Modified Jupyter Notebook Data Science Stack

Original version from https://github.com/jupyter/docker-stacks/blob/master/datascience-notebook/README.md

This is a modification of the Jupyter Data Science Stack Docker file to include much more packages in Julia out of the box.

I removed the R packages as this is aimed at Julia. I invite to use the awesome data science stack from Jupyter if you want R in docker.

Important notes:

Use Python 3 kernel to try rampy and gcvspline.

Spectra works fairly well with the Julia kernel.

Charles Le Losq.

What it Gives You

Jupyter Notebook 5.0.x
Conda Python 3.x and Python 2.7.x environments (Python 3.x recommended to use with Rampy and GCVspline)
pandas, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, cloudpickle, dill, numba, bokeh, rampy, gcvspline pre-installed
Julia v0.5.x with Gadfly, RDatasets, HDF5, JuMP, Ipopt, Spectra and many other packages pre-installed
Unprivileged user jovyan (uid=1000, configurable, see options) in group users (gid=100) with ownership over /home/jovyan and /opt/conda
tini as the container entrypoint and start-notebook.sh as the default command
A start-singleuser.sh script useful for running a single-user instance of the Notebook server, as required by JupyterHub
A start.sh script useful for running alternative commands in the container (e.g. ipython, jupyter kernelgateway, jupyter lab)
Options for a self-signed HTTPS certificate and passwordless sudo

Running the Python/Julia stuff in Docker

Starting guide at https://github.com/jupyter/docker-stacks/tree/master/datascience-notebook

Install docker, see Docker website for your system (available on Linux/Mac/Windows). Then in a terminal (see Docker guide for your operating system)

docker pull charlesll/julia:latest

Now you're ready to launch Docker. Create a working directory folder and get its path. For instance, in this example, I want to do some work on the Forsterite data so will name it /Users/charles/Labodata/SPECTROSCOPY/Infrared/Forsterite

You can run the container with access to the folder by typing:

docker run -it --rm -p 8888:8888 -v /Users/charles/Labodata/SPECTROSCOPY/Infrared/Forsterite:/home/jovyan/work charlesll/julia:latest

From Jupyter notes: Take note of the authentication token included in the notebook startup log messages. Include it in the URL you visit to access the Notebook server or enter it in the Notebook login form.

In practice copy-paste what the terminal tells you to copy, paste the link in the browser, and you're good to go!

Notes

Of course, change the /Users/charles/Desktop/TestNotebook to reflect your own working path. Do NOT change the /home/jovyan/work part, this is in the container.
It happened to me that the dowload of the docker container was stuck. Just relaunch it.
if it tells you that there is an error because port 8888 is already busy, just input another port like 7777 (change the 8888:8888 to 7777:7777 or 7777:8888 for instance)

Notebook Options

The Docker container executes a start-notebook.sh script script by default. The start-notebook.sh script handles the NB_UID, NB_GID and GRANT_SUDO features documented in the next section, and then executes the jupyter notebook.

You can pass Jupyter command line options through the start-notebook.sh script when launching the container. For example, to secure the Notebook server with a custom password hashed using IPython.lib.passwd() instead of the default token, run the following:

docker run -d -p 8888:8888 jupyter/datascience-notebook start-notebook.sh --NotebookApp.password='sha1:74ba40f8a388:c913541b7ee99d15d5ed31d4226bf7838f83a50e'

For example, to set the base URL of the notebook server, run the following:

docker run -d -p 8888:8888 jupyter/datascience-notebook start-notebook.sh --NotebookApp.base_url=/some/path

For example, to disable all authentication mechanisms (not a recommended practice):

docker run -d -p 8888:8888 jupyter/datascience-notebook start-notebook.sh --NotebookApp.token=''

You can sidestep the start-notebook.sh script and run your own commands in the container. See the Alternative Commands section later in this document for more information.

Docker Options

You may customize the execution of the Docker container and the command it is running with the following optional arguments.

-e GEN_CERT=yes - Generates a self-signed SSL certificate and configures Jupyter Notebook to use it to accept encrypted HTTPS connections.
-e NB_UID=1000 - Specify the uid of the jovyan user. Useful to mount host volumes with specific file ownership. For this option to take effect, you must run the container with --user root. (The start-notebook.sh script will su jovyan after adjusting the user id.)
-e NB_GID=100 - Specify the gid of the jovyan user. Useful to mount host volumes with specific file ownership. For this option to take effect, you must run the container with --user root. (The start-notebook.sh script will su jovyan after adjusting the group id.)
-e GRANT_SUDO=yes - Gives the jovyan user passwordless sudo capability. Useful for installing OS packages. For this option to take effect, you must run the container with --user root. (The start-notebook.sh script will su jovyan after adding jovyan to sudoers.) You should only enable sudo if you trust the user or if the container is running on an isolated host.
-v /some/host/folder/for/work:/home/jovyan/work - Host mounts the default working directory on the host to preserve work even when the container is destroyed and recreated (e.g., during an upgrade).

SSL Certificates

You may mount SSL key and certificate files into a container and configure Jupyter Notebook to use them to accept HTTPS connections. For example, to mount a host folder containing a notebook.key and notebook.crt:

docker run -d -p 8888:8888 \
    -v /some/host/folder:/etc/ssl/notebook \
    jupyter/datascience-notebook start-notebook.sh \
    --NotebookApp.keyfile=/etc/ssl/notebook/notebook.key
    --NotebookApp.certfile=/etc/ssl/notebook/notebook.crt

Alternatively, you may mount a single PEM file containing both the key and certificate. For example:

docker run -d -p 8888:8888 \
    -v /some/host/folder/notebook.pem:/etc/ssl/notebook.pem \
    jupyter/datascience-notebook start-notebook.sh \
    --NotebookApp.certfile=/etc/ssl/notebook.pem

In either case, Jupyter Notebook expects the key and certificate to be a base64 encoded text file. The certificate file or PEM may contain one or more certificates (e.g., server, intermediate, and root).

For additional information about using SSL, see the following:

The docker-stacks/examples for information about how to use Let's Encrypt certificates when you run these stacks on a publicly visible domain.
The jupyter_notebook_config.py file for how this Docker image generates a self-signed certificate.
The Jupyter Notebook documentation for best practices about running a public notebook server in general, most of which are encoded in this image.

Conda Environments

The default Python 3.x Conda environment resides in /opt/conda. A second Python 2.x Conda environment exists in /opt/conda/envs/python2. You can switch to the python2 environment in a shell by entering the following:

source activate python2

You can return to the default environment with this command:

source deactivate

The commands jupyter, ipython, python, pip, easy_install, and conda (among others) are available in both environments. For convenience, you can install packages into either environment regardless of what environment is currently active using commands like the following:

# install a package into the python2 environment
pip2 install some-package
conda install -n python2 some-package

# install a package into the default (python 3.x) environment
pip3 install some-package
conda install -n python3 some-package

Alternative Commands

start-singleuser.sh

JupyterHub requires a single-user instance of the Jupyter Notebook server per user. To use this stack with JupyterHub and DockerSpawner, you must specify the container image name and override the default container run command in your jupyterhub_config.py:

# Spawn user containers from this image
c.DockerSpawner.container_image = 'jupyter/datascience-notebook'

# Have the Spawner override the Docker run command
c.DockerSpawner.extra_create_kwargs.update({
	'command': '/usr/local/bin/start-singleuser.sh'
})

start.sh

The start.sh script supports the same features as the default start-notebook.sh script (e.g., GRANT_SUDO), but allows you to specify an arbitrary command to execute. For example, to run the text-based ipython console in a container, do the following:

docker run -it --rm jupyter/datascience-notebook start.sh ipython

This script is particularly useful when you derive a new Dockerfile from this image and install additional Jupyter applications with subcommands like jupyter console, jupyter kernelgateway, and jupyter lab.

Others

You can bypass the provided scripts and specify your an arbitrary start command. If you do, keep in mind that certain features documented above will not function (e.g., GRANT_SUDO).

onurcanbektas/Docker-Julia