Custodian is a simple, robust and flexible just-in-time (JIT) job management framework written in Python. Using custodian, you can create wrappers that perform error checking, job management and error recovery. It has a simple plugin framework that allows you to develop specific job management workflows for different applications.

Error recovery is an important aspect of many high-throughput projects that generate data on a large scale. When you are running on the order of hundreds of thousands of jobs, even an error rate of 1% would mean thousands of errored jobs that would be impossible to deal with on a case-by-case basis.

The specific use case for custodian is for long running jobs, with potentially random errors. For example, there may be a script that takes several days to run on a server, with a 1% chance of some IO error causing the job to fail. Using custodian, one can develop a mechanism to gracefully recover from the error, and restart the job with modified parameters if necessary.

The current version of Custodian also comes with two sub-packages for error handling for Vienna Ab Initio Simulation Package (VASP), NwChem and QChem calculations.

Change log


  1. Pymatgen 4.0.0 compatible release.


  1. Custodian now comes with a “cstdn” script that enables the arbitrary creation of simple job sequences using a yaml file, and the running of calculations based on these yaml specifications.

Older versions

Getting custodian

Stable version

The version at the Python Package Index (PyPI) is always the latest stable release that will be hopefully, be relatively bug-free. The easiest way to install custodian on any system is to use easy_install or pip, as follows:

easy_install custodian


pip install custodian

Some plugins (e.g., vasp management) require additional setup (please see pymatgen’s documentation).

Developmental version

The bleeding edge developmental version is at the custodian’s Github repo. The developmental version is likely to be more buggy, but may contain new features. The Github version include test files as well for complete unit testing. After cloning the source, you can type:

python install

or to install the package in developmental mode:

python develop


Custodian requires Python 2.7+. There are no other required dependencies.

Optional dependencies

Optional libraries that are required if you need certain features:

  1. Python Materials Genomics (pymatgen) 2.8.10+: To use the plugin for VASP, NwChem and Qchem. Please install using:

    pip install pymatgen

    For more information, please consult pymatgen’s documentation.

  2. nose - For complete unittesting.


The main class in the workflow is known as Custodian, which manages a series of jobs with a list of error handlers. The general workflow for Custodian is presented in the figure below.

Custodian workflow

Overview of the Custodian workflow.

The Custodian class takes in two general inputs - a list of Jobs and a list of ErrorHandlers. Jobs should be subclasses of the custodian.custodian.Job abstract base class and ErrorHandlers should be subclasses of the custodian.custodian.ErrorHandler abstract base class. To use custodian, you need to implement concrete implementations of these abstract base classes.

Simple example

An very simple example implementation is given in the script in the scripts directory. We will now go through the example in detail here.

The ExampleJob has the following code.

class ExampleJob(Job):
    This example job simply sums a random sequence of 100 numbers between 0
    and 1, adds it to an initial value and puts the value in 'total'
    key in params. Note that it subclasses the Job abstract base class.

    def __init__(self, jobid, params={"initial": 0, "total": 0}):
        The initialization of the ExampleJob requires a jobid,
        something to simply identify a job, and a params argument,
        which is a mutable dict that enables storage of the results and can
        be transferred from Job to Handler.
        self.jobid = jobid
        self.params = params

    def setup(self):
        The setup sets the initial and total values to zero at the start of
        a Job.
        self.params["initial"] = 0
        self.params["total"] = 0

    def run(self):
        Doing the actual run, i.e., generating a random sequence of 100
        numbers between 0 and 1, summing it and adding it to the inital
        value to get the total value.
        print "Running job {}".format(self.jobid)
        sequence = [random.uniform(0, 1) for i in range(100)]
        self.params["total"] = self.params["initial"] + sum(sequence)
        print "Current total = {}".format(self.params["total"])

    def postprocess(self):
        # Simply just print a success message.
        print "Success for job {}".format(self.jobid)

    def name(self):
        A name for the job.
        return "ExampleJob{}".format(self.jobid)

    def to_dict(self):
        All Jobs must implement a to_dict property that returns a JSON
        serializable dict to enable Custodian to log the job information in
        a json file.
        return {"jobid": self.jobid}

    def from_dict(d):
        Similarly, all Jobs must implement a from_dict static method
        that takes in a dict of the form returned by to_dict and returns a
        actual Job.
        return ExampleJob(d["jobid"])

The ExampleJob simply sums a random sequence of 100 numbers between 0 and 1, adds it to an initial value and puts the value in ‘total’ variable. The ExampleJob subclasses the Job abstract base class, and implements the necessary API comprising of just three key methods: setup(), run(), and postprocess().

Let us now define an ErrorHandler that will check if the total value is >= 50, and if it is not, it will increment the initial value by 1 and rerun the ExampleJob again.

class ExampleHandler(ErrorHandler):
    This example error handler checks if the value of total is >= 50. If it
    is not, the handler increments the initial value and rerun the
    ExampleJob until a total >= 50 is obtained.

    def __init__(self, params):
        The initialization of the ExampleHandler takes in the same params
        argument, which should contain the results from the ExampleJob.
        self.params = params

    def check(self):
        The check() step should return a boolean indicating if there are
        errors. In this case, we define an error to be a situation where the
        total is less than 50.
        return self.params["total"] < 50

    def correct(self):
        The correct() step should fix any errors and return a dict
        summarizing the actions taken. In this case, we increment the initial
        value by 1 in an attempt to increase the total.
        self.params["initial"] += 1
        print "Total < 50. Incrementing initial to {}".format(
        return {"errors": "total < 50", "actions": "increment by 1"}

    def is_monitor(self):
        This property indicates whether this handler is a monitor, i.e.,
        whether it turns in the background as the run is taking place and
        correcting errors.
        return False

    def to_dict(self):
        Similar to Jobs, ErrorHandlers should have a to_dict property that
        returns a JSON-serializable dict.
        return {}

    def from_dict(d):
        Similar to Jobs, ErrorHandlers should have a from_dict static property
        that returns the Example Handler from a JSON-serializable dict.
        return ExampleHandler()

As you can see above, the ExampleHandler subclasses the ErrorHandler abstract base class, and implements the necessary API comprising of just two key methods: check() and correct().

The transfer of information between the Job and ErrorHandler is done using the params argument in this example, which is not ideal but is sufficiently for demonstrating the Custodian API. In real world usage, a more common transfer of information may involve the Job writing the output to a file, and the ErrorHandler checking the contents of those files to detect error situations.

To run the job, one simply needs to supply a list of ExampleJobs and ErrorHandlers to a Custodian.

njobs = 100
params = {"initial": 0, "total": 0}
c = Custodian([ExampleHandler(params)],
              [ExampleJob(i, params) for i in xrange(njobs)],

If you run in the scripts directory, you will noticed that a custodian.json file was generated, which summarizes the jobs that have been run and any corrections performed.

Practical example: Electronic structure calculations

A practical example where the Custodian framework is particularly useful is in the area of electronic structure calculations. Electronic structure calculations tend to be long running and often terminates due to errors, random or otherwise. Such errors become a major issue in projects that performs such calculations in high throughput, such as the Materials Project.

The Custodian package comes with a fairly comprehensive plugin to deal with jobs ( and errors (custodian.vasp.handlers) in electronic structure calculations based on the Vienna Ab Initio Simulation Package (VASP). To do this, Custodian uses the Python Materials Genomics (pymatgen) package to perform analysis and io from VASP input and output files.

A simple example of a script using Custodian to run a two-relaxation VASP job is as follows:

from custodian.custodian import Custodian
from custodian.vasp.handlers import VaspErrorHandler, \
    UnconvergedErrorHandler, PoscarErrorHandler, DentetErrorHandler
from import VaspJob

handlers = [VaspErrorHandler(), UnconvergedErrorHandler(),
            PoscarErrorHandler(), DentetErrorHandler()]
jobs = VaspJob.double_relaxation_run(args.command.split())
c = Custodian(handlers, jobs, max_errors=10)

The above will gracefully deal with many VASP errors encountered during relaxation. For example, it will correct ISMEAR to 0 if there are insufficient KPOINTS to use ISMEAR = -5.

Using custodian, you can even setup potentially indefinite jobs, e.g. kpoints convergence jobs with a target energy convergence. Please see the converge_kpoints script in the scripts for an example.

New in version 0.4.3: A new package for dealing with NwChem calculations has been added. NwChem is an open-source code for performing computational chemistry calculations.

API/Reference Docs

The API docs are generated using Sphinx auto-doc and outlines the purpose of all modules and classes, and the expected argument and returned objects for most methods. They are available at the link below.

custodian API docs

How to cite custodian

If you use custodian in your research, especially the VASP component, please consider citing the following work:

Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent Chevrier, Kristin A. Persson, Gerbrand Ceder. Python Materials Genomics (pymatgen) : A Robust, Open-Source Python Library for Materials Analysis. Computational Materials Science, 2013, 68, 314–319. doi:10.1016/j.commatsci.2012.10.028


Custodian is released under the MIT License. The terms of the license are as follows:

The MIT License (MIT)
Copyright (c) 2011-2012 MIT & LBNL

Permission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the "Software")
, to deal in the Software without restriction, including without limitation
the rights to use, copy, modify, merge, publish, distribute, sublicense,
and/or sell copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.