Sample Binder Repositories

Below we list several sample Binder repositories that demonstrate how to compose build files in order to create Binders with varying environments. You can find all of the repositories listed on this page at the binder-examples GitHub organization.

Managing languages


Python environment with requirements.txt

Binder | repo link

A Binder-compatible repo with a requirements.txt file.

Access this Binder at the following URL:

http://mybinder.org/v2/gh/binder-examples/requirements/master

Notes

The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:

pip install -r requirements.txt

The base Binder image contains no extra dependencies, so be as explicit as possible in defining the packages that you need. This includes specifying explicit versions wherever possible.

In this example we include the library seaborn which will be installed in the environment, and our notebook uses it to plot a figure.

Files in this repository

index.ipynb
requirements.txt



Conda environment with environment.yml

Binder | repo link

A Binder-compatible repo with an environment.yml file.

Access this Binder at the following URL:

http://mybinder.org/v2/gh/binder-examples/conda_environment/v1.0?filepath=index.ipynb

Notes

The environment.yml file should list all Python libraries on which your notebooks depend, specified as though they were created using the following conda commands:

source activate example-environment
conda env export > environment.yml

Note that the only libraries available to you will be the ones specified in the environment.yml, so be sure to include everything that you need!

Files in this repository

environment.yml
index.ipynb



python package with setup.py

Binder | repo link

A Binder-compatible repo with a python package and a setup.py file.

Notes

It is convenient to provide an example Jupyter notebook for a new package and add the hooks necessary to run the example with Binder. However, normally the package will not be included in the python path. To do that, one needs a setup.py for the package (see binder docs). Once this is done, it is possible to import the package in a notebook running within Binder.

This setup.py was originally adapted from https://github.com/kennethreitz/setup.py by @cranmer

Files in this repository

MANIFEST.in
example_notebook
mypackage
setup.py



Julia and Python environments

Binder | repo link

This example shows how you can install a Julia and Python environment side-by-side. In this repository are both an environment.yml file as well as a REQURE file. The former corresponds to an anaconda python environment, and the latter corresponds to a Julia environment. Both kernels will be available to you in a built Binder environment.

Files in this repository

REQUIRE
environment.yml
julia.ipynb
python-and-julia.ipynb
python.ipynb



Julia Binder demo

This is a demo of Julia functionality for the Binder project. Simply go to the URL below and it will launch an interactive Julia environment:

Binder | repo link

Files in this repository

REQUIRE
demo.ipynb



Specifying an R environment with a runtime.txt file

Jupyter+R: Binder

RStudio: Binder

RShiny: Binder

Binder supports using R and RStudio, with libraries pinned to a specific snapshot on MRAN.

You need to have a runtime.txt file that is formatted like:

r-<YYYY>-<MM>-<DD>

where YYYY-MM-DD is a snapshot at MRAN that will be used for installing libraries.

You can also have an install.R file that will be executed during build, and can be used to install libraries.

Both RStudio and IRKernel are installed by default, so you can use either the Jupyter notebook interface or the RStudio interface.

This repository also contains an example of a Shiny app.

Files in this repository

bus-dashboard
index.ipynb
install.R
runtime.txt



Specifying an R environment by having a DESCRIPTION file

Jupyter+R: Binder

RStudio: Binder

Binder supports using R and RStudio, with libraries pinned to a specific snapshot on MRAN.

If you specify a runtime.txt file that is formatted like:

r-<YYYY>-<MM>-<DD>

where YYYY-MM-DD it will use the MRAN snapshot of that day for setting up the R runtime.

Without specifying a runtime.txt it will use a 2-day old snapshot of MRAN.

Both RStudio and IRKernel are installed by default, so you can use either the Jupyter notebook interface or the RStudio interface.

Files in this repository

DESCRIPTION
NAMESPACE
R
test-library.ipynb



Octave on mybinder.org

Binder | repo link

An example of using Octave on mybinder.org.

This shows you how to make Matlab code that works with Octave run on mybinder.org.

The example notebook is taken from the octave_kernel repository.

Files in this repository

apt.txt
environment.yml
index.ipynb


User interfaces


JupyterLab + Binder

Binder | repo link

JupyterLab is packaged with Binder repositories by default. In order to run a JupyterLab session, you have two options:

Start JupyterLab after you start your Binder

Do the following:

  1. Launch a Binder instance (e.g., by clicking the Binder badge)
  2. Replace tree at the end of your URL with lab.
  3. That’s it!

Files in this repository

.profile
binder
geojson-extension.geojson
index.ipynb



Enabling Jupyter Extensions with post-build commands

Binder | repo link

This example demonstrates how to enable Jupyter extensions with Binder. We’ll cover a few in this repo because some are idiosyncratic in how they’re enabled.

We accomplish each using a requirements.txt file to install the extensions, then a postBuild file to enable them.

ipywidgets

Ipywidgets lets you create interactive widgets in your notebook. Installation is fairly straightforward. You install the python package, then enable the extension.

python-markdown

The python-markdown extension is useful for interweaving computational cells (e.g., python cells) and markdown cells. As this extension automatically runs code in the notebook, you need to be sure to “trust” the notebooks in your postBuild script (see the script in this repo for example).

Files in this repository

index.ipynb
postBuild
requirements.txt



Creating interactive presentations on Binder with RISE

Binder | repo link

RISE allows you to quickly generate a live, interactive presentation from a Jupyter Notebook that is connected to the underlying Kernel of the notebook. Using a new feature for automatically launching the RISE plugin when a notebook is opened, RISE can be used to share interactive presentations that run in the cloud with Binder. This repository demonstrates how to accomplish this.

To make your RISE presentation automatically-launch with it is open, add an autolaunch=true configuration parameter to a notebook’s livereveal section in the metadata. E.g.:

...
"livereveal": {
        "autolaunch": true
        }
...

When the notebook is launched, your presentation will automatically begin.

See the RISE Documentation for more information.

Files in this repository

environment.yml
index.ipynb



Interactive apps from Jupyter Notebooks

Binder | repo link

This repository demonstrates how to create interactive webapps from a Jupyter Notebook. This is similar to how Shiny apps work in R.

Using the appmode Jupyter plugin, a notebook’s code will be run, and then only the markdown cells and code outputs will be shown.

You can check out the appmode repository here: https://github.com/oschuett/appmode

Files in this repository

environment.yml
index.ipynb
ipyvolume_demo.ipynb
postBuild



Running a bokeh server with Binder

Binder | repo link

This repository demonstrates how to run a Bokeh server from within Binder. To do so, we did the following things:

  1. Created a bokeh-app directory in the repo with a main.py file in it. This is the application that will be served. We’ve added the Bokeh weather example as a demo.

  2. Installed bokeh for the viz and nbserverproxy which we’ll use to direct people to the port on which Bokeh runs. See environment.yml.

  3. Added a custom server extension (bokehserverextension.py) that will be run to direct people to the Bokeh app (which is run on a port)

  4. Used postBuild to enable the nbserverproxy extension, then set up and enable our custom server extension for Bokeh.

  5. Created a Binder link that uses urlpath to point users to the port on which the Bokeh server will run:

    https://mybinder.org/v2/gh/binder-examples/bokeh/master?urlpath=/proxy/5006/bokeh-app
    

When people click on the Binder link, they should be directed to the running Bokeh app.

Files in this repository

bokeh-app
bokehserverextension.py
environment.yml
postBuild



stencila-py

Demo for Stencila

&amp;

DAR on binder with Python code.

Binder | repo link

Learn more about Stencila and its integration with Binder in this blog post.

Files in this repository

article


System environents


Specifying a Python 2 environment with runtime.txt

Binder | repo link

We can specify various runtime parameters with a runtime.txt file. In this repository, we demonstrate how to install python 2 with the environment.

If you specify python-2.7 in runtime.txt, then:

  • A python3 environment is created & installed (this is what the notebook runs from)
  • A python2 environment is created and registered
  • The contents of requirements.txt are installed into the python2 environment

important: Make sure that you save your notebooks with a python 2 kernel activated, as this defines which kernel Binder will use when a notebook is opened.

note: If you also wish to install python 3 dependencies, you may do so by including a file called requirements3.txt. The packages inside will be installed into the python 3 environment.

Files in this repository

index.ipynb
requirements.txt
runtime.txt



Mixing Python 2 and 3 kernels with runtime.txt

Binder | repo link

Sometimes you want both Python 2 and Python 3 (e.g., if you have a mixture of notebooks that use each version of the language). This repository demonstrates how to handle these cases with repo2docker. You can specify a Python 2.7 environment with the runtime.txt file. In this case, repo2docker will install Python 2 alongside Python 3 (though all commands will default to Python 2). In this case, you can install Python 3 dependencies with requirements3.txt, while a file called requirements.txt alone will install to the Python 2 environment.

Files in this repository

index2.ipynb
index3.ipynb
requirements.txt
requirements3.txt
runtime.txt



Using latex with Binder

Binder | repo link

This repository demonstrates how to install latex alongside matplotlib for Binder. This repository also makes use of JupyterLab Latex to render latex files in Jupyter lab. This requires a few different build components:

  • apt.txt for apt-installing the latex components
  • environment.yml for installing the python dependencies
  • postBuild for forcing matplotlib to build the font cache and for installing JupyterLab Latex.

Thanks to m-weigand for giving inspiration for this repo!

Files in this repository

apt.txt
environment.yml
index.ipynb
postBuild
sample.tex



Installing packages from apt repositories

Binder | repo link

Sometimes you want packages that exist outside of the language-specific packaging ecosystems of Python/R/Julia. Binder makes it possible to apt-install packages from the ubuntu apt repository. This repository demonstrates how to do this by specifying names in an apt.txt file.

Files in this repository

apt.txt
index.ipynb
postBuild



Multi-language demo.

This is a demo showing how you can intermingle Python, R, Rust, Fortran, Cython, C.

You can try it :

Binder | repo link

And read the accompanying blog post.

Files in this repository

23-Cross-Language-Integration.ipynb
REQUIRE
apt.txt
data.csv
environment.yml
julia.ipynb
polyglot-ds-prep.ipynb
polyglot-ds.ipynb
postBuild



Using conda with pip in the same build

Binder | repo link

If you use environment.yml, then Binder will use a Miniconda distribution to install your packages. However, you may still want to use pip. In this case, you should not use a requirements.txt file, but instead use a - pip section in environment.yml. This repository is an example of how to construct your environment.yml file to accomplish this.

Files in this repository

environment.yml
index.ipynb


Data and reproducibility


Remote Storage with Binder

Binder | repo link

A Binder-compatible repo that shows accessing data from remote sources.

Access this Binder at the following URL:

http://mybinder.org/v2/gh/binder-examples/remote_storage/master

Notes

The notebooks use boto and requests to load both images and tables from S3. The image loading makes use of PIL and the table loading makes use of pandas.

Files in this repository

index.ipynb
requirements.txt



Importing data with Quilt

Binder | repo link

Pull data into Binder notebooks

This example uses Quilt to inject data packages into a Jupyter notebook.

Data packages are versioned, immutable snapshots of data. Data packages may contain data of any size. Here is an example of data package: uciml/iris.

How to specify data dependencies in your own Binder

  1. Add quilt to requirements.txt
  2. Specify data package dependencies in quilt.yml (docs). For example:
packages:
  - vgauthier/DynamicPopEstimate   # get the latest version
  - danWebster/sgRNAs:a972d92      # get a specific hash (short hash)
  - akarve/sales:tag:latest        # get a specific tag
  - asah/snli:v:1.0                # get a specific version
  1. Include the following lines at the top of postBuild. (postBuild should be executable: chmod +x postBuild on UNIX, git update-index --chmod=+x postBuild for Windows).
###!/bin/bash
quilt install

If you are adopting the binder folder pattern for your repo2docker configuration files, and including quilt.yml, your postBuild file should look like this:

###!/bin/bash
quilt install @./binder/quilt.yml

More info about how to install data packages via the quilt install command is available here.

  1. Now you can access the package data in your Jupyter notebooks:
In [1]: from quilt.data.akarve import sales
In [2]: sales.transactions()
Out[2]: 
      Row ID  Order ID Order Date Order Priority  Order Quantity       Sales  \
0          1         3 2010-10-13            Low               6    261.5400   
1         49       293 2012-10-01           High              49  10123.0200   
2         50       293 2012-10-01           High              27    244.5700   
...

Files in this repository

index.ipynb
postBuild
quilt.yml
requirements.txt



Nixpkgs BinderHub example

Binder | repo link

Why Nix?

Nix would be a great addition to reproducible data science. It is a unique package manager. Some notable features:

  • 100% reproducible environments (pin to exact commit in repository)
  • both a source and binary package repository
  • allows customized compilation and version of every package
  • can run identical environment outside of docker (all linux distros + dawin)
  • as of now 45,000+ packages
  • fully declarative environments
  • packages: python, javascript, julia, R, haskell, perl, and many other languages (some better than others).

Assuming that you have nix installed (compatible with all linux distributions and darwin (Mac OS)) you can run this repository locally (no need for binderhub). It will be identical assuming you have pinned repositories. Nix can coexist fine with other package managers.

This derivation installs python37, numpy, and scipy.

For a more detailed example see the detailed binderhub example costrouc/nix-binder-example

Files in this repository

LICENSE.md
default.nix
nix-introduction.ipynb


Dockerfile environments


Minimal Dockerfiles for Binder

Binder | repo link

Binder needs only one thing for images to work:

  • to be able to launch jupyter notebook as a specified user (passed via docker build args as NB_UID/NB_USER)

What this means in practice is that the notebook package must be installed and on PATH:

RUN pip install --no-cache notebook

That’s almost everything.

The remaining piece is that the specified user must be able to start the notebook, which requires certain permissions like being able to write to the home directory.

The absolute bare minimum for this is to set HOME to /tmp so that it’s writable, which would make the shortest, smallest Dockerfile that works on Binder:

FROM python:3.7-slim
RUN pip install --no-cache notebook
ENV HOME=/tmp

which you can try out: Binder

However, it would be better to consume the NB_UID/NB_USER arguments and create a real user:

### create user with a home directory
ARG NB_USER
ARG NB_UID
ENV USER ${NB_USER}
ENV HOME /home/${NB_USER}

RUN adduser --disabled-password \
    --gecos "Default user" \
    --uid ${NB_UID} \
    ${NB_USER}
WORKDIR ${HOME}

From this point, you can start adding files, installing packages, etc.

Files in this repository

Dockerfile



Using a Docker image from the Jupyter docker-stacks repository

Binder | repo link

Sometimes you just want to inherit from one of the pre-built images maintained by the Jupyter Project’s Docker Stacks, and perhaps add just an extra library or two. This example shows you how to do that - check out the Dockerfile.

Note that in this case we are using a docker image that already satisfies the criteria for use on binder, we don’t need to install notebook or anything manually.

Files in this repository

Dockerfile



Specifying an R environment with a runtime.txt file

Jupyter+R: Binder

RStudio: Binder

RShiny: Binder

Both RStudio and IRKernel are installed by default, so you can use either the Jupyter notebook interface or the RStudio interface.

This repository also contains an example of a Shiny app.

Files in this repository

Dockerfile
bus-dashboard
index.ipynb
install.R