This module will work with Python 2.7+ on Linux systems.

To install, use the standard python method:

git clone
cd python-cluster
python ./ install --user

In general you want user level install even if you have sudo access, as most cluster environments share /home/<user> across the cluster, making this module available everywhere.

Note: While the name is python-cluster you will import it just as cluster:

import cluster


The only external module that I use in this software is dill. It isn’t 100% required but it makes function submission much more stable.

If you choose to use dill, it must be installed cluster wide.

Function Submission

In order to submit functions to the cluster, this module must import them on the compute node. This means that all of your python modules must be available on every compute node. To avoid pain and debugging, you can do this manually by running this on your loggin node:

freeze --local | grep -v '^\-e' | cut -d = -f 1 > module_list.txt

And then on the compute nodes:

cat module_list.txt | xargs pip install --user

This will ensure that all of your modules are installed globally.

In general it is a good idea to install modules as –user with pip to avoid this issue.

Simple Usage

Setting Environment

To set the environement, set queue.MODE to one of [‘torque’, ‘slurm’, ‘local’], or run get_cluster_environment().

Simple Job Submission

At its simplest, this module can be used by just executing submit(<command>), where command is a function or system command/shell script. The module will autodetect the cluster, generate an intuitive name, run the job, and write all outputs to files in the current directory. These can be cleaned with clean_dir().

To run with dependency tracking, run:

import cluster
job  = cluster.submit(<command1>)
job2 = cluster.submit(<command2>, dependencies=job1)
exitcode, stdout, stderr = job2.get()  # Will block until job completes


The submit function works well with python functions as well as with shell scripts and shell commands.

However, in order for this to work, cluster ends up importing your original script file on the nodes. This means that all code in your file will be executed, so anything that isn’t a function or class must be protected with an:

if __name__ == '__main__':

protecting statment.

If you do not do this you can end up with multi-submission and infinate recursion, which could mess up your jobs or just crash the job, but either way, it won’t be good.

File Submission

If you want to just submit a file, that can be done like this:

from cluster import submit_file
submit_file('/path/to/script', dependencies=[7, 9])

This will return the job number and will enter the job into the queue as dependant on jobs 007 and 009. The dependencies can be omitted.

The Job Class

The core of this submission system is a Job class, this class allows easy job handling and debugging. All of the above commands work well with the Job class also, but more fine grained control is possible. For example:

my_job = """#!/bin/bash
parallel /usr/bin/parser {} ::: folder/*.txt
for i in folder/*.txt; do
    echo $i >> my_output.txt
    echo job_$i done!
job = cluster.Job(my_job, cores=16)
if job.exitcode != 0:

More is also possible, for a full description, see the API documentation here: Job Documentation


While this software is designed to be used as a python library, several scripts are provided to make life easier.


Uses python-cluster to check the job queue for only one user’s jobs. Produces a very simple display, for full job information, the regular tools can be used (e.g squeue):

Choose jobs to show, default is all:
  -r, --running  Show running jobs only
  -q, --queued   Show queued jobs only

Choose alternate output style:
  -c, --count    Display count only
  -l, --list     Print space separated list of job numbers


This script allows the user to save cluster keyword arguments in a config file located at ~/.python-cluster.

Rather than edit that file directly, use this script to add profiles and options.

There are two classes of options: global options, and profiles.

Global options will be used in all profiles, but only if the option is not already present in the profile definition. Profiles must be called every time and allow bundled keyword arguments, they can also be overridden by providing keyword arguments at runtime.

Global options are great for saving a default queue.


  :list:   Display all global options and profiles.

Profile Management:
  :add:    Add a profile
           Usage: add profile_name keyword:arg [keyword:arg ...]
  :edit:   Edit an existing profile
           Usage: edit profile_name keyword:arg [keyword:arg ...]
  :remove: Delete an existing profile (The default profile will be recreated
           if it does not exist when a job is submitted.
           Usage: remove|del profile_name

Global Option Management:
  :add-global:    Add a global keyword
                  Usage: add-global keyword:arg [keyword:arg ...]
  :remove-global: Remove a global keyword
                  Usage: remove-global|del-global keyword [keyword ...]

  :reset: Completely reset your entire profile to the defaults.


Blocks until provided jobs complete. Allows to monitor by user, partition, or simple job list:

Arguments are cumulative except user. For example::
  auto_resubmit -p bob -j 172436 172437
user can be 'self'
This command will monitor all jobs in the bob partition as
well as the two jobs specified directly.
  monitor_jobs -p bob -u fred
This command will only monitor fred's jobs in bob (the union).


Uses the cluster.job.clean_dir() function to clean all job files in the current directory.

Caution: The clean() function will delete EVERY file with extensions matching those these:

.<suffix>.sbatch & .cluster.script for slurm mode
.<suffix>.qsub for torque mode
.<suffix> for local mode


Will work with no commands.

optional arguments:
  -h, --help                        show this help message and exit
  -d DIR, --dir DIR                 Directory to clean
  -s, --suffix SUFFIX               Directory to clean
  -q, --qtype {torque,slurm,local}  Limit deletions to this qtype
  -n, --no-confirm                  Do not confirm before deleting (for scripts)
  -v, --verbose                     Show debug information


Prints simple help information on the available keyword arguments. It calls the cluster_help() function, which means that keyword information is always up to date.

Queue Management

This module provides simple queue management functions

To generate a queue object, do the following:

import cluster
q = cluster.Queue(user='self')

This will give you a simple queue object containg a list of jobs that belong to you. If you do not provide user, all jobs are included for all users. You can provide qtype to explicitly force the queue object to contain jobs from one queing system (e.g. local or torque).

To get a dictionary of all jobs, running jobs, queued jobs, and complete jobs, use:

Every job has a number of attributes, including owner, nodes, cores, memory.

Advanced Usage

Keyword Arguments

To make submission easier, this module defines a number of keyword arguments in the file that can be used for all submission and Job() functions. These include things like ‘cores’ and ‘nodes’ and ‘mem’.

The following is a complete list of arguments that can be used in this version:

Used in every mode::
cores:      Number of cores to use for the job
            Type: int; Default: 1
modules:    Modules to load with the `module load` command
            Type: list; Default: None
filedir:    Folder to write cluster files to, must be accessible to the compute
            Type: str; Default: .
dir:        The working directory for the job
            Type: str; Default: path argument
suffix:     A suffix to append to job files (e.g. job.suffix.qsub)
            Type: str; Default: cluster
outfile:    File to write STDOUT to
            Type: str; Default: None
errfile:    File to write STDERR to
            Type: str; Default: None

Used for function calls::
imports:    Imports to be used in function calls (e.g. sys, os) if not provided,
            defaults to all current imports, which may not work if you use complex
            imports. The list can include the import call, or just be a name, e.g.
            ['from os import path', 'sys']
            Type: list; Default: None

Used only in local mode::
threads:    Number of threads to use on the local machine
            Type: int; Default: 8

Options that work in both slurm and torque::
nodes:      Number of nodes to request
            Type: int; Default: 1
features:   A comma-separated list of node features to require
            Type: list; Default: None
time:       Walltime in HH:MM:SS
            Type: str; Default: 12:00:00
mem:        Memory to use in MB (e.g. 4000)
            Type: ['int', 'str']; Default: 4000
partition:  The partition/queue to run in (e.g. local/batch)
            Type: str; Default: None
account:    Account to be charged
            Type: str; Default: None
export:     Comma separated list of environmental variables to export
            Type: str; Default: None

Used for slurm only::
begin:      Start after this much time
            Type: str; Default: None

In addition some synonyms are allowed:

cpus:                             cores
memory:                           mem
queue:                            partition
depend, dependencies, dependency: depends

Note: Type is enforced, any provided argument must match that python type (automatic conversion is attempted), the default is just a recommendation and is not currently used. These arguments are passed like regular arguments to the submission and Job() functions, eg:

Job(nodes=1, cores=4, mem='20MB')

This will be interpretted correctly on any system. If torque or slurm are not available, any cluster arguments will be ignored. The module will attempt to honor the cores request, but if it exceeds the maximum number of cores on the local machine, then the request will be trimmed accordingly (i.e. a 50 core request will become 8 cores on an 8 core machine).

### Adding your own keywords

There are many more options available for torque and slurm, to add your own, edit the file, and look for CLUSTER_OPTS (or TORQUE/SLURM if your keyword option is only availble on one system). Add your option using the same format as is present in that file. The format is:

('name', {'slurm': '--option-str={}', 'torque': '--torque-option={}',
          'help': 'This is an option!', 'type': str, 'default': None})

You can also add list options, but they must include ‘sjoin’ and ‘tjoin’ keys to define how to merge the list for slurm and torque, or you must write custom option handling code in cluster.options.options_to_string(). For an excellent example of both approaches included in a single option, see the ‘features’ keyword above.

I happily accept pull requests for new option additions (any any other improvements for that matter).

Profiles and the Config File

To avoid having to enter all keyword arguments every time, profiles can be used. These profiles can store any of the above keywords and drastically simplify submission. For example:

job = submit(my_function, profile='large')

Instead of:

job = submit(my_funtion, nodes=2, cores=16, mem='64GB', partition='bigjobs',
             features=['highmem'], export='PYTHONPATH')

These profiles are saved in a config file at ~/.python-cluster and can be editted in that file directly, or using the below functions. To edit them in the file directly, you must make sure that the section is labelled ‘prof_<name>’ where <name> is whatever you want it to be called. e.g.:

nodes = 1
cores = 16
time = 24:00:00
mem = 32000

Note: a default profile must always exist, it will be added back if it does not exist.

The easiest way to manage profiles is with the cluster_profile script in bin. It defines several easy methods to manage both profiles and global options, see the scripts section above for information.

Alternatively, the functions cluster.config_file.set_profile() and cluster.config_file.get_profile() can be used:

cluster.config_file.set_profile('small', {'nodes': 1, 'cores': 1,
                                          'mem': '2GB'})

To see all profiles run:


Other options are defined in the config file, including the maximum number of jobs in the queue, the time to sleep between submissions, and other options. To see these run:


You can set options with:


The defaults can be directly edited in, they are clearly documented.

Job Files

All jobs write out a job file before submission, even though this is not necessary (or useful) with multiprocessing. In local mode, this is a .cluster file, in slurm is is a .cluster.sbatch and a .cluster.script file, in torque it is a .cluster.qsub file. ‘cluster’ is set by the suffix keyword, and can be overridden.

To change the directory these files are written to, use the ‘filedir’ keyword argument to Job or submit.

NOTE: This directory must be accessible to the compute nodes!!!

All jobs are assigned a name that is used to generate the output files, including STDOUT and STDERR files. The default name for the out files is STDOUT: name.cluster.out and STDERR: name.cluster.err. These can be overwridden with keyword arguments.

All Job objects have a clean() method that will delete any left over files. In addition there is a clean_job_files script that will delete all files made by this package in any given directory. Be very careful with the script though, it can clobber a lot of work all at once if it is used wrong.

Dependecy Tracking

Dependency tracking is supported in all modes. Local mode uses a unique queueing system that works similarly to torque and slurm and which is defined in

To use dependency tracking in any mode pass a list of job ids to submit or submit_file with the dependencies keyword argument.


I use a custion logging script called logme to log errors. To get verbose output, set logme.MIN_LEVEL to ‘debug’. To reduce output, set logme.MIN_LEVEL to ‘warn’.

Code Overview

There are two important classes for interaction with the batch system: Job and Queue. The essential flow of a job submission is:

job = Job(command/function, arguments, name)
job.write()  # Writes the job submission files
job.submit() # Submits the job
job.wait()   # Waits for the job to complete
job.stdout   # Prints the output from the job
job.clean()  # Delete all of the files written

You can also wait for many jobs with the Queue class:

q = Queue(user='self')
q.wait([job1, job2])

The jobs in this case can be either a Job class or a job number.

Why the Name?

I gave this project the name ‘Fyrd’ in honour of my grandmother, Hélène Sandolphen, who was a scholar of old English. It is the old Anglo-Saxon word for ‘army’, and this code gives you an army of workers on any machine so it seemed appropriate.

The project used to be called “Python Cluster”, which is more descriptive but frankly boring. Also, about half a dozen other projects have almost the same name, so it made no sense to keep that name and put the project onto PyPI.

Issues and Contributing

If you have any trouble with this software add an issue in

If you want to help improve it, please fork the repo and send me pull requests when you are done.


Right now this software is in _beta_, to get to version 1.0 it needs to be tested by users and demonstrated to be stable. In addition, I would like to implement the following features prior to the release of v1.0:

  • Auto update Job scripts when attributes are changed until files are already written.
  • DONE: Profile managing script in bin
  • Update of all bin scripts to work with new options
  • Persistent job tracking in an sqlite database stored in $HOME
  • Mac OS X functionality
  • Autoadjusting of job options based on queue features (i.e. implement a ‘max’ option and try to guess the max cores available for a request on any machine)
  • Allow users to define their own keyword arguments in their configuration

If you have any other feature suggestions please email them to me at or open an issue.