We **highly recommend** you to **run Paddle Serving in Docker**, please visit [Run in Docker](doc/RUN_IN_DOCKER.md). See the [document](doc/DOCKER_IMAGES.md) for more docker images.
**Attention:**: Currently, the default GPU environment of paddlepaddle 2.0 is Cuda 10.2, so the sample code of GPU Docker is based on Cuda 10.2. We also provides docker images and whl packages for other GPU environments. If users use other environments, they need to carefully check and select the appropriate version.
**Attention:**: Currently, the default GPU environment of paddlepaddle 2.1 is Cuda 10.2, so the sample code of GPU Docker is based on Cuda 10.2. We also provides docker images and whl packages for other GPU environments. If users use other environments, they need to carefully check and select the appropriate version.
**Attention:** the following so-called 'python' or 'pip' stands for one of Python 3.6/3.7/3.8.
nvidia-docker run -p 9292:9292 --name test -dit registry.baidubce.com/paddlepaddle/serving:0.6.0-cuda10.2-cudnn8-devel bash
nvidia-docker exec -it test bash
git clone https://github.com/PaddlePaddle/Serving
```
install python dependencies
```
cd Serving
pip install -r python/requirements.txt
```
```shell
pip install paddle-serving-client==0.5.0
pip install paddle-serving-server==0.5.0 # CPU
pip install paddle-serving-app==0.3.0
pip install paddle-serving-server-gpu==0.5.0.post102 #GPU with CUDA10.2 + TensorRT7
pip install paddle-serving-client==0.6.0
pip install paddle-serving-server==0.6.0 # CPU
pip install paddle-serving-app==0.6.0
pip install paddle-serving-server-gpu==0.6.0.post102 #GPU with CUDA10.2 + TensorRT7
# DO NOT RUN ALL COMMANDS! check your GPU env and select the right one
pip install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0
pip install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0
pip install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6
pip install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7
pip install paddle-serving-server-gpu==0.6.0.post101 # GPU with CUDA10.1 + TensorRT6
pip install paddle-serving-server-gpu==0.6.0.post11 # GPU with CUDA10.1 + TensorRT7
```
You may need to use a domestic mirror source (in China, you can use the Tsinghua mirror source, add `-i https://pypi.tuna.tsinghua.edu.cn/simple` to pip command) to speed up the download.
...
...
@@ -115,28 +120,31 @@ If you need install modules compiled with develop branch, please download packag
Packages of paddle-serving-server and paddle-serving-server-gpu support Centos 6/7, Ubuntu 16/18, Windows 10.
Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python2.7/3.5/3.6/3.7/3.8.
Packages of paddle-serving-client and paddle-serving-app support Linux and Windows, but paddle-serving-client only support python3.6/3.7/3.8.
**For latest version, Cuda 9.0 or Cuda 10.0 are no longer supported, Python2.7/3.5 is no longer supported.**
Recommended to install paddle >= 2.1.0
Recommended to install paddle >= 2.0.0
```
# CPU users, please run
pip install paddlepaddle==2.0.0
pip install paddlepaddle==2.1.0
# GPU Cuda10.2 please run
pip install paddlepaddle-gpu==2.0.0
pip install paddlepaddle-gpu==2.1.0
```
**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
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)
the default `paddlepaddle-gpu==2.1.0` 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.
...
...
@@ -211,6 +219,34 @@ the response is
```
{"result":{"price":[[18.901151657104492]]}}
```
<h3align="center">Pipeline Service</h3>
Paddle Serving provides industry-leading multi-model tandem services, which strongly supports the actual operating business scenarios of major companies, please refer to [OCR word recognition](./python/examples/pipeline/ocr).
@@ -253,6 +292,7 @@ If you want to contribute code to Paddle Serving, please reference [Contribution
- Special Thanks to [@BeyondYourself](https://github.com/BeyondYourself) in complementing the gRPC tutorial, updating the FAQ doc and modifying the mdkir command
- Special Thanks to [@mcl-stone](https://github.com/mcl-stone) in updating faster_rcnn benchmark
- Special Thanks to [@cg82616424](https://github.com/cg82616424) in updating the unet benchmark and modifying resize comment error
- Special Thanks to [@cuicheng01](https://github.com/cuicheng01) for providing 11 PaddleClas models
**Tips:** If you want to use CPU server and GPU server at the same time, you should check the gcc version, only Cuda10.1/10.2/11 can run with CPU server owing to the same gcc version(8.2).
for kunlun user who uses arm-xpu or x86-xpu can download the wheel packages as follows. Users should use the xpu-beta docker [DOCKER IMAGES](./DOCKER_IMAGES.md)
for ARM user who uses [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) can download the wheel packages as follows. And ARM user should use the xpu-beta docker [DOCKER IMAGES](./DOCKER_IMAGES.md)
- download the serving server whl package and bin package, and make sure they are for the same environment
- download the serving client whl and serving app whl, pay attention to the Python version.
-`pip install ` the serving and `tar xf ` the binary package, then `export SERVING_BIN=$PWD/serving-gpu-cuda10-0.0.0/serving` (take Cuda 10.0 as the example)
-`pip install ` the serving and `tar xf ` the binary package, then `export SERVING_BIN=$PWD/serving-gpu-cuda11-0.0.0/serving` (take Cuda 11 as the example)
@@ -8,7 +8,7 @@ This document guides users how to build Paddle Serving service on the Windows pl
### Running Paddle Serving on Native Windows System
**Configure Python environment variables to PATH**: **We only support Python 3.5+ on Native Windows System.**. First, you need to add the directory where the Python executable program is located to the PATH. Usually in **System Properties/My Computer Properties**-**Advanced**-**Environment Variables**, click Path and add the path at the beginning. For example, `C:\Users\$USER\AppData\Local\Programs\Python\Python36`, and finally click **OK** continuously. If you enter python on Powershell, you can enter the python interactive interface, indicating that the environment variable configuration is successful.
**Configure Python environment variables to PATH**: **We only support Python 3.6+ on Native Windows System.**. First, you need to add the directory where the Python executable program is located to the PATH. Usually in **System Properties/My Computer Properties**-**Advanced**-**Environment Variables**, click Path and add the path at the beginning. For example, `C:\Users\$USER\AppData\Local\Programs\Python\Python36`, and finally click **OK** continuously. If you enter python on Powershell, you can enter the python interactive interface, indicating that the environment variable configuration is successful.
**Install wget**: Because all the downloads in the tutorial and the built-in model download function in `paddle_serving_app` all use the wget tool, download the binary package at the [link](http://gnuwin32.sourceforge.net/packages/wget.htm), unzip and copy it to `C:\Windows\System32`, if there is a security prompt, you need to pass it.
