@@ -62,9 +62,9 @@ This chapter guides you through the installation and deployment steps. It is str
-[Build Paddle Serving from Source with Docker](doc/Compile_EN.md)
-[Deploy Paddle Serving on Kubernetes(Chinese)](doc/Run_On_Kubernetes_CN.md)
-[Deploy Paddle Serving with Security gateway(Chinese)](doc/Serving_Auth_Docker_CN.md)
- Deploy on more hardwares[[百度昆仑](doc/Run_On_XPU_CN.md)、[华为昇腾](doc/Run_On_NPU_CN.md)、[海光DCU](doc/Run_On_DCU_CN.md)、[Jetson](doc/Run_On_JETSON_CN.md)]
- Deploy on more hardwares[[ARM CPU、百度昆仑](doc/Run_On_XPU_EN.md)、[华为昇腾](doc/Run_On_NPU_CN.md)、[海光DCU](doc/Run_On_DCU_CN.md)、[Jetson](doc/Run_On_JETSON_CN.md)]
@@ -108,13 +108,13 @@ For Paddle Serving developers, we provide extended documents such as custom OP,
<h2align="center">Model Zoo</h2>
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 45 models.
Paddle Serving works closely with the Paddle model suite, and implements a large number of service deployment examples, including image classification, object detection, language and text recognition, Chinese part of speech, sentiment analysis, content recommendation and other types of examples, for a total of 46 models.
Install the service whl package. There are three types of client, app and server. The server is divided into CPU and GPU. Choose one installation according to the environment.
- GPU with CUDA10.2 + Cudnn7 + TensorRT6(Recommended)
For **Windows 10 users**, please refer to the document [Paddle Serving Guide for Windows Platform](Windows_Tutorial_CN.md).
## 5. Installation check
After the above steps are completed, you can use the command line to run the environment check function to automatically run the Paddle Serving related examples to verify the environment-related configuration.
```
python3 -m paddle_serving_server.serve check
```
For details, please refer to the [Environmental Check Documentation](./Check_Env_CN.md)
Check the following table, and copy the address of hyperlink then run `pip3 install`. For example, if you want to install `paddle-serving-server-0.0.0-py3-non-any.whl`, right click the hyper link and copy the link address, the final command is `pip3 install https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server-0.0.0-py3-none-any.whl`.
for most users, we do not need to read this section. But if you deploy your Paddle Serving on a machine without network, you will encounter a problem that the binary executable tar file cannot be downloaded. Therefore, here we give you all the download links for various environment.
-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=$PWD/serving-gpu-cuda11-0.0.0/serving` (take Cuda 11 as the example)
for kunlun user who uses arm-xpu or x86-xpu can download the wheel packages as follows. Users should use the xpu-beta docker [DOCKER IMAGES](./Docker_Images_CN.md)
Check the following table, and copy the address of hyperlink then run `pip3 install`. For example, if you want to install `paddle-serving-server-0.0.0-py3-non-any.whl`, right click the hyper link and copy the link address, the final command is `pip3 install https://paddle-serving.bj.bcebos.com/test-dev/whl/paddle_serving_server-0.0.0-py3-none-any.whl`.
| | develop whl | develop bin | stable whl | stable bin |
for most users, we do not need to read this section. But if you deploy your Paddle Serving on a machine without network, you will encounter a problem that the binary executable tar file cannot be downloaded. Therefore, here we give you all the download links for various environment.
### How to setup SERVING_BIN offline?
- download the serving server whl package and bin package, and make sure they are for the same environment
- download the serving client whl and serving app whl, pay attention to the Python version.
-`pip install ` the serving and `tar xf ` the binary package, then `export SERVING_BIN=$PWD/serving-gpu-cuda11-0.0.0/serving` (take Cuda 11 as the example)
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_CN.md)
