgz
+```
+
+## 2. 配置后端
+
+如下C++代码保存为`infer_paddle_onnxruntime.cc`
+
+``` c++
+#include "fastdeploy/runtime.h"
+
+namespace fd = fastdeploy;
+
+int main(int argc, char* argv[]) {
+  std::string model_file = "mobilenetv2/inference.pdmodel";
+  std::string params_file = "mobilenetv2/inference.pdiparams";
+
+  // setup option
+  fd::RuntimeOption runtime_option;
+  runtime_option.SetModelPath(model_file, params_file, fd::ModelFormat::PADDLE);
+  runtime_option.UseOrtBackend();
+  runtime_option.SetCpuThreadNum(12);
+  // init runtime
+  std::unique_ptr<fd::Runtime> runtime =
+      std::unique_ptr<fd::Runtime>(new fd::Runtime());
+  if (!runtime->Init(runtime_option)) {
+    std::cerr << "--- Init FastDeploy Runitme Failed! "
+              << "\n--- Model:  " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "--- Init FastDeploy Runitme Done! "
+              << "\n--- Model:  " << model_file << std::endl;
+  }
+  // init input tensor shape
+  fd::TensorInfo info = runtime->GetInputInfo(0);
+  info.shape = {1, 3, 224, 224};
+
+  std::vector<fd::FDTensor> input_tensors(1);
+  std::vector<fd::FDTensor> output_tensors(1);
+
+  std::vector<float> inputs_data;
+  inputs_data.resize(1 * 3 * 224 * 224);
+  for (size_t i = 0; i < inputs_data.size(); ++i) {
+    inputs_data[i] = std::rand() % 1000 / 1000.0f;
+  }
+  input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
+
+  //get input name
+  input_tensors[0].name = info.name;
+
+  runtime->Infer(input_tensors, &output_tensors);
+
+  output_tensors[0].PrintInfo();
+  return 0;
+}
+```
+加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
+```
+[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
+```
+
+## 3. 准备CMakeLists.txt
+
+FastDeploy中包含多个依赖库，直接采用`g++`或编译器编译较为繁杂，推荐使用cmake进行编译配置。示例配置如下，
+
+```cmake
+PROJECT(runtime_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
+```
+
+## 4. 编译可执行程序
+
+打开命令行终端，进入`infer_paddle_onnxruntime.cc`和`CMakeLists.txt`所在的目录，执行如下命令
+
+```bash
+cd examples/runtime/cpp
+mkdir build & cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
+make -j
+```
+
+```fastdeploy_cpp_sdk``` 为FastDeploy C++部署库路径
+
+编译完成后，使用如下命令执行可得到预测结果
+```bash
+./runtime_demo
+```
+执行时如提示`error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`，说明程序执行时没有找到FastDeploy的库路径，可通过执行如下命令，将FastDeploy的库路径添加到环境变量之后，重新执行二进制程序。
+```bash
+source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
+```
+
+本示例代码在各平台(Windows/Linux/Mac)上通用，但编译过程仅支持(Linux/Mac)，Windows上使用msbuild进行编译，具体使用方式参考[Windows平台使用FastDeploy C++ SDK](../../faq/use_sdk_on_windows.md)
+
+## 其它文档
+
+- [不同后端Runtime demo示例](../../../../examples/runtime/README.md)
+- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)

docs/cn/quick_start/runtime/python.md

@@ -1 +1,52 @@
 # Python推理
+
+确认开发环境已安装FastDeploy，参考[FastDeploy安装](../../build_and_install/)安装预编译的FastDeploy，或根据自己需求进行编译安装。
+
+本文档以 PaddleClas 分类模型 MobileNetV2 为例展示CPU上的推理示例
+
+## 1. 获取模型
+
+``` python
+import fastdeploy as fd
+
+model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
+fd.download_and_decompress(model_url, path=".")
+```
+
+## 2. 配置后端
+
+- 更多后端的示例可参考[examples/runtime](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/runtime)
+
+``` python
+option = fd.RuntimeOption()
+
+option.set_model_path("mobilenetv2/inference.pdmodel",
+                      "mobilenetv2/inference.pdiparams")
+
+# **** CPU 配置 ****
+option.use_cpu()
+option.use_ort_backend()
+option.set_cpu_thread_num(12)
+
+# 初始化构造runtime
+runtime = fd.Runtime(option)
+
+# 获取模型输入名
+input_name = runtime.get_input_info(0).name
+
+# 构造随机数据进行推理
+results = runtime.infer({
+    input_name: np.random.rand(1, 3, 224, 224).astype("float32")
+})
+
+print(results[0].shape)
+```
+加载完成，会输出提示如下，说明初始化的后端，以及运行的硬件设备
+```
+[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
+```
+
+## 其它文档
+
+- [不同后端Runtime demo示例](../../../../examples/runtime/README.md)
+- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)