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ThisĬan scale to large number of machines & users. JupyterHub from scratch, assuming they fit your use case. These are generally easier to set up than setting up Contents ¶ Distributions ¶Ī JupyterHub distribution is tailored towards a particular set of The JupyterHub team and Project Jupyter value our community, and JupyterHubįollows the Jupyter Community Guides. The Hub configures the proxy to forward URL prefixes to the single-userįor convenient administration of the Hub, its users, and services, The Hub handles user login and spawns single-user servers on demand The proxy forwards all requests to the Hub by default JupyterHub performs the following functions: A simplification of the whole system can be seen in the figure below: Multiple single-user Jupyter notebook servers (Python/IPython/tornado) that are monitored by SpawnersĪn authentication class that manages how users can access the systemīesides these central pieces, you can add optional configurations through a config.py file and manage users kernels on an admin panel. Modifications are synchronized in real time. Ī Hub (tornado process) that is the heart of JupyterHubĪ configurable http proxy (node-http-proxy) that receives the requests from the client’s browser You can share your Jupyter notebooks privately with project collaborators. Take a look at the Zero to JupyterHub with Kubernetes. If you need to allow for even more users, a dynamic amount of servers can be used on a cloud, If you need a simple case for a small amount of users (0-100) and single server Take a look at them before continuing with the configuration of the broad To make life easier, JupyterHub has distributions. Instances of the single-user Jupyter notebook server. It is a multi-user Hub that spawns, manages, and proxies multiple It can be used in a class of students, a corporate data science group or scientific JupyterHub is the best way to serve Jupyter notebook for multiple users.