@@ -124,7 +124,7 @@ Recommended to install paddle >= 2.0.0
pip install paddlepaddle==2.0.0
# GPU Cuda10.2 please run
pip install paddlepaddle-gpu==2.0.0
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
...
...
@@ -135,8 +135,12 @@ The url corresponding to `cuda9.0_cudnn7-mkl`, copy it and run
the default `paddlepaddle-gpu==2.0.0` is Cuda 10.2 with no TensorRT. If you want to install PaddlePaddle with TensorRT. please also check the documentation-multi-version whl package list and find key word `cuda10.2-cudnn8.0-trt7.1.3`. More info please check [Paddle Serving uses TensorRT](./doc/TENSOR_RT.md)
If it is other environment and Python version, please find the corresponding link in the table and install it with pip.
For **Windows Users**, please read the document [Paddle Serving for Windows Users](./doc/WINDOWS_TUTORIAL.md)
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
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
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.
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
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.
