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A simple dataflow engine with scalable semantics.
Pydra is a rewrite of the Nipype engine with mapping and joining as first-class operations. It forms the core of the Nipype 2.0 ecosystem.
The goal of pydra is to provide a lightweight Python dataflow engine for DAG construction, manipulation, and distributed execution.
attrs <https://www.attrs.org/en/stable/>_splitter and combiner provides many ways of compressing complex loop semanticsAPI Documentation <https://nipype.github.io/pydra/>_
SciPy 2020 Proceedings <http://conference.scipy.org/proceedings/scipy2020/pydra.html>_PyCon 2020 Poster <https://docs.google.com/presentation/d/10tS2I34rS0G9qz6v29qVd77OUydjP_FdBklrgAGmYSw/edit?usp=sharing>_Explore Pydra interactively <https://github.com/nipype/pydra-tutorial>_ (the tutorial can be also run using Binder service)|Binder|
.. |Binder| image:: https://mybinder.org/badge_logo.svg :alt: Binder
Please note that mybinder times out after an hour.
::
pip install pydra
Note that installation fails with older versions of pip on Windows. Upgrade pip before installing:
::
pip install –upgrade pip pip install pydra
Pydra requires Python 3.7+. To install in developer mode:
::
git clone git@github.com:nipype/pydra.git
cd pydra
pip install -e ".[dev]"
In order to run pydra's test locally:
::
pytest -vs pydra
If you want to test execution with Dask:
::
git clone git@github.com:nipype/pydra.git
cd pydra
pip install -e ".[dask]"
It is also useful to install pre-commit:
::
pip install pre-commit
pre-commit
FAQs
Pydra dataflow engine
We found that pydra demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 6 open source maintainers collaborating on the project.
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