Jug: A Task-Based Parallelization Framework


If you use Jug to generate results for a scientific publication, please cite

Coelho, L.P., (2017). Jug: Software for Parallel Reproducible Computation in Python. Journal of Open Research Software. 5(1), p.30.


What is Jug?

Jug allows you to write code that is broken up into tasks and run different tasks on different processors.

It currently has two backends. The first uses the filesystem to communicate between processes and works correctly over NFS, so you can coordinate processes on different machines. The second is based on redis so the processes only need the capability to connect to a common redis server.

Jug also takes care of saving all the intermediate results to the backend in a way that allows them to be retrieved later.


Short Example

Here is a one minute example. Save the following to a file called primes.py:

from jug import TaskGenerator

def is_prime(n):
    from time import sleep

    # Sleep for 1 second, this runs too fast and is not a good demo
    for j in range(2, n - 1):
        if (n % j) == 0:
            return False
    return True

def count_primes(ps):
    return sum(ps)

def write_output(n):
    with open('output.txt', 'wt') as output:
        output.write("Found {0} primes <= 100.\n".format(n))

primes100 = []
for n in range(2, 101):

n_primes = count_primes(primes100)

Of course, this is only for didactical purposes, normally you would use a better method. Similarly, the sleep function is so that it does not run too fast.

Now type jug status primes.py to get:

 Waiting       Ready    Finished     Running  Task name
       1           0           0           0  primes.count_primes
       0          99           0           0  primes.is_prime
       1           0           0           0  primes.write_output
       2          99           0           0  Total

This tells you that you have 99 tasks called primes.is_prime ready to run, while both other tasks are _waiting_ (i.e., they need the primes.is_prime tasks to finish). So run jug execute primes.py &. You can even run multiple instances in the background (if you have multiple cores, for example). After starting 4 instances and waiting a few seconds, you can check the status again (with jug status primes.py):

 Waiting       Ready    Finished     Running  Task name
       1           0           0           0  primes.count_primes
       0          63          32           4  primes.is_prime
       1           0           0           0  primes.write_output
       2          99           0           0  Total

Now you have 32 tasks finished, 4 running, and 63 still ready. Eventually, they will all finish (including count_primes and write_output`) and you can inspect the results with ``jug shell primes.py. This will give you an ipython shell. The primes100 variable is available, but it is an ugly list of jug.Task objects. To get the actual value, you call the value function:

In [1]: primes100 = value(primes100)

In [2]: primes100[:10]
Out[2]: [True, True, False, True, False, True, False, False, False, True]

More Examples

There is a worked out example in the tutorial, and another, fully functioning in the examples/ directory.

How do I get Jug?

The simplest is using pip:

pip install jug

You can either get the git repository at


Or download the package from PyPI


“I’ve been using jug with great success to distribute the running of a reasonably large set of parameter combinations” - Andreas Longva

Documentation Contents

What do I need to run Jug?

It is a Python only package. Jug is continuously tested with Python 2.6 and up (including Python 3.3 and up).

How does it work?

Read the tutorial.

What’s the status of the project?

Since version 1.0, jug should be considered stable.

Indices and tables