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Batchspawner: A spawner for Jupyterhub to spawn notebooks using batch resource managers.

Project description

batchspawner for Jupyterhub

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This is a custom spawner for Jupyterhub that is designed for installations on clusters using batch scheduling software.

This began as a generalization of mkgilbert's batchspawner which in turn was inspired by Andrea Zonca's blog post where he explains his implementation for a spawner that uses SSH and Torque. His github repo is found here.

This package formerly included WrapSpawner and ProfilesSpawner, which provide mechanisms for runtime configuration of spawners. These have been split out and moved to the wrapspawner package.


  1. from root directory of this repo (where is), run pip install -e .

    If you don't actually need an editable version, you can simply run pip install batchspawner

  2. add lines in for the spawner you intend to use, e.g.

       c = get_config()
       c.JupyterHub.spawner_class = 'batchspawner.TorqueSpawner'
       import batchspawner    # Even though not used, needed to register batchspawner interface
  3. Depending on the spawner, additional configuration will likely be needed.

Batch Spawners

For information on the specific spawners, see


This file contains an abstraction layer for batch job queueing systems (BatchSpawnerBase), and implements Jupyterhub spawners for Torque, Moab, SLURM, SGE, HTCondor, LSF, and eventually others. Common attributes of batch submission / resource manager environments will include notions of:

  • queue names, resource manager addresses
  • resource limits including runtime, number of processes, memory
  • singleuser child process running on (usually remote) host not known until runtime
  • job submission and monitoring via resource manager utilities
  • remote execution via submission of templated scripts
  • job names instead of PIDs

BatchSpawnerBase provides several general mechanisms:

  • configurable traits req_foo that are exposed as {foo} in job template scripts. Templates (submit scripts in particular) may also use the full power of jinja2. Templates are automatically detected if a {{ or {% is present, otherwise str.format() used.
  • configurable command templates for submitting/querying/cancelling jobs
  • a generic concept of job-ID and ID-based job state tracking
  • overrideable hooks for subclasses to plug in logic at numerous points


Every effort has been made to accommodate highly diverse systems through configuration only. This example consists of the (lightly edited) configuration used by the author to run Jupyter notebooks on an academic supercomputer cluster.

# Select the Torque backend and increase the timeout since batch jobs may take time to start
import batchspawner
c.JupyterHub.spawner_class = 'batchspawner.TorqueSpawner'
c.Spawner.http_timeout = 120

# BatchSpawnerBase configuration
#    These are simply setting parameters used in the job script template below
c.BatchSpawnerBase.req_nprocs = '2'
c.BatchSpawnerBase.req_queue = 'mesabi'
c.BatchSpawnerBase.req_host = ''
c.BatchSpawnerBase.req_runtime = '12:00:00'
c.BatchSpawnerBase.req_memory = '4gb'
# TorqueSpawner configuration
#    The script below is nearly identical to the default template, but we needed
#    to add a line for our local environment. For most sites the default templates
#    should be a good starting point.
c.TorqueSpawner.batch_script = '''#!/bin/sh
#PBS -q {queue}@{host}
#PBS -l walltime={runtime}
#PBS -l nodes=1:ppn={nprocs}
#PBS -l mem={memory}
#PBS -N jupyterhub-singleuser
#PBS -v {keepvars}
module load python3
# For our site we need to munge the execution hostname returned by qstat
c.TorqueSpawner.state_exechost_exp = r'int-\'


Unless otherwise stated for a specific spawner, assume that spawners do evaluate shell environment for users and thus the security requirements of JupyterHub security for untrusted users are not fulfilled because some (most?) spawners do start a user shell which will execute arbitrary user environment configuration (.profile, .bashrc and the like) unless users do not have access to their own cluster user account. This is something which we are working on.

Provide different configurations of BatchSpawner


ProfilesSpawner, available as part of the wrapspawner package, allows the Jupyterhub administrator to define a set of different spawning configurations, both different spawners and different configurations of the same spawner. The user is then presented a dropdown menu for choosing the most suitable configuration for their needs.

This method provides an easy and safe way to provide different configurations of BatchSpawner to the users, see an example below.


The following is based on the author's configuration (at the same site as the example above) showing how to give users access to multiple job configurations on the batch scheduled clusters, as well as an option to run a local notebook directly on the jupyterhub server.

# Same initial setup as the previous example
import batchspawner
c.JupyterHub.spawner_class = 'wrapspawner.ProfilesSpawner'
c.Spawner.http_timeout = 120
# BatchSpawnerBase configuration
#   Providing default values that we may omit in the profiles
c.BatchSpawnerBase.req_host = ''
c.BatchSpawnerBase.req_runtime = '12:00:00'
c.TorqueSpawner.state_exechost_exp = r'in-\'
# ProfilesSpawner configuration
# List of profiles to offer for selection. Signature is:
#   List(Tuple( Unicode, Unicode, Type(Spawner), Dict ))
# corresponding to profile display name, unique key, Spawner class,
# dictionary of spawner config options.
# The first three values will be exposed in the input_template as {display},
# {key}, and {type}
c.ProfilesSpawner.profiles = [
   ( "Local server", 'local', 'jupyterhub.spawner.LocalProcessSpawner', {'ip':''} ),
   ('Mesabi - 2 cores, 4 GB, 8 hours', 'mesabi2c4g12h', 'batchspawner.TorqueSpawner',
      dict(req_nprocs='2', req_queue='mesabi', req_runtime='8:00:00', req_memory='4gb')),
   ('Mesabi - 12 cores, 128 GB, 4 hours', 'mesabi128gb', 'batchspawner.TorqueSpawner',
      dict(req_nprocs='12', req_queue='ram256g', req_runtime='4:00:00', req_memory='125gb')),
   ('Mesabi - 2 cores, 4 GB, 24 hours', 'mesabi2c4gb24h', 'batchspawner.TorqueSpawner',
      dict(req_nprocs='2', req_queue='mesabi', req_runtime='24:00:00', req_memory='4gb')),
   ('Interactive Cluster - 2 cores, 4 GB, 8 hours', 'lab', 'batchspawner.TorqueSpawner',
      dict(req_nprocs='2', req_host='', req_queue='lab',
          req_runtime='8:00:00', req_memory='4gb', state_exechost_exp='')),
c.ProfilesSpawner.ip = ''

Debugging batchspawner

Sometimes it can be hard to debug batchspawner, but it's not really once you know how the pieces interact. Check the following places for error messages:

  • Check the JupyterHub logs for errors.

  • Check the JupyterHub logs for the batch script that got submitted and the command used to submit it. Are these correct? (Note that there are submission environment variables too, which aren't displayed.)

  • At this point, it's a matter of checking the batch system. Is the job ever scheduled? Does it run? Does it succeed? Check the batch system status and output of the job. The most comon failure patterns are a) job never starting due to bad scheduler options, b) job waiting in the queue beyond the start_timeout, causing JupyterHub to kill the job.

  • At this point the job starts. Does it fail immediately, or before Jupyter starts? Check the scheduler output files (stdout/stderr of the job), wherever it is stored. To debug the job script, you can add debugging into the batch script, such as an env or set -x.

  • At this point Jupyter itself starts - check its error messages. Is it starting with the right options? Can it communicate with the hub? At this point there usually isn't anything batchspawner-specific, with the one exception below. The error log would be in the batch script output (same file as above). There may also be clues in the JupyterHub logfile.

Common problems:

  • Did you import batchspawner in the file? This is needed in order to activate the batchspawer API in JupyterHub.



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