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MiG-SwarmSpawner enables JupyterHub to spawn jupyter notebooks that can interact with the MiG infrastructure.

Project description

https://travis-ci.org/rasmunk/SwarmSpawner.svg?branch=master

MiG SwarmSpawner enables JupyterHub to spawn jupyter notebooks that can interact with the MiG infrastructure.

More info about Docker Services here.

Prerequisites

Python version 3.3 and above is required.

Installation

pip install mig-swarmspawner

Installation from GitHub

git clone https://github.com/rasmunk/SwarmSpawner
cd SwarmSpawner
python setup.py install

Configuration

You can find an example jupyter_config.py inside examples.

The spawner

Docker Engine in Swarm mode and the related services work in a different way compared to Docker containers.

Tell JupyterHub to use SwarmSpawner by adding the following lines to your jupyterhub_config.py:

c.JupyterHub.spawner_class = 'mig.SwarmSpawner'
c.JupyterHub.hub_ip = '0.0.0.0'
# This should be the name of the jupyterhub service
c.SwarmSpawner.jupyterhub_service_name = 'NameOfTheService'

What is jupyterhub_service_name?

Inside a Docker engine in Swarm mode the services use a name instead of a ip to communicate with each other. ‘jupyterhub_service_name’ is the name of ther service for the JupyterHub.

Networks

It’s important to put the JupyterHub service (also the proxy) and the services that are running jupyter notebook inside the same network, otherwise they couldn’t reach each other. SwarmSpawner use the service’s name instead of the service’s ip, as a consequence JupyterHub and servers should share the same overlay network (network across nodes).

#list of networks
c.SwarmSpawner.networks = ["mynetwork"]

Define the services inside jupyterhub_config.py

You can define container_spec, resource_spec and networks inside jupyterhub_config.py.

Container_spec

The command and args definitions depends on the image that you are using. I.e the command must be possible to execute in the selected image The ‘/usr/local/bin/start-singleuser.sh’ is provided by the jupyter base-notebook The start-singleuser.sh args assumes that the launched image is extended from a version of this.

c.SwarmSpawner.container_spec = {
              # The command to run inside the service
              'args' : ['/usr/local/bin/start-singleuser.sh']
      }

Note: in a container spec, args sets the equivalent of CMD in the Dockerfile, command sets the equivalent of ENTRYPOINT. The notebook server command should not be the ENTRYPOINT, so generally use args, not command, to specify how to launch the notebook server.

See this issue for more info.

Dockerimages

To define which images are available to the users, a list of dockerimages must be declared The individual dictionaries also makes it possible to define whether the image should mount any volumes when it is spawned

# Available docker images the user can spawn
c.SwarmSpawner.dockerimages = [
    {'image': 'jupyter/base-notebook:30f16d52126f',
     'name': 'Minimal python notebook'},
    {'image': 'nielsbohr/base-notebook:latest',
     'name': 'Image with automatic {replace_me} mount, supports Py2/3 and R,',
     'mounts': mounts}
]

To make the user able to select between the available images, the following must be set. If this is not the case, the user will simply spawn an instance of the default image. i.e. dockerimages[0]

# Before the user can select which image to spawn,
# user_options has to be enabled
c.SwarmSpawner.use_user_options = True

This enables an image select form in the users /hub/home url path when a notebook hasen’t been spawned already.

Bind a Host dir

With 'type':'bind' you mount a local directory of the host inside the container.

Remember that source should exist in the node where you are creating the service.

notebook_dir = os.environ.get('NOTEBOOK_DIR') or '/home/jovyan/work'
c.SwarmSpawner.notebook_dir = notebook_dir
mounts = [{'type' : 'bind',
        'source' : 'MountPointOnTheHost',
        'target' : 'MountPointInsideTheContainer',}]

Volumes

With 'type':'volume' you mount a Docker Volume inside the container. If the volume doesn’t exist it will be created.

mounts = [{'type' : 'volume',
        'source' : 'NameOfTheVolume',
        'target' : 'MountPointInsideTheContainer',}]

Named path

For both types, volume and bind, you can specify a {username} inside the source:

mounts = [{'type' : 'volume',
        'source' : 'jupyterhub-user-{username}',
        'target' : 'MountPointInsideTheContainer',}]

username will be the hashed version of the username.

Mount an anonymous volume

This kind of volume will be removed with the service.

mounts = [{'type' : 'volume',
        'target' : 'MountPointInsideTheContainer',}]

SSHFS mount

It is also possible to mount a volume that is an sshfs mount to another host supports either passing {id_rsa} or {password} that should be used to authenticate, in addition the typical sshfs flags are supported, defaults to port 22

mounts = [{'type': 'volume',
           'driver_config': 'rasmunk/sshfs:latest',
           'driver_options': {'sshcmd': '{sshcmd}', 'id_rsa': '{id_rsa}',
                              'big_writes': '', 'allow_other': '',
                              'reconnect': '', 'port': '2222'},
           'source': 'sshvolume-user-{username}',
           'target': '/home/jovyan/work'
           }]

Resource_spec

You can also specify some resource for each service

c.SwarmSpawner.resource_spec = {
                'cpu_limit' : 1000, # (int) – CPU limit in units of 10^9 CPU shares.
                'mem_limit' : int(512 * 1e6), # (int) – Memory limit in Bytes.
                'cpu_reservation' : 1000, # (int) – CPU reservation in units of 10^9 CPU shares.
                'mem_reservation' : int(512 * 1e6), # (int) – Memory reservation in Bytes
                }

Using user_options

There is the possibility to set parameters using user_options

# To use user_options in service creation
c.SwarmSpawner.use_user_options = False

To control the creation of the services you have 2 ways, using jupyterhub_config.py or user_options.

Remember that at the end you are just using the Docker Engine API.

user_options, if used, will overwrite jupyter_config.py for services.

If you set c.SwarmSpawner.use_user_option = True the spawner will use the dict passed through the form or as json body when using the Hub Api.

The spawner expect a dict with these keys:

user_options = {
        'container_spec' : {
                # (string or list) command to run in the image.
                'args' : ['/usr/local/bin/start-singleuser.sh'],
                # name of the image
                'Image' : '',
                'mounts' : mounts,
                'resource_spec' : {
                        # (int) – CPU limit in units of 10^9 CPU shares.
                        'cpu_limit': int(1 * 1e9),
                        # (int) – Memory limit in Bytes.
                        'mem_limit': int(512 * 1e6),
                        # (int) – CPU reservation in units of 10^9 CPU shares.
                        'cpu_reservation': int(1 * 1e9),
                        # (int) – Memory reservation in bytes
                        'mem_reservation': int(512 * 1e6),
                        },
                # list of constrains
                'placement' : [],
                # list of networks
                'network' : [],
                # name of service
                'name' : ''
                }
        }

Names of the Jupyter notebook service inside Docker engine in Swarm mode

When JupyterHub spawns a new Jupyter notebook server the name of the service will be {service_prefix}-{service_owner}-{service_suffix}

You can change the service_prefix in this way:

Prefix of the service in Docker

c.SwarmSpawner.service_prefix = "jupyterhub"

service_owner is the hexdigest() of the hashed user.name.

In case of named servers (more than one server for user) service_suffix is the name of the server, otherwise is always 1.

Downloading images

Docker Engine in Swarm mode downloads images automatically from the repository. Either the image is available on the remote repository or locally, if not you will get an error.

Because before starting the service you have to complete the download of the image is better to have a longer timeout (default is 30 secs)

c.SwarmSpawner.start_timeout = 60 * 5

You can use all the docker images inside the Jupyter docker-stacks.

License

All code is licensed under the terms of the revised BSD license.

Project details


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