Useful dockers for DataScience
By default docker starts jupyter notebook on port 8888 with PASSWORD=default
docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder ferres/sci:cpu
# if nvidia-docker is not installed
# ./nvidia-docker-install.sh
nvidia-docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder ferres/sci:gpu
docker build -t ferres/sci:gpu -f sci/Dockerfile.gpu --build-arg PASSWORD=new_password ./sci
docker run -d -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder -e PASSWORD=new_password ferres/sci:cpu
Builds dockers in the repository for cpu/gpu usage
./build.sh -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
./push.sh -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
./nvidia-docker-install.sh
Content type
Image
Digest
Size
596.8 MB
Last updated
over 9 years ago
docker pull ferres/ds-dockers:openai.cpu