All-in-one Docker image with common machine learning tools

Here are Dockerfiles to get you up and running with a fully functional deep learning machine. It contains all the popular deep learning frameworks with CPU and GPU support (CUDA and cuDNN included). The CPU version should work on Linux, Windows and OS X. The GPU version will, however, only work on Linux machines. See OS support for details

Setup

Prerequisites

1. Install Docker following the installation guide for your platform: https://docs.docker.com/engine/installation/

2. GPU Version Only: Install Nvidia drivers on your machine either from Nvidia directly or follow the instructions here. Note that you don't have to install CUDA or cuDNN. These are included in the Docker container.

3. GPU Version Only: Install nvidia-docker: https://github.com/NVIDIA/nvidia-docker, following the instructions here. This will install a replacement for the docker CLI. It takes care of setting up the Nvidia host driver environment inside the Docker containers and a few other things.

Common used docker commands

Clean up container

docker container ps -a
docker container rm 69b8da54bc43

Remove images

docker rmi 69b8da54bc43

Download dock files

git clone https://github.com/zhenliu/dl-docker

Builder docker image

docker build -t zhenliu/dl-docker:cpu -f Dockerfile.cpu .

start docker container with ipython port

docker run -it -p 8888:8888 -p 6006:6006 -v ~/dl_root:/root/dl_root zhenliu/dl-docker:cpu bash

start an existing container after machine was reboot

docker start d3b7d7463857

start an interactive shell to an existing container

docker exec -it d3b7d7463857 /bin/bash

export the current container into a different image

docker export -o /home/zhenliu/myfile.tar d3b7d7463857

Import the image. Make sure the same image does not exist.

docker import /home/qisun/myfile.tar

Execute command in background in docker

docker exec d3b7d7463857 /bin/bash -c "bwa aln mydata >& log" &

Check the status of the execution

docker exec d3b7d7463857 ps -ef

Clean all idle containers

docker container prune