Two additional tools have enhanced Jupyter’s usability. One is JupyterHub, a service that allows institutions to provide Jupyter notebooks to large pools of users. The IT team at the University of California, Berkeley, where Pérez is a faculty member, has deployed one such hub, which Pérez uses to ensure that all students on his data-science course have identical computing environments. “We cannot possibly manage IT support for 800 students, helping them debug why the installation on their laptop is not working; that’s simply infeasible,” he says.
The other development is Binder, an open-source service that allows users to use Jupyter notebooks on GitHub in a web browser without having to install the software or any programming libraries. Users can also execute Jupyter notebooks on the Google cloud by inserting https://colab.research.google.com/github before the URL of a notebook on GitHub, or using the commercial service Code Ocean. In September, Code Ocean rolled out a new user interface for its cloud-based code-sharing and code-execution service, also based on Jupyter.
Why Jupyter is data scientists’ computational notebook of choice