If you want to customize your Serving based on source code, use the version with the suffix - devel.
**cuda10.1-cudnn7-gcc54 image is not ready, you should run from dockerfile if you need it.**
| Description | OS | TAG | Dockerfile |
If you need to develop and compile based on the source code, please use the version with the suffix -devel.
**In the TAG column, 0.7.0 can also be replaced with the corresponding version number, such as 0.5.0/0.4.1, etc., but it should be noted that some development environments only increase with a certain version iteration, so not all environments All have the corresponding version number can be used.**
Running Images is lighter than Develop Images, and Running Images are made up with serving whl and bin, but without develop tools like cmake because of lower image size. If you want to know about it, plese check the document [Paddle Serving on Kubernetes.](./Run_On_Kubernetes_CN.md).
**Tips:** If you want to use CPU server and GPU server (version>=0.5.0) 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).
**Strongly recommend** you build **Paddle Serving** in Docker, please check [How to run PaddleServing in Docker](Run_In_Docker_CN.md). For more images, please refer to [Docker Image List](Docker_Images_CN.md).
**Strongly recommend** you build **Paddle Serving** in Docker. For more images, please refer to [Docker Image List](Docker_Images_CN.md).
**Tip-1**: This project only supports <mark>**Python3.6/3.7/3.8**</mark>, all subsequent operations related to Python/Pip need to select the correct Python version.
One of the biggest benefits of Docker is portability, which can be deployed on multiple operating systems and mainstream cloud computing platforms. The Paddle Serving Docker image can be deployed on Linux, Mac and Windows platforms.
## Requirements
Docker (GPU version requires nvidia-docker to be installed on the GPU machine)
This document takes Python2 as an example to show how to run Paddle Serving in docker. You can also use Python3 to run related commands by replacing `python` with `python3`.
## CPU
### Get docker image
Refer to [this document](Docker_Images_EN.md) for a docker image:
docker run -p 9292:9292 --nametest-dit registry.baidubce.com/paddlepaddle/serving:latest-devel
docker exec-ittest bash
```
The `-p` option is to map the `9292` port of the container to the `9292` port of the host.
### Install PaddleServing
Please refer to the instructions on the homepage to download the pip package of the corresponding version.
## GPU
The GPU version is basically the same as the CPU version, with only some differences in interface naming (GPU version requires nvidia-docker to be installed on the GPU machine).
### Get docker image
Refer to [this document](Docker_Images_EN.md) for a docker image, the following is an example of an `cuda9.0-cudnn7` image:
nvidia-docker run -p 9292:9292 --nametest-dit registry.baidubce.com/paddlepaddle/serving:latest-cuda10.2-cudnn8-devel
nvidia-docker exec-ittest bash
```
or
```bash
docker run --gpus all -p 9292:9292 --nametest-dit registry.baidubce.com/paddlepaddle/serving:latest-cuda10.2-cudnn8-devel
docker exec-ittest bash
```
The `-p` option is to map the `9292` port of the container to the `9292` port of the host.
### Install PaddleServing
The mirror comes with `paddle_serving_server_gpu`, `paddle_serving_client`, and `paddle_serving_app` corresponding to the mirror tag version. If users don’t need to change the version, they can use it directly, which is suitable for environments without extranet services.
If you need to change the version, please refer to the instructions on the homepage to download the pip package of the corresponding version. [LATEST_PACKAGES](./Latest_Packages_CN.md)
## Precautious
- Runtime images cannot be used for compilation. If you want to compile from source, refer to [COMPILE](Compile_EN.md).
- If you use Cuda9 and Cuda10 docker images, you cannot use `paddle_serving_server` CPU version at the same time, due to the limitation of gcc version. If you want to use both in one docker image, please choose images of Cuda10.1, Cuda10.2 and Cuda11.
