What is a Jupyter Notebook?
is an open-source web
application that allows you to create and share documents that contain live
code, equations, visualizations and narrative text. Uses include: data
cleaning and transformation, numerical simulation, statistical modeling,
data visualization, machine learning, and much more.
What is JupyterHub?
power of notebooks to groups of users. It gives users access to
computational environments and resources without burdening the users with
installation and maintenance tasks. Users - including students, researchers,
and data scientists - can get their work done in their own workspaces on
shared resources which can be managed efficiently by system administrators.
Who can use this service?
This JupyterHub service is open to UCB instructors and students; as this is
a proof-of-concept service, students should ensure they are backing up their
notebooks regularly if they are using this service for course activities.
How do I distribute course content to students?
We recommend using
to distribute content to students. You can construct a
, and send it to your students. When they click it, the course content
will be pulled into their home directories, and the appropriate file will be
What packages & libraries are available?
This JupyterHub comes with Python 3.8 and R 4.0 installed. Many commonly
used packages are pre-installed. This
file lists the python packages installed, while this
file lists the R packages installed.
Can I install my own packages?
Yes, you can! In a Jupyter Notebook, you can use
!pip install package-name or
!conda install package-name to install a Python package. With
R, you can use
install.packages(). To prevent individual user
environments diverging from each other too much, your user installed
packages only last the length of your server. So we
recommend putting those package installation lines in the beginning of the
notebook / file that you are using, so it can be run each time as necessary.
Can a package be installed for all my students?
Most likely! Please open a
with your request. Even better, make a pull request with changes to the
file (for Python packages) and
file for R packages.
I have many other questions!
open an issues
and we will help answer them!