| This project is deprecated, please check DockerSpawner. |
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| ============================== |
| SwarmSpawner |
SwarmSpawner enables JupyterHub to spawn single user notebook servers in Docker Services.
More info about Docker Services here.
Python version 3.3 and above is required.
You can find an example jupyter_config.py inside examples.
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`:
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.
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).
You can define container_spec, resource_spec and networks inside jupyterhub_config.py.
command and args depends on the image that you are using.
If you use one of the images from the Jupyter docker-stack you need to specify args as: /usr/local/bin/start-singleuser.sh
If you are using a specific image, well it's up to you to specify the right command.
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.
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.
With 'type':'volume' you mount a Docker Volume inside the container. If the volume doesn't exist it will be created.
For both types, volume and bind, you can specify a {username} inside the source:
username will be the hashed version of the username.
This kind of volume will be removed with the service.
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
}There is the possibility to set parameters using user_options
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' : ''
}
}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
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.
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)
You can use all the docker images inside the Jupyter docker-stacks.
If you would like to contribute to the project, please read contributor documentation.
For a development install, clone the repository and then install from source:
All code is licensed under the terms of the revised BSD license.
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