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Parsl - Parallel Scripting Library
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@benclifford @yadudoc
benclifford and yadudoc Fix #935 broken extreme scale executor, and add a regression test (#936) 
* Add a CI test for exex - broken, see issue #916

* install flake8 from test deps, extreme_scale deps; use pip not setup.py

* Make mpi worker pool report number of workers, to fix #916
Latest commit 7e54e7d May 23, 2019
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docker Adding sample predict functions as python files Mar 28, 2018
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parsl Fix #935 broken extreme scale executor, and add a regression test (#936) May 23, 2019
.flake8 Remove un-needed D203 override from flake8: no error is reported beca… Mar 19, 2019
.gitignore Merge branch 'master' into monitoring-viz Oct 5, 2018
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.travis.yml Fix #935 broken extreme scale executor, and add a regression test (#936) May 23, 2019
CITATION.md update inprocessdings Apr 30, 2019
CONTRIBUTING.rst Make contributor notes on tests more reflect reality of what runs in … Feb 19, 2019
LICENSE Initial commit Sep 28, 2017
MANIFEST.in change MANIFEST.in to install more static files Feb 19, 2019
README.rst 0.7.2 release prep (#820) Mar 14, 2019
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mypy.ini Introduce mypy stub for typeguard, and type-check parsl.load() (#874) Apr 24, 2019
requirements.txt switch to using typeguard as it is more maintained Apr 14, 2019
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test-requirements.txt Pin pytest-xdist as newer versions are incompatible with pinned pytest ( Apr 7, 2019

README.rst

Parsl - Parallel Scripting Library

Apache Licence V2.0 Build status Documentation Status

Parsl is a parallel scripting library that enables easy parallelism and workflow design. The latest version available on PyPi is v0.7.2.

QuickStart

Parsl is now available on PyPI, but first make sure you have Python3.5+

$ python3 --version

Install Parsl using pip:

$ pip3 install parsl

To run the Parsl tutorial notebooks you will need to install Jupyter:

$ pip3 install jupyter

Detailed information about setting up Jupyter with Python3.5 is available here

Note: Parsl uses an opt-in model to collect anonymous usage statistics for reporting and improvement purposes. To understand what stats are collected and enable collection please refer to the usage tracking guide

Documentation

The complete parsl documentation is hosted here.

The Parsl tutorial is here and the same tutorial set hosted on live Jupyter notebooks are available here

For Developers

  1. Download Parsl:

    $ git clone https://github.com/Parsl/parsl

  2. Install:

    $ cd parsl
$ python3 setup.py install

  3. Use Parsl!

Requirements

Parsl is supported in Python 3.5+. Requirements can be found here. Requirements for running tests can be found here.

Contributing

We welcome contributions from the community. Please see our contributing guide.

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