Metadata-Version: 2.4
Name: Jug
Version: 2.6.0
Summary: A Task Based Parallelization Framework
Author-email: Luis Pedro Coelho <luis@luispedro.org>
License: MIT
Project-URL: Homepage, https://jug.readthedocs.io
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Operating System :: POSIX
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Classifier: Topic :: System :: Distributed Computing
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: COPYING.MIT
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: numpy; extra == "dev"
Requires-Dist: polars; extra == "dev"
Requires-Dist: pyyaml; extra == "dev"
Requires-Dist: redis; extra == "dev"
Dynamic: license-file

# Jug: A Task-Based Parallelization Framework

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

[![image](https://github.com/luispedro/jug/actions/workflows/python-package.yml/badge.svg)](https://github.com/luispedro/jug/actions/workflows/python-package.yml)
[![image](https://zenodo.org/badge/205237.svg)](https://zenodo.org/badge/latestdoi/205237)
[![image](https://img.shields.io/badge/install%20with-conda-brightgreen.svg?style=flat)](https://anaconda.org/conda-forge/jug)
[![image](https://static.pepy.tech/personalized-badge/jug?period=total&units=international_system&left_color=black&right_color=blue&left_text=Downloads)](https://pepy.tech/project/jug)
[![Downloads](https://static.pepy.tech/badge/jug/month)](https://pepy.tech/project/jug)
[![image](https://img.shields.io/badge/CITATION-doi.org%2F10.5334%2Fjors.161-green.svg)](https://doi.org/10.5334/jors.161)

It uses the filesystem to communicate between processes and works
correctly over NFS, so you can coordinate processes on different
machines.

Jug is a pure Python implementation and should work on any platform.

Python versions 3.8 and above are supported (will almost certainly work
on earlier versions as well, but they are not part of the [CI
suite](<https://github.com/luispedro/jug/actions>)).

*Documentation*: <https://jug.readthedocs.io/>

*Video*: On [vimeo](http://vimeo.com/8972696) or
[showmedo](http://showmedo.com/videotutorials/video?name=9750000;fromSeriesID=975)

*Mailing List*: <https://groups.google.com/group/jug-users>

## Testimonials

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

## Install

You can install Jug with pip:

    pip install Jug

If you want to use `jug shell`, install IPython as well:

    pip install Jug ipython

Or, if you use [conda](https://anaconda.org/), you can install
jug from [conda-forge](https://conda-forge.github.io/) using the
following commands:

    conda config --add channels conda-forge
    conda install jug

## Citation

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.
>
> <https://doi.org/10.5334/jors.161>

## Short Example

Here is a one minute example. Save the following to a file called
`primes.py` (if you have installed jug, you can obtain a slightly longer
version of this example by running `jug demo` on the command line):

    from jug import TaskGenerator
    from time import sleep

    @TaskGenerator
    def is_prime(n):
        sleep(1.)
        for j in range(2,n-1):
            if (n % j) == 0:
                return False
        return True

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

This is a brute-force way to find all the prime numbers up to 100. Of
course, this is only for didactic purposes, normally you would use a
better method. Similarly, the `sleep` function is so that it does not
run too fast. Still, it illustrates the basic functionality of Jug for
embarrassingly parallel problems.

Type `jug status primes.py` to get:

    Task name                  Waiting       Ready    Finished     Running
    ----------------------------------------------------------------------
    primes.is_prime                  0          99           0           0
    ......................................................................
    Total:                           0          99           0           0

This tells you that you have 99 tasks called `primes.is_prime` ready to
run. 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`):

    Task name                  Waiting       Ready    Finished     Running
    ----------------------------------------------------------------------
    primes.is_prime                  0          63          32           4
    ......................................................................
    Total:                           0          63          32           4

Now you have 32 tasks finished, 4 running, and 63 still ready.
Eventually, they will all finish and you can inspect the results with
`jug shell primes.py`. This requires `ipython` to be installed and 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]

## What's New

## Version 2.6.0

*Released 20 September 2026*

### Compatibility notes

Two changes alter task hashes, so cached results for the affected tasks will
be recomputed the first time you run them with this version:

- The pickle protocol used for hashing is now pinned to protocol 4. This only
  affects users of Python 3.14 and 3.15 (where pickle's default is protocol 5);
  Python 3.13 and earlier already used protocol 4. From now on, task hashes are
  stable across Python versions.
- Tasklets built from `lambda` functions are now hashed using the lambda's
  constants, referenced names, closure and default arguments (previously only
  its bytecode was used, so lambdas that differed only in those hashed
  identically).

### User-visible improvements

- Better error message when loading results fails (patch by Justin R.
  Porter, [GH #92](https://github.com/luispedro/jug/pull/92))
- Configuration files are now read as UTF-8 regardless of locale
- `jug cleanup` now reports the number of removed objects and of removed
  locks separately

### Internal improvements

- Use `@property` and `@abstractmethod` instead of the deprecated
  `abstractproperty`
- Modernize Python idioms and update documentation

### Bugfixes

- Fix `write_task_out` for numpy arrays, which are now written in `.npy`
  format (they were silently pickled instead)
- Fall back to pickle for numpy arrays of object dtype, which cannot be
  saved in numpy's native format
- `file_store.cleanup()` no longer deletes the temporary files of workers
  that are still running
- Fix the `tput` fallback in `get_terminal_size`
- Fix wrong exception type in `dict_store.cleanup()`
- Fix saving to/loading from a file backend in `jug.backend.dict_store`
  on Python 3 (pickle files must be opened in binary mode)
- Fix help text of `jug status --cache-file`

## Version 2.5.0

*Released 12 March 2026*

### User-visible improvements

- Special case saving `polars` DataFrames in `file_store` for speed.
- More flexible parsing of booleans in `jug.options`.
- Support project-local configuration files (`.jugrc` or `jugrc`). Jug
  now walks up the directory tree from the current working directory (up
  to the git project root) looking for local configuration files. See
  `configuration` for details.
- Ship the Jug assistant skill in the Python package and add
  `jug install-skills --output DIR` to install it into Codex or Claude
  Code skills directories. See `ai-assistants` for usage
  details.

### Bugfixes

- Fix `_get_terminal_size_linux` for Python 3.14, which changed how
  `fcntl.ioctl` handles string arguments. Use `os.get_terminal_size()`
  instead (patch by justinrporter, [GH
  #90](https://github.com/luispedro/jug/issues/90)).
- Fix `jug.backend.dict_store` for Python 3.
- Fix `describe` in `jug.task` for Python 3.

## Version 2.4.0

*Released 8 May 2025*

### User-visible improvements

- Adds support for lambda functions in `Tasklets`
- Adds `NoHash` class to disable hashing for some arguments. This is in
  `jug.unsafe` as it can be used to \"fool\" Jug, but it can be useful
  when there are nuisance arguments that are not relevant for the task
  (e.g., number of threads)
- jug.file_store: create files with better permissions

### Internal improvements

- Convert to `pyproject.toml` for building

### Bugfixes

- Better error detection for permission problems
- Bugfix when using local imports and `jug pack`

Drops support for versions of Python older than 3.7. Technically, it
should still work, but they are too old to test in Github CI, so we will
not support them.

## Version 2.3.1

*Released 5 November 2023*

- Update for Python 3.12

## Version 2.3.0

*Released 25 June 2023*

- jug shell: Add `get_filtered_tasks()`
- jug: Fix `jug --version` (which had been broken in the refactoring to
  use subcommands)
- jug shell: Fix message in jug shell when there are no dependencies (it
  would repeatedly print the message stating *this will only be run
  once*)
- jug pack: Make it much faster to invalidate elements
- file_store: ensure that the temporary directory exists
- Drops support for Python 3.4

For older version see `ChangeLog` file or the [full
history](https://jug.readthedocs.io/en/latest/history.html).
