Authenticators

The Authenticator is the mechanism for authorizing users to use the Hub and single user notebook servers.

The default PAM Authenticator

JupyterHub ships with the default PAM-based Authenticator, for logging in with local user accounts via a username and password.

The OAuthenticator

Some login mechanisms, such as OAuth, don’t map onto username and password authentication, and instead use tokens. When using these mechanisms, you can override the login handlers.

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

JupyterHub’s OAuthenticator currently supports the following popular services:

  • Auth0
  • Bitbucket
  • CILogon
  • GitHub
  • GitLab
  • Globus
  • Google
  • MediaWiki
  • Okpy
  • OpenShift

A generic implementation, which you can use for OAuth authentication with any provider, is also available.

The Dummy Authenticator

When testing, it may be helpful to use the :class:~jupyterhub.auth.DummyAuthenticator. This allows for any username and password unless if a global password has been set. Once set, any username will still be accepted but the correct password will need to be provided.

Additional Authenticators

A partial list of other authenticators is available on the JupyterHub wiki.

Technical Overview of Authentication

How the Base Authenticator works

The base authenticator uses simple username and password authentication.

The base Authenticator has one central method:

Authenticator.authenticate method

Authenticator.authenticate(handler, data)

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

  • username
  • password

The authenticate method’s job is simple:

  • return the 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 IPython.utils.traitlets import Dict
from jupyterhub.auth import Authenticator

class DictionaryAuthenticator(Authenticator):

    passwords = Dict(config=True,
        help="""dict of username:password for authentication"""
    )

    async def authenticate(self, handler, data):
        if self.passwords.get(data['username']) == data['password']:
            return data['username']

Normalize 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'
}

Validate 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.*'

How to write a custom authenticator

You can use custom Authenticator subclasses to enable authentication via other mechanisms. 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. For example, the Authenticator methods, pre_spawn_start(user, spawner) and post_spawn_stop(user, spawner), are hooks that can be used to do auth-related startup (e.g. opening PAM sessions) and cleanup (e.g. closing PAM sessions).

See a list of custom Authenticators on the wiki.

If you are interested in writing a custom authenticator, you can read this tutorial.

Registering custom Authenticators via entry points

As of JupyterHub 1.0, custom authenticators can register themselves via the jupyterhub.authenticators entry point metadata. To do this, in your setup.py add:

setup(
  ...
  entry_points={
    'jupyterhub.authenticators': [
        'myservice = mypackage:MyAuthenticator',
    ],
  },
)

If you have added this metadata to your package, users can select your authenticator with the configuration:

c.JupyterHub.authenticator_class = 'myservice'

instead of the full

c.JupyterHub.authenticator_class = 'mypackage:MyAuthenticator'

previously required. Additionally, configurable attributes for your authenticator will appear in jupyterhub help output and auto-generated configuration files via jupyterhub --generate-config.

Authentication state

JupyterHub 0.8 adds the ability to persist state related to authentication, such as auth-related tokens. If such state should be persisted, .authenticate() should return a dictionary of the form:

{
  'name': username,
  'auth_state': {
    'key': 'value',
  }
}

where username is the username that has been authenticated, and auth_state is any JSON-serializable dictionary.

Because auth_state may contain sensitive information, it is encrypted before being stored in the database. To store auth_state, two conditions must be met:

  1. persisting auth state must be enabled explicitly via configuration

    c.Authenticator.enable_auth_state = True
    
  2. encryption must be enabled by the presence of JUPYTERHUB_CRYPT_KEY environment variable, which should be a hex-encoded 32-byte key. For example:

    export JUPYTERHUB_CRYPT_KEY=$(openssl rand -hex 32)
    

JupyterHub uses Fernet to encrypt auth_state. To facilitate key-rotation, JUPYTERHUB_CRYPT_KEY may be a semicolon-separated list of encryption keys. If there are multiple keys present, the first key is always used to persist any new auth_state.

Using auth_state

Typically, if auth_state is persisted it is desirable to affect the Spawner environment in some way. This may mean defining environment variables, placing certificate in the user’s home directory, etc. The Authenticator.pre_spawn_start method can be used to pass information from authenticator state to Spawner environment:

class MyAuthenticator(Authenticator):
    @gen.coroutine
    def authenticate(self, handler, data=None):
        username = yield identify_user(handler, data)
        upstream_token = yield token_for_user(username)
        return {
            'name': username,
            'auth_state': {
                'upstream_token': upstream_token,
            },
        }

    @gen.coroutine
    def pre_spawn_start(self, user, spawner):
        """Pass upstream_token to spawner via environment variable"""
        auth_state = yield user.get_auth_state()
        if not auth_state:
            # auth_state not enabled
            return
        spawner.environment['UPSTREAM_TOKEN'] = auth_state['upstream_token']

pre_spawn_start and post_spawn_stop hooks

Authenticators uses two hooks, pre_spawn_start(user, spawner) and post_spawn_stop(user, spawner) to add pass additional state information between the authenticator and a spawner. These hooks are typically used auth-related startup, i.e. opening a PAM session, and auth-related cleanup, i.e. closing a PAM session.

JupyterHub as an OAuth provider

Beginning with version 0.8, JupyterHub is an OAuth provider.