.. image:: pics/nutsml_logo.gif :align: center
Introduction <https://maet3608.github.io/nuts-ml/introduction.html>_Installation <https://maet3608.github.io/nuts-ml/installation.html>_Tutorial <https://maet3608.github.io/nuts-ml/tutorial/introduction.html>_Documentation <https://maet3608.github.io/nuts-ml/>_Examples <https://github.com/maet3608/nuts-ml/tree/master/nutsml/examples>_Github <https://github.com/maet3608/nuts-ml>_nuts-ml is a data pre-processing library for GPU based deep learning that provides common pre-processing functions as independent, reusable units. These so called 'nuts' can be freely arranged to build data flows that are efficient, easy to read and modify.
The following example gives a taste of a nuts-ml data-flow that trains a network on image data and prints training loss and accuracy
.. code:: python
(train_samples >> Stratify(1) >> read_image >> transform >> augment >>
Shuffle(100) >> build_batch >> network.train() >>
Print('train loss:{} acc:{}') >> Consume())
nuts-ml is based on nuts-flow <https://github.com/maet3608/nuts-flow>,
which is described here <https://maet3608.github.io/nuts-flow/>.
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