add prebuild package usage docs on windows

docs/en/02-how-to-run/prebuilt_package_windows.md

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+# How to use prebuilt package on Windows10
+
+- [How to use prebuilt package on Windows10](#how-to-use-prebuilt-package-on-windows10)
+  - [Prerequisite](#prerequisite)
+    - [ONNX Runtime](#onnx-runtime)
+    - [TensorRT](#tensorrt)
+  - [Model Convert](#model-convert)
+    - [ONNX Runtime Example](#onnx-runtime-example)
+    - [TensorRT Example](#tensorrt-example)
+  - [Model Inference](#model-inference)
+    - [Backend Inference](#backend-inference)
+      - [ONNXRuntime](#onnxruntime)
+      - [TensorRT](#tensorrt-1)
+    - [Python SDK](#python-sdk)
+      - [ONNXRuntime](#onnxruntime-1)
+      - [TensorRT](#tensorrt-2)
+    - [C SDK](#c-sdk)
+      - [ONNXRuntime](#onnxruntime-2)
+      - [TensorRT](#tensorrt-3)
+  - [Troubleshooting](#troubleshooting)
+
+______________________________________________________________________
+
+This tutorial takes `mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1.zip` and `mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip` as examples to show how to use the prebuilt packages.
+
+The directory structure of the prebuilt package is as follows, where the `dist` folder is about model converter, and the `sdk` folder is related to model inference.
+
+```
+.
+|-- dist
+`-- sdk
+    |-- bin
+    |-- example
+    |-- include
+    |-- lib
+    `-- python
+```
+
+## Prerequisite
+
+In order to use the prebuilt package, you need to install some third-party dependent libraries.
+
+1. Follow the [get_started](../get_started.md) documentation to create a virtual python environment and install pytorch, torchvision and mmcv-full. To use the C interface of the SDK, you need to install [vs2019+](https://visualstudio.microsoft.com/), [OpenCV](https://github.com/opencv/opencv/releases).
+
+   :point_right: It is recommended to use `pip` instead of `conda` to install pytorch and torchvision
+
+2. Clone the mmdeploy repository
+
+   ```bash
+   git clone https://github.com/open-mmlab/mmdeploy.git
+   ```
+
+   :point_right: The main purpose here is to use the configs, so there is no need to compile `mmdeploy`.
+
+3. Install mmclassification
+
+   ```bash
+   git clone https://github.com/open-mmlab/mmclassification.git
+   cd mmclassification
+   pip install -e .
+   ```
+
+4. Prepare a PyTorch model as our example
+
+   Download the pth [resnet18_8xb32_in1k_20210831-fbbb1da6.pth](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth). The corresponding config of the model is [resnet18_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py)
+
+After the above work is done, the structure of the current working directory should be:
+
+```
+.
+|-- mmclassification
+|-- mmdeploy
+|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+```
+
+### ONNX Runtime
+
+In order to use `ONNX Runtime` backend, you should also do the following steps.
+
+5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
+
+   ```bash
+   # download mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1.zip
+   pip install .\mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\dist\mmdeploy-0.6.0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\sdk\python\mmdeploy_python-0.6.0-cp38-none-win_amd64.whl
+   ```
+
+   :point_right: If you have installed it before, please uninstall it first.
+
+6. Install onnxruntime package
+
+   ```
+   pip install onnxruntime==1.8.1
+   ```
+
+7. Download [`onnxruntime`](https://github.com/microsoft/onnxruntime/releases/tag/v1.8.1), and add environment variable.
+
+   As shown in the figure, add the lib directory of onnxruntime to the PATH.
+
+   ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png)
+   :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`.
+
+### TensorRT
+
+In order to use `TensorRT` backend, you should also do the following steps.
+
+5. Install `mmdeploy` (Model Converter) and `mmdeploy_python` (SDK Python API).
+
+   ```bash
+   # download mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0.zip
+   pip install .\mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\dist\mmdeploy-0.6.0-py38-none-win_amd64.whl
+   pip install .\mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\python\mmdeploy_python-0.6.0-cp38-none-win_amd64.whl
+   ```
+
+   :point_right: If you have installed it before, please uninstall it first.
+
+6. Install TensorRT related package and set environment variables
+
+   - CUDA Toolkit 11.1
+   - TensorRT 8.2.3.0
+   - cuDNN 8.2.1.0
+
+   Add the runtime libraries of TensorRT and cuDNN to the PATH. You can refer to the path setting of onnxruntime. Don't forget to install python package of TensorRT.
+
+   :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by echo `$env:PATH`.
+
+   :exclamation: It is recommended to add only one version of the TensorRT/cuDNN runtime libraries to the PATH. It is better not to copy the runtime libraries of TensorRT/cuDNN to the cuda directory in `C:\`.
+
+7. Install pycuda by `pip install pycuda`
+
+## Model Convert
+
+### ONNX Runtime Example
+
+The following describes how to use the prebuilt package to do model conversion based on the previous downloaded pth.
+
+After the above work is done, the structure of the current working directory should be：
+
+```
+..
+|-- mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1
+|-- mmclassification
+|-- mmdeploy
+`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+```
+
+The python to convert the model
+
+```python
+from mmdeploy.apis import torch2onnx
+from mmdeploy.backend.sdk.export_info import export2SDK
+
+img = 'mmclassification/demo/demo.JPEG'
+work_dir = 'work_dir/onnx/resnet'
+save_file = 'end2end.onnx'
+deploy_cfg = 'mmdeploy/configs/mmcls/classification_onnxruntime_dynamic.py'
+model_cfg = 'mmclassification/configs/resnet/resnet18_8xb32_in1k.py'
+model_checkpoint = 'resnet18_8xb32_in1k_20210831-fbbb1da6.pth'
+device = 'cpu'
+
+# 1. convert model to onnx
+torch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg,
+  model_checkpoint, device)
+
+# 2. extract pipeline info for sdk use (dump-info)
+export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint)
+```
+
+The structure of the converted model directory:
+
+```bash
+.\work_dir\
+`-- onnx
+    `-- resnet
+        |-- deploy.json
+        |-- detail.json
+        |-- end2end.onnx
+        `-- pipeline.json
+```
+
+### TensorRT Example
+
+The following describes how to use the prebuilt package to do model conversion based on the previous downloaded ckpt.
+
+After the above work is done, the structure of the current working directory should be：
+
+```
+..
+|-- mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmclassification
+|-- mmdeploy
+`-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+```
+
+The python to convert the model
+
+```python
+from mmdeploy.apis import torch2onnx
+from mmdeploy.apis.tensorrt import onnx2tensorrt
+from mmdeploy.backend.sdk.export_info import export2SDK
+import os
+
+img = 'mmclassification/demo/demo.JPEG'
+work_dir = 'work_dir/trt/resnet'
+save_file = 'end2end.onnx'
+deploy_cfg = 'mmdeploy/configs/mmcls/classification_tensorrt_static-224x224.py'
+model_cfg = 'mmclassification/configs/resnet/resnet18_8xb32_in1k.py'
+model_checkpoint = 'resnet18_8xb32_in1k_20210831-fbbb1da6.pth'
+device = 'cpu'
+
+# 1. convert model to IR(onnx)
+torch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg,
+  model_checkpoint, device)
+
+# 2. convert IR to tensorrt
+onnx_model = os.path.join(work_dir, save_file)
+save_file = 'end2end.engine'
+model_id = 0
+device = 'cuda'
+onnx2tensorrt(work_dir, save_file, model_id, deploy_cfg, onnx_model, device)
+
+# 3. extract pipeline info for sdk use (dump-info)
+export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint)
+```
+
+The structure of the converted model directory:
+
+```
+.\work_dir\
+`-- trt
+    `-- resnet
+        |-- deploy.json
+        |-- detail.json
+        |-- end2end.engine
+        |-- end2end.onnx
+        `-- pipeline.json
+```
+
+## Model Inference
+
+You can obtain two model folders after model conversion.
+
+```
+.\work_dir\onnx\resnet
+.\work_dir\trt\resnet
+```
+
+The structure of current working directory：
+
+```
+.
+|-- mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0
+|-- mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1
+|-- mmclassification
+|-- mmdeploy
+|-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
+`-- work_dir
+```
+
+### Backend Inference
+
+:exclamation: It should be emphasized that this interface is not for deployment, but shields the difference of backend inference api. The main purpose of this api is to check whether the converted model can be inferred normally.
+
+#### ONNXRuntime
+
+The Python code to inference model
+
+```python
+from mmdeploy.apis import inference_model
+
+model_cfg = 'mmclassification/configs/resnet/resnet18_8xb32_in1k.py'
+deploy_cfg = 'mmdeploy/configs/mmcls/classification_onnxruntime_dynamic.py'
+backend_files = ['work_dir/onnx/resnet/end2end.onnx']
+img = 'mmclassification/demo/demo.JPEG'
+device = 'cpu'
+result = inference_model(model_cfg, deploy_cfg, backend_files, img, device)
+```
+
+#### TensorRT
+
+The Python code to inference model
+
+```python
+from mmdeploy.apis import inference_model
+
+model_cfg = 'mmclassification/configs/resnet/resnet18_8xb32_in1k.py'
+deploy_cfg = 'mmdeploy/configs/mmcls/classification_tensorrt_static-224x224.py'
+backend_files = ['work_dir/trt/resnet/end2end.engine']
+img = 'mmclassification/demo/demo.JPEG'
+device = 'cuda'
+result = inference_model(model_cfg, deploy_cfg, backend_files, img, device)
+```
+
+### Python SDK
+
+The following describes how to use the SDK's Python API for inference
+
+#### ONNXRuntime
+
+The inference code
+
+```bash
+python .\mmdeploy\demo\python\image_classification.py .\work_dir\onnx\resnet\ .\mmclassification\demo\demo.JPEG
+```
+
+#### TensorRT
+
+The inference code
+
+```
+ python .\mmdeploy\demo\python\image_classification.py .\work_dir\trt\resnet\ .\mmclassification\demo\demo.JPEG --device-name cuda
+```
+
+### C SDK
+
+The following describes how to use the SDK's C API for inference
+
+#### ONNXRuntime
+
+1. Build examples
+
+   Under `mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\sdk\example` directory
+
+   ```
+   // Path should be modified according to the actual location
+   mkdir build
+   cd build
+   cmake .. -A x64 -T v142 `
+     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\sdk\lib\cmake\MMDeploy `
+     -DONNXRUNTIME_DIR=C:\Deps\onnxruntime\onnxruntime-win-gpu-x64-1.8.1
+
+   cmake --build . --config Release
+   ```
+
+2. Add environment variables or copy the runtime libraries to the same level directory of exe
+
+   :point_right: The purpose is to make the exe find the relevant dll
+
+   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\sdk\bin`) to the PATH.
+
+   If choose to copy the dynamic libraries, copy the dll in the bin directory to the same level directory of the just compiled exe (build/Release).
+
+3. Inference：
+
+   It is recommended to use `CMD` here.
+
+   Under `mmdeploy-0.6.0-windows-amd64-onnxruntime1.8.1\\sdk\\example\\build\\Release` directory：
+
+   ```
+   .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmclassification\demo\demo.JPEG
+   ```
+
+#### TensorRT
+
+1. Build examples
+
+   Under `mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example` directory
+
+   ```
+   // Path should be modified according to the actual location
+   mkdir build
+   cd build
+   cmake .. -A x64 -T v142 `
+     -DOpenCV_DIR=C:\Deps\opencv\build\x64\vc15\lib `
+     -DMMDeploy_DIR=C:\workspace\mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8 2.3.0\sdk\lib\cmake\MMDeploy `
+     -DTENSORRT_DIR=C:\Deps\tensorrt\TensorRT-8.2.3.0 `
+     -DCUDNN_DIR=C:\Deps\cudnn\8.2.1
+   cmake --build . --config Release
+   ```
+
+2. Add environment variables or copy the runtime libraries to the same level directory of exe
+
+   :point_right: The purpose is to make the exe find the relevant dll
+
+   If choose to add environment variables, add the runtime libraries path of `mmdeploy` (`mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\sdk\bin`) to the PATH.
+
+   If choose to copy the dynamic libraries, copy the dll in the bin directory to the same level directory of the just compiled exe (build/Release).
+
+3. Inference
+
+   It is recommended to use `CMD` here.
+
+   Under `mmdeploy-0.6.0-windows-amd64-cuda11.1-tensorrt8.2.3.0\\sdk\\example\\build\\Release` directory
+
+   ```
+   .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmclassification\demo\demo.JPEG
+   ```
+
+## Troubleshooting
+
+If you encounter problems, please refer to [FAQ](../faq.md)