Ubuntu configures docker image for deep learning

Take ubuntu16.04, cuda10.1 as examples

1. Preparation

Install the graphics card driver corresponding to the cuda version in ubuntu, refer to the link
Installed docker and toolkits, refer to the link
Use docker without sudo, refer to the link
Test whether docker's --gpus parameter is available, not available refer to the link above to reinstall docker

docker run --help | grep -i gpus

2. Pull the official image

You need to pull a mirror that encapsulates cuda and cudnn from the official as the initial mirror

Go to docker hub to find the required mirror
Ctrl+ fsearch keywords, such as cuda10.1+cudnn8, search10.1-cudnn8
Copy the docker pull command and pull the image in the terminal

docker pull nvidia/cuda:10.1-cudnn8-devel-ubuntu16.04

Download may be very slow, speed up docker pull in advance, refer to the link

3. Create a container

View currently existing mirrors

docker images

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nvidia/cuda is the image just pulled, pay attention to the REPOSITORY , TAG , IMAGE ID of the image

Create a container by mirroring, and mount the local folder, the folder path is written according to the actual situation

docker run --gpus all -it -v /home/kkjsk/projects:/projects 1e33220823f3(容器ID) /bin/bash

Create container by image ID

docker run --gpus all -it -v /home/name/projects:/projects nvidia/cuda:10.1-cudnn8-devel-ubutnu16.04(REPOSITORY:TAG) /bin/bash

Create a container with REPOSITORY and TAG

/ home / name / projects: / projects means that the local / home / name / projects path mounted to the container / projects , the name can own

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Successfully created and run the container, and enter ls on the command line, you can see the mounted folder

Check whether the graphics card driver can be called

nvidia-smi

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Check if cuda is available

nvcc -V

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No problem, the container can successfully call the graphics card and use cuda

4. Install python

Python is not installed in the official image, and there is no pip, so you need to install the required python version in the container yourself. Install python by referring to the source code

Pay attention to downloadPlace the source code package under the mounted folder, Otherwise it cannot be found in the container
Install python in the container,Remove sudo from the command line
Some posts will modify the python installation path, it is not recommended to modify it in the docker container

5. Packaging

You can package a basic environment directly, or you can package the dependent packages needed by the code after you install them, so that the packaging is convenient for transplantation

Save the container as a new image and package the image

Start another terminal to view the existing container

docker ps -a

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Create a new image through the container

docker commit b0a9e5a5a411(容器ID) kkjsk/docker:python(新镜像的DREPOSITORY：TAG)

View the newly created image

docker images

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You can see that the mirror kkjsk/docker:python was successfully created

Save the image as a local compressed package,Suffix tar

docker save kkjsk/docker:python(REPOSITORY：TAG) cuda10.1_docker.tar(压缩包名)

Then you can see the saved compressed package locally

6. Import the image

When using the packaged image on other computers, you need to import the image through the compressed package first

docker load --input cuda10.1_docker.tar