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


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

    • both DockerSpawner and SystemUserSpawner also work with Docker Swarm for launching containers on remote machines

  • SudoSpawner enables JupyterHub to run without being root, by spawning an intermediate process via sudo

  • BatchSpawner for spawning remote servers using batch systems

  • YarnSpawner for spawning notebook servers in YARN containers on a Hadoop cluster

  • SSHSpawner to spawn notebooks on a remote server using SSH

Spawner control methods


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.

The return value of Spawner.start should be the (ip, port) of the running 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.ip = ''
    self.port = random_port()
    # get environment variables,
    # several of which are required for configuring the single-user server
    env = self.get_env()
    cmd = []
    # get jupyterhub command to run,
    # typically ['jupyterhub-singleuser']

    yield self._actually_start_server_somehow(cmd, env)
    return (self.ip, self.port)

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 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. On Windows, it uses psutil.pid_exists.


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()
        state['pid'] =
    return state

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

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


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.


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.start is called, this dictionary is accessible as self.user_options.

Writing a custom spawner

If you are interested in building a custom spawner, you can read this tutorial.

Registering custom Spawners via entry points

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

    'jupyterhub.spawners': [
        'myservice = mypackage:MySpawner',

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

c.JupyterHub.spawner_class = 'myservice'

instead of the full

c.JupyterHub.spawner_class = 'mypackage:MySpawner'

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

Spawners, resource limits, and guarantees (Optional)

Some spawners of the single-user notebook servers allow setting limits or guarantees on resources, such as CPU and memory. To provide a consistent experience for sysadmins and users, we provide a standard way to set and discover these resource limits and guarantees, such as for memory and CPU. For the limits and guarantees to be useful, the spawner must implement support for them. For example, LocalProcessSpawner, the default spawner, does not support limits and guarantees. One of the spawners that supports limits and guarantees is the systemdspawner.

Memory Limits & Guarantees

c.Spawner.mem_limit: A limit specifies the maximum amount of memory that may be allocated, though there is no promise that the maximum amount will be available. In supported spawners, you can set c.Spawner.mem_limit to limit the total amount of memory that a single-user notebook server can allocate. Attempting to use more memory than this limit will cause errors. The single-user notebook server can discover its own memory limit by looking at the environment variable MEM_LIMIT, which is specified in absolute bytes.

c.Spawner.mem_guarantee: Sometimes, a guarantee of a minimum amount of memory is desirable. In this case, you can set c.Spawner.mem_guarantee to to provide a guarantee that at minimum this much memory will always be available for the single-user notebook server to use. The environment variable MEM_GUARANTEE will also be set in the single-user notebook server.

The spawner’s underlying system or cluster is responsible for enforcing these limits and providing these guarantees. If these values are set to None, no limits or guarantees are provided, and no environment values are set.

CPU Limits & Guarantees

c.Spawner.cpu_limit: In supported spawners, you can set c.Spawner.cpu_limit to limit the total number of cpu-cores that a single-user notebook server can use. These can be fractional - 0.5 means 50% of one CPU core, 4.0 is 4 cpu-cores, etc. This value is also set in the single-user notebook server’s environment variable CPU_LIMIT. The limit does not claim that you will be able to use all the CPU up to your limit as other higher priority applications might be taking up CPU.

c.Spawner.cpu_guarantee: You can set c.Spawner.cpu_guarantee to provide a guarantee for CPU usage. The environment variable CPU_GUARANTEE will be set in the single-user notebook server when a guarantee is being provided.

The spawner’s underlying system or cluster is responsible for enforcing these limits and providing these guarantees. If these values are set to None, no limits or guarantees are provided, and no environment values are set.


Communication between the Proxy, Hub, and Notebook can be secured by turning on internal_ssl in For a custom spawner to utilize these certs, there are two methods of interest on the base Spawner class: .create_certs and .move_certs.

The first method, .create_certs will sign a key-cert pair using an internally trusted authority for notebooks. During this process, .create_certs can apply ip and dns name information to the cert via an alt_names kwarg. This is used for certificate authentication (verification). Without proper verification, the Notebook will be unable to communicate with the Hub and vice versa when internal_ssl is enabled. For example, given a deployment using the DockerSpawner which will start containers with ips from the docker subnet pool, the DockerSpawner would need to instead choose a container ip prior to starting and pass that to .create_certs (TODO: edit).

In general though, this method will not need to be changed and the default ip/dns (localhost) info will suffice.

When .create_certs is run, it will .create_certs in a default, central location specified by c.JupyterHub.internal_certs_location. For Spawners that need access to these certs elsewhere (i.e. on another host altogether), the .move_certs method can be overridden to move the certs appropriately. Again, using DockerSpawner as an example, this would entail moving certs to a directory that will get mounted into the container this spawner starts.