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https://github.com/kostrykin/repype

Reproducible, efficient, flexible batch processing using pipelines for sustainable software experiments
https://github.com/kostrykin/repype

batch-processing experiment-design pipeline python python-workflow scientific-computing sustainability sustainable-computing

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Reproducible, efficient, flexible batch processing using pipelines for sustainable software experiments

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README

        

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To support the sustainability of your software experiments


repype

PyPI version
conda-forge version





**Installation:**

.. code::

git clone [email protected]:kostrykin/repype.git
cd repype && python setup.py install

**Documentation:** https://repype.readthedocs.io

**Examples:**

- https://github.com/kostrykin/repype/tree/master/examples
- https://github.com/BMCV/SuperDSM

**Development instructions:**

- To run the test suite, first install the testing dependencies::

pip install -r tests/requirements.txt
python -m unittest

- Instead of using ``python -m unittest``, use coverage.py to also produce a test coverage report::

coverage run -m unittest && coverage combine && coverage html

This requires `coverage.py `_ to be installed additionally, like ``pip install coverage``.

- To build the documentation locally::

pip install -r docs/requirements.txt
cd docs
make html

You can then open ``build/html/index.html`` to view the documentation.

- To run the Textual interface with debug console::

textual run --dev repype.textual.app:Repype

after running ``textual console`` on a different terminal.

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Copyright (c) 2017-2024 Leonid Kostrykin, Biomedical Computer Vision Group, Heidelberg University

This work is licensed under the terms of the MIT license. For a copy, see LICENSE.