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All Projects → bethgelab → docker

bethgelab / docker

Licence: MIT license
Information and scripts to run and develop the Bethge Lab Docker containers

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THIS REPOSITORY AND THE DOCKER IMAGES LISTED HERE ARE OUTDATED. OUR NEW DEEPLEARNING DOCKER IMAGES AND SCRIPTS CAN BE FOUND AT https://github.com/bethgelab/docker-deeplearning.

Docker Toolchain of the Bethge Lab

This repository includes utilities to build and run the Docker images of the Bethge Lab. The toolchain is composed of four different images (more details below):

  • The xserver image adds user-authentication and Xserver capabilities to a base Ubuntu-image. In particular, this images fixes a file permission problem: new files created from within the container are owned by root and thus conflict with user permissions.

  • The jupyter-notebook image is a fork of the official jupyter/notebook image but is based on xserver.

  • The jupyter-scipyserver image is based on jupyter-notebook and adds many python packages needed for scientific computing such as Numpy and Scipy (both compiled against OpenBlas), Theano, Lasagne, Pandas, Seaborn and more.

  • The jupyter-deeplearning image is based on jupyter-scipyserver (including Lasagne) but adds some libraries such as Caffe, Keras, Tensorflow, Scikit-image, Joblib and others.

  • The jupyter-torch image is based on jupyter-deeplearning and adds Torch including FBLuaLib and loadcaffe.

All images come with different (or no) CUDA-libraries installed. Currently we support the following configurations:

  • ubuntu-14.04: plain Ubuntu 14.04
  • cuda6.5: Ubuntu 14.04 + Cuda 6.5
  • cuda7.0-cudnn2: Ubuntu 14.04 + Cuda 7.0 + CuDNN v2
  • cuda7.0-cudnn3: Ubuntu 14.04 + Cuda 7.0 + CuDNN v3
  • cuda7.0-cudnn4: Ubuntu 14.04 + Cuda 7.0 + CuDNN v4
  • cuda7.5-cudnn5: Ubuntu 14.04 + Cuda 7.5 + CuDNN v5
  • cuda8.0-cudnn5: Ubuntu 14.04 + Cuda 8.0 + CuDNN v5

All images are readily available from Docker Hub. To pull for example the image jupyter-deeplearning with Cuda 8.0 and CuDNN v5 you would do

docker pull bethgelab/jupyter-deeplearning-x:cuda8.0-cudnn5

Available tags are ubuntu-14.04, cuda6.5, cuda7.0-cudnn2, cuda7.0-cudnn3, cuda7.0-cudnn4, cuda7.5-cudnn5, cuda8.0-cudnn5.

AGMB Docker wrapper

To make the employment of the containers as painless as possible we have wrapped all important flags in the script nv-agmb-docker (see root directory of repo), which wraps nvidia-docker (https://github.com/NVIDIA/nvidia-docker). To run a container, first pull the image from Docker Hub (important - otherwise the CUDA version cannot be detected) before running the command

GPU=0 ./nv-agmb-docker run -d bethgelab/jupyter-deeplearning-x:cuda8.0-cudnn5

or equivalently for any other image or tag. This command has to be run in the folder in which the agmb-docker script was placed. The script takes care of setting up the NVIDIA host driver environment inside the Docker container, adds the current user, mounts his home-directory in which it finally starts the jupyter notebook. Some properties are specific to users within the AG Bethge lab, but as an external user one can override all settings. As the most stripped-down version, use

GPU=0 ./nv-agmb-docker run -e GROUPS=sudo -e USER_HOME=$HOME -d bethgelab/jupyter-deeplearning-x:cuda8.0-cudnn5

Note that all the usual docker flags can be given. In addition, some environmental variables have a special meaning

  • USER -- The username that is added to the container
  • USER_HOME -- The user's home directory location (default /gpfs01/bethge/home/$USER_NAME)
  • USER_ID -- The user ID for the new user
  • USER_GROUPS -- The groups to which the user is added (default: sudo,bethgelab:1011,cin:1019); the first group will act as the primary group
  • USER_ENCRYPTED_PASSWORD -- your user password (encrypted). To generate it: perl -e 'print crypt('"PASSWORD"', "aa"),"\n"'

GPUs are exported through a list of comma-separated IDs using the environment variable GPU. The numbering is the same as reported by nvidia-smi or when running CUDA code with CUDA_DEVICE_ORDER=PCI_BUS_ID, it is however different from the default CUDA ordering.

Note

The above applies to the tags from CUDA 8.0 onwards. For older images (i.e. CUDA 7.5 and before) replace nv-agmb-docker by agmb-docker.

xserver: LDAP, Xserver & OpenBLAS

This image is a modification and extension of a Dockerfile by Alexander Ecker. It enables the following features:

  1. Using LDAP user within a Docker container (more precisely: emulates it by using a local user with the same uid).
  2. Runs an X server.
  3. SSH daemon, i.e. allows ssh -X to run GUI within the Docker container.
  4. Installs OpenBLAS.

Note that one should not override the CMD in this image. If you need to execute additional programs when starting the container, add them to /usr/local/bin/startup as follows:

RUN echo "./mycmd" >> /usr/local/bin/startup

jupyter-notebook

This image is a fork of the official jupyter/notebook image with some modifications to allow a shift of the base image from plain Ubuntu to our CUDA-enhanced ldap-xserver images. The Jupyter Notebook runs as the User and listens to port 8888. At runtime the container will initialize (and display) a port-forwarding between host and container, the choice can be overriden by setting the forward manually, e.g.

GPU=0 ./agmb-docker run -p 534:8888 -d bethgelab/jupyter-deeplearning:cuda7.0-cudnn3

The notebook can then be reached by

http://localhost:534

By default the notebook will start from user home.

jupyter-scipyserver

This image is based on jupyter-notebook and adds the following packages to both Python 2.7 and Python 3.4:

  • Numpy (compiled against OpenBLAS)
  • Scipy (compiled against OpenBLAS)
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • h5py
  • yt
  • sympy
  • patsy
  • ggplot
  • statsmodels
  • Theano (from master)
  • Lasagne (from master)
  • Bokeh
  • mock
  • pytest

jupyter-deeplearning

This image is based on jupyter-scipyserver and adds Caffe 0.14 (binaries by NVIDIA). In addition, the following packages are installed for Python 2.7:

  • scikit-image
  • h5py
  • leveldb
  • networkx
  • joblib
  • bloscpack
  • keras
  • TensorFlow

jupyter-torch

This image is based on jupyter-deeplearning and adds Torch including FBLuaLib and loadcaffe.

Trouble-Shooting

If you run into an "[[: not found" error upon executing the agmb-docker script, then you are invoking dash and not bash. To invoke bash explicitely, do

GPU=0 bash agmb-docker run -d bethgelab/jupyter-deeplearning:cuda7.0-cudnn3

Issues and Contributing

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].