(简体中文|English)
This document maintains a list of docker images provided by Paddle Serving.
You can get images in two ways:
-
Pull image directly from
hub.baidubce.comordocker.iothrough TAG:docker pull hub.baidubce.com/paddlepaddle/serving:<TAG> # hub.baidubce.com docker pull paddlepaddle/serving:<TAG> # hub.docker.com
-
Building image based on dockerfile
Create a new folder and copy Dockerfile to this folder, and run the following command:
docker build -t <image-name>:<images-tag> .
Runtime images cannot be used for compilation. If you want to customize your Serving based on source code, use the version with the suffix - devel.
| Description | OS | TAG | Dockerfile |
|---|---|---|---|
| CPU runtime | CentOS7 | latest | Dockerfile |
| CPU development | CentOS7 | latest-devel | Dockerfile.devel |
| GPU (cuda9.0-cudnn7) runtime | CentOS7 | latest-cuda9.0-cudnn7 | Dockerfile.cuda9.0-cudnn7 |
| GPU (cuda9.0-cudnn7) development | CentOS7 | latest-cuda9.0-cudnn7-devel | Dockerfile.cuda9.0-cudnn7.devel |
| GPU (cuda10.0-cudnn7) runtime | CentOS7 | latest-cuda10.0-cudnn7 | Dockerfile.cuda10.0-cudnn7 |
| GPU (cuda10.0-cudnn7) development | CentOS7 | latest-cuda10.0-cudnn7-devel | Dockerfile.cuda10.0-cudnn7.devel |
| GPU (cuda10.1-cudnn7-tensorRT6) runtime | Ubuntu16 | latest-cuda10.1-cudnn7 | Dockerfile.cuda10.1-cudnn7 |
| GPU (cuda10.1-cudnn7-tensorRT6) development | Ubuntu16 | latest-cuda10.1-cudnn7-devel | Dockerfile.cuda10.1-cudnn7.devel |
| GPU (cuda10.2-cudnn8-tensorRT7) runtime | Ubuntu16 | latest-cuda10.2-cudnn8 | Dockerfile.cuda10.2-cudnn8 |
| GPU (cuda10.2-cudnn8-tensorRT7) development | Ubuntu16 | latest-cuda10.2-cudnn8-devel | Dockerfile.cuda10.2-cudnn8.devel |
| GPU (cuda11-cudnn8-tensorRT7) runtime | Ubuntu18 | latest-cuda11-cudnn8 | Dockerfile.cuda11-cudnn8 |
| GPU (cuda11-cudnn8-tensorRT7) development | Ubuntu18 | latest-cuda11-cudnn8-devel | Dockerfile.cuda11-cudnn8.devel |
Java Client:
hub.baidubce.com/paddlepaddle/serving:latest-java
XPU:
hub.baidubce.com/paddlepaddle/serving:xpu-beta
Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using.
The machine running the CUDA container only requires the NVIDIA driver, the CUDA toolkit doesn't have to be installed.
For the relationship between CUDA toolkit version, Driver version and GPU architecture, please refer to nvidia-docker wiki.