@@ -55,12 +55,12 @@ The goal of Paddle Serving is to provide high-performance, flexible and easy-to-
This chapter guides you through the installation and deployment steps. It is strongly recommended to use Docker to deploy Paddle Serving. If you do not use docker, ignore the docker-related steps. Paddle Serving can be deployed on cloud servers using Kubernetes, running on many commonly hardwares such as ARM CPU, Intel CPU, Nvidia GPU, Kunlun XPU. The latest development kit of the develop branch is compiled and generated every day for developers to use.
-[Install Paddle Serving using docker(stable wheel packages)](doc/Install_EN.md)
-[Install Paddle Serving using docker](doc/Install_EN.md)
-[Build Paddle Serving from Source with Docker](doc/Compile_EN.md)
-[Deploy Paddle Serving on Kubernetes](doc/Run_On_Kubernetes_CN.md)
-[Deploy Paddle Serving with Security gateway(Chinese)](doc/Serving_Auth_Docker_CN.md)
-[Deploy Paddle Serving on more hardwares](doc/Run_On_XPU_EN.md)
We first need to pull related images for the environment we need. Under the **Environment** column in the above table, except for the CPU, the rest (Cuda**+Cudnn**) belong to the GPU environment.
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)
