With version 0.7, JupyterHub adds support for Services.

This section provides the following information about Services:

Definition of a Service

When working with JupyterHub, a Service is defined as a process that interacts with the Hub’s REST API. A Service may perform a specific or action or task. For example, the following tasks can each be a unique Service:

  • shutting down individuals’ single user notebook servers that have been idle for some time
  • registering additional web servers which should use the Hub’s authentication and be served behind the Hub’s proxy.

Two key features help define a Service:

  • Is the Service managed by JupyterHub?
  • Does the Service have a web server that should be added to the proxy’s table?

Currently, these characteristics distinguish two types of Services:

  • A Hub-Managed Service which is managed by JupyterHub
  • An Externally-Managed Service which runs its own web server and communicates operation instructions via the Hub’s API.

Properties of a Service

A Service may have the following properties:

  • name: str - the name of the service
  • admin: bool (default - false) - whether the service should have administrative privileges
  • url: str (default - None) - The URL where the service is/should be. If a url is specified for where the Service runs its own web server, the service will be added to the proxy at /services/:name
  • api_token: str (default - None) - For Externally-Managed Services you need to specify an API token to perform API requests to the Hub

If a service is also to be managed by the Hub, it has a few extra options:

  • command: (str/Popen list) - Command for JupyterHub to spawn the service. - Only use this if the service should be a subprocess. - If command is not specified, the Service is assumed to be managed externally. - If a command is specified for launching the Service, the Service will be started and managed by the Hub.
  • environment: dict - additional environment variables for the Service.
  • user: str - the name of a system user to manage the Service. If unspecified, run as the same user as the Hub.

Hub-Managed Services

A Hub-Managed Service is started by the Hub, and the Hub is responsible for the Service’s actions. A Hub-Managed Service can only be a local subprocess of the Hub. The Hub will take care of starting the process and restarts it if it stops.

While Hub-Managed Services share some similarities with notebook Spawners, there are no plans for Hub-Managed Services to support the same spawning abstractions as a notebook Spawner.

If you wish to run a Service in a Docker container or other deployment environments, the Service can be registered as an Externally-Managed Service, as described below.

Launching a Hub-Managed Service

A Hub-Managed Service is characterized by its specified command for launching the Service. For example, a ‘cull idle’ notebook server task configured as a Hub-Managed Service would include:

  • the Service name,
  • admin permissions, and
  • the command to launch the Service which will cull idle servers after a timeout interval

This example would be configured as follows in = [
        'name': 'cull-idle',
        'admin': True,
        'command': ['python', '/path/to/', '--timeout']

A Hub-Managed Service may also be configured with additional optional parameters, which describe the environment needed to start the Service process:

  • environment: dict - additional environment variables for the Service.
  • user: str - name of the user to run the server if different from the Hub. Requires Hub to be root.
  • cwd: path directory in which to run the Service, if different from the Hub directory.

The Hub will pass the following environment variables to launch the Service:

JUPYTERHUB_SERVICE_NAME:   The name of the service
JUPYTERHUB_API_TOKEN:      API token assigned to the service
JUPYTERHUB_API_URL:        URL for the JupyterHub API (default,
JUPYTERHUB_BASE_URL:       Base URL of the Hub (https://mydomain[:port]/)
JUPYTERHUB_SERVICE_PREFIX: URL path prefix of this service (/services/:service-name/)
JUPYTERHUB_SERVICE_URL:    Local URL where the service is expected to be listening.
                           Only for proxied web services.

For the previous ‘cull idle’ Service example, these environment variables would be passed to the Service when the Hub starts the ‘cull idle’ Service:

JUPYTERHUB_API_TOKEN: API token assigned to the service
JUPYTERHUB_BASE_URL: https://mydomain[:port]
JUPYTERHUB_SERVICE_PREFIX: /services/cull-idle/

See the JupyterHub GitHub repo for additional information about the cull-idle example.

Externally-Managed Services

You may prefer to use your own service management tools, such as Docker or systemd, to manage a JupyterHub Service. These Externally-Managed Services, unlike Hub-Managed Services, are not subprocesses of the Hub. You must tell JupyterHub which API token the Externally-Managed Service is using to perform its API requests. Each Externally-Managed Service will need a unique API token, because the Hub authenticates each API request and the API token is used to identify the originating Service or user.

A configuration example of an Externally-Managed Service with admin access and running its own web server is: = [
        'name': 'my-web-service',
        'url': '',
        'api_token': 'super-secret',

In this case, the url field will be passed along to the Service as JUPYTERHUB_SERVICE_URL.

Writing your own Services

When writing your own services, you have a few decisions to make (in addition to what your service does!):

  1. Does my service need a public URL?
  2. Do I want JupyterHub to start/stop the service?
  3. Does my service need to authenticate users?

When a Service is managed by JupyterHub, the Hub will pass the necessary information to the Service via the environment variables described above. A flexible Service, whether managed by the Hub or not, can make use of these same environment variables.

When you run a service that has a url, it will be accessible under a /services/ prefix, such as For your service to route proxied requests properly, it must take JUPYTERHUB_SERVICE_PREFIX into account when routing requests. For example, a web service would normally service its root handler at '/', but the proxied service would need to serve JUPYTERHUB_SERVICE_PREFIX + '/'.

Hub Authentication and Services

JupyterHub 0.7 introduces some utilities for using the Hub’s authentication mechanism to govern access to your service. When a user logs into JupyterHub, the Hub sets a cookie (jupyterhub-services). The service can use this cookie to authenticate requests.

JupyterHub ships with a reference implementation of Hub authentication that can be used by services. You may go beyond this reference implementation and create custom hub-authenticating clients and services. We describe the process below.

The reference, or base, implementation is the HubAuth class, which implements the requests to the Hub.

To use HubAuth, you must set the .api_token, either programmatically when constructing the class, or via the JUPYTERHUB_API_TOKEN environment variable.

Most of the logic for authentication implementation is found in the HubAuth.user_for_cookie method, which makes a request of the Hub, and returns:

  • None, if no user could be identified, or

  • a dict of the following form:

      "name": "username",
      "groups": ["list", "of", "groups"],
      "admin": False, # or True

You are then free to use the returned user information to take appropriate action.

HubAuth also caches the Hub’s response for a number of seconds, configurable by the cookie_cache_max_age setting (default: five minutes).

Flask Example

For example, you have a Flask service that returns information about a user. JupyterHub’s HubAuth class can be used to authenticate requests to the Flask service. See the service-whoami-flask example in the JupyterHub GitHub repo for more details.

from functools import wraps
import json
import os
from urllib.parse import quote

from flask import Flask, redirect, request, Response

from import HubAuth

prefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/')

auth = HubAuth(

app = Flask(__name__)

def authenticated(f):
    """Decorator for authenticating with the Hub"""
    def decorated(*args, **kwargs):
        cookie = request.cookies.get(auth.cookie_name)
        if cookie:
            user = auth.user_for_cookie(cookie)
            user = None
        if user:
            return f(user, *args, **kwargs)
            # redirect to login url on failed auth
            return redirect(auth.login_url + '?next=%s' % quote(request.path))
    return decorated

@app.route(prefix + '/')
def whoami(user):
    return Response(
        json.dumps(user, indent=1, sort_keys=True),

Authenticating tornado services with JupyterHub

Since most Jupyter services are written with tornado, we include a mixin class, HubAuthenticated, for quickly authenticating your own tornado services with JupyterHub.

Tornado’s @web.authenticated method calls a Handler’s .get_current_user method to identify the user. Mixing in HubAuthenticated defines get_current_user to use HubAuth. If you want to configure the HubAuth instance beyond the default, you’ll want to define an initialize method, such as:

class MyHandler(HubAuthenticated, web.RequestHandler):
    hub_users = {'inara', 'mal'}

    def initialize(self, hub_auth):
        self.hub_auth = hub_auth

    def get(self):

The HubAuth will automatically load the desired configuration from the Service environment variables.

If you want to limit user access, you can whitelist users through either the .hub_users attribute or .hub_groups. These are sets that check against the username and user group list, respectively. If a user matches neither the user list nor the group list, they will not be allowed access. If both are left undefined, then any user will be allowed.

Implementing your own Authentication with JupyterHub

If you don’t want to use the reference implementation (e.g. you find the implementation a poor fit for your Flask app), you can implement authentication via the Hub yourself. We recommend looking at the HubAuth class implementation for reference, and taking note of the following process:

  1. retrieve the cookie jupyterhub-services from the request.

  2. Make an API request GET /hub/api/authorizations/cookie/jupyterhub-services/cookie-value, where cookie-value is the url-encoded value of the jupyterhub-services cookie. This request must be authenticated with a Hub API token in the Authorization header. For example, with requests:

    r = requests.get(
                   quote(encrypted_cookie, safe=''),
        headers = {
            'Authorization' : 'token %s' % api_token,
    user = r.json()
  3. On success, the reply will be a JSON model describing the user:

      "name": "inara",
      "groups": ["serenity", "guild"],

An example of using an Externally-Managed Service and authentication is nbviewer, and an example of its configuration is found here. nbviewer can also be run as a Hub-Managed Service as described here.