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.


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



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 explict versions wherever possible.

In this example we include the library seaborn, 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



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



Using latex with Binder

Binder | repo link

This repository demonstrates how to install latex alongside matplotlib for Binder. This requires a few different build components:

  • apt.txt for apt-installing the latex components
  • requirements.txt for installing the python dependencies
  • postBuild for forcing matplotlib to build the font cache

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

Files in this repository

apt.txt
index.ipynb
postBuild
requirements.txt



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
.test
.tmp
binder
geojson-extension.geojson
index.ipynb



Specifying an R environment with a runtime.txt file

Jupyter+R: Binder

RStudio: Binder

Binder supports using R + 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.

Files in this repository

index.Rmd
index.ipynb
install.R
runtime.txt



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



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



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



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



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



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



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



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

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