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

  • 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 DummyAuthenticator. This allows for any username and password unless a global password has been set. Once set, any username will still be accepted but the correct password will need to be provided.

Added in version 5.0: The DummyAuthenticator’s default allow_all is True, unlike most other Authenticators.

Additional Authenticators#

Additional authenticators can be found on GitHub by searching for topic:jupyterhub topic:authenticator.

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:



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

If authentication is successful the authenticate method must return either:

  • the username (non-empty str) of the authenticated user

  • or a dictionary with fields:

Otherwise, it must return None.

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

from secrets import compare_digest
from 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):
        username = data["username"]
        password = data["password"]
        check_password = self.passwords.get(username, "")
        # always call compare_digest, for timing attacks
        if compare_digest(check_password, password) and username in self.passwords:
            return username
            return None

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'

When using PAMAuthenticator, you can set c.PAMAuthenticator.pam_normalize_username = True, which will normalize usernames using PAM (basically round-tripping them: username to uid to username), which is useful in case you use some external service that allows multiple usernames mapping to the same user (such as ActiveDirectory, yes, this really happens). When pam_normalize_username is on, usernames are not normalized to lowercase.

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, Authenticator.pre_spawn_start() and Authenticator.post_spawn_stop(), are hooks that can be used to do auth-related startup (e.g. opening PAM sessions) and cleanup (e.g. closing PAM sessions).

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 add:

    'jupyterhub.authenticators': [
        'myservice = mypackage:MyAuthenticator',

If you have added this metadata to your package, admins 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.

Allowing access#

When dealing with logging in, there are generally two separate steps:


identifying who is trying to log in, and


deciding whether an authenticated user is allowed to access your JupyterHub

Authenticator.authenticate() is responsible for authenticating users. It is perfectly fine in the simplest cases for Authenticator.authenticate to be responsible for authentication and authorization, in which case authenticate may return None if the user is not authorized.

However, Authenticators also have two methods, check_allowed() and check_blocked_users(), which are called after successful authentication to further check if the user is allowed.

If check_blocked_users() returns False, authorization stops and the user is not allowed.

If Authenticator.allow_all is True OR check_allowed() returns True, authorization proceeds.

Added in version 5.0: Authenticator.allow_all and Authenticator.allow_existing_users are new in JupyterHub 5.0.

By default, allow_all is False, which is a change from pre-5.0, where allow_all was implicitly True if allowed_users was empty.

Overriding check_allowed#

Changed in version 5.0: check_allowed() is not called if allow_all is True.

Changed in version 5.0: Starting with 5.0, check_allowed() should NOT return True if no allow config is specified (allow_all should be used instead).

The base implementation of check_allowed() checks:

  • if username is in the allowed_users set, return True

  • else return False

Changed in version 5.0: Prior to 5.0, this would also return True if allowed_users was empty.

For clarity, this is no longer the case. A new allow_all property (default False) has been added which is checked before calling check_allowed. If allow_all is True, this takes priority over check_allowed, which will be ignored.

If your Authenticator subclass similarly returns True when no allow config is defined, this is fully backward compatible for your users, but means allow_all = False has no real effect.

You can make your Authenticator forward-compatible with JupyterHub 5 by defining allow_all as a boolean config trait on your class:

class MyAuthenticator(Authenticator):

    # backport allow_all from JupyterHub 5
    allow_all = Bool(False, config=True)

    def check_allowed(self, username, authentication):
        if self.allow_all:
            # replaces previous "if no auth config"
            return True

If an Authenticator defines additional sources of allow configuration, such as membership in a group or other information, it should override check_allowed to account for this.


allow_ configuration should generally be additive, i.e. if access is granted by any allow configuration, a user should be authorized.

JupyterHub recommends that Authenticators applying restrictive configuration should use names like block_ or require_, and check this during check_blocked_users or authenticate, not check_allowed.

In general, an Authenticator’s skeleton should look like:

class MyAuthenticator(Authenticator):
    # backport allow_all for compatibility with JupyterHub < 5
    allow_all = Bool(False, config=True)
    require_something = List(config=True)
    allowed_something = Set()

    def authenticate(self, data, handler):
        if success:
            return {"username": username, "auth_state": {...}}
            return None

    def check_blocked_users(self, username, authentication=None):
        """Apply _restrictive_ configuration"""

        if self.require_something and not has_something(username, self.request_):
            return False
        # repeat for each restriction
        if restriction_defined and restriction_not_met:
            return False
        return super().check_blocked_users(self, username, authentication)

    def check_allowed(self, username, authentication=None):
        """Apply _permissive_ configuration

        Only called if check_blocked_users returns True
        AND allow_all is False
        if self.allow_all:
            # check here to backport allow_all behavior
            # from JupyterHub 5
            # this branch will never be taken with jupyterhub >=5
            return True
        if self.allowed_something and user_has_something(username):
            return True
        # repeat for each allow
        if allow_config and allow_met:
            return True
        # should always have this at the end
        if self.allowed_users and username in self.allowed_users:
            return True
        # do not call super!
        # super().check_allowed is not safe with JupyterHub < 5.0,
        # as it will return True if allowed_users is empty
        return False

Key points:

  • allow_all is backported from JupyterHub 5, for consistent behavior in all versions of JupyterHub (optional)

  • restrictive configuration is checked in check_blocked_users

  • if any restriction is not met, check_blocked_users returns False

  • permissive configuration is checked in check_allowed

  • if any allow condition is met, check_allowed returns True

So the logical expression for a user being authorized should look like:

if ALL restrictions are met AND ANY admissions are met: user is authorized

Custom error messages#

Any of these authentication and authorization methods may raise a web.HTTPError Exception

from tornado import web

raise web.HTTPError(403, "informative message")

if you want to show a more informative login failure message rather than the generic one.

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):
    async def authenticate(self, handler, data=None):
        username = await identify_user(handler, data)
        upstream_token = await token_for_user(username)
        return {
            'name': username,
            'auth_state': {
                'upstream_token': upstream_token,

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

Note that environment variable names and values are always strings, so passing multiple values means setting multiple environment variables or serializing more complex data into a single variable, e.g. as a JSON string.

auth state can also be used to configure the spawner via config without subclassing by setting c.Spawner.auth_state_hook. This function will be called with (spawner, auth_state), only when auth_state is defined.

For example: (for KubeSpawner)

def auth_state_hook(spawner, auth_state):
    spawner.volumes = auth_state['user_volumes']
    spawner.mounts = auth_state['user_mounts']

c.Spawner.auth_state_hook = auth_state_hook

Authenticator-managed group membership#

Added in version 2.2.

Some identity providers may have their own concept of group membership that you would like to preserve in JupyterHub. This is now possible with Authenticator.manage_groups.

You can set the config:

c.Authenticator.manage_groups = True

to enable this behavior. The default is False for Authenticators that ship with JupyterHub, but may be True for custom Authenticators. Check your Authenticator’s documentation for manage_groups support.

If True, Authenticator.authenticate() and Authenticator.refresh_user() may include a field groups which is a list of group names the user should be a member of:

  • Membership will be added for any group in the list

  • Membership in any groups not in the list will be revoked

  • Any groups not already present in the database will be created

  • If None is returned, no changes are made to the user’s group membership

If authenticator-managed groups are enabled, all group-management via the API is disabled, and roles cannot be specified with load_groups traitlet.

Authenticator-managed roles#

Added in version 5.0.

Some identity providers may have their own concept of role membership that you would like to preserve in JupyterHub. This is now possible with Authenticator.manage_roles.

You can set the config:

c.Authenticator.manage_roles = True

to enable this behavior. The default is False for Authenticators that ship with JupyterHub, but may be True for custom Authenticators. Check your Authenticator’s documentation for manage_roles support.

If True, Authenticator.authenticate() and Authenticator.refresh_user() may include a field roles which is a list of roles that user should be assigned to:

  • User will be assigned each role in the list

  • User will be revoked roles not in the list (but they may still retain the role privileges if they inherit the role from their group)

  • Any roles not already present in the database will be created

  • Attributes of the roles (description, scopes, groups, users, and services) will be updated if given

  • If None is returned, no changes are made to the user’s roles

If authenticator-managed roles are enabled, all role-management via the API is disabled, and roles cannot be assigned to groups nor users via load_roles traitlet (roles can still be created via load_roles or assigned to services).

When an authenticator manages roles, the initial roles and role assignments can be loaded from role specifications returned by the Authenticator.load_managed_roles() method.

The authenticator-manged roles and role assignment will be deleted after restart if:

pre_spawn_start and post_spawn_stop hooks#

Authenticators use two hooks, Authenticator.pre_spawn_start() and Authenticator.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.