semvec/pypi-packages huggingface dataset:
drain 0.0.6
pipeline library
License: MIT license
Keywords: drain

drain
=====

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Drain is a lightweight framework for writing reproducible data science workflows in Python. The core features are:

* Turn a Python workflow (`DAG <https://en.wikipedia.org/wiki/Directed_acyclic_graph#Data_processing_networks>`_) into steps that can be run by a tool like `make`.

* Transparently pass the results of one step as the input to another, handling any caching that the user requests using efficient tools like `HDF <http://www.pytables.org/>`_ and `joblib <https://pythonhosted.org/joblib/generated/joblib.dump.html>`_.

* Enable easy *parallel* execution of workflows.

* Execute only those steps that are determined to be necessary based on timestamps (both source code and data) and dependencies, virtually guaranteeing *reproducibility* of results and efficient development.

Drain is designed around these principles:

* *Simplicity*: drain is very lightweight and easy to use. The core is just a few hundred lines of code. The steps you write in drain get executed with minimal overhead, making drain workflows easy to debug and manage.

* *Reusability*: Drain leverages mature tools `drake <https://github.com/Factual/drake/>`_ to execute workflows. Drain provides a library of steps for data science workflows including feature generation and selection, model fitting and comparison.

* *Generality*: Virtually any workflow can be realized in drain. The core was written with extensibility in mind so new storage backends and job schedulers, for example, will be easy to incorporate.


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   :target: https://pypi.python.org/pypi/drain
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   :target: https://travis-ci.org/potash/drain
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   :target: https://drain.readthedocs.io/en/latest/?badge=latest
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   :target: https://opensource.org/licenses/MIT