JupyterHub¶

JupyterHub is a server that gives multiple users access to Jupyter notebooks, running an independent Jupyter notebook server for each user.

To use JupyterHub, you need a Unix server (typically Linux) running somewhere that is accessible to your team on the network. The JupyterHub server can be on an internal network at your organisation, or it can run on the public internet (in which case, take care with security). Users access JupyterHub in a web browser, by going to the IP address or domain name of the server.

Different authenticators control access to JupyterHub. The default one (pam) uses the user accounts on the server where JupyterHub is running. If you use this, you will need to create a user account on the system for each user on your team. Using other authenticators, you can allow users to sign in with e.g. a Github account, or with any single-sign-on system your organisation has.

Next, spawners control how JupyterHub starts the individual notebook server for each user. The default spawner will start a notebook server on the same machine running under their system username. The other main option is to start each server in a separate container, often using Docker.

JupyterHub runs as three separate parts:

The multi-user Hub (Python & Tornado)
A configurable http proxy (NodeJS)
Multiple single-user Jupyter notebook servers (Python & Tornado)

Basic principles:

Hub spawns proxy
Proxy forwards ~all requests to hub by default
Hub handles login, and spawns single-user servers on demand
Hub configures proxy to forward url prefixes to single-user servers

Contents:

Getting started with JupyterHub¶

This document describes some of the basics of configuring JupyterHub to do what you want. JupyterHub is highly customizable, so there’s a lot to cover.

Installation¶

See the readme for help installing JupyterHub.

Overview¶

JupyterHub is a set of processes that together provide a multiuser Jupyter Notebook server. There are three main categories of processes run by the jupyterhub command line program:

Single User Server: a dedicated, single-user, Jupyter Notebook is started for each user on the system when they log in. The object that starts these processes is called a Spawner.
Proxy: the public facing part of the server that uses a dynamic proxy to route HTTP requests to the Hub and Single User Servers.
Hub: manages user accounts and authentication and coordinates Single Users Servers using a Spawner.

JupyterHub’s default behavior¶

IMPORTANT: In its default configuration, JupyterHub requires SSL encryption (HTTPS) to run. You should not run JupyterHub without SSL encryption on a public network. See Security documentation for how to configure JupyterHub to use SSL, and in certain cases, e.g. behind SSL termination in nginx, allowing the hub to run with no SSL by requiring --no-ssl (as of version 0.5).

To start JupyterHub in its default configuration, type the following at the command line:

sudo jupyterhub

The default Authenticator that ships with JupyterHub authenticates users with their system name and password (via PAM). Any user on the system with a password will be allowed to start a single-user notebook server.

The default Spawner starts servers locally as each user, one dedicated server per user. These servers listen on localhost, and start in the given user’s home directory.

By default, the Proxy listens on all public interfaces on port 8000. Thus you can reach JupyterHub through either:

http://localhost:8000

or any other public IP or domain pointing to your system.

In their default configuration, the other services, the Hub and Single-User Servers, all communicate with each other on localhost only.

By default, starting JupyterHub will write two files to disk in the current working directory:

jupyterhub.sqlite is the sqlite database containing all of the state of the Hub. This file allows the Hub to remember what users are running and where, as well as other information enabling you to restart parts of JupyterHub separately.
jupyterhub_cookie_secret is the encryption key used for securing cookies. This file needs to persist in order for restarting the Hub server to avoid invalidating cookies. Conversely, deleting this file and restarting the server effectively invalidates all login cookies. The cookie secret file is discussed in the [Cookie Secret documentation](#Cookie secret).

The location of these files can be specified via configuration, discussed below.

How to configure JupyterHub¶

JupyterHub is configured in two ways:

Configuration file
Command-line arguments

Configuration file¶

By default, JupyterHub will look for a configuration file (which may not be created yet) named jupyterhub_config.py in the current working directory. You can create an empty configuration file with:

jupyterhub --generate-config

This empty configuration file has descriptions of all configuration variables and their default values. You can load a specific config file with:

jupyterhub -f /path/to/jupyterhub_config.py

See also: general docs on the config system Jupyter uses.

Command-line arguments¶

Type the following for brief information about the command-line arguments:

jupyterhub -h

or:

jupyterhub --help-all

for the full command line help.

All configurable options are technically configurable on the command-line, even if some are really inconvenient to type. Just replace the desired option, c.Class.trait, with –Class.trait. For example, to configure c.Spawner.notebook_dir = ‘~/assignments’ from the command-line:

jupyterhub --Spawner.notebook_dir='~/assignments'

Networking¶

Configuring the Proxy’s IP address and port¶

The Proxy’s main IP address setting determines where JupyterHub is available to users. By default, JupyterHub is configured to be available on all network interfaces ('') on port 8000. Note: Use of '*' is discouraged for IP configuration; instead, use of '0.0.0.0' is preferred.

Changing the IP address and port can be done with the following command line arguments:

jupyterhub --ip=192.168.1.2 --port=443

Or by placing the following lines in a configuration file:

c.JupyterHub.ip = '192.168.1.2'
c.JupyterHub.port = 443

Port 443 is used as an example since 443 is the default port for SSL/HTTPS.

Configuring only the main IP and port of JupyterHub should be sufficient for most deployments of JupyterHub. However, more customized scenarios may need additional networking details to be configured.

Configuring the Proxy’s REST API communication IP address and port (optional)¶

The Hub service talks to the proxy via a REST API on a secondary port, whose network interface and port can be configured separately. By default, this REST API listens on port 8081 of localhost only.

If running the Proxy separate from the Hub, configure the REST API communication IP address and port with:

# ideally a private network address
c.JupyterHub.proxy_api_ip = '10.0.1.4'
c.JupyterHub.proxy_api_port = 5432

Configuring the Hub if Spawners or Proxy are remote or isolated in containers¶

The Hub service also listens only on localhost (port 8080) by default. The Hub needs needs to be accessible from both the proxy and all Spawners. When spawning local servers, an IP address setting of localhost is fine. If either the Proxy or (more likely) the Spawners will be remote or isolated in containers, the Hub must listen on an IP that is accessible.

c.JupyterHub.hub_ip = '10.0.1.4'
c.JupyterHub.hub_port = 54321

Security¶

IMPORTANT: In its default configuration, JupyterHub requires SSL encryption (HTTPS) to run. You should not run JupyterHub without SSL encryption on a public network.

Security is the most important aspect of configuring Jupyter. There are three main aspects of the security configuration:

SSL encryption (to enable HTTPS)
Cookie secret (a key for encrypting browser cookies)
Proxy authentication token (used for the Hub and other services to authenticate to the Proxy)

SSL encryption¶

Since JupyterHub includes authentication and allows arbitrary code execution, you should not run it without SSL (HTTPS). This will require you to obtain an official, trusted SSL certificate or create a self-signed certificate. Once you have obtained and installed a key and certificate you need to specify their locations in the configuration file as follows:

c.JupyterHub.ssl_key = '/path/to/my.key'
c.JupyterHub.ssl_cert = '/path/to/my.cert'

It is also possible to use letsencrypt (https://letsencrypt.org/) to obtain a free, trusted SSL certificate. If you run letsencrypt using the default options, the needed configuration is (replace your.domain.com by your fully qualified domain name):

c.JupyterHub.ssl_key = '/etc/letsencrypt/live/your.domain.com/privkey.pem'
c.JupyterHub.ssl_cert = '/etc/letsencrypt/live/your.domain.com/fullchain.pem'

Some cert files also contain the key, in which case only the cert is needed. It is important that these files be put in a secure location on your server, where they are not readable by regular users.

Note: In certain cases, e.g. behind SSL termination in nginx, allowing no SSL running on the hub may be desired. To run the Hub without SSL, you must opt in by configuring and confirming the --no-ssl option, added as of version 0.5.

Proxy authentication token¶

The Hub authenticates its requests to the Proxy using a secret token that the Hub and Proxy agree upon. The value of this string should be a random string (for example, generated by openssl rand -hex 32). You can pass this value to the Hub and Proxy using either the CONFIGPROXY_AUTH_TOKEN environment variable:

export CONFIGPROXY_AUTH_TOKEN=`openssl rand -hex 32`

This environment variable needs to be visible to the Hub and Proxy.

Or you can set the value in the configuration file:

c.JupyterHub.proxy_auth_token = '0bc02bede919e99a26de1e2a7a5aadfaf6228de836ec39a05a6c6942831d8fe5'

If you don’t set the Proxy authentication token, the Hub will generate a random key itself, which means that any time you restart the Hub you must also restart the Proxy. If the proxy is a subprocess of the Hub, this should happen automatically (this is the default configuration).

Another time you must set the Proxy authentication token yourself is if you want other services, such as nbgrader to also be able to connect to the Proxy.

Configuring authentication¶

The default Authenticator uses PAM to authenticate system users with their username and password. The default behavior of this Authenticator is to allow any user with an account and password on the system to login. You can restrict which users are allowed to login with Authenticator.whitelist:

c.Authenticator.whitelist = {'mal', 'zoe', 'inara', 'kaylee'}

Admin users of JupyterHub have the ability to take actions on users’ behalf, such as stopping and restarting their servers, and adding and removing new users from the whitelist. Any users in the admin list are automatically added to the whitelist, if they are not already present. The set of initial Admin users can configured as follows:

c.Authenticator.admin_users = {'mal', 'zoe'}

If JupyterHub.admin_access is True (not default), then admin users have permission to log in as other users on their respective machines, for debugging. You should make sure your users know if admin_access is enabled.

Adding and removing users¶

Users can be added and removed to the Hub via the admin panel or REST API. These users will be added to the whitelist and database. Restarting the Hub will not require manually updating the whitelist in your config file, as the users will be loaded from the database. This means that after starting the Hub once, it is not sufficient to remove users from the whitelist in your config file. You must also remove them from the database, either by discarding the database file, or via the admin UI.

The default PAMAuthenticator is one case of a special kind of authenticator, called a LocalAuthenticator, indicating that it manages users on the local system. When you add a user to the Hub, a LocalAuthenticator checks if that user already exists. Normally, there will be an error telling you that the user doesn’t exist. If you set the configuration value

c.LocalAuthenticator.create_system_users = True

however, adding a user to the Hub that doesn’t already exist on the system will result in the Hub creating that user via the system adduser command line tool. This option is typically used on hosted deployments of JupyterHub, to avoid the need to manually create all your users before launching the service. It is not recommended when running JupyterHub in situations where JupyterHub users maps directly onto UNIX users.

Configuring single-user servers¶

Since the single-user server is an instance of jupyter notebook, an entire separate multi-process application, there are many aspect of that server can configure, and a lot of ways to express that configuration.

At the JupyterHub level, you can set some values on the Spawner. The simplest of these is Spawner.notebook_dir, which lets you set the root directory for a user’s server. This root notebook directory is the highest level directory users will be able to access in the notebook dashboard. In this example, the root notebook directory is set to ~/notebooks, where ~ is expanded to the user’s home directory.

c.Spawner.notebook_dir = '~/notebooks'

You can also specify extra command-line arguments to the notebook server with:

c.Spawner.args = ['--debug', '--profile=PHYS131']

This could be used to set the users default page for the single user server:

c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']

Since the single-user server extends the notebook server application, it still loads configuration from the ipython_notebook_config.py config file. Each user may have one of these files in $HOME/.ipython/profile_default/. IPython also supports loading system-wide config files from /etc/ipython/, which is the place to put configuration that you want to affect all of your users.

External services¶

JupyterHub has a REST API that can be used to run external services. More detail on this API will be added in the future.

File locations¶

It is recommended to put all of the files used by JupyterHub into standard UNIX filesystem locations.

/srv/jupyterhub for all security and runtime files
/etc/jupyterhub for all configuration files
/var/log for log files

Example¶

In the following example, we show a configuration files for a fairly standard JupyterHub deployment with the following assumptions:

JupyterHub is running on a single cloud server
Using SSL on the standard HTTPS port 443
You want to use GitHub OAuth for login
You need the users to exist locally on the server
You want users’ notebooks to be served from ~/assignments to allow users to browse for notebooks within other users home directories
You want the landing page for each user to be a Welcome.ipynb notebook in their assignments directory.
All runtime files are put into /srv/jupyterhub and log files in /var/log.

Let’s start out with jupyterhub_config.py:

# jupyterhub_config.py
c = get_config()

import os
pjoin = os.path.join

runtime_dir = os.path.join('/srv/jupyterhub')
ssl_dir = pjoin(runtime_dir, 'ssl')
if not os.path.exists(ssl_dir):
    os.makedirs(ssl_dir)


# https on :443
c.JupyterHub.port = 443
c.JupyterHub.ssl_key = pjoin(ssl_dir, 'ssl.key')
c.JupyterHub.ssl_cert = pjoin(ssl_dir, 'ssl.cert')

# put the JupyterHub cookie secret and state db
# in /var/run/jupyterhub
c.JupyterHub.cookie_secret_file = pjoin(runtime_dir, 'cookie_secret')
c.JupyterHub.db_url = pjoin(runtime_dir, 'jupyterhub.sqlite')
# or `--db=/path/to/jupyterhub.sqlite` on the command-line

# put the log file in /var/log
c.JupyterHub.log_file = '/var/log/jupyterhub.log'

# use GitHub OAuthenticator for local users

c.JupyterHub.authenticator_class = 'oauthenticator.LocalGitHubOAuthenticator'
c.GitHubOAuthenticator.oauth_callback_url = os.environ['OAUTH_CALLBACK_URL']
# create system users that don't exist yet
c.LocalAuthenticator.create_system_users = True

# specify users and admin
c.Authenticator.whitelist = {'rgbkrk', 'minrk', 'jhamrick'}
c.Authenticator.admin_users = {'jhamrick', 'rgbkrk'}

# start single-user notebook servers in ~/assignments,
# with ~/assignments/Welcome.ipynb as the default landing page
# this config could also be put in
# /etc/ipython/ipython_notebook_config.py
c.Spawner.notebook_dir = '~/assignments'
c.Spawner.args = ['--NotebookApp.default_url=/notebooks/Welcome.ipynb']

Using the GitHub Authenticator requires a few additional env variables, which we will need to set when we launch the server:

export GITHUB_CLIENT_ID=github_id
export GITHUB_CLIENT_SECRET=github_secret
export OAUTH_CALLBACK_URL=https://example.com/hub/oauth_callback
export CONFIGPROXY_AUTH_TOKEN=super-secret
jupyterhub -f /path/to/aboveconfig.py

How JupyterHub works¶

JupyterHub is a multi-user server that manages and proxies multiple instances of the single-user ~~IPython~~ Jupyter notebook server.

There are three basic processes involved:

multi-user Hub (Python/Tornado)
configurable http proxy (node-http-proxy)
multiple single-user IPython notebook servers (Python/IPython/Tornado)

The proxy is the only process that listens on a public interface. The Hub sits behind the proxy at /hub. Single-user servers sit behind the proxy at /user/[username].

Logging in¶

When a new browser logs in to JupyterHub, the following events take place:

Login data is handed to the Authenticator instance for validation
The Authenticator returns the username, if login information is valid
A single-user server instance is Spawned for the logged-in user
When the server starts, the proxy is notified to forward /user/[username]/* to the single-user server
Two cookies are set, one for /hub/ and another for /user/[username], containing an encrypted token.
The browser is redirected to /user/[username], which is handled by the single-user server

Logging into a single-user server is authenticated via the Hub:

On request, the single-user server forwards the encrypted cookie to the Hub for verification
The Hub replies with the username if it is a valid cookie
If the user is the owner of the server, access is allowed
If it is the wrong user or an invalid cookie, the browser is redirected to /hub/login

Customizing JupyterHub¶

There are two basic extension points for JupyterHub: How users are authenticated, and how their server processes are started. Each is governed by a customizable class, and JupyterHub ships with just the most basic version of each.

To enable custom authentication and/or spawning, subclass Authenticator or Spawner, and override the relevant methods.

Authentication¶

Authentication is customizable via the Authenticator class. Authentication can be replaced by any mechanism, such as OAuth, Kerberos, etc.

JupyterHub only ships with PAM authentication, which requires the server to be run as root, or at least with access to the PAM service, which regular users typically do not have (on Ubuntu, this requires being added to the shadow group).

More info on custom Authenticators.

See a list of custom Authenticators on the wiki.

Spawning¶

Each single-user server is started by a Spawner. The Spawner represents an abstract interface to a process, and needs to be able to take three actions:

start the process
poll whether the process is still running
stop the process

More info on custom Spawners.

See a list of custom Spawners on the wiki.

Web Security in JupyterHub¶

JupyterHub is designed to be a simple multi-user server for modestly sized groups of semi-trusted users. While the design reflects serving semi-trusted users, JupyterHub is not necessarily unsuitable for serving untrusted users. Using JupyterHub with untrusted users does mean more work and much care is required to secure a Hub against untrusted users, with extra caution on protecting users from each other as the Hub is serving untrusted users.

One aspect of JupyterHub’s design simplicity for semi-trusted users is that the Hub and single-user servers are placed in a single domain, behind a proxy. As a result, if the Hub is serving untrusted users, many of the web’s cross-site protections are not applied between single-user servers and the Hub, or between single-user servers and each other, since browsers see the whole thing (proxy, Hub, and single user servers) as a single website.

To protect users from each other, a user must never be able to write arbitrary HTML and serve it to another user on the Hub’s domain. JupyterHub’s authentication setup prevents this because only the owner of a given single-user server is allowed to view user-authored pages served by their server. To protect all users from each other, JupyterHub administrators must ensure that:

A user does not have permission to modify their single-user server:
- A user may not install new packages in the Python environment that runs their server.
- If the PATH is used to resolve the single-user executable (instead of an absolute path), a user may not create new files in any PATH directory that precedes the directory containing jupyterhub-singleuser.
- A user may not modify environment variables (e.g. PATH, PYTHONPATH) for their single-user server.
A user may not modify the configuration of the notebook server (the ~/.jupyter or JUPYTER_CONFIG_DIR directory).

If any additional services are run on the same domain as the Hub, the services must never display user-authored HTML that is neither sanitized nor sandboxed (e.g. IFramed) to any user that lacks authentication as the author of a file.

Mitigations¶

There are two main configuration options provided by JupyterHub to mitigate these issues:

Subdomains¶

JupyterHub 0.5 adds the ability to run single-user servers on their own subdomains, which means the cross-origin protections between servers has the desired effect, and user servers and the Hub are protected from each other. A user’s server will be at username.jupyter.mydomain.com, etc. This requires all user subdomains to point to the same address, which is most easily accomplished with wildcard DNS. Since this spreads the service across multiple domains, you will need wildcard SSL, as well. Unfortunately, for many institutional domains, wildcard DNS and SSL are not available, but if you do plan to serve untrusted users, enabling subdomains is highly encouraged, as it resolves all of the cross-site issues.

Disabling user config¶

If subdomains are not available or not desirable, 0.5 also adds an option Spawner.disable_user_config, which you can set to prevent the user-owned configuration files from being loaded. This leaves only package installation and PATHs as things the admin must enforce.

For most Spawners, PATH is not something users an influence, but care should be taken to ensure that the Spawn does not evaluate shell configuration files prior to launching the server.

Package isolation is most easily handled by running the single-user server in a virtualenv with disabled system-site-packages.

Extra notes¶

It is important to note that the control over the environment only affects the single-user server, and not the environment(s) in which the user’s kernel(s) may run. Installing additional packages in the kernel environment does not pose additional risk to the web application’s security.

Writing a custom Authenticator¶

The Authenticator is the mechanism for authorizing users. Basic authenticators use simple username and password authentication. JupyterHub ships only with a PAM-based Authenticator, for logging in with local user accounts.

You can use custom Authenticator subclasses to enable authentication via other systems. One such example is using GitHub OAuth.

Because the username is passed from the Authenticator to the Spawner, a custom Authenticator and Spawner are often used together.

See a list of custom Authenticators on the wiki.

Basics of Authenticators¶

A basic Authenticator has one central method:

Authenticator.authenticate¶

Authenticator.authenticate(handler, data)

This method is passed the tornado RequestHandler and the POST data from the login form. Unless the login form has been customized, data will have two keys:

username (self-explanatory)
password (also self-explanatory)

authenticate‘s job is simple:

return a username (non-empty str) of the authenticated user if authentication is successful
return None otherwise

Writing an Authenticator that looks up passwords in a dictionary requires only overriding this one method:

from tornado import gen
from IPython.utils.traitlets import Dict
from jupyterhub.auth import Authenticator

class DictionaryAuthenticator(Authenticator):

    passwords = Dict(config=True,
        help="""dict of username:password for authentication"""
    )
    
    @gen.coroutine
    def authenticate(self, handler, data):
        if self.passwords.get(data['username']) == data['password']:
            return data['username']

Authenticator.whitelist¶

Authenticators can specify a whitelist of usernames to allow authentication. For local user authentication (e.g. PAM), this lets you limit which users can login.

Normalizing and validating usernames¶

Since the Authenticator and Spawner both use the same username, sometimes you want to transform the name coming from the authentication service (e.g. turning email addresses into local system usernames) before adding them to the Hub service. Authenticators can define normalize_username, which takes a username. The default normalization is to cast names to lowercase

For simple mappings, a configurable dict Authenticator.username_map is used to turn one name into another:

c.Authenticator.username_map  = {
  'service-name': 'localname'
}

Validating usernames¶

In most cases, there is a very limited set of acceptable usernames. Authenticators can define validate_username(username), which should return True for a valid username and False for an invalid one. The primary effect this has is improving error messages during user creation.

The default behavior is to use configurable Authenticator.username_pattern, which is a regular expression string for validation.

To only allow usernames that start with ‘w’:

c.Authenticator.username_pattern = r'w.*'

OAuth and other non-password logins¶

Some login mechanisms, such as OAuth, don’t map onto username+password. For these, you can override the login handlers.

You can see an example implementation of an Authenticator that uses GitHub OAuth at OAuthenticator.

Writing a custom Spawner¶

A Spawner starts each single-user notebook server. The Spawner represents an abstract interface to a process, and a custom Spawner needs to be able to take three actions:

start the process
poll whether the process is still running
stop the process

Examples¶

Custom Spawners for JupyterHub can be found on the JupyterHub wiki. Some examples include:

DockerSpawner for spawning user servers in Docker containers
- dockerspawner.DockerSpawner for spawning identical Docker containers for each users
- dockerspawner.SystemUserSpawner for spawning Docker containers with an environment and home directory for each users
SudoSpawner enables JupyterHub to run without being root, by spawning an intermediate process via sudo
BatchSpawner for spawning remote servers using batch systems
RemoteSpawner to spawn notebooks and a remote server and tunnel the port via SSH
SwarmSpawner for spawning containers using Docker Swarm

Spawner control methods¶

Spawner.start¶

Spawner.start should start the single-user server for a single user. Information about the user can be retrieved from self.user, an object encapsulating the user’s name, authentication, and server info.

When Spawner.start returns, it should have stored the IP and port of the single-user server in self.user.server.

NOTE: When writing coroutines, never yield in between a database change and a commit.

Most Spawner.start functions will look similar to this example:

def start(self):
    self.user.server.ip = 'localhost' # or other host or IP address, as seen by the Hub
    self.user.server.port = 1234 # port selected somehow
    self.db.commit() # always commit before yield, if modifying db values
    yield self._actually_start_server_somehow()

When Spawner.start returns, the single-user server process should actually be running, not just requested. JupyterHub can handle Spawner.start being very slow (such as PBS-style batch queues, or instantiating whole AWS instances) via relaxing the Spawner.start_timeout config value.

Spawner.poll¶

Spawner.poll should check if the spawner is still running. It should return None if it is still running, and an integer exit status, otherwise.

For the local process case, Spawner.poll uses os.kill(PID, 0) to check if the local process is still running.

Spawner.stop¶

Spawner.stop should stop the process. It must be a tornado coroutine, which should return when the process has finished exiting.

Spawner state¶

JupyterHub should be able to stop and restart without tearing down single-user notebook servers. To do this task, a Spawner may need to persist some information that can be restored later. A JSON-able dictionary of state can be used to store persisted information.

Unlike start, stop, and poll methods, the state methods must not be coroutines.

For the single-process case, the Spawner state is only the process ID of the server:

def get_state(self):
    """get the current state"""
    state = super().get_state()
    if self.pid:
        state['pid'] = self.pid
    return state

def load_state(self, state):
    """load state from the database"""
    super().load_state(state)
    if 'pid' in state:
        self.pid = state['pid']

def clear_state(self):
    """clear any state (called after shutdown)"""
    super().clear_state()
    self.pid = 0

Spawner options form¶

(new in 0.4)

Some deployments may want to offer options to users to influence how their servers are started. This may include cluster-based deployments, where users specify what resources should be available, or docker-based deployments where users can select from a list of base images.

This feature is enabled by setting Spawner.options_form, which is an HTML form snippet inserted unmodified into the spawn form. If the Spawner.options_form is defined, when a user tries to start their server, they will be directed to a form page, like this:

spawn-form

If Spawner.options_form is undefined, the user’s server is spawned directly, and no spawn page is rendered.

See this example for a form that allows custom CLI args for the local spawner.

`Spawner.options_from_form`¶

Options from this form will always be a dictionary of lists of strings, e.g.:

{
  'integer': ['5'],
  'text': ['some text'],
  'select': ['a', 'b'],
}

When formdata arrives, it is passed through Spawner.options_from_form(formdata), which is a method to turn the form data into the correct structure. This method must return a dictionary, and is meant to interpret the lists-of-strings into the correct types. For example, the options_from_form for the above form would look like:

def options_from_form(self, formdata):
    options = {}
    options['integer'] = int(formdata['integer'][0]) # single integer value
    options['text'] = formdata['text'][0] # single string value
    options['select'] = formdata['select'] # list already correct
    options['notinform'] = 'extra info' # not in the form at all
    return options

which would return:

{
  'integer': 5,
  'text': 'some text',
  'select': ['a', 'b'],
  'notinform': 'extra info',
}

When Spawner.spawn is called, this dictionary is accessible as self.user_options.

Troubleshooting¶

This document is under active development.

When troubleshooting, you may see unexpected behaviors or receive an error message. These two lists provide links to identifying the cause of the problem and how to resolve it.

Behavior problems¶

JupyterHub proxy fails to start

Errors¶

500 error after spawning a single-user server

JupyterHub proxy fails to start¶

If you have tried to start the JupyterHub proxy and it fails to start:

check if the JupyterHub IP configuration setting is c.JupyterHub.ip = '*'; if it is, try c.JupyterHub.ip = ''
Try starting with jupyterhub --ip=0.0.0.0

500 error after spawning my single-user server¶

You receive a 500 error when accessing the URL /user/you/.... This is often seen when your single-user server cannot check your cookies with the Hub.

There are two likely reasons for this:

The single-user server cannot connect to the Hub’s API (networking configuration problems)
The single-user server cannot authenticate its requests (invalid token)

Symptoms:¶

The main symptom is a failure to load any page served by the single-user server, met with a 500 error. This is typically the first page at /user/you after logging in or clicking “Start my server”. When a single-user server receives a request, it makes an API request to the Hub to check if the cookie corresponds to the right user. This request is logged.

If everything is working, it will look like this:

200 GET /hub/api/authorizations/cookie/jupyter-hub-token-name/[secret] (@10.0.1.4) 6.10ms

You should see a similar 200 message, as above, in the Hub log when you first visit your single-user server. If you don’t see this message in the log, it may mean that your single-user server isn’t connecting to your Hub.

If you see 403 (forbidden) like this, it’s a token problem:

403 GET /hub/api/authorizations/cookie/jupyter-hub-token-name/[secret] (@10.0.1.4) 4.14ms

Check the logs of the single-user server, which may have more detailed information on the cause.

Causes and resolutions:¶

No authorization request¶

If you make an API request and it is not received by the server, you likely have a network configuration issue. Often, this happens when the Hub is only listening on 127.0.0.1 (default) and the single-user servers are not on the same ‘machine’ (can be physically remote, or in a docker container or VM). The fix for this case is to make sure that c.JupyterHub.hub_ip is an address that all single-user servers can connect to, e.g.:

c.JupyterHub.hub_ip = '10.0.0.1'

The JupyterHub API¶

Release:	0.6.1
Date:	Oct 04, 2016

Authenticators¶

Module: `jupyterhub.auth`¶

Base Authenticator class and the default PAM Authenticator

class jupyterhub.auth.Authenticator(**kwargs)¶

A class for authentication.

The primary API is one method, authenticate, a tornado coroutine for authenticating users.

add_user(user)¶

Add a new user

By default, this just adds the user to the whitelist.

Subclasses may do more extensive things, such as adding actual unix users, but they should call super to ensure the whitelist is updated.

Parameters:	user (User) – The User wrapper object

authenticate(handler, data)¶

Authenticate a user with login form data.

This must be a tornado gen.coroutine. It must return the username on successful authentication, and return None on failed authentication.

Checking the whitelist is handled separately by the caller.

Parameters:	handler (tornado.web.RequestHandler) – the current request handler data (dict) – The formdata of the login form. The default form has ‘username’ and ‘password’ fields.
Returns:	the username of the authenticated user None: Authentication failed
Return type:	str

check_whitelist(username)¶

Check a username against our whitelist.

Return True if username is allowed, False otherwise. No whitelist means any username should be allowed.

Names are normalized before being checked against the whitelist.

delete_user(user)¶

Triggered when a user is deleted.

Removes the user from the whitelist. Subclasses should call super to ensure the whitelist is updated.

Parameters:	user (User) – The User wrapper object

get_authenticated_user(handler, data)¶

This is the outer API for authenticating a user.

This calls authenticate, which should be overridden in subclasses, normalizes the username if any normalization should be done, and then validates the name in the whitelist.

Subclasses should not need to override this method. The various stages can be overridden separately:

authenticate turns formdata into a username
normalize_username normalizes the username
check_whitelist checks against the user whitelist

get_handlers(app)¶

Return any custom handlers the authenticator needs to register

(e.g. for OAuth).

Parameters:	app (JupyterHub Application) – the application object, in case it needs to be accessed for info.
Returns:	list of `('/url', Handler)` tuples passed to tornado. The Hub prefix is added to any URLs.
Return type:	list

login_url(base_url)¶

Override to register a custom login handler

Generally used in combination with get_handlers.

Parameters:	base_url (str) – the base URL of the Hub (e.g. /hub/)
Returns:	The login URL, e.g. ‘/hub/login’
Return type:	str

logout_url(base_url)¶

Override to register a custom logout handler.

Generally used in combination with get_handlers.

Parameters:	base_url (str) – the base URL of the Hub (e.g. /hub/)
Returns:	The logout URL, e.g. ‘/hub/logout’
Return type:	str

normalize_username(username)¶

Normalize a username.

Override in subclasses if usernames should have some normalization. Default: cast to lowercase, lookup in username_map.

post_spawn_stop(user, spawner)¶

Hook called after stopping a user container.

Can be used to do auth-related cleanup, e.g. closing PAM sessions.

pre_spawn_start(user, spawner)¶

Hook called before spawning a user’s server.

Can be used to do auth-related startup, e.g. opening PAM sessions.

validate_username(username)¶

Validate a (normalized) username.

Return True if username is valid, False otherwise.

class jupyterhub.auth.LocalAuthenticator(**kwargs)¶

Base class for Authenticators that work with local Linux/UNIX users

Checks for local users, and can attempt to create them if they exist.

add_system_user(user)¶: Create a new Linux/UNIX user on the system. Works on FreeBSD and Linux, at least.

add_user(user)¶

Add a new user

If self.create_system_users, the user will attempt to be created.

static system_user_exists(user)¶: Check if the user exists on the system

class jupyterhub.auth.PAMAuthenticator(**kwargs)¶: Authenticate local Linux/UNIX users with PAM

Spawners¶

Module: `jupyterhub.spawner`¶

Class for spawning single-user notebook servers.

`Spawner`¶

class jupyterhub.spawner.Spawner(**kwargs)¶

Base class for spawning single-user notebook servers.

Subclass this, and override the following methods:

load_state
get_state
start
stop
poll

get_args()¶: Return the arguments to be passed after self.cmd

get_env()¶

Return the environment dict to use for the Spawner.

This applies things like env_keep, anything defined in Spawner.environment, and adds the API token to the env.

Use this to access the env in Spawner.start to allow extension in subclasses.

get_state()¶

store the state necessary for load_state

A black box of extra state for custom spawners. Subclasses should call super.

Returns:	state – a JSONable dict of state
Return type:	dict

options_from_form(form_data)¶

Interpret HTTP form data

Form data will always arrive as a dict of lists of strings. Override this function to understand single-values, numbers, etc.

This should coerce form data into the structure expected by self.user_options, which must be a dict.

Instances will receive this data on self.user_options, after passing through this function, prior to Spawner.start.

poll()¶

Check if the single-user process is running

return None if it is, an exit status (0 if unknown) if it is not.

start()¶: Start the single-user process

stop(now=False)¶: Stop the single-user process

class jupyterhub.spawner.LocalProcessSpawner(**kwargs)¶

A Spawner that just uses Popen to start local processes as users.

Requires users to exist on the local system.

This is the default spawner for JupyterHub.

Users¶

Module: `jupyterhub.user`¶

`User`¶

class jupyterhub.user.Server¶

class jupyterhub.user.User(orm_user, settings, **kwargs)¶

name¶: The user’s name

server¶: The user’s Server data object if running, None otherwise. Has ip, port attributes.

spawner¶: The user’s Spawner instance.

escaped_name¶: My name, escaped for use in URLs, cookies, etc.

Summary of changes in JupyterHub¶

See git log for a more detailed summary.

0.6¶

0.6.1¶

Bugfixes on 0.6:

statsd is an optional dependency, only needed if in use
Notice more quickly when servers have crashed
Better error pages for proxy errors
Add Stop All button to admin panel for stopping all servers at once

0.6.0¶

JupyterHub has moved to a new jupyterhub namespace on GitHub and Docker. What was juptyer/jupyterhub is now jupyterhub/jupyterhub, etc.
jupyterhub/jupyterhub image on DockerHub no longer loads the jupyterhub_config.py in an ONBUILD step. A new jupyterhub/jupyterhub-onbuild image does this
Add statsd support, via c.JupyterHub.statsd_{host,port,prefix}
Update to traitlets 4.1 @default, @observe APIs for traits
Allow disabling PAM sessions via c.PAMAuthenticator.open_sessions = False. This may be needed on SELinux-enabled systems, where our PAM session logic often does not work properly
Add Spawner.environment configurable, for defining extra environment variables to load for single-user servers
JupyterHub API tokens can be pregenerated and loaded via JupyterHub.api_tokens, a dict of token: username.
JupyterHub API tokens can be requested via the REST API, with a POST request to /api/authorizations/token. This can only be used if the Authenticator has a username and password.
Various fixes for user URLs and redirects

0.5¶

Single-user server must be run with Jupyter Notebook ≥ 4.0
Require --no-ssl confirmation to allow the Hub to be run without SSL (e.g. behind SSL termination in nginx)
Add lengths to text fields for MySQL support
Add Spawner.disable_user_config for preventing user-owned configuration from modifying single-user servers.
Fixes for MySQL support.
Add ability to run each user’s server on its own subdomain. Requires wildcard DNS and wildcard SSL to be feasible. Enable subdomains by setting JupyterHub.subdomain_host = 'https://jupyterhub.domain.tld[:port]'.
Use 127.0.0.1 for local communication instead of localhost, avoiding issues with DNS on some systems.
Fix race that could add users to proxy prematurely if spawning is slow.

0.4¶

0.4.1¶

Fix removal of /login page in 0.4.0, breaking some OAuth providers.

0.4.0¶

Add Spawner.user_options_form for specifying an HTML form to present to users, allowing users to influence the spawning of their own servers.
Add Authenticator.pre_spawn_start and Authenticator.post_spawn_stop hooks, so that Authenticators can do setup or teardown (e.g. passing credentials to Spawner, mounting data sources, etc.). These methods are typically used with custom Authenticator+Spawner pairs.
0.4 will be the last JupyterHub release where single-user servers running IPython 3 is supported instead of Notebook ≥ 4.0.

0.3¶

No longer make the user starting the Hub an admin
start PAM sessions on login
hooks for Authenticators to fire before spawners start and after they stop, allowing deeper interaction between Spawner/Authenticator pairs.
login redirect fixes