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Last pushed: a year ago
Short Description
Useful dockers for DataScience
Full Description


Useful dockers for DataScience


By default docker starts jupyter notebook on port 8888 with PASSWORD=default

CPU version

docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder ferres/sci:cpu

GPU version

# if nvidia-docker is not installed
# ./
nvidia-docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder ferres/sci:gpu


build time (set default password)

docker build -t ferres/sci:gpu -f sci/Dockerfile.gpu --build-arg PASSWORD=new_password ./sci

runtime (preferred, much easier)

docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder -e PASSWORD=new_password ferres/sci:cpu

Some scripts

Builds dockers in the repository for cpu/gpu usage

./ -bs
# -b : base docker with Theano/TensorFlow and jupyter (base:{cpu,gpu})
# -s : data science docker with all you need (sci:{cpu,gpu})

Pushes dockers to remote repository

./ -bs -r repository
# -b : base docker
# -s : data science docker with all you need
# -r repo : used as following `docker push $REPO/base:gpu && docker push $REPO/base:cpu`, ferres repo by default

Configures aws for gpu docker, needs to be excecuted on aws instance. Then you can use nvidia-docker

Docker Pull Command
Source Repository