fix

doc/TENSOR_RT.md

+## Paddle Serving uses TensorRT
+
+(English|[Simplified Chinese]((./TENSOR_RT_CN.md)))
+
+### Background
+
+Deploying models trained on mainstream frameworks through the tensorRT tool launched by Nvidia can greatly increase the speed of model inference, which is often at least 1 times faster than the original framework, and it also takes up more device memory. less. Therefore, it is very useful for all users who need to deploy models to master the method of deploying deep learning models with tensorRT. Paddle Serving provides comprehensive TensorRT ecological support.
+
+### surroundings
+
+Serving Cuda10.1 Cuda10.2 and Cuda11 versions support TensorRT.
+
+#### Install Paddle
+
+In [Development using Docker environment](./RUN_IN_DOCKER.md) and [Docker image list](./DOCKER_IMAGES.md), we give the development image of TensorRT. After using the mirror to start, you need to install the Paddle whl package that supports TensorRT, refer to the documentation on the home page
+
+```
+# GPU Cuda10.2 environment please execute
+pip install paddlepaddle-gpu==2.0.0
+```
+
+**Note**: If your Cuda version is not 10.2, please do not execute the above commands directly, you need to refer to [Paddle official documentation-multi-version whl package list
+](https://www.paddlepaddle.org.cn/documentation/docs/en/install/Tables_en.html#multi-version-whl-package-list-release)
+
+Select the URL link of the corresponding GPU environment and install it. For example, for Python2.7 users of Cuda 10.1, please select `cp27-cp27mu` and
+`cuda10.1-cudnn7.6-trt6.0.1.5` corresponding url, copy it and execute
+```
+pip install https://paddle-wheel.bj.bcebos.com/with-trt/2.0.0-gpu-cuda10.1-cudnn7-mkl/paddlepaddle_gpu-2.0.0.post101-cp27-cp27mu-linux_x86_64.whl
+```
+Since the default `paddlepaddle-gpu==2.0.0` is Cuda 10.2 and TensorRT is not built, if you need to use TensorRT on `paddlepaddle-gpu`, you need to find `cuda10 in the above multi-version whl package list .2-cudnn8.0-trt7.1.3`, download the corresponding Python version.
+
+
+#### Install Paddle Serving
+```
+# Cuda10.2
+pip install paddle-server-server==${VERSION}.post102
+# Cuda 10.1
+pip install paddle-server-server==${VERSION}.post101
+# Cuda 11
+pip install paddle-server-server==${VERSION}.post11
+```
+
+### Use TensorRT
+
+#### RPC mode
+
+In [Serving model example](../python/examples), we have given models that can be accelerated using TensorRT, such as [Faster_RCNN model](../python/examples/detection/faster_rcnn_r50_fpn_1x_coco) under detection
+
+We just need
+```
+wget --no-check-certificate https://paddle-serving.bj.bcebos.com/pddet_demo/2.0/faster_rcnn_r50_fpn_1x_coco.tar
+tar xf faster_rcnn_r50_fpn_1x_coco.tar
+python -m paddle_serving_server_gpu.serve --model serving_server --port 9494 --gpu_ids 0 --use_trt
+```
+The TensorRT version of the faster_rcnn model server is started
+
+
+#### Local Predictor mode
+
+In [local_predictor](../python/paddle_serving_app/local_predict.py#L52), users can explicitly specify `use_trt=True` and pass it to `load_model_config`.
+Other methods are no different from other Local Predictor methods, and you need to pay attention to the compatibility of the model with TensorRT.
+
+#### Pipeline Mode
+
+In [Pipeline mode](./PIPELINE_SERVING.md), our [imagenet example](../python/examples/pipeline/imagenet/config.yml#L23) gives the way to set TensorRT.

doc/TENSOR_RT_CN.md

 ## Paddle Serving 使用 TensorRT
 
-([English](./TENSOR)|)
+([English](./TENSOR_RT.md)|简体中文)
 
 ### 背景
 
@@ -44,7 +44,7 @@ pip install paddle-server-server==${VERSION}.post11
 
 #### RPC模式
 
-在[Serving模型示例](../python/examples)当中，我们有给出可以使用TensorRT加速的模型，例如detection下的[faster_rcnn模型](python/examples/detection/faster_rcnn_r50_fpn_1x_coco)
+在[Serving模型示例](../python/examples)当中，我们有给出可以使用TensorRT加速的模型，例如detection下的[Faster_RCNN模型](../python/examples/detection/faster_rcnn_r50_fpn_1x_coco)
 
 我们只需
 ```
@@ -57,7 +57,7 @@ TensorRT版本的faster_rcnn模型服务端就启动了
 
 #### Local Predictor模式
 
-在 [local_predicotr 实现当中](../python/paddle_serving_app/local_predict.py#L52)当中，用户可以显式制定`use_trt=True`传入到`load_model_config`当中。
+在 [local_predictor](../python/paddle_serving_app/local_predict.py#L52)当中，用户可以显式制定`use_trt=True`传入到`load_model_config`当中。
 其他方式和其他Local Predictor使用方法没有区别，需要注意模型对TensorRT的兼容性。
 
 #### Pipeline模式