Package: batchspawner
batchspawner for JupyterhubThis is a custom spawner forJupyterhubthat is designed for installations on clusters using batch scheduling software.This began as a generalization ofmkgilbert's batchspawnerwhich in turn was inspired byAndrea Zonca's blog postwhere he explains his implementation for a spawner that uses SSH and Torque. His github repo is foundhere.This package formerly included WrapSpawner and ProfilesSpawner, which provide mechanisms for runtime configuration of spawners. These have been split out and moved to thewrapspawnerpackage.Installationfrom root directory of this repo (where setup.py is), runpip install -e .If you don't actually need an editable version, you can simply runpip install batchspawneradd lines in jupyterhub_config.py for the spawner you intend to use, e.g.c=get_config()c.JupyterHub.spawner_class='batchspawner.TorqueSpawner'importbatchspawner# Even though not used, needed to register batchspawner interfaceDepending on the spawner, additional configuration will likely be needed.Batch SpawnersFor information on the specific spawners, seeSPAWNERS.md.OverviewThis 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 addressesresource limits including runtime, number of processes, memorysingleuser child process running on (usually remote) host not known until runtimejob submission and monitoring via resource manager utilitiesremote execution via submission of templated scriptsjob names instead of PIDsBatchSpawnerBaseprovides several general mechanisms:configurable traitsreq_foothat are exposed as{foo}in job template scripts. Templates (submit scripts in particular) may also use the full power ofjinja2. Templates are automatically detected if a{{or{%is present, otherwise str.format() used.configurable command templates for submitting/querying/cancelling jobsa generic concept of job-ID and ID-based job state trackingoverrideable hooks for subclasses to plug in logic at numerous pointsExampleEvery 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 startimportbatchspawnerc.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='mesabi.xyz.edu'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{cmd}'''# For our site we need to munge the execution hostname returned by qstatc.TorqueSpawner.state_exechost_exp=r'int-\1.mesabi.xyz.edu'SecurityUnless otherwise stated for a specific spawner, assume that spawnersdoevaluate shell environment for users and thus thesecurity
requirements of JupyterHub security for untrusted
usersare not fulfilled because some (most?) spawnersdostart a user
shell which will execute arbitrary user environment configuration
(.profile,.bashrcand 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 BatchSpawnerOverviewProfilesSpawner, available as part of thewrapspawnerpackage, 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 ofBatchSpawnerto the
users, see an example below.ExampleThe 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 exampleimportbatchspawnerc.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='mesabi.xyz.edu'c.BatchSpawnerBase.req_runtime='12:00:00'c.TorqueSpawner.state_exechost_exp=r'in-\1.mesabi.xyz.edu'#------------------------------------------------------------------------------# 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':'0.0.0.0'}),('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='labhost.xyz.edu',req_queue='lab',req_runtime='8:00:00',req_memory='4gb',state_exechost_exp='')),]c.ProfilesSpawner.ip='0.0.0.0'Debugging batchspawnerSometimes 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 thestart_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 anenvorset -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.Are you running on an NFS filesystem? It's possible for Jupyter to
experience issues due to varying implementations of the fcntl() system
call. (See alsoJupyterhub-Notes and Tips: SQLite)Common problems:Did youimport batchspawnerin thejupyterhub_config.pyfile?
This is needed in order to activate the batchspawer API in
JupyterHub.ChangelogSeeCHANGELOG.md.