jupyterhub/docs/source/explanation/concepts.md

What is Jupyter and JupyterHub?

JupyterHub is not what you think it is. Most things you think are part of JupyterHub are actually handled by some other component, and it's not always obvious how the parts relate. This document was originally written to assist in debugging: very often, the actual problem is not where one thinks it is and thus people can't easily debug. In order to tell this story, we start at JupyterHub and go all the way down to the fundamental components of Jupyter.

We occasionally leave things out or bend the truth where it helps in explanation, and give our explanations in terms of Python even though many other languages can be used instead.

This guide is long, but after reading it you will be know of all major components in the Jupyter ecosystem and everything else you read should make sense.

Just what is Jupyter?

Before we get too far, let's remember what our end goal is. A Jupyter Notebook is really nothing more than a Python process (or some language) which is getting commands from a web browser and displaying the output via a browser. What the process actually sees can roughly be considered getting data on standard input and writing to standard output (*). There is nothing intrinsically special about this process

• it can do anything a normal Python process can do, and nothing more. The kernel handles capturing output and converting things like graphics to a form usable by the browser.

Everything we explain below is building up to this, going through many different layers which give you many ways of customizing how this process runs. But this process is not too special.

JupyterHub

JupyterHub is the central piece that provides multi-user login. Despite this, the end user only briefly interacts with it and most of the actual Jupyter session does not relate to the hub at all. In short, anything which is related to starting the user's workspace is about JupyterHub, anything about running usually isn't.

If you have problems connecting the authentication, spawning, and the proxy (explained below), the issues is usually with JupyterHub. To debug, JupyterHub has extensive logs which get printed to its console and can be used to discover most problems.

JupyterHub consists of the main pieces below:

Authenticators

JupyterHub itself doesn't actually (necessarily) manage your users. It has a database of users, but it is usually connected with some other system that manages the usernames and passwords. When someone tries to log in to JupyteHub, it just asks the authenticator if the username/password is valid. The authenticator can also return user groups and admin status of users, so that JupyterHub can roughly manage users to services.

The following authenticators are included with JupyterHub:

• PAMAuthenticator uses the standard Unix/Linux operating system functions to check users. Roughly, if someone already has access to the machine (they can log in by ssh or otherwise), they will be able to log in to JupyterHub automatically. Thus, JupyterHub fills the role of a ssh server, but providing a web-browser based way to access the machine.

But those are fairly limited, and thus there are plenty of others to choose from. You can connect to almost any other existing service to manage your users. You either use all users from this other service (e.g. your company), or whitelist only the allowed users (e.g. your group's Github users). Some other popular authenticators include:

• OAuthenticator uses the standard OAuth protocol to verify users. For example, you can easily use Github to authenticate your users - people have a "click to login with Github" button. This is often done with a whitelist to only allow certain users.

• NativeAuthenticator actually stores its own usernames and passwords, unlike most other authenticators. Thus, you can manage all your users within JupyterHUb only. (include one more example here)

• There are authenticators for LTI (learning management systems), Shibboleth, Kerberos - and so on.

The authenticator is configured with the c.JupyterHub.authenticator_class configuration option in the jupyterhub_config.py file.

The authenticator runs internally to the Hub process but communicates with outside services.

If you have trouble logging in, this is usually a problem of the authenticator. The authenticator debug information goes to the JupyterHub logs, but there may also be hints in whatever external services you are using.

Spawners

The spawner is the real core of JupyterHub: when someone wants a notebook server, it finds resources and starts the server. It could run on the current server, on another server, on some cloud service, or even more. They can limit resources (CPU, memory) or isolate users from each other - if the spawner supports it. They can also do no limiting and allow any user to access any other user's files if they are not configured properly.

Some basic spawners included in JupyterHub is:

LocalProcessSpawner is build in to JupyterHub and basically starts tries to switch user to the given username and start Jupyter. It requires that the hub be run as root (because only root has permission to start processes as other user IDs). LocalProcessSpawner is no different than a user logging in with something like ssh and running jobs. PAMAuthenticator and LocalProcessSpawner is the most basic way of using JupyterHub (and what it does out of the box) and makes the hub not too dissimilar to an advanced ssh server.

There are many more advanced fancy spawners:

• SudoSpawner is like LocalProcessSpawner but lets you run JupyterHub without root. sudo has to be configured to allow the hub's user to run processes under other user IDs.

• SystemdSpawner uses Systemd to start other processes. It can isolate users from each other and provide some limits.

• DockerSpawner runs stuff in Docker, a containerization system. This lets you fully isolate users, limit CPU, memory, and provide other operating system images to fully customize the environment.

• KubeSpawner runs on the Kubernetes, a cloud orchestration system. The spawner can easily limit users and provide cloud scaling - but the spawner doesn't actually do that, Kubernetes does.

• BatchSpawner runs on computer clusters with batch queuing systems. The user processes are run as batch jobs, having access to all the data and software that the users normally will.

In short, spawners are the interface to the rest of the operating system, and to configure them right you need to know a bit about how the corresponding operating system service works.

The spawner is responsible for the environment of the single-user notebook servers (described in the next section). In the end, it just makes a choice about how to start these processes: for example, the Docker spawner starts a normal Docker container and runs the right command inside of it. Thus, the spawner is responsible for setting what kind of software and data is available to the user.

The spawner runs internally to the Hub process but communicates with outside services. It is configured by c.JupyterHub.spawner_class in jupyterhub_config.py.

If a user tries to launch a notebook server and it doesn't work, the error is usually with the spawner or the notebook server (as described in the next section). Each spawner outputs some logs to the main JupyterHub logs, but may also have logs in other places depending on what services it interacts with (for example, the Docker spawner somehow puts logs in the Docker system services).

Proxy

Previously, we said that the hub is between the user and the user's notebook servers. It actually isn't directly between, because the JupyterHub proxy relays connections between the users and their single-user notebook servers. What this basically means is that the hub itself can shut down, and if the proxy can continue to allow users to communicate with their notebook servers. (This just further emphasizes that the hub is responsible for starting, not running, the notebooks). By default, the hub starts the proxy automatically (so that you don't realize there is a separate proxy) and stops the proxy when the hub stops (so that connections get interrupted). But when you configure the proxy to run separately, your users connections will stay working even without the hub.

The default proxy is ConfigurableHttpProxy which is simple but effective. A more advanced option is the Traefik Proxy, which gives you redundancy and high-availability.

When users "connect to JupyterHub", they always first connect to the proxy and the proxy relays the connection to the hub. Thus, the proxy is responsible for SSL and accepting connections from the rest of the internet.

The hub has to connect to the proxy to adjust the routes (The web path /user/someone goes to the server of someone at a certain address). The proxy has to be able to connect to both the hub and all the single-user servers.

The proxy always runs as a separate process to JupyterHub (even though JupyterHub can start it for you). JupyterHub has one set of configuration options for the proxy addresses (bind_url) and one for the hub (bind_url). If bind_url is given, it is just passed to the automatic proxy to tell it what to do.

If you have problems after users are redirected to their single-user notebook servers, or making the first connection to the hub, it is usually caused by the proxy. The ConfigurableHttpProxy's logs are mixed with JupyterHub's logs if it's started through the hub (the default case), otherwise from whatever system runs the proxy (if you do it, you'll know).

Services

JupyterHub has the concept of services, which are other web services started by the hub, but otherwise are not really related to the hub itself. They are often used to do things related to Jupyter (things that user interacts with, usually not the hub), but could always be run some other way. Running from the hub provides an easy way to get Hub API tokens and authenticate users against the hub.

The configuration option c.JupyterHub.services (??) is used to start services from the hub.

Let's use the often-requested question of sharing files using hubshare as an example. Hubshare would work as an external service which user notebooks talk to and use Hub authentication, but otherwise it isn't directly a matter of the hub. You could equally well share files by other extensions to the single-user notebook servers or configuring the spawners to access shared storage spaces.

When a service is started from JupyterHub automatically, its logs are included in the JupyterHub logs.

Single-user notebook server

The single-user notebook server is the same thing you get by running jupyter notebook or jupyter lab from the command line - the actual Jupyter user interface for a single person.

The role of the spawner is to start this server - basically, running the command jupyter notebook. Actually it doesn't run that, it runs jupyterhub-singleuser which first communicates with the hub to say "I'm alive" before running a completely normal Jupyter server. The single-user server can be JupyterLab or classic notebooks. By this point, the hub is almost completely out of the picture (the web traffic is going through proxy unchanged). By this time, the spawner has already decided the environment which this single-user server will have and the single-user server has to deal with that.

The spawner starts the server using jupyterhub-singleuser with some environment variables like JUPYTERHUB_API_TOKEN and JUPYTERHUB_BASE_URL which tell the single-user server how to connect back to the hub in order to say that it's ready.

The single-user server options are JupyterLab and classic Jupyter Notebook. Really, there isn't that much difference between them, they run through the same backend server process and the web frontend is an option when it is starting. The spawner can choose the command line when it starts the single-user server. Extensions are a property of the single-user server (in two parts: there can be a part that runs in server process, and parts that run in javascript in lab or notebook).

After the single-user notebook server is started, any errors are only an issue of the single-user notebook server. Sometimes, it seems like the spawner is failing, but really the spawner is working but the single-user notebook server dies right away (in this case, you need to find the problem with the single-user server and adjust the spawner to start it correctly). This can happen, for example, if the spawner doesn't set an environment variable or doesn't provide storage.

The single-user server's logs are handled by the spawner, so if you notice problems at this phase you need to check your spawner for instructions for accessing the single-user logs. For example, the LocalProcessSpawner logs are just outputted to the same JupyterHub output logs (TODO is this correct?), the SystemdSpawner logs are written to the Systemd journal, Docker and Kubernetes logs are written to Docker and Kubernetes respectively, and batchspawner output goes to the normal output places of batch jobs and is an explicit configuration option of the spawner.

Notebook

(Jupyter) Notebook is the classic interface, where each notebook opens in a separate tab.

Does anything need to be said here?

Lab

JupyterLab is the new interface, where multiple notebooks are openable in the same tab in an IDE-like environment. JupyterLab is run thorugh the same server file, but at a path /lab instead of /tree.

Both Notebook and Lab use the same .ipynb file format.

Does anything need to be said here?

• how extensions work in lab compared to notebook

Kernel

Normally, our tour of the Jupyter ecosystem would stop here. But, since if you've read this far you probably need to know every last bit, let's go further and talk about the kernels. The commands you run in the notebook session are not executed in the same process as the notebook itself, but in a separate kernel. There are many kernels available.

As a basic approximation, a Jupyter kernel is a process which accepts commands (cells that are run) and returns the output to Jupyter to display. One example is the IPython Jupyter kernel, which runs Python and adds the IPython magic functions (%, %%, !, etc. commands). There is nothing special about it, it can be considered a normal Python process. Like we said above, the kernel process can be approximated as a process that takes commands on stdin and returns stuff on stdout. Actually, a kernel is more fancy, because it can communicate over the network and add in magic commands.

Kernel communication is via the the ZeroMQ protocol on the local computer. Kernels are separate processes from the main single-user notebook server (and thus obviously, different from the JupyterHub process and everything else). By default (and unless you do something special), kernels share the same environment as the notebook server (data, resource limits, permissions, user id, etc.). But there are things like the Jupyter Kernel Gateway / Enterprise Gateway, which allow you to run the kernels on a different machine and possibly with a different environment.

What does this mean? There is yet another layer of configurability. Each kernel can run a different programming language, with different software, and so on. By default, they would run in the same environment as the single-user notebook server, and the most common other way they are configured is by running in different Python virtual environments or conda environments. They can be started and killed independently (there is normally one per notebook you have open). The kernels is what uses most of your memory and CPU if you have large amounts of data open or are using a lot of compute power.

You can list your installed kernels with jupyter kernelspec list. If you look at one of kernel.json files in those directories, you will see exactly what command is run. These are normally automatically made by the kernels, but can be edited as needed. The spec tells you even more.

The kernel has to be reachable by the single-user notebook server.

If you get problems with "Kernel died" or some other error in a single notebook but the single-user notebook server stays working, it is usually a problem with the kernel. It could be that you are trying to use more resources than you are allowed and the symptom is the kernel getting killed. It could be that it crashes for some other reason. The debug logs for the kernel are normally mixed in with the single-user notebook server logs.

JupyterHub distributions

There are several "distributions" which automatically install all of the things above and configure them for a certain purpose. They are good ways to get started, but if you are doing very custom things eventually it may become hard to adapt them to your needs.

• Zero to JupyterHub with Kubernetes installs an entire scaleable system using Kubernetes. Uses KubeSpawner, ....Authenticator, ....

• The Littlest JupyterHub installs JupyterHub on a single system using SystemdSpawner and NativeAuthenticator (which manages users itself).

• JupyterHub the hard way takes you through everything yourself. It is a natural companion to this guide, since you get to experience every little bit.

I want to...

Share files between users. Spawner to share data, or JupyterNotebook/Lab user interface + some service for distributing files.

What's next?

Now you know everything. Well, you know how everything relates, but there are still plenty of details, implementations, and exceptions. When setting up JupyterHub, the first step is to consider the above layers and see what options are suitable for you. Then, put everything together.